ekf2: estimator aid source status (mag heading, mag 3d)

.ci/Jenkinsfile-hardware

@@ -864,12 +864,15 @@ void printTopics() {
   sh './Tools/HIL/run_nsh_cmd.py --device `find /dev/serial -name *usb-*` --cmd "listener cpuload" || true'
   sh './Tools/HIL/run_nsh_cmd.py --device `find /dev/serial -name *usb-*` --cmd "listener distance_sensor" || true'
   sh './Tools/HIL/run_nsh_cmd.py --device `find /dev/serial -name *usb-*` --cmd "listener ekf2_timestamps" || true'
-  sh './Tools/HIL/run_nsh_cmd.py --device `find /dev/serial -name *usb-*` --cmd "listener estimator_gps_status" || true'
   sh './Tools/HIL/run_nsh_cmd.py --device `find /dev/serial -name *usb-*` --cmd "listener esc_status" || true'
+  sh './Tools/HIL/run_nsh_cmd.py --device `find /dev/serial -name *usb-*` --cmd "listener estimator_aid_src_fake_pos" || true'
+  sh './Tools/HIL/run_nsh_cmd.py --device `find /dev/serial -name *usb-*` --cmd "listener estimator_aid_src_gnss_pos" || true'
+  sh './Tools/HIL/run_nsh_cmd.py --device `find /dev/serial -name *usb-*` --cmd "listener estimator_aid_src_gnss_vel" || true'
   sh './Tools/HIL/run_nsh_cmd.py --device `find /dev/serial -name *usb-*` --cmd "listener estimator_attitude" || true'
   sh './Tools/HIL/run_nsh_cmd.py --device `find /dev/serial -name *usb-*` --cmd "listener estimator_baro_bias" || true'
   sh './Tools/HIL/run_nsh_cmd.py --device `find /dev/serial -name *usb-*` --cmd "listener estimator_event_flags" || true'
   sh './Tools/HIL/run_nsh_cmd.py --device `find /dev/serial -name *usb-*` --cmd "listener estimator_global_position" || true'
+  sh './Tools/HIL/run_nsh_cmd.py --device `find /dev/serial -name *usb-*` --cmd "listener estimator_gps_status" || true'
   sh './Tools/HIL/run_nsh_cmd.py --device `find /dev/serial -name *usb-*` --cmd "listener estimator_innovation_test_ratios" || true'
   sh './Tools/HIL/run_nsh_cmd.py --device `find /dev/serial -name *usb-*` --cmd "listener estimator_innovation_variances" || true'
   sh './Tools/HIL/run_nsh_cmd.py --device `find /dev/serial -name *usb-*` --cmd "listener estimator_innovations" || true'

msg/CMakeLists.txt

@@ -77,6 +77,9 @@ set(msg_files
 	estimator_states.msg
 	estimator_status.msg
 	estimator_status_flags.msg
+	estimator_aid_source_1d.msg
+	estimator_aid_source_2d.msg
+	estimator_aid_source_3d.msg
 	event.msg
 	follow_target.msg
 	failure_detector_status.msg

msg/estimator_aid_source_1d.msg

@@ -0,0 +1,23 @@
+uint64 timestamp             # time since system start (microseconds)
+uint64 timestamp_sample      # the timestamp of the raw data (microseconds)
+
+uint8 estimator_instance
+
+uint32 device_id
+
+uint64 time_last_fuse
+
+float32 observation
+float32 observation_variance
+
+float32 innovation
+float32 innovation_variance
+float32 test_ratio
+
+bool fusion_enabled          # true when measurements are being fused
+bool innovation_rejected     # true if the observation has been rejected
+bool fused                   # true if the sample was successfully fused
+
+# TOPICS estimator_aid_source_1d
+# TOPICS estimator_aid_src_baro_hgt estimator_aid_src_rng_hgt
+# TOPICS estimator_aid_src_mag_heading

msg/estimator_aid_source_2d.msg

@@ -0,0 +1,22 @@
+uint64 timestamp             # time since system start (microseconds)
+uint64 timestamp_sample      # the timestamp of the raw data (microseconds)
+
+uint8 estimator_instance
+
+uint32 device_id
+
+uint64[2] time_last_fuse
+
+float32[2] observation
+float32[2] observation_variance
+
+float32[2] innovation
+float32[2] innovation_variance
+float32[2] test_ratio
+
+bool[2] fusion_enabled       # true when measurements are being fused
+bool[2] innovation_rejected  # true if the observation has been rejected
+bool[2] fused                # true if the sample was successfully fused
+
+# TOPICS estimator_aid_source_2d
+# TOPICS estimator_aid_src_fake_pos

msg/estimator_aid_source_3d.msg

@@ -0,0 +1,23 @@
+uint64 timestamp             # time since system start (microseconds)
+uint64 timestamp_sample      # the timestamp of the raw data (microseconds)
+
+uint8 estimator_instance
+
+uint32 device_id
+
+uint64[3] time_last_fuse
+
+float32[3] observation
+float32[3] observation_variance
+
+float32[3] innovation
+float32[3] innovation_variance
+float32[3] test_ratio
+
+bool[3] fusion_enabled       # true when measurements are being fused
+bool[3] innovation_rejected  # true if the observation has been rejected
+bool[3] fused                # true if the sample was successfully fused
+
+# TOPICS estimator_aid_source_3d
+# TOPICS estimator_aid_src_gnss_pos estimator_aid_src_gnss_vel
+# TOPICS estimator_aid_src_mag

msg/estimator_event_flags.msg

@@ -16,6 +16,10 @@ bool starting_vision_pos_fusion         #  9 - true when the filter starts using
 bool starting_vision_vel_fusion         # 10 - true when the filter starts using vision system velocity measurements to correct the state estimates
 bool starting_vision_yaw_fusion         # 11 - true when the filter starts using vision system yaw  measurements to correct the state estimates
 bool yaw_aligned_to_imu_gps             # 12 - true when the filter resets the yaw to an estimate derived from IMU and GPS data
+bool reset_hgt_to_baro                  # 13 - true when the vertical position state is reset to the baro measurement
+bool reset_hgt_to_gps                   # 14 - true when the vertical position state is reset to the gps measurement
+bool reset_hgt_to_rng                   # 15 - true when the vertical position state is reset to the rng measurement
+bool reset_hgt_to_ev                    # 16 - true when the vertical position state is reset to the ev measurement
 
 # warning events
 uint32 warning_event_changes            # number of warning event changes

msg/estimator_status_flags.msg

@@ -65,9 +65,6 @@ bool reject_hor_vel                       #  0 - true if horizontal velocity obs
 bool reject_ver_vel                       #  1 - true if vertical velocity observations have been rejected
 bool reject_hor_pos                       #  2 - true if horizontal position observations have been rejected
 bool reject_ver_pos                       #  3 - true if vertical position observations have been rejected
-bool reject_mag_x                         #  4 - true if the X magnetometer observation has been rejected
-bool reject_mag_y                         #  5 - true if the Y magnetometer observation has been rejected
-bool reject_mag_z                         #  6 - true if the Z magnetometer observation has been rejected
 bool reject_yaw                           #  7 - true if the yaw observation has been rejected
 bool reject_airspeed                      #  8 - true if the airspeed observation has been rejected
 bool reject_sideslip                      #  9 - true if the synthetic sideslip observation has been rejected

src/modules/ekf2/EKF/common.h

@@ -438,9 +438,9 @@ union innovation_fault_status_u {
 		bool reject_ver_vel   : 1; ///< 1 - true if vertical velocity observations have been rejected
 		bool reject_hor_pos   : 1; ///< 2 - true if horizontal position observations have been rejected
 		bool reject_ver_pos   : 1; ///< 3 - true if true if vertical position observations have been rejected
-		bool reject_mag_x     : 1; ///< 4 - true if the X magnetometer observation has been rejected
-		bool reject_mag_y     : 1; ///< 5 - true if the Y magnetometer observation has been rejected
-		bool reject_mag_z     : 1; ///< 6 - true if the Z magnetometer observation has been rejected
+		bool __reject_mag_x     : 1; ///< 4 - true if the X magnetometer observation has been rejected
+		bool __reject_mag_y     : 1; ///< 5 - true if the Y magnetometer observation has been rejected
+		bool __reject_mag_z     : 1; ///< 6 - true if the Z magnetometer observation has been rejected
 		bool reject_yaw       : 1; ///< 7 - true if the yaw observation has been rejected
 		bool reject_airspeed  : 1; ///< 8 - true if the airspeed observation has been rejected
 		bool reject_sideslip  : 1; ///< 9 - true if the synthetic sideslip observation has been rejected
@@ -550,6 +550,10 @@ union information_event_status_u {
 		bool starting_vision_vel_fusion : 1; ///< 10 - true when the filter starts using vision system velocity measurements to correct the state estimates
 		bool starting_vision_yaw_fusion : 1; ///< 11 - true when the filter starts using vision system yaw  measurements to correct the state estimates
 		bool yaw_aligned_to_imu_gps     : 1; ///< 12 - true when the filter resets the yaw to an estimate derived from IMU and GPS data
+		bool reset_hgt_to_baro          : 1; ///< 13 - true when the vertical position state is reset to the baro measurement
+		bool reset_hgt_to_gps           : 1; ///< 14 - true when the vertical position state is reset to the gps measurement
+		bool reset_hgt_to_rng           : 1; ///< 15 - true when the vertical position state is reset to the rng measurement
+		bool reset_hgt_to_ev            : 1; ///< 16 - true when the vertical position state is reset to the ev measurement
 	} flags;
 	uint32_t value;
 };

src/modules/ekf2/EKF/control.cpp

@@ -125,8 +125,8 @@ void Ekf::controlFusionModes()
 
 	if (_range_buffer) {
 		// Get range data from buffer and check validity
-		bool is_rng_data_ready = _range_buffer->pop_first_older_than(_imu_sample_delayed.time_us, _range_sensor.getSampleAddress());
-		_range_sensor.setDataReadiness(is_rng_data_ready);
+		_rng_data_ready = _range_buffer->pop_first_older_than(_imu_sample_delayed.time_us, _range_sensor.getSampleAddress());
+		_range_sensor.setDataReadiness(_rng_data_ready);
 
 		// update range sensor angle parameters in case they have changed
 		_range_sensor.setPitchOffset(_params.rng_sens_pitch);
@@ -778,6 +778,34 @@ void Ekf::checkVerticalAccelerationHealth()
 	bool is_inertial_nav_falling = false;
 	bool are_vertical_pos_and_vel_independant = false;
 
+	if (_control_status.flags.gps) {
+		// GNSS velocity
+		const auto &gps_vel = _aid_src_gnss_vel;
+
+		if (gps_vel.time_last_fuse[2] > _vert_vel_fuse_time_us) {
+			_vert_vel_fuse_time_us = gps_vel.time_last_fuse[2];
+			_vert_vel_innov_ratio = gps_vel.innovation[2] / sqrtf(gps_vel.innovation_variance[2]);
+		}
+
+		// GNSS position
+		const auto &gps_pos = _aid_src_gnss_pos;
+
+		if (gps_pos.time_last_fuse[2] > _vert_pos_fuse_attempt_time_us) {
+			_vert_pos_fuse_attempt_time_us = gps_pos.time_last_fuse[2];
+			_vert_pos_innov_ratio = gps_pos.innovation[2] / sqrtf(gps_pos.innovation_variance[2]);
+		}
+	}
+
+	if (_control_status.flags.baro_hgt) {
+		// baro height
+		const auto &baro_hgt = _aid_src_baro_hgt;
+
+		if (baro_hgt.time_last_fuse > _vert_pos_fuse_attempt_time_us) {
+			_vert_pos_fuse_attempt_time_us = baro_hgt.time_last_fuse;
+			_vert_pos_innov_ratio = baro_hgt.innovation / sqrtf(baro_hgt.innovation_variance);
+		}
+	}
+
 	if (isRecent(_vert_pos_fuse_attempt_time_us, 1000000)) {
 		if (isRecent(_vert_vel_fuse_time_us, 1000000)) {
 			// If vertical position and velocity come from independent sensors then we can
@@ -923,25 +951,23 @@ void Ekf::controlHeightFusion()
 	updateBaroHgtOffset();
 	updateGroundEffect();
 
-	if (_control_status.flags.baro_hgt) {
+	if (_baro_data_ready) {
+		updateBaroHgt(_baro_sample_delayed, _aid_src_baro_hgt);
 
-		if (_baro_data_ready && !_baro_hgt_faulty) {
-			fuseBaroHgt();
+		if (_control_status.flags.baro_hgt && !_baro_hgt_faulty) {
+			fuseBaroHgt(_aid_src_baro_hgt);
 		}
+	}
 
-	} else if (_control_status.flags.gps_hgt) {
+	if (_rng_data_ready) {
+		updateRngHgt(_aid_src_rng_hgt);
 
-		if (_gps_data_ready) {
-			fuseGpsHgt();
+		if (_control_status.flags.rng_hgt && _range_sensor.isDataHealthy()) {
+			fuseRngHgt(_aid_src_rng_hgt);
 		}
+	}
 
-	} else if (_control_status.flags.rng_hgt) {
-
-		if (_range_sensor.isDataHealthy()) {
-			fuseRngHgt();
-		}
-
-	} else if (_control_status.flags.ev_hgt) {
+	if (_control_status.flags.ev_hgt) {
 
 		if (_control_status.flags.ev_hgt && _ev_data_ready) {
 			fuseEvHgt();

src/modules/ekf2/EKF/covariance.cpp

@@ -984,13 +984,24 @@ void Ekf::fixCovarianceErrors(bool force_symmetry)
 		const float down_dvel_bias = _state.delta_vel_bias.dot(Vector3f(_R_to_earth.row(2)));
 
 		// check that the vertical component of accel bias is consistent with both the vertical position and velocity innovation
-		bool bad_acc_bias = (fabsf(down_dvel_bias) > dVel_bias_lim
-				     && ((down_dvel_bias * _gps_vel_innov(2) < 0.0f && _control_status.flags.gps)
-					 || (down_dvel_bias * _ev_vel_innov(2) < 0.0f && _control_status.flags.ev_vel))
-				     && ((down_dvel_bias * _gps_pos_innov(2) < 0.0f && _control_status.flags.gps_hgt)
-					 || (down_dvel_bias * _baro_hgt_innov < 0.0f && _control_status.flags.baro_hgt)
-					 || (down_dvel_bias * _rng_hgt_innov < 0.0f && _control_status.flags.rng_hgt)
-					 || (down_dvel_bias * _ev_pos_innov(2) < 0.0f && _control_status.flags.ev_hgt)));
+		bool bad_acc_bias = false;
+		if (fabsf(down_dvel_bias) > dVel_bias_lim) {
+
+			bool bad_vz_gps = _control_status.flags.gps    && (down_dvel_bias * _aid_src_gnss_vel.innovation[2] < 0.0f);
+			bool bad_vz_ev  = _control_status.flags.ev_vel && (down_dvel_bias * _ev_vel_innov(2) < 0.0f);
+
+			if (bad_vz_gps || bad_vz_ev) {
+				bool bad_z_baro = _control_status.flags.baro_hgt && (down_dvel_bias * _aid_src_baro_hgt.innovation    < 0.0f);
+				bool bad_z_gps  = _control_status.flags.gps_hgt  && (down_dvel_bias * _aid_src_gnss_pos.innovation[2] < 0.0f);
+				bool bad_z_rng  = _control_status.flags.rng_hgt  && (down_dvel_bias * _aid_src_rng_hgt.innovation     < 0.0f);
+				bool bad_z_ev   = _control_status.flags.ev_hgt   && (down_dvel_bias * _ev_pos_innov(2) < 0.0f);
+
+
+				if (bad_z_baro || bad_z_gps || bad_z_rng || bad_z_ev) {
+					bad_acc_bias = true;
+				}
+			}
+		}
 
 		// record the pass/fail
 		if (!bad_acc_bias) {

src/modules/ekf2/EKF/ekf.h

@@ -48,6 +48,10 @@
 #include "EKFGSF_yaw.h"
 #include "baro_bias_estimator.hpp"
 
+#include <uORB/topics/estimator_aid_source_1d.h>
+#include <uORB/topics/estimator_aid_source_2d.h>
+#include <uORB/topics/estimator_aid_source_3d.h>
+
 class Ekf final : public EstimatorInterface
 {
 public:
@@ -82,13 +86,13 @@ public:
 	void getEvVelPosInnovVar(float hvel[2], float &vvel, float hpos[2], float &vpos) const;
 	void getEvVelPosInnovRatio(float &hvel, float &vvel, float &hpos, float &vpos) const;
 
-	void getBaroHgtInnov(float &baro_hgt_innov) const { baro_hgt_innov = _baro_hgt_innov; }
-	void getBaroHgtInnovVar(float &baro_hgt_innov_var) const { baro_hgt_innov_var = _baro_hgt_innov_var; }
-	void getBaroHgtInnovRatio(float &baro_hgt_innov_ratio) const { baro_hgt_innov_ratio = _baro_hgt_test_ratio; }
+	void getBaroHgtInnov(float &baro_hgt_innov) const { baro_hgt_innov = _aid_src_baro_hgt.innovation; }
+	void getBaroHgtInnovVar(float &baro_hgt_innov_var) const { baro_hgt_innov_var = _aid_src_baro_hgt.innovation_variance; }
+	void getBaroHgtInnovRatio(float &baro_hgt_innov_ratio) const { baro_hgt_innov_ratio = _aid_src_baro_hgt.test_ratio; }
 
-	void getRngHgtInnov(float &rng_hgt_innov) const { rng_hgt_innov = _rng_hgt_innov; }
-	void getRngHgtInnovVar(float &rng_hgt_innov_var) const { rng_hgt_innov_var = _rng_hgt_innov_var; }
-	void getRngHgtInnovRatio(float &rng_hgt_innov_ratio) const { rng_hgt_innov_ratio = _rng_hgt_test_ratio; }
+	void getRngHgtInnov(float &rng_hgt_innov) const { rng_hgt_innov = _aid_src_rng_hgt.innovation; }
+	void getRngHgtInnovVar(float &rng_hgt_innov_var) const { rng_hgt_innov_var = _aid_src_rng_hgt.innovation_variance; }
+	void getRngHgtInnovRatio(float &rng_hgt_innov_ratio) const { rng_hgt_innov_ratio = _aid_src_rng_hgt.test_ratio; }
 
 	void getAuxVelInnov(float aux_vel_innov[2]) const;
 	void getAuxVelInnovVar(float aux_vel_innov[2]) const;
@@ -108,9 +112,9 @@ public:
 	void getHeadingInnovVar(float &heading_innov_var) const { heading_innov_var = _heading_innov_var; }
 	void getHeadingInnovRatio(float &heading_innov_ratio) const { heading_innov_ratio = _yaw_test_ratio; }
 
-	void getMagInnov(float mag_innov[3]) const { _mag_innov.copyTo(mag_innov); }
-	void getMagInnovVar(float mag_innov_var[3]) const { _mag_innov_var.copyTo(mag_innov_var); }
-	void getMagInnovRatio(float &mag_innov_ratio) const { mag_innov_ratio = _mag_test_ratio.max(); }
+	void getMagInnov(float mag_innov[3]) const { memcpy(mag_innov, _aid_src_mag.innovation, sizeof(_aid_src_mag.innovation)); }
+	void getMagInnovVar(float mag_innov_var[3]) const { memcpy(mag_innov_var, _aid_src_mag.innovation_variance, sizeof(_aid_src_mag.innovation_variance)); }
+	void getMagInnovRatio(float &mag_innov_ratio) const { mag_innov_ratio = Vector3f(_aid_src_mag.test_ratio).max(); }
 
 	void getDragInnov(float drag_innov[2]) const { _drag_innov.copyTo(drag_innov); }
 	void getDragInnovVar(float drag_innov_var[2]) const { _drag_innov_var.copyTo(drag_innov_var); }
@@ -336,6 +340,17 @@ public:
 
 	const BaroBiasEstimator::status &getBaroBiasEstimatorStatus() const { return _baro_b_est.getStatus(); }
 
+	const auto &aid_src_baro_hgt() const { return _aid_src_baro_hgt; }
+	const auto &aid_src_rng_hgt() const { return _aid_src_rng_hgt; }
+
+	const auto &aid_src_fake_pos() const { return _aid_src_fake_pos; }
+
+	const auto &aid_src_mag_heading() const { return _aid_src_mag_heading; }
+	const auto &aid_src_mag() const { return _aid_src_mag; }
+
+	const auto &aid_src_gnss_vel() const { return _aid_src_gnss_vel; }
+	const auto &aid_src_gnss_pos() const { return _aid_src_gnss_pos; }
+
 private:
 
 	// set the internal states and status to their default value
@@ -377,6 +392,7 @@ private:
 	// booleans true when fresh sensor data is available at the fusion time horizon
 	bool _gps_data_ready{false};	///< true when new GPS data has fallen behind the fusion time horizon and is available to be fused
 	bool _baro_data_ready{false};	///< true when new baro height data has fallen behind the fusion time horizon and is available to be fused
+	bool _rng_data_ready{false};
 	bool _flow_data_ready{false};	///< true when the leading edge of the optical flow integration period has fallen behind the fusion time horizon
 	bool _ev_data_ready{false};	///< true when new external vision system data has fallen behind the fusion time horizon and is available to be fused
 	bool _tas_data_ready{false};	///< true when new true airspeed data has fallen behind the fusion time horizon and is available to be fused
@@ -394,7 +410,6 @@ private:
 	uint64_t _time_last_flow_terrain_fuse{0}; ///< time the last fusion of optical flow measurements for terrain estimation were performed (uSec)
 	uint64_t _time_last_arsp_fuse{0};	///< time the last fusion of airspeed measurements were performed (uSec)
 	uint64_t _time_last_beta_fuse{0};	///< time the last fusion of synthetic sideslip measurements were performed (uSec)
-	uint64_t _time_last_fake_pos_fuse{0};	///< last time we faked position measurements to constrain tilt errors during operation without external aiding (uSec)
 	uint64_t _time_last_zero_velocity_fuse{0}; ///< last time of zero velocity update (uSec)
 	uint64_t _time_last_gps_yaw_fuse{0};	///< time the last fusion of GPS yaw measurements were performed (uSec)
 	uint64_t _time_last_gps_yaw_data{0};	///< time the last GPS yaw measurement was available (uSec)
@@ -438,33 +453,18 @@ private:
 	float _vert_vel_innov_ratio{0.f};		///< standard deviation of vertical velocity innovation
 	uint64_t _vert_vel_fuse_time_us{0};	///< last system time in usec time vertical velocity measurement fuson was attempted
 
-	Vector3f _gps_vel_innov{};	///< GPS velocity innovations (m/sec)
-	Vector3f _gps_vel_innov_var{};	///< GPS velocity innovation variances ((m/sec)**2)
-
-	Vector3f _gps_pos_innov{};	///< GPS position innovations (m)
-	Vector3f _gps_pos_innov_var{};	///< GPS position innovation variances (m**2)
-
 	Vector3f _ev_vel_innov{};	///< external vision velocity innovations (m/sec)
 	Vector3f _ev_vel_innov_var{};	///< external vision velocity innovation variances ((m/sec)**2)
 
 	Vector3f _ev_pos_innov{};	///< external vision position innovations (m)
 	Vector3f _ev_pos_innov_var{};	///< external vision position innovation variances (m**2)
 
-	float _baro_hgt_innov{};		///< baro hgt innovations (m)
-	float _baro_hgt_innov_var{};	///< baro hgt innovation variances (m**2)
-
-	float _rng_hgt_innov{};	///< range hgt innovations (m)
-	float _rng_hgt_innov_var{};	///< range hgt innovation variances (m**2)
-
 	Vector3f _aux_vel_innov{};	///< horizontal auxiliary velocity innovations: (m/sec)
 	Vector3f _aux_vel_innov_var{};	///< horizontal auxiliary velocity innovation variances: ((m/sec)**2)
 
 	float _heading_innov{0.0f};	///< heading measurement innovation (rad)
 	float _heading_innov_var{0.0f};	///< heading measurement innovation variance (rad**2)
 
-	Vector3f _mag_innov{};		///< earth magnetic field innovations (Gauss)
-	Vector3f _mag_innov_var{};	///< earth magnetic field innovation variance (Gauss**2)
-
 	Vector2f _drag_innov{};		///< multirotor drag measurement innovation (m/sec**2)
 	Vector2f _drag_innov_var{};	///< multirotor drag measurement innovation variance ((m/sec**2)**2)
 
@@ -491,6 +491,17 @@ private:
 	bool _inhibit_flow_use{false};	///< true when use of optical flow and range finder is being inhibited
 	Vector2f _flow_compensated_XY_rad{};	///< measured delta angle of the image about the X and Y body axes after removal of body rotation (rad), RH rotation is positive
 
+	estimator_aid_source_1d_s _aid_src_baro_hgt{};
+	estimator_aid_source_1d_s _aid_src_rng_hgt{};
+
+	estimator_aid_source_2d_s _aid_src_fake_pos{};
+
+	estimator_aid_source_1d_s _aid_src_mag_heading{};
+	estimator_aid_source_3d_s _aid_src_mag{};
+
+	estimator_aid_source_3d_s _aid_src_gnss_vel{};
+	estimator_aid_source_3d_s _aid_src_gnss_pos{};
+
 	// output predictor states
 	Vector3f _delta_angle_corr{};	///< delta angle correction vector (rad)
 	Vector3f _vel_err_integ{};	///< integral of velocity tracking error (m)
@@ -521,8 +532,12 @@ private:
 
 	// Variables used to perform in flight resets and switch between height sources
 	AlphaFilter<Vector3f> _mag_lpf{0.1f};	///< filtered magnetometer measurement for instant reset (Gauss)
-	float _hgt_sensor_offset{0.0f};		///< set as necessary if desired to maintain the same height after a height reset (m)
+
 	float _baro_hgt_offset{0.0f};		///< baro height reading at the local NED origin (m)
+	float _gps_hgt_offset{0.0f};		///< GPS height reading at the local NED origin (m)
+	float _rng_hgt_offset{0.0f};		///< Range height reading at the local NED origin (m)
+	float _ev_hgt_offset{0.0f};		///< EV height reading at the local NED origin (m)
+
 	float _baro_hgt_bias{0.0f};
 	float _baro_hgt_bias_var{1.f};
 
@@ -585,7 +600,7 @@ private:
 	void predictCovariance();
 
 	// ekf sequential fusion of magnetometer measurements
-	void fuseMag(const Vector3f &mag);
+	void fuseMag(estimator_aid_source_3d_s &aid_src_mag, const Vector3f &mag);
 
 	// fuse the first euler angle from either a 321 or 312 rotation sequence as the observation (currently measures yaw using the magnetometer)
 	void fuseHeading(float measured_hdg = NAN, float obs_var = NAN);
@@ -633,11 +648,13 @@ private:
 	// fuse body frame drag specific forces for multi-rotor wind estimation
 	void fuseDrag(const dragSample &drag_sample);
 
-	void fuseBaroHgt();
-	void fuseGpsHgt();
-	void fuseRngHgt();
+	void fuseBaroHgt(estimator_aid_source_1d_s &baro_hgt);
+	void fuseRngHgt(estimator_aid_source_1d_s &range_hgt);
 	void fuseEvHgt();
 
+	void updateBaroHgt(const baroSample &baro_sample, estimator_aid_source_1d_s &baro_hgt);
+	void updateRngHgt(estimator_aid_source_1d_s &rng_hgt);
+
 	// fuse single velocity and position measurement
 	bool fuseVelPosHeight(const float innov, const float innov_var, const int obs_index);
 
@@ -676,12 +693,16 @@ private:
 				  Vector3f &innov_var, Vector2f &test_ratio);
 
 	bool fuseHorizontalPosition(const Vector3f &innov, float innov_gate, const Vector3f &obs_var,
-				    Vector3f &innov_var, Vector2f &test_ratiov, bool inhibit_gate = false);
+				    Vector3f &innov_var, Vector2f &test_ratiov);
 
 	bool fuseVerticalPosition(float innov, float innov_gate, float obs_var,
 				  float &innov_var, float &test_ratio);
 
-	void fuseGpsVelPos();
+	void updateGpsVel(const gpsSample &gps_sample);
+	void fuseGpsVel();
+
+	void updateGpsPos(const gpsSample &gps_sample);
+	void fuseGpsPos();
 
 	// calculate optical flow body angular rate compensation
 	// returns false if bias corrected body rate data is unavailable
@@ -1048,6 +1069,80 @@ private:
 	bool isYawEmergencyEstimateAvailable() const;
 
 	void resetGpsDriftCheckFilters();
+
+	bool resetEstimatorAidStatusFlags(estimator_aid_source_1d_s &status) const
+	{
+		if (status.timestamp_sample != 0) {
+			status.timestamp_sample = 0;
+			status.fusion_enabled = false;
+			status.innovation_rejected = false;
+			status.fused = false;
+
+			return true;
+		}
+
+		return false;
+	}
+
+	void resetEstimatorAidStatus(estimator_aid_source_1d_s &status) const
+	{
+		if (resetEstimatorAidStatusFlags(status)) {
+			status.observation = 0;
+			status.observation_variance = 0;
+
+			status.innovation = 0;
+			status.innovation_variance = 0;
+			status.test_ratio = 0;
+		}
+	}
+
+	template <typename T>
+	bool resetEstimatorAidStatusFlags(T &status) const
+	{
+		if (status.timestamp_sample != 0) {
+			status.timestamp_sample = 0;
+
+			for (size_t i = 0; i < (sizeof(status.fusion_enabled) / sizeof(status.fusion_enabled[0])); i++) {
+				status.fusion_enabled[i] = false;
+				status.innovation_rejected[i] = false;
+				status.fused[i] = false;
+			}
+
+			return true;
+		}
+
+		return false;
+	}
+
+	template <typename T>
+	void resetEstimatorAidStatus(T &status) const
+	{
+		if (resetEstimatorAidStatusFlags(status)) {
+			for (size_t i = 0; i < (sizeof(status.fusion_enabled) / sizeof(status.fusion_enabled[0])); i++) {
+				status.observation[i] = 0;
+				status.observation_variance[i] = 0;
+
+				status.innovation[i] = 0;
+				status.innovation_variance[i] = 0;
+				status.test_ratio[i] = 0;
+			}
+		}
+	}
+
+	void setEstimatorAidStatusTestRatio(estimator_aid_source_1d_s &status, float innovation_gate) const
+	{
+		status.test_ratio = sq(status.innovation) / (sq(innovation_gate) * status.innovation_variance);
+		status.innovation_rejected = (status.test_ratio > 1.f);
+	}
+
+	template <typename T>
+	void setEstimatorAidStatusTestRatio(T &status, float innovation_gate) const
+	{
+		for (size_t i = 0; i < (sizeof(status.test_ratio) / sizeof(status.test_ratio[0])); i++) {
+			status.test_ratio[i] = sq(status.innovation[i]) / (sq(innovation_gate) * status.innovation_variance[i]);
+			status.innovation_rejected[i] = (status.test_ratio[i] > 1.f);
+		}
+	}
 };
 
 #endif // !EKF_EKF_H

src/modules/ekf2/EKF/ekf_helper.cpp

@@ -241,7 +241,10 @@ void Ekf::resetVerticalPositionTo(const float new_vert_pos)
 
 void Ekf::resetHeightToBaro()
 {
-	resetVerticalPositionTo(-_baro_sample_delayed.hgt + _baro_hgt_offset);
+	ECL_INFO("reset height to baro");
+	_information_events.flags.reset_hgt_to_baro = true;
+
+	resetVerticalPositionTo(-(_baro_sample_delayed.hgt - _baro_hgt_offset));
 
 	// the state variance is the same as the observation
 	P.uncorrelateCovarianceSetVariance<1>(9, sq(_params.baro_noise));
@@ -249,6 +252,9 @@ void Ekf::resetHeightToBaro()
 
 void Ekf::resetHeightToGps()
 {
+	ECL_INFO("reset height to GPS");
+	_information_events.flags.reset_hgt_to_gps = true;
+
 	const float z_pos_before_reset = _state.pos(2);
 	resetVerticalPositionTo(-_gps_sample_delayed.hgt + _gps_alt_ref);
 
@@ -261,6 +267,9 @@ void Ekf::resetHeightToGps()
 
 void Ekf::resetHeightToRng()
 {
+	ECL_INFO("reset height to RNG");
+	_information_events.flags.reset_hgt_to_rng = true;
+
 	float dist_bottom;
 
 	if (_control_status.flags.in_air) {
@@ -273,7 +282,7 @@ void Ekf::resetHeightToRng()
 
 	// update the state and associated variance
 	const float z_pos_before_reset = _state.pos(2);
-	resetVerticalPositionTo(-dist_bottom + _hgt_sensor_offset);
+	resetVerticalPositionTo(-dist_bottom + _rng_hgt_offset);
 
 	// the state variance is the same as the observation
 	P.uncorrelateCovarianceSetVariance<1>(9, sq(_params.range_noise));
@@ -284,6 +293,9 @@ void Ekf::resetHeightToRng()
 
 void Ekf::resetHeightToEv()
 {
+	ECL_INFO("reset height to EV");
+	_information_events.flags.reset_hgt_to_ev = true;
+
 	const float z_pos_before_reset = _state.pos(2);
 	resetVerticalPositionTo(_ev_sample_delayed.pos(2));
 
@@ -352,7 +364,8 @@ bool Ekf::realignYawGPS(const Vector3f &mag)
 	}
 
 	// check for excessive horizontal GPS velocity innovations
-	const bool badVelInnov = (_gps_vel_test_ratio(0) > 1.0f) && _control_status.flags.gps;
+	const float gps_vel_test_ratio = fmaxf(_aid_src_gnss_vel.test_ratio[0], _aid_src_gnss_vel.test_ratio[1]);
+	const bool badVelInnov = (gps_vel_test_ratio > 1.0f) && _control_status.flags.gps;
 
 	// calculate GPS course over ground angle
 	const float gpsCOG = atan2f(_gps_sample_delayed.vel(1), _gps_sample_delayed.vel(0));
@@ -593,30 +606,33 @@ Vector3f Ekf::calcEarthRateNED(float lat_rad) const
 
 void Ekf::getGpsVelPosInnov(float hvel[2], float &vvel, float hpos[2],  float &vpos) const
 {
-	hvel[0] = _gps_vel_innov(0);
-	hvel[1] = _gps_vel_innov(1);
-	vvel    = _gps_vel_innov(2);
-	hpos[0] = _gps_pos_innov(0);
-	hpos[1] = _gps_pos_innov(1);
-	vpos    = _gps_pos_innov(2);
+	hvel[0] = _aid_src_gnss_vel.innovation[0];
+	hvel[1] = _aid_src_gnss_vel.innovation[1];
+	vvel    = _aid_src_gnss_vel.innovation[2];
+
+	hpos[0] = _aid_src_gnss_pos.innovation[0];
+	hpos[1] = _aid_src_gnss_pos.innovation[1];
+	vpos    = _aid_src_gnss_pos.innovation[2];
 }
 
 void Ekf::getGpsVelPosInnovVar(float hvel[2], float &vvel, float hpos[2], float &vpos)  const
 {
-	hvel[0] = _gps_vel_innov_var(0);
-	hvel[1] = _gps_vel_innov_var(1);
-	vvel    = _gps_vel_innov_var(2);
-	hpos[0] = _gps_pos_innov_var(0);
-	hpos[1] = _gps_pos_innov_var(1);
-	vpos    = _gps_pos_innov_var(2);
+	hvel[0] = _aid_src_gnss_vel.innovation_variance[0];
+	hvel[1] = _aid_src_gnss_vel.innovation_variance[1];
+	vvel    = _aid_src_gnss_vel.innovation_variance[2];
+
+	hpos[0] = _aid_src_gnss_pos.innovation_variance[0];
+	hpos[1] = _aid_src_gnss_pos.innovation_variance[1];
+	vpos    = _aid_src_gnss_pos.innovation_variance[2];
 }
 
 void Ekf::getGpsVelPosInnovRatio(float &hvel, float &vvel, float &hpos, float &vpos) const
 {
-	hvel = _gps_vel_test_ratio(0);
-	vvel = _gps_vel_test_ratio(1);
-	hpos = _gps_pos_test_ratio(0);
-	vpos = _gps_pos_test_ratio(1);
+	hvel = fmaxf(_aid_src_gnss_vel.test_ratio[0], _aid_src_gnss_vel.test_ratio[1]);
+	vvel = _aid_src_gnss_vel.test_ratio[2];
+
+	hpos = fmaxf(_aid_src_gnss_pos.test_ratio[0], _aid_src_gnss_pos.test_ratio[1]);
+	vpos = _aid_src_gnss_pos.test_ratio[2];
 }
 
 void Ekf::getEvVelPosInnov(float hvel[2], float &vvel, float hpos[2], float &vpos) const
@@ -728,7 +744,7 @@ void Ekf::get_ekf_gpos_accuracy(float *ekf_eph, float *ekf_epv) const
 	// and using state variances for accuracy reporting is overly optimistic in these situations
 	if (_control_status.flags.inertial_dead_reckoning) {
 		if (_control_status.flags.gps) {
-			hpos_err = math::max(hpos_err, sqrtf(sq(_gps_pos_innov(0)) + sq(_gps_pos_innov(1))));
+			hpos_err = math::max(hpos_err, Vector2f(_aid_src_gnss_pos.innovation).norm());
 		}
 
 		if (_control_status.flags.ev_pos) {
@@ -750,7 +766,7 @@ void Ekf::get_ekf_lpos_accuracy(float *ekf_eph, float *ekf_epv) const
 	// The reason is that complete rejection of measurements is often caused by heading misalignment or inertial sensing errors
 	// and using state variances for accuracy reporting is overly optimistic in these situations
 	if (_deadreckon_time_exceeded && _control_status.flags.gps) {
-		hpos_err = math::max(hpos_err, sqrtf(sq(_gps_pos_innov(0)) + sq(_gps_pos_innov(1))));
+		hpos_err = math::max(hpos_err, Vector2f(_aid_src_gnss_pos.innovation).norm());
 	}
 
 	*ekf_eph = hpos_err;
@@ -774,7 +790,7 @@ void Ekf::get_ekf_vel_accuracy(float *ekf_evh, float *ekf_evv) const
 		}
 
 		if (_control_status.flags.gps) {
-			vel_err_conservative = math::max(vel_err_conservative, sqrtf(sq(_gps_pos_innov(0)) + sq(_gps_pos_innov(1))));
+			vel_err_conservative = math::max(vel_err_conservative, Vector2f(_aid_src_gnss_pos.innovation).norm());
 
 		} else if (_control_status.flags.ev_pos) {
 			vel_err_conservative = math::max(vel_err_conservative, sqrtf(sq(_ev_pos_innov(0)) + sq(_ev_pos_innov(1))));
@@ -894,17 +910,17 @@ void Ekf::get_innovation_test_status(uint16_t &status, float &mag, float &vel, f
 	status = _innov_check_fail_status.value;
 
 	// return the largest magnetometer innovation test ratio
-	mag = sqrtf(math::max(_yaw_test_ratio, _mag_test_ratio.max()));
+	mag = sqrtf(math::max(_yaw_test_ratio, Vector3f(_aid_src_mag.test_ratio).max()));
 
 	// return the largest velocity and position innovation test ratio
 	vel = NAN;
 	pos = NAN;
 
 	if (_control_status.flags.gps) {
-		float gps_vel = sqrtf(math::max(_gps_vel_test_ratio(0), _gps_vel_test_ratio(1)));
+		float gps_vel = sqrtf(Vector3f(_aid_src_gnss_vel.test_ratio).max());
 		vel = math::max(gps_vel, FLT_MIN);
 
-		float gps_pos = sqrtf(_gps_pos_test_ratio(0));
+		float gps_pos = sqrtf(Vector2f(_aid_src_gnss_pos.test_ratio).max());
 		pos = math::max(gps_pos, FLT_MIN);
 	}
 
@@ -925,13 +941,13 @@ void Ekf::get_innovation_test_status(uint16_t &status, float &mag, float &vel, f
 
 	// return the vertical position innovation test ratio
 	if (_control_status.flags.baro_hgt) {
-		hgt = math::max(sqrtf(_baro_hgt_test_ratio), FLT_MIN);
+		hgt = math::max(sqrtf(_aid_src_baro_hgt.test_ratio), FLT_MIN);
 
 	} else if (_control_status.flags.gps_hgt) {
-		hgt = math::max(sqrtf(_gps_pos_test_ratio(1)), FLT_MIN);
+		hgt = math::max(sqrtf(_aid_src_gnss_pos.test_ratio[2]), FLT_MIN);
 
 	} else if (_control_status.flags.rng_hgt) {
-		hgt = math::max(sqrtf(_rng_hgt_test_ratio), FLT_MIN);
+		hgt = math::max(sqrtf(_aid_src_rng_hgt.test_ratio), FLT_MIN);
 
 	} else if (_control_status.flags.ev_hgt) {
 		hgt = math::max(sqrtf(_ev_pos_test_ratio(1)), FLT_MIN);
@@ -965,9 +981,9 @@ void Ekf::get_ekf_soln_status(uint16_t *status) const
 	soln_status.flags.const_pos_mode = !soln_status.flags.velocity_horiz;
 	soln_status.flags.pred_pos_horiz_rel = soln_status.flags.pos_horiz_rel;
 	soln_status.flags.pred_pos_horiz_abs = soln_status.flags.pos_horiz_abs;
-	const bool gps_vel_innov_bad = (_gps_vel_test_ratio(0) > 1.0f) || (_gps_vel_test_ratio(1) > 1.0f);
-	const bool gps_pos_innov_bad = (_gps_pos_test_ratio(0) > 1.0f);
-	const bool mag_innov_good = (_mag_test_ratio.max() < 1.0f) && (_yaw_test_ratio < 1.0f);
+	const bool gps_vel_innov_bad = Vector3f(_aid_src_gnss_vel.test_ratio).max() > 1.f;
+	const bool gps_pos_innov_bad = Vector2f(_aid_src_gnss_pos.test_ratio).max() > 1.f;
+	const bool mag_innov_good = (Vector3f(_aid_src_mag.test_ratio).max() < 1.0f) && (_yaw_test_ratio < 1.0f);
 	soln_status.flags.gps_glitch = (gps_vel_innov_bad || gps_pos_innov_bad) && mag_innov_good;
 	soln_status.flags.accel_error = _fault_status.flags.bad_acc_vertical;
 	*status = soln_status.value;
@@ -1000,6 +1016,7 @@ void Ekf::update_deadreckoning_status()
 				      || isRecent(_time_last_hor_vel_fuse, _params.no_aid_timeout_max));
 
 	const bool optFlowAiding = _control_status.flags.opt_flow && isRecent(_time_last_of_fuse, _params.no_aid_timeout_max);
+
 	const bool airDataAiding = _control_status.flags.wind &&
 				   isRecent(_time_last_arsp_fuse, _params.no_aid_timeout_max) &&
 				   isRecent(_time_last_beta_fuse, _params.no_aid_timeout_max);
@@ -1219,12 +1236,18 @@ void Ekf::stopMag3DFusion()
 	if (_control_status.flags.mag_3D) {
 		saveMagCovData();
 		_control_status.flags.mag_3D = false;
+
+		resetEstimatorAidStatus(_aid_src_mag);
 	}
 }
 
 void Ekf::stopMagHdgFusion()
 {
-	_control_status.flags.mag_hdg = false;
+	if (_control_status.flags.mag_hdg) {
+		_control_status.flags.mag_hdg = false;
+
+		resetEstimatorAidStatus(_aid_src_mag_heading);
+	}
 }
 
 void Ekf::startMagHdgFusion()
@@ -1254,7 +1277,8 @@ void Ekf::startBaroHgtFusion()
 
 		// We don't need to set a height sensor offset
 		// since we track a separate _baro_hgt_offset
-		_hgt_sensor_offset = 0.0f;
+
+		ECL_INFO("starting baro height fusion");
 	}
 }
 
@@ -1262,17 +1286,19 @@ void Ekf::startGpsHgtFusion()
 {
 	if (!_control_status.flags.gps_hgt) {
 		if (_control_status.flags.rng_hgt) {
-			// swith out of range aid
+			// switch out of range aid
 			// calculate height sensor offset such that current
 			// measurement matches our current height estimate
-			_hgt_sensor_offset = _gps_sample_delayed.hgt - _gps_alt_ref + _state.pos(2);
+			_gps_hgt_offset = (_gps_sample_delayed.hgt - _gps_alt_ref) + _state.pos(2);
 
 		} else {
-			_hgt_sensor_offset = 0.f;
+			_gps_hgt_offset = 0.f;
 			resetHeightToGps();
 		}
 
 		setControlGPSHeight();
+
+		ECL_INFO("starting GPS height fusion");
 	}
 }
 
@@ -1283,12 +1309,14 @@ void Ekf::startRngHgtFusion()
 
 		// Range finder is the primary height source, the ground is now the datum used
 		// to compute the local vertical position
-		_hgt_sensor_offset = 0.f;
+		_rng_hgt_offset = 0.f;
 
 		if (!_control_status_prev.flags.ev_hgt) {
 			// EV and range finders are using the same height datum
 			resetHeightToRng();
 		}
+
+		ECL_INFO("starting RNG height fusion");
 	}
 }
 
@@ -1299,7 +1327,9 @@ void Ekf::startRngAidHgtFusion()
 
 		// calculate height sensor offset such that current
 		// measurement matches our current height estimate
-		_hgt_sensor_offset = _terrain_vpos;
+		_rng_hgt_offset = _terrain_vpos;
+
+		ECL_INFO("starting RNG aid height fusion");
 	}
 }
 
@@ -1312,6 +1342,8 @@ void Ekf::startEvHgtFusion()
 			// EV and range finders are using the same height datum
 			resetHeightToEv();
 		}
+
+		ECL_INFO("starting EV height fusion");
 	}
 }
 
@@ -1557,6 +1589,8 @@ void Ekf::stopGpsFusion()
 	if (_control_status.flags.gps) {
 		stopGpsPosFusion();
 		stopGpsVelFusion();
+
+		_control_status.flags.gps = false;
 	}
 
 	if (_control_status.flags.gps_yaw) {
@@ -1570,27 +1604,27 @@ void Ekf::stopGpsFusion()
 
 void Ekf::stopGpsPosFusion()
 {
-	_control_status.flags.gps = false;
+	ECL_INFO("stopping GPS position fusion");
 
 	if (_control_status.flags.gps_hgt) {
+		ECL_INFO("stopping GPS height fusion");
 		startBaroHgtFusion();
 	}
 
-	_gps_pos_innov.setZero();
-	_gps_pos_innov_var.setZero();
-	_gps_pos_test_ratio.setZero();
+	resetEstimatorAidStatus(_aid_src_gnss_pos);
 }
 
 void Ekf::stopGpsVelFusion()
 {
-	_gps_vel_innov.setZero();
-	_gps_vel_innov_var.setZero();
-	_gps_vel_test_ratio.setZero();
+	ECL_INFO("stopping GPS velocity fusion");
+
+	resetEstimatorAidStatus(_aid_src_gnss_vel);
 }
 
 void Ekf::startGpsYawFusion()
 {
-	if (resetYawToGps()) {
+	if (!_control_status.flags.gps_yaw && resetYawToGps()) {
+		ECL_INFO("starting GPS yaw fusion");
 		_control_status.flags.yaw_align = true;
 		_control_status.flags.mag_dec = false;
 		stopEvYawFusion();
@@ -1598,12 +1632,14 @@ void Ekf::startGpsYawFusion()
 		stopMag3DFusion();
 		_control_status.flags.gps_yaw = true;
 	}
-
 }
 
 void Ekf::stopGpsYawFusion()
 {
-	_control_status.flags.gps_yaw = false;
+	if (_control_status.flags.gps_yaw) {
+		ECL_INFO("stopping GPS yaw fusion");
+		_control_status.flags.gps_yaw = false;
+	}
 }
 
 void Ekf::startEvPosFusion()

src/modules/ekf2/EKF/estimator_interface.h

@@ -329,15 +329,10 @@ protected:
 
 	// innovation consistency check monitoring ratios
 	float _yaw_test_ratio{};		// yaw innovation consistency check ratio
-	AlphaFilter<float>_yaw_signed_test_ratio_lpf{0.1f}; // average signed test ratio used to detect a bias in the state
-	Vector3f _mag_test_ratio{};		// magnetometer XYZ innovation consistency check ratios
-	Vector2f _gps_vel_test_ratio{};		// GPS velocity innovation consistency check ratios
-	Vector2f _gps_pos_test_ratio{};		// GPS position innovation consistency check ratios
+	AlphaFilter<float> _yaw_signed_test_ratio_lpf{0.1f}; // average signed test ratio used to detect a bias in the state
 	Vector2f _ev_vel_test_ratio{};		// EV velocity innovation consistency check ratios
 	Vector2f _ev_pos_test_ratio{};		// EV position innovation consistency check ratios
 	Vector2f _aux_vel_test_ratio{};		// Auxiliary horizontal velocity innovation consistency check ratio
-	float _baro_hgt_test_ratio{};	// baro height innovation consistency check ratios
-	float _rng_hgt_test_ratio{};		// range finder height innovation consistency check ratios
 	float _optflow_test_ratio{};		// Optical flow innovation consistency check ratio
 	float _tas_test_ratio{};		// tas innovation consistency check ratio
 	float _hagl_test_ratio{};		// height above terrain measurement innovation consistency check ratio

src/modules/ekf2/EKF/fake_pos_control.cpp

@@ -40,9 +40,14 @@
 
 void Ekf::controlFakePosFusion()
 {
+	auto &fake_pos = _aid_src_fake_pos;
+
+	// clear
+	resetEstimatorAidStatusFlags(fake_pos);
+
 	// If we aren't doing any aiding, fake position measurements at the last known position to constrain drift
 	// During intial tilt aligment, fake position is used to perform a "quasi-stationary" leveling of the EKF
-	const bool fake_pos_data_ready = isTimedOut(_time_last_fake_pos_fuse, (uint64_t)2e5); // Fuse fake position at a limited rate
+	const bool fake_pos_data_ready = isTimedOut(fake_pos.time_last_fuse[0], (uint64_t)2e5); // Fuse fake position at a limited rate
 
 	if (fake_pos_data_ready) {
 		const bool continuing_conditions_passing = !isHorizontalAidingActive();
@@ -52,7 +57,7 @@ void Ekf::controlFakePosFusion()
 			if (continuing_conditions_passing) {
 				fuseFakePosition();
 
-				const bool is_fusion_failing = isTimedOut(_time_last_fake_pos_fuse, (uint64_t)4e5);
+				const bool is_fusion_failing = isTimedOut(fake_pos.time_last_fuse[0], (uint64_t)4e5);
 
 				if (is_fusion_failing) {
 					resetFakePosFusion();
@@ -79,6 +84,7 @@ void Ekf::controlFakePosFusion()
 void Ekf::startFakePosFusion()
 {
 	if (!_using_synthetic_position) {
+		ECL_INFO("start fake position fusion");
 		_using_synthetic_position = true;
 		_fuse_hpos_as_odom = false; // TODO: needed?
 		resetFakePosFusion();
@@ -87,39 +93,68 @@ void Ekf::startFakePosFusion()
 
 void Ekf::resetFakePosFusion()
 {
+	ECL_INFO("reset fake position fusion");
 	_last_known_posNE = _state.pos.xy();
+
 	resetHorizontalPositionToLastKnown();
 	resetHorizontalVelocityToZero();
-	_time_last_fake_pos_fuse = _time_last_imu;
+
+	_aid_src_fake_pos.time_last_fuse[0] = _time_last_imu;
+	_aid_src_fake_pos.time_last_fuse[1] = _time_last_imu;
 }
 
 void Ekf::stopFakePosFusion()
 {
-	_using_synthetic_position = false;
+	if (_using_synthetic_position) {
+		ECL_INFO("stop fake position fusion");
+		_using_synthetic_position = false;
+
+		resetEstimatorAidStatus(_aid_src_fake_pos);
+	}
 }
 
 void Ekf::fuseFakePosition()
 {
-	Vector3f fake_pos_obs_var;
+	Vector2f obs_var;
 
 	if (_control_status.flags.in_air && _control_status.flags.tilt_align) {
-		fake_pos_obs_var(0) = fake_pos_obs_var(1) = sq(fmaxf(_params.pos_noaid_noise, _params.gps_pos_noise));
+		obs_var(0) = obs_var(1) = sq(fmaxf(_params.pos_noaid_noise, _params.gps_pos_noise));
 
 	} else if (!_control_status.flags.in_air && _control_status.flags.vehicle_at_rest) {
 		// Accelerate tilt fine alignment by fusing more
 		// aggressively when the vehicle is at rest
-		fake_pos_obs_var(0) = fake_pos_obs_var(1) = sq(0.01f);
+		obs_var(0) = obs_var(1) = sq(0.01f);
 
 	} else {
-		fake_pos_obs_var(0) = fake_pos_obs_var(1) = sq(0.5f);
+		obs_var(0) = obs_var(1) = sq(0.5f);
 	}
 
-	_gps_pos_innov.xy() = Vector2f(_state.pos) - _last_known_posNE;
+	const float innov_gate = 3.f;
 
-	const float fake_pos_innov_gate = 3.f;
+	auto &fake_pos = _aid_src_fake_pos;
 
-	if (fuseHorizontalPosition(_gps_pos_innov, fake_pos_innov_gate, fake_pos_obs_var,
-	                           _gps_pos_innov_var, _gps_pos_test_ratio, true)) {
-		_time_last_fake_pos_fuse = _time_last_imu;
+	for (int i = 0; i < 2; i++) {
+		fake_pos.observation[i] = _last_known_posNE(i);
+		fake_pos.observation_variance[i] = obs_var(i);
+
+		fake_pos.innovation[i] = _state.pos(i) - _last_known_posNE(i);
+		fake_pos.innovation_variance[i] = P(7 + i, 7 + i) + obs_var(i);
 	}
+
+	setEstimatorAidStatusTestRatio(fake_pos, innov_gate);
+
+	// fuse
+	for (int i = 0; i < 2; i++) {
+		// always protect against extreme values that could result in a NaN
+		fake_pos.fusion_enabled[i] = fake_pos.test_ratio[i] < sq(100.0f / innov_gate);
+
+		if (fake_pos.fusion_enabled[i] && !fake_pos.innovation_rejected[i]) {
+			if (fuseVelPosHeight(fake_pos.innovation[i], fake_pos.innovation_variance[i], 3 + i)) {
+				fake_pos.fused[i] = true;
+				fake_pos.time_last_fuse[i] = _time_last_imu;
+			}
+		}
+	}
+
+	fake_pos.timestamp_sample = _time_last_imu;
 }

src/modules/ekf2/EKF/gps_control.cpp

@@ -48,6 +48,14 @@ void Ekf::controlGpsFusion()
 
 	// Check for new GPS data that has fallen behind the fusion time horizon
 	if (_gps_data_ready) {
+
+		// reset flags
+		resetEstimatorAidStatusFlags(_aid_src_gnss_vel);
+		resetEstimatorAidStatusFlags(_aid_src_gnss_pos);
+
+		updateGpsVel(_gps_sample_delayed);
+		updateGpsPos(_gps_sample_delayed);
+
 		const bool gps_checks_passing = isTimedOut(_last_gps_fail_us, (uint64_t)5e6);
 		const bool gps_checks_failing = isTimedOut(_last_gps_pass_us, (uint64_t)5e6);
 
@@ -67,7 +75,8 @@ void Ekf::controlGpsFusion()
 				if (continuing_conditions_passing
 				    || !isOtherSourceOfHorizontalAidingThan(_control_status.flags.gps)) {
 
-					fuseGpsVelPos();
+					fuseGpsVel();
+					fuseGpsPos();
 
 					if (shouldResetGpsFusion()) {
 						const bool was_gps_signal_lost = isTimedOut(_time_prev_gps_us, 1000000);

src/modules/ekf2/EKF/gps_fusion.cpp

@@ -39,39 +39,143 @@
 /* #include <mathlib/mathlib.h> */
 #include "ekf.h"
 
-void Ekf::fuseGpsVelPos()
+void Ekf::updateGpsVel(const gpsSample &gps_sample)
 {
-	Vector3f gps_pos_obs_var;
+	const float vel_var = sq(gps_sample.sacc);
+	const Vector3f obs_var{vel_var, vel_var, vel_var * sq(1.5f)};
+
+	// innovation gate size
+	const float innov_gate = fmaxf(_params.gps_vel_innov_gate, 1.f);
+
+	auto &gps_vel = _aid_src_gnss_vel;
+
+	for (int i = 0; i < 3; i++) {
+		gps_vel.observation[i] = gps_sample.vel(i);
+		gps_vel.observation_variance[i] = obs_var(i);
+
+		gps_vel.innovation[i] = _state.vel(i) - gps_sample.vel(i);
+		gps_vel.innovation_variance[i] = P(4 + i, 4 + i) + obs_var(i);
+	}
+
+	setEstimatorAidStatusTestRatio(gps_vel, innov_gate);
+
+	// vz special case if there is bad vertical acceleration data, then don't reject measurement,
+	// but limit innovation to prevent spikes that could destabilise the filter
+	if (_fault_status.flags.bad_acc_vertical && gps_vel.innovation_rejected[2]) {
+		const float innov_limit = innov_gate * sqrtf(gps_vel.innovation_variance[2]);
+		gps_vel.innovation[2] = math::constrain(gps_vel.innovation[2], -innov_limit, innov_limit);
+		gps_vel.innovation_rejected[2] = false;
+	}
+
+	gps_vel.timestamp_sample = gps_sample.time_us;
+}
+
+void Ekf::updateGpsPos(const gpsSample &gps_sample)
+{
+	Vector3f position;
+	position(0) = gps_sample.pos(0);
+	position(1) = gps_sample.pos(1);
+
+	// vertical position - gps measurement has opposite sign to earth z axis
+	position(2) = -(gps_sample.hgt - _gps_alt_ref - _gps_hgt_offset);
 
 	const float lower_limit = fmaxf(_params.gps_pos_noise, 0.01f);
 
+	Vector3f obs_var;
+
 	if (isOtherSourceOfHorizontalAidingThan(_control_status.flags.gps)) {
 		// if we are using other sources of aiding, then relax the upper observation
 		// noise limit which prevents bad GPS perturbing the position estimate
-		gps_pos_obs_var(0) = gps_pos_obs_var(1) = sq(fmaxf(_gps_sample_delayed.hacc, lower_limit));
+		obs_var(0) = obs_var(1) = sq(fmaxf(gps_sample.hacc, lower_limit));
 
 	} else {
 		// if we are not using another source of aiding, then we are reliant on the GPS
 		// observations to constrain attitude errors and must limit the observation noise value.
 		float upper_limit = fmaxf(_params.pos_noaid_noise, lower_limit);
-		gps_pos_obs_var(0) = gps_pos_obs_var(1) = sq(math::constrain(_gps_sample_delayed.hacc, lower_limit, upper_limit));
+		obs_var(0) = obs_var(1) = sq(math::constrain(gps_sample.hacc, lower_limit, upper_limit));
 	}
 
+	obs_var(2) = getGpsHeightVariance();
 
+	// innovation gate size
+	float innov_gate = fmaxf(_params.gps_pos_innov_gate, 1.f);
 
-	const float vel_var = sq(_gps_sample_delayed.sacc);
-	const Vector3f gps_vel_obs_var{vel_var, vel_var, vel_var * sq(1.5f)};
+	auto &gps_pos = _aid_src_gnss_pos;
 
-	// calculate innovations
-	_gps_vel_innov = _state.vel - _gps_sample_delayed.vel;
-	_gps_pos_innov.xy() = Vector2f(_state.pos) - _gps_sample_delayed.pos;
+	for (int i = 0; i < 3; i++) {
+		gps_pos.observation[i] = position(i);
+		gps_pos.observation_variance[i] = obs_var(i);
 
-	// set innovation gate size
-	const float pos_innov_gate = fmaxf(_params.gps_pos_innov_gate, 1.f);
-	const float vel_innov_gate = fmaxf(_params.gps_vel_innov_gate, 1.f);
+		gps_pos.innovation[i] = _state.pos(i) - position(i);
+		gps_pos.innovation_variance[i] = P(7 + i, 7 + i) + obs_var(i);
+	}
 
-	// fuse GPS measurement
-	fuseHorizontalVelocity(_gps_vel_innov, vel_innov_gate, gps_vel_obs_var, _gps_vel_innov_var, _gps_vel_test_ratio);
-	fuseVerticalVelocity(_gps_vel_innov, vel_innov_gate, gps_vel_obs_var, _gps_vel_innov_var, _gps_vel_test_ratio);
-	fuseHorizontalPosition(_gps_pos_innov, pos_innov_gate, gps_pos_obs_var, _gps_pos_innov_var, _gps_pos_test_ratio);
+	setEstimatorAidStatusTestRatio(gps_pos, innov_gate);
+
+	// z special case if there is bad vertical acceleration data, then don't reject measurement,
+	// but limit innovation to prevent spikes that could destabilise the filter
+	if (_fault_status.flags.bad_acc_vertical && gps_pos.innovation_rejected[2]) {
+		const float innov_limit = innov_gate * sqrtf(gps_pos.innovation_variance[2]);
+		gps_pos.innovation[2] = math::constrain(gps_pos.innovation[2], -innov_limit, innov_limit);
+		gps_pos.innovation_rejected[2] = false;
+	}
+
+	gps_pos.timestamp_sample = gps_sample.time_us;
+}
+
+void Ekf::fuseGpsVel()
+{
+	// velocity
+	auto &gps_vel = _aid_src_gnss_vel;
+
+	// vx & vy
+	gps_vel.fusion_enabled[0] = true;
+	gps_vel.fusion_enabled[1] = true;
+
+	if (!gps_vel.innovation_rejected[0] && !gps_vel.innovation_rejected[1]) {
+		for (int i = 0; i < 2; i++) {
+			if (fuseVelPosHeight(gps_vel.innovation[i], gps_vel.innovation_variance[i], i)) {
+				gps_vel.fused[i] = true;
+				gps_vel.time_last_fuse[i] = _time_last_imu;
+			}
+		}
+	}
+
+	// vz
+	gps_vel.fusion_enabled[2] = true;
+
+	if (gps_vel.fusion_enabled[2] && !gps_vel.innovation_rejected[2]) {
+		if (fuseVelPosHeight(gps_vel.innovation[2], gps_vel.innovation_variance[2], 2)) {
+			gps_vel.fused[2] = true;
+			gps_vel.time_last_fuse[2] = _time_last_imu;
+		}
+	}
+}
+
+void Ekf::fuseGpsPos()
+{
+	auto &gps_pos = _aid_src_gnss_pos;
+
+	// x & y
+	gps_pos.fusion_enabled[0] = true;
+	gps_pos.fusion_enabled[1] = true;
+
+	if (!gps_pos.innovation_rejected[0] && !gps_pos.innovation_rejected[1]) {
+		for (int i = 0; i < 2; i++) {
+			if (fuseVelPosHeight(gps_pos.innovation[i], gps_pos.innovation_variance[i], 3 + i)) {
+				gps_pos.fused[i] = true;
+				gps_pos.time_last_fuse[i] = _time_last_imu;
+			}
+		}
+	}
+
+	// z
+	gps_pos.fusion_enabled[2] = _control_status.flags.gps_hgt;
+
+	if (gps_pos.fusion_enabled[2] && !gps_pos.innovation_rejected[2]) {
+		if (fuseVelPosHeight(gps_pos.innovation[2], gps_pos.innovation_variance[2], 5)) {
+			gps_pos.fused[2] = true;
+			gps_pos.time_last_fuse[2] = _time_last_imu;
+		}
+	}
 }

src/modules/ekf2/EKF/gps_yaw_fusion.cpp

@@ -147,7 +147,7 @@ void Ekf::fuseGpsYaw()
 	_yaw_test_ratio = sq(_heading_innov) / (sq(innov_gate) * _heading_innov_var);
 
 	// we are no longer using 3-axis fusion so set the reported test levels to zero
-	_mag_test_ratio.setZero();
+	memset(_aid_src_mag.test_ratio, 0, sizeof(_aid_src_mag.test_ratio));
 
 	if (_yaw_test_ratio > 1.0f) {
 		_innov_check_fail_status.flags.reject_yaw = true;

src/modules/ekf2/EKF/height_fusion.cpp

@@ -38,12 +38,23 @@
 
 #include "ekf.h"
 
-void Ekf::fuseBaroHgt()
+void Ekf::updateBaroHgt(const baroSample &baro_sample, estimator_aid_source_1d_s &baro_hgt)
 {
-	// vertical position innovation - baro measurement has opposite sign to earth z axis
-	const float unbiased_baro = _baro_sample_delayed.hgt - _baro_b_est.getBias();
+	// reset flags
+	resetEstimatorAidStatusFlags(baro_hgt);
 
-	_baro_hgt_innov = _state.pos(2) + unbiased_baro - _baro_hgt_offset;
+	// innovation gate size
+	float innov_gate = fmaxf(_params.baro_innov_gate, 1.f);
+
+	// observation variance - user parameter defined
+	float obs_var = sq(fmaxf(_params.baro_noise, 0.01f));
+
+	// vertical position innovation - baro measurement has opposite sign to earth z axis
+	baro_hgt.observation = -(_baro_sample_delayed.hgt - _baro_b_est.getBias() - _baro_hgt_offset);
+	baro_hgt.observation_variance = obs_var;
+
+	baro_hgt.innovation = _state.pos(2) - baro_hgt.observation;
+	baro_hgt.innovation_variance = P(9, 9) + obs_var;
 
 	// Compensate for positive static pressure transients (negative vertical position innovations)
 	// caused by rotor wash ground interaction by applying a temporary deadzone to baro innovations.
@@ -52,55 +63,84 @@ void Ekf::fuseBaroHgt()
 		const float deadzone_start = 0.0f;
 		const float deadzone_end = deadzone_start + _params.gnd_effect_deadzone;
 
-		if (_baro_hgt_innov < -deadzone_start) {
-			if (_baro_hgt_innov <= -deadzone_end) {
-				_baro_hgt_innov += deadzone_end;
+		if (baro_hgt.innovation < -deadzone_start) {
+			if (baro_hgt.innovation <= -deadzone_end) {
+				baro_hgt.innovation += deadzone_end;
 
 			} else {
-				_baro_hgt_innov = -deadzone_start;
+				baro_hgt.innovation = -deadzone_start;
 			}
 		}
 	}
 
-	// innovation gate size
-	float innov_gate = fmaxf(_params.baro_innov_gate, 1.f);
+	setEstimatorAidStatusTestRatio(baro_hgt, innov_gate);
 
-	// observation variance - user parameter defined
-	float obs_var = sq(fmaxf(_params.baro_noise, 0.01f));
+	// special case if there is bad vertical acceleration data, then don't reject measurement,
+	// but limit innovation to prevent spikes that could destabilise the filter
+	if (_fault_status.flags.bad_acc_vertical && baro_hgt.innovation_rejected) {
+		const float innov_limit = innov_gate * sqrtf(baro_hgt.innovation_variance);
+		baro_hgt.innovation = math::constrain(baro_hgt.innovation, -innov_limit, innov_limit);
+		baro_hgt.innovation_rejected = false;
+	}
 
-	fuseVerticalPosition(_baro_hgt_innov, innov_gate, obs_var,
-			     _baro_hgt_innov_var, _baro_hgt_test_ratio);
+	baro_hgt.fusion_enabled = _control_status.flags.baro_hgt;
+
+	baro_hgt.timestamp_sample = baro_sample.time_us;
 }
 
-void Ekf::fuseGpsHgt()
+void Ekf::fuseBaroHgt(estimator_aid_source_1d_s &baro_hgt)
 {
-	// vertical position innovation - gps measurement has opposite sign to earth z axis
-	_gps_pos_innov(2) = _state.pos(2) + _gps_sample_delayed.hgt - _gps_alt_ref - _hgt_sensor_offset;
+	if (baro_hgt.fusion_enabled
+	    && !baro_hgt.innovation_rejected
+	    && fuseVelPosHeight(baro_hgt.innovation, baro_hgt.innovation_variance, 5)) {
 
-	// innovation gate size
-	float innov_gate = fmaxf(_params.baro_innov_gate, 1.f);
-
-	float obs_var = getGpsHeightVariance();
-
-	// _gps_pos_test_ratio(1) is the vertical test ratio
-	fuseVerticalPosition(_gps_pos_innov(2), innov_gate, obs_var,
-			     _gps_pos_innov_var(2), _gps_pos_test_ratio(1));
+		baro_hgt.fused = true;
+		baro_hgt.time_last_fuse = _time_last_imu;
+	}
 }
 
-void Ekf::fuseRngHgt()
+void Ekf::updateRngHgt(estimator_aid_source_1d_s &rng_hgt)
 {
-	// use range finder with tilt correction
-	_rng_hgt_innov = _state.pos(2) - (-math::max(_range_sensor.getDistBottom(),
-					  _params.rng_gnd_clearance)) - _hgt_sensor_offset;
-
-	// innovation gate size
-	float innov_gate = fmaxf(_params.range_innov_gate, 1.f);
+	// reset flags
+	resetEstimatorAidStatusFlags(rng_hgt);
 
 	// observation variance - user parameter defined
 	float obs_var = fmaxf(sq(_params.range_noise) + sq(_params.range_noise_scaler * _range_sensor.getDistBottom()), 0.01f);
 
-	fuseVerticalPosition(_rng_hgt_innov, innov_gate, obs_var,
-			     _rng_hgt_innov_var, _rng_hgt_test_ratio);
+	// innovation gate size
+	float innov_gate = fmaxf(_params.range_innov_gate, 1.f);
+
+	// vertical position innovation, use range finder with tilt correction
+	rng_hgt.observation = (-math::max(_range_sensor.getDistBottom(), _params.rng_gnd_clearance)) + _rng_hgt_offset;
+	rng_hgt.observation_variance = obs_var;
+
+	rng_hgt.innovation = _state.pos(2) - rng_hgt.observation;
+	rng_hgt.innovation_variance = P(9, 9) + obs_var;
+
+	setEstimatorAidStatusTestRatio(rng_hgt, innov_gate);
+
+	// special case if there is bad vertical acceleration data, then don't reject measurement,
+	// but limit innovation to prevent spikes that could destabilise the filter
+	if (_fault_status.flags.bad_acc_vertical && rng_hgt.innovation_rejected) {
+		const float innov_limit = innov_gate * sqrtf(rng_hgt.innovation_variance);
+		rng_hgt.innovation = math::constrain(rng_hgt.innovation, -innov_limit, innov_limit);
+		rng_hgt.innovation_rejected = false;
+	}
+
+	rng_hgt.fusion_enabled = _control_status.flags.rng_hgt;
+
+	rng_hgt.timestamp_sample = _range_sensor.getSampleAddress()->time_us;
+}
+
+void Ekf::fuseRngHgt(estimator_aid_source_1d_s &rng_hgt)
+{
+	if (rng_hgt.fusion_enabled
+	    && !rng_hgt.innovation_rejected
+	    && fuseVelPosHeight(rng_hgt.innovation, rng_hgt.innovation_variance, 5)) {
+
+		rng_hgt.fused = true;
+		rng_hgt.time_last_fuse = _time_last_imu;
+	}
 }
 
 void Ekf::fuseEvHgt()

src/modules/ekf2/EKF/mag_control.cpp

@@ -68,6 +68,10 @@ void Ekf::controlMagFusion()
 	}
 
 	if (mag_data_ready) {
+		// reset flags
+		resetEstimatorAidStatusFlags(_aid_src_mag);
+		resetEstimatorAidStatusFlags(_aid_src_mag_heading);
+
 		checkMagFieldStrength(mag_sample.mag);
 	}
 
@@ -145,6 +149,11 @@ void Ekf::controlMagFusion()
 
 		runMagAndMagDeclFusions(mag_sample.mag);
 	}
+
+	if (mag_data_ready) {
+		_aid_src_mag.timestamp_sample = mag_sample.time_us;
+		_aid_src_mag_heading.timestamp_sample = mag_sample.time_us;
+	}
 }
 
 bool Ekf::noOtherYawAidingThanMag() const
@@ -369,13 +378,13 @@ void Ekf::run3DMagAndDeclFusions(const Vector3f &mag)
 		// states for the first few observations.
 		fuseDeclination(0.02f);
 		_mag_decl_cov_reset = true;
-		fuseMag(mag);
+		fuseMag(_aid_src_mag, mag);
 
 	} else {
 		// The normal sequence is to fuse the magnetometer data first before fusing
 		// declination angle at a higher uncertainty to allow some learning of
 		// declination angle over time.
-		fuseMag(mag);
+		fuseMag(_aid_src_mag, mag);
 
 		if (_control_status.flags.mag_dec) {
 			fuseDeclination(0.5f);

src/modules/ekf2/EKF/mag_fusion.cpp

@@ -45,7 +45,7 @@
 
 #include <mathlib/mathlib.h>
 
-void Ekf::fuseMag(const Vector3f &mag)
+void Ekf::fuseMag(estimator_aid_source_3d_s &aid_src_mag, const Vector3f &mag)
 {
 	// assign intermediate variables
 	const float q0 = _state.quat_nominal(0);
@@ -58,10 +58,11 @@ void Ekf::fuseMag(const Vector3f &mag)
 	const float magD = _state.mag_I(2);
 
 	// XYZ Measurement uncertainty. Need to consider timing errors for fast rotations
-	const float R_MAG = sq(fmaxf(_params.mag_noise, 0.0f));
+	const float R_MAG = sq(fmaxf(_params.mag_noise, 0.f));
 
 	// calculate intermediate variables used for X axis innovation variance, observation Jacobians and Kalman gains
-	const char* numerical_error_covariance_reset_string = "numerical error - covariance reset";
+	static constexpr const char numerical_error_covariance_reset_string[] {"numerical error - covariance reset"};
+
 	const float HKX0 = -magD*q2 + magE*q3 + magN*q0;
 	const float HKX1 = magD*q3 + magE*q2 + magN*q1;
 	const float HKX2 = magE*q1;
@@ -87,21 +88,9 @@ void Ekf::fuseMag(const Vector3f &mag)
 	const float HKX22 = HKX10*P(1,17) - HKX11*P(1,18) + HKX12*P(1,1) + HKX13*P(0,1) - HKX14*P(1,2) + HKX15*P(1,3) + HKX6*P(1,16) + P(1,19);
 	const float HKX23 = HKX10*P(17,19) - HKX11*P(18,19) + HKX12*P(1,19) + HKX13*P(0,19) - HKX14*P(2,19) + HKX15*P(3,19) + HKX6*P(16,19) + P(19,19);
 
-	_mag_innov_var(0) = HKX10*HKX20 - HKX11*HKX18 + HKX12*HKX22 + HKX13*HKX16 - HKX14*HKX19 + HKX15*HKX21 + HKX17*HKX6 + HKX23 + R_MAG;
+	aid_src_mag.innovation_variance[0] = HKX10*HKX20 - HKX11*HKX18 + HKX12*HKX22 + HKX13*HKX16 - HKX14*HKX19 + HKX15*HKX21 + HKX17*HKX6 + HKX23 + R_MAG;
 
-	if (_mag_innov_var(0) < R_MAG) {
-		// the innovation variance contribution from the state covariances is negative which means the covariance matrix is badly conditioned
-		_fault_status.flags.bad_mag_x = true;
-
-		// we need to re-initialise covariances and abort this fusion step
-		resetMagRelatedCovariances();
-		ECL_ERR("magX %s", numerical_error_covariance_reset_string);
-		return;
-	}
-
-	_fault_status.flags.bad_mag_x = false;
-
-	const float HKX24 = 1.0F/_mag_innov_var(0);
+	const float HKX24 = 1.f / aid_src_mag.innovation_variance[0];
 
 	// intermediate variables for calculation of innovations variances for Y and Z axes
 	// don't calculate all terms needed for observation jacobians and Kalman gains because
@@ -126,69 +115,91 @@ void Ekf::fuseMag(const Vector3f &mag)
 	const float IV17 = 2*IV0 - 2*IV1;
 	const float IV18 = IV10 - IV8 + IV9;
 
-	_mag_innov_var(1) = IV11*P(17,20) + IV11*(IV11*P(17,17) + IV2*P(17,18) - IV3*P(16,17) + IV4*P(2,17) + IV5*P(0,17) + IV6*P(1,17) - IV7*P(3,17) + P(17,20)) + IV2*P(18,20) + IV2*(IV11*P(17,18) + IV2*P(18,18) - IV3*P(16,18) + IV4*P(2,18) + IV5*P(0,18) + IV6*P(1,18) - IV7*P(3,18) + P(18,20)) - IV3*P(16,20) - IV3*(IV11*P(16,17) + IV2*P(16,18) - IV3*P(16,16) + IV4*P(2,16) + IV5*P(0,16) + IV6*P(1,16) - IV7*P(3,16) + P(16,20)) + IV4*P(2,20) + IV4*(IV11*P(2,17) - IV12 + IV2*P(2,18) - IV3*P(2,16) + IV4*P(2,2) + IV5*P(0,2) + IV6*P(1,2) + P(2,20)) + IV5*P(0,20) + IV5*(IV11*P(0,17) + IV14 + IV2*P(0,18) - IV3*P(0,16) + IV4*P(0,2) + IV5*P(0,0) - IV7*P(0,3) + P(0,20)) + IV6*P(1,20) + IV6*(IV11*P(1,17) + IV13 + IV2*P(1,18) - IV3*P(1,16) + IV4*P(1,2) + IV6*P(1,1) - IV7*P(1,3) + P(1,20)) - IV7*P(3,20) - IV7*(IV11*P(3,17) + IV15 + IV2*P(3,18) - IV3*P(3,16) + IV5*P(0,3) + IV6*P(1,3) - IV7*P(3,3) + P(3,20)) + P(20,20) + R_MAG;
-	_mag_innov_var(2) = IV16*P(16,21) + IV16*(IV16*P(16,16) - IV17*P(16,17) + IV18*P(16,18) + IV4*P(3,16) - IV5*P(1,16) + IV6*P(0,16) + IV7*P(2,16) + P(16,21)) - IV17*P(17,21) - IV17*(IV16*P(16,17) - IV17*P(17,17) + IV18*P(17,18) + IV4*P(3,17) - IV5*P(1,17) + IV6*P(0,17) + IV7*P(2,17) + P(17,21)) + IV18*P(18,21) + IV18*(IV16*P(16,18) - IV17*P(17,18) + IV18*P(18,18) + IV4*P(3,18) - IV5*P(1,18) + IV6*P(0,18) + IV7*P(2,18) + P(18,21)) + IV4*P(3,21) + IV4*(IV12 + IV16*P(3,16) - IV17*P(3,17) + IV18*P(3,18) + IV4*P(3,3) - IV5*P(1,3) + IV6*P(0,3) + P(3,21)) - IV5*P(1,21) - IV5*(IV14 + IV16*P(1,16) - IV17*P(1,17) + IV18*P(1,18) + IV4*P(1,3) - IV5*P(1,1) + IV7*P(1,2) + P(1,21)) + IV6*P(0,21) + IV6*(-IV13 + IV16*P(0,16) - IV17*P(0,17) + IV18*P(0,18) + IV4*P(0,3) + IV6*P(0,0) + IV7*P(0,2) + P(0,21)) + IV7*P(2,21) + IV7*(IV15 + IV16*P(2,16) - IV17*P(2,17) + IV18*P(2,18) - IV5*P(1,2) + IV6*P(0,2) + IV7*P(2,2) + P(2,21)) + P(21,21) + R_MAG;
+	aid_src_mag.innovation_variance[1] = IV11*P(17,20) + IV11*(IV11*P(17,17) + IV2*P(17,18) - IV3*P(16,17) + IV4*P(2,17) + IV5*P(0,17) + IV6*P(1,17) - IV7*P(3,17) + P(17,20)) + IV2*P(18,20) + IV2*(IV11*P(17,18) + IV2*P(18,18) - IV3*P(16,18) + IV4*P(2,18) + IV5*P(0,18) + IV6*P(1,18) - IV7*P(3,18) + P(18,20)) - IV3*P(16,20) - IV3*(IV11*P(16,17) + IV2*P(16,18) - IV3*P(16,16) + IV4*P(2,16) + IV5*P(0,16) + IV6*P(1,16) - IV7*P(3,16) + P(16,20)) + IV4*P(2,20) + IV4*(IV11*P(2,17) - IV12 + IV2*P(2,18) - IV3*P(2,16) + IV4*P(2,2) + IV5*P(0,2) + IV6*P(1,2) + P(2,20)) + IV5*P(0,20) + IV5*(IV11*P(0,17) + IV14 + IV2*P(0,18) - IV3*P(0,16) + IV4*P(0,2) + IV5*P(0,0) - IV7*P(0,3) + P(0,20)) + IV6*P(1,20) + IV6*(IV11*P(1,17) + IV13 + IV2*P(1,18) - IV3*P(1,16) + IV4*P(1,2) + IV6*P(1,1) - IV7*P(1,3) + P(1,20)) - IV7*P(3,20) - IV7*(IV11*P(3,17) + IV15 + IV2*P(3,18) - IV3*P(3,16) + IV5*P(0,3) + IV6*P(1,3) - IV7*P(3,3) + P(3,20)) + P(20,20) + R_MAG;
+	aid_src_mag.innovation_variance[2] = IV16*P(16,21) + IV16*(IV16*P(16,16) - IV17*P(16,17) + IV18*P(16,18) + IV4*P(3,16) - IV5*P(1,16) + IV6*P(0,16) + IV7*P(2,16) + P(16,21)) - IV17*P(17,21) - IV17*(IV16*P(16,17) - IV17*P(17,17) + IV18*P(17,18) + IV4*P(3,17) - IV5*P(1,17) + IV6*P(0,17) + IV7*P(2,17) + P(17,21)) + IV18*P(18,21) + IV18*(IV16*P(16,18) - IV17*P(17,18) + IV18*P(18,18) + IV4*P(3,18) - IV5*P(1,18) + IV6*P(0,18) + IV7*P(2,18) + P(18,21)) + IV4*P(3,21) + IV4*(IV12 + IV16*P(3,16) - IV17*P(3,17) + IV18*P(3,18) + IV4*P(3,3) - IV5*P(1,3) + IV6*P(0,3) + P(3,21)) - IV5*P(1,21) - IV5*(IV14 + IV16*P(1,16) - IV17*P(1,17) + IV18*P(1,18) + IV4*P(1,3) - IV5*P(1,1) + IV7*P(1,2) + P(1,21)) + IV6*P(0,21) + IV6*(-IV13 + IV16*P(0,16) - IV17*P(0,17) + IV18*P(0,18) + IV4*P(0,3) + IV6*P(0,0) + IV7*P(0,2) + P(0,21)) + IV7*P(2,21) + IV7*(IV15 + IV16*P(2,16) - IV17*P(2,17) + IV18*P(2,18) - IV5*P(1,2) + IV6*P(0,2) + IV7*P(2,2) + P(2,21)) + P(21,21) + R_MAG;
 
-	// chedk innovation variances for being badly conditioned
+	// check innovation variances for being badly conditioned
+	if (_control_status.flags.mag_3D) {
 
-	if (_mag_innov_var(1) < R_MAG) {
-		// the innovation variance contribution from the state covariances is negtive which means the covariance matrix is badly conditioned
-		_fault_status.flags.bad_mag_y = true;
+		if (aid_src_mag.innovation_variance[0] < R_MAG) {
+			// the innovation variance contribution from the state covariances is negative which means the covariance matrix is badly conditioned
+			_fault_status.flags.bad_mag_x = true;
 
-		// we need to re-initialise covariances and abort this fusion step
-		resetMagRelatedCovariances();
-		ECL_ERR("magY %s", numerical_error_covariance_reset_string);
-		return;
+			// we need to re-initialise covariances and abort this fusion step
+			resetMagRelatedCovariances();
+			ECL_ERR("magX %s", numerical_error_covariance_reset_string);
+			return;
+
+		} else {
+			_fault_status.flags.bad_mag_x = false;
+		}
+
+		if (aid_src_mag.innovation_variance[1] < R_MAG) {
+			// the innovation variance contribution from the state covariances is negtive which means the covariance matrix is badly conditioned
+			_fault_status.flags.bad_mag_y = true;
+
+			// we need to re-initialise covariances and abort this fusion step
+			resetMagRelatedCovariances();
+			ECL_ERR("magY %s", numerical_error_covariance_reset_string);
+			return;
+
+		} else {
+			_fault_status.flags.bad_mag_y = false;
+		}
+
+		if (aid_src_mag.innovation_variance[2] < R_MAG) {
+			// the innovation variance contribution from the state covariances is negative which means the covariance matrix is badly conditioned
+			_fault_status.flags.bad_mag_z = true;
+
+			// we need to re-initialise covariances and abort this fusion step
+			resetMagRelatedCovariances();
+			ECL_ERR("magZ %s", numerical_error_covariance_reset_string);
+			return;
+
+		} else {
+			_fault_status.flags.bad_mag_z = false;
+		}
 	}
 
-	_fault_status.flags.bad_mag_y = false;
-
-	if (_mag_innov_var(2) < R_MAG) {
-		// the innovation variance contribution from the state covariances is negative which means the covariance matrix is badly conditioned
-		_fault_status.flags.bad_mag_z = true;
-
-		// we need to re-initialise covariances and abort this fusion step
-		resetMagRelatedCovariances();
-		ECL_ERR("magZ %s", numerical_error_covariance_reset_string);
-		return;
-	}
-
-	_fault_status.flags.bad_mag_z = false;
+	// compute magnetometer innovations
+	// Perform an innovation consistency check and report the result
+	float innov_gate = math::max(_params.mag_innov_gate, 1.f);
 
 	// rotate magnetometer earth field state into body frame
 	const Dcmf R_to_body = quatToInverseRotMat(_state.quat_nominal);
-
 	const Vector3f mag_I_rot = R_to_body * _state.mag_I;
 
 	// compute magnetometer innovations
-	_mag_innov = mag_I_rot + _state.mag_B - mag;
+	const Vector3f mag_observation = mag - _state.mag_B;
+	const Vector3f mag_innov = mag_I_rot - mag_observation;
+
+	mag_observation.copyTo(aid_src_mag.observation);
+	mag_innov.copyTo(aid_src_mag.innovation);
+
+	for (int i = 0; i < 3; i++) {
+		aid_src_mag.observation_variance[i] = R_MAG;
+		// aid_src_mag.innovation_variance[i] // computed separately
+		aid_src_mag.test_ratio[i] = sq(aid_src_mag.innovation[i]) / (sq(innov_gate) * aid_src_mag.innovation_variance[i]);
+
+		aid_src_mag.innovation_rejected[i] = (aid_src_mag.test_ratio[i] > 1.f);
+	}
 
 	// do not use the synthesized measurement for the magnetomter Z component for 3D fusion
 	if (_control_status.flags.synthetic_mag_z) {
-		_mag_innov(2) = 0.0f;
-	}
-
-	// Perform an innovation consistency check and report the result
-	bool all_innovation_checks_passed = true;
-
-	for (uint8_t index = 0; index <= 2; index++) {
-		_mag_test_ratio(index) = sq(_mag_innov(index)) / (sq(math::max(_params.mag_innov_gate, 1.0f)) * _mag_innov_var(index));
-
-		if (_mag_test_ratio(index) > 1.0f) {
-			all_innovation_checks_passed = false;
-			_innov_check_fail_status.value |= (1 << (index + 3));
-
-		} else {
-			_innov_check_fail_status.value &= ~(1 << (index + 3));
-		}
+		aid_src_mag.innovation[2] = 0.0f;
+		aid_src_mag.innovation_rejected[2] = false;
 	}
 
 	// we are no longer using heading fusion so set the reported test level to zero
 	_yaw_test_ratio = 0.0f;
 
 	// if any axis fails, abort the mag fusion
-	if (!all_innovation_checks_passed) {
-		return;
+	if (_control_status.flags.mag_3D) {
+		for (auto& innovation_rejected : aid_src_mag.innovation_rejected) {
+			if (innovation_rejected) {
+				return;
+			}
+		}
 	}
 
 	// For the first few seconds after in-flight alignment we allow the magnetic field state estimates to stabilise
@@ -200,223 +211,251 @@ void Ekf::fuseMag(const Vector3f &mag)
 	Vector24f Kfusion;
 
 	// update the states and covariance using sequential fusion of the magnetometer components
-	for (uint8_t index = 0; index <= 2; index++) {
+	// Calculate Kalman gains and observation jacobians
 
-		// Calculate Kalman gains and observation jacobians
-		if (index == 0) {
-			// Calculate X axis observation jacobians
-			Hfusion.at<0>() = 2*HKX0;
-			Hfusion.at<1>() = 2*HKX1;
-			Hfusion.at<2>() = 2*HKX2 - 2*HKX3 - 2*HKX4;
-			Hfusion.at<3>() = 2*HKX5;
-			Hfusion.at<16>() = HKX6;
-			Hfusion.at<17>() = 2*HKX7;
-			Hfusion.at<18>() = 2*HKX8 - 2*HKX9;
-			Hfusion.at<19>() = 1;
+	// Calculate X axis observation jacobians
+	Hfusion.at<0>() = 2*HKX0;
+	Hfusion.at<1>() = 2*HKX1;
+	Hfusion.at<2>() = 2*HKX2 - 2*HKX3 - 2*HKX4;
+	Hfusion.at<3>() = 2*HKX5;
+	Hfusion.at<16>() = HKX6;
+	Hfusion.at<17>() = 2*HKX7;
+	Hfusion.at<18>() = 2*HKX8 - 2*HKX9;
+	Hfusion.at<19>() = 1;
 
-			// Calculate X axis Kalman gains
-			if (update_all_states) {
-				Kfusion(0) = HKX16*HKX24;
-				Kfusion(1) = HKX22*HKX24;
-				Kfusion(2) = HKX19*HKX24;
-				Kfusion(3) = HKX21*HKX24;
+	// Calculate X axis Kalman gains
+	if (update_all_states) {
+		Kfusion(0) = HKX16*HKX24;
+		Kfusion(1) = HKX22*HKX24;
+		Kfusion(2) = HKX19*HKX24;
+		Kfusion(3) = HKX21*HKX24;
 
-				for (unsigned row = 4; row <= 15; row++) {
-					Kfusion(row) = HKX24*(HKX10*P(row,17) - HKX11*P(row,18) + HKX12*P(1,row) + HKX13*P(0,row) - HKX14*P(2,row) + HKX15*P(3,row) + HKX6*P(row,16) + P(row,19));
-				}
-
-				for (unsigned row = 22; row <= 23; row++) {
-					Kfusion(row) = HKX24*(HKX10*P(17,row) - HKX11*P(18,row) + HKX12*P(1,row) + HKX13*P(0,row) - HKX14*P(2,row) + HKX15*P(3,row) + HKX6*P(16,row) + P(19,row));
-				}
-			}
-
-			Kfusion(16) = HKX17*HKX24;
-			Kfusion(17) = HKX20*HKX24;
-			Kfusion(18) = HKX18*HKX24;
-			Kfusion(19) = HKX23*HKX24;
-
-			for (unsigned row = 20; row <= 21; row++) {
-				Kfusion(row) = HKX24*(HKX10*P(17,row) - HKX11*P(18,row) + HKX12*P(1,row) + HKX13*P(0,row) - HKX14*P(2,row) + HKX15*P(3,row) + HKX6*P(16,row) + P(19,row));
-			}
-
-		} else if (index == 1) {
-
-			// recalculate innovation variance becasue states and covariances have changed due to previous fusion
-			const float HKY0 = magD*q1 + magE*q0 - magN*q3;
-			const float HKY1 = magD*q0 - magE*q1 + magN*q2;
-			const float HKY2 = magD*q3 + magE*q2 + magN*q1;
-			const float HKY3 = magD*q2;
-			const float HKY4 = magE*q3;
-			const float HKY5 = magN*q0;
-			const float HKY6 = q1*q2;
-			const float HKY7 = q0*q3;
-			const float HKY8 = ecl::powf(q0, 2) - ecl::powf(q1, 2) + ecl::powf(q2, 2) - ecl::powf(q3, 2);
-			const float HKY9 = q0*q1 + q2*q3;
-			const float HKY10 = 2*HKY9;
-			const float HKY11 = -2*HKY6 + 2*HKY7;
-			const float HKY12 = 2*HKY2;
-			const float HKY13 = 2*HKY0;
-			const float HKY14 = 2*HKY1;
-			const float HKY15 = -2*HKY3 + 2*HKY4 + 2*HKY5;
-			const float HKY16 = HKY10*P(0,18) - HKY11*P(0,16) + HKY12*P(0,2) + HKY13*P(0,0) + HKY14*P(0,1) - HKY15*P(0,3) + HKY8*P(0,17) + P(0,20);
-			const float HKY17 = HKY10*P(17,18) - HKY11*P(16,17) + HKY12*P(2,17) + HKY13*P(0,17) + HKY14*P(1,17) - HKY15*P(3,17) + HKY8*P(17,17) + P(17,20);
-			const float HKY18 = HKY10*P(16,18) - HKY11*P(16,16) + HKY12*P(2,16) + HKY13*P(0,16) + HKY14*P(1,16) - HKY15*P(3,16) + HKY8*P(16,17) + P(16,20);
-			const float HKY19 = HKY10*P(3,18) - HKY11*P(3,16) + HKY12*P(2,3) + HKY13*P(0,3) + HKY14*P(1,3) - HKY15*P(3,3) + HKY8*P(3,17) + P(3,20);
-			const float HKY20 = HKY10*P(18,18) - HKY11*P(16,18) + HKY12*P(2,18) + HKY13*P(0,18) + HKY14*P(1,18) - HKY15*P(3,18) + HKY8*P(17,18) + P(18,20);
-			const float HKY21 = HKY10*P(1,18) - HKY11*P(1,16) + HKY12*P(1,2) + HKY13*P(0,1) + HKY14*P(1,1) - HKY15*P(1,3) + HKY8*P(1,17) + P(1,20);
-			const float HKY22 = HKY10*P(2,18) - HKY11*P(2,16) + HKY12*P(2,2) + HKY13*P(0,2) + HKY14*P(1,2) - HKY15*P(2,3) + HKY8*P(2,17) + P(2,20);
-			const float HKY23 = HKY10*P(18,20) - HKY11*P(16,20) + HKY12*P(2,20) + HKY13*P(0,20) + HKY14*P(1,20) - HKY15*P(3,20) + HKY8*P(17,20) + P(20,20);
-
-			_mag_innov_var(1) = (HKY10*HKY20 - HKY11*HKY18 + HKY12*HKY22 + HKY13*HKY16 + HKY14*HKY21 - HKY15*HKY19 + HKY17*HKY8 + HKY23 + R_MAG);
-
-			if (_mag_innov_var(1) < R_MAG) {
-				// the innovation variance contribution from the state covariances is negative which means the covariance matrix is badly conditioned
-				_fault_status.flags.bad_mag_y = true;
-
-				// we need to re-initialise covariances and abort this fusion step
-				resetMagRelatedCovariances();
-				ECL_ERR("magY %s", numerical_error_covariance_reset_string);
-				return;
-			}
-			const float HKY24 = 1.0F/_mag_innov_var(1);
-
-			// Calculate Y axis observation jacobians
-			Hfusion.setZero();
-			Hfusion.at<0>() = 2*HKY0;
-			Hfusion.at<1>() = 2*HKY1;
-			Hfusion.at<2>() = 2*HKY2;
-			Hfusion.at<3>() = 2*HKY3 - 2*HKY4 - 2*HKY5;
-			Hfusion.at<16>() = 2*HKY6 - 2*HKY7;
-			Hfusion.at<17>() = HKY8;
-			Hfusion.at<18>() = 2*HKY9;
-			Hfusion.at<20>() = 1;
-
-			// Calculate Y axis Kalman gains
-			if (update_all_states) {
-				Kfusion(0) = HKY16*HKY24;
-				Kfusion(1) = HKY21*HKY24;
-				Kfusion(2) = HKY22*HKY24;
-				Kfusion(3) = HKY19*HKY24;
-
-				for (unsigned row = 4; row <= 15; row++) {
-					Kfusion(row) = HKY24*(HKY10*P(row,18) - HKY11*P(row,16) + HKY12*P(2,row) + HKY13*P(0,row) + HKY14*P(1,row) - HKY15*P(3,row) + HKY8*P(row,17) + P(row,20));
-				}
-
-				for (unsigned row = 22; row <= 23; row++) {
-					Kfusion(row) = HKY24*(HKY10*P(18,row) - HKY11*P(16,row) + HKY12*P(2,row) + HKY13*P(0,row) + HKY14*P(1,row) - HKY15*P(3,row) + HKY8*P(17,row) + P(20,row));
-				}
-			}
-
-			Kfusion(16) = HKY18*HKY24;
-			Kfusion(17) = HKY17*HKY24;
-			Kfusion(18) = HKY20*HKY24;
-			Kfusion(19) = HKY24*(HKY10*P(18,19) - HKY11*P(16,19) + HKY12*P(2,19) + HKY13*P(0,19) + HKY14*P(1,19) - HKY15*P(3,19) + HKY8*P(17,19) + P(19,20));
-			Kfusion(20) = HKY23*HKY24;
-			Kfusion(21) = HKY24*(HKY10*P(18,21) - HKY11*P(16,21) + HKY12*P(2,21) + HKY13*P(0,21) + HKY14*P(1,21) - HKY15*P(3,21) + HKY8*P(17,21) + P(20,21));
-
-		} else if (index == 2) {
-
-			// we do not fuse synthesized magnetomter measurements when doing 3D fusion
-			if (_control_status.flags.synthetic_mag_z) {
-				continue;
-			}
-
-			// recalculate innovation variance becasue states and covariances have changed due to previous fusion
-			const float HKZ0 = magD*q0 - magE*q1 + magN*q2;
-			const float HKZ1 = magN*q3;
-			const float HKZ2 = magD*q1;
-			const float HKZ3 = magE*q0;
-			const float HKZ4 = -magD*q2 + magE*q3 + magN*q0;
-			const float HKZ5 = magD*q3 + magE*q2 + magN*q1;
-			const float HKZ6 = q0*q2 + q1*q3;
-			const float HKZ7 = q2*q3;
-			const float HKZ8 = q0*q1;
-			const float HKZ9 = ecl::powf(q0, 2) - ecl::powf(q1, 2) - ecl::powf(q2, 2) + ecl::powf(q3, 2);
-			const float HKZ10 = 2*HKZ6;
-			const float HKZ11 = -2*HKZ7 + 2*HKZ8;
-			const float HKZ12 = 2*HKZ5;
-			const float HKZ13 = 2*HKZ0;
-			const float HKZ14 = -2*HKZ1 + 2*HKZ2 + 2*HKZ3;
-			const float HKZ15 = 2*HKZ4;
-			const float HKZ16 = HKZ10*P(0,16) - HKZ11*P(0,17) + HKZ12*P(0,3) + HKZ13*P(0,0) - HKZ14*P(0,1) + HKZ15*P(0,2) + HKZ9*P(0,18) + P(0,21);
-			const float HKZ17 = HKZ10*P(16,18) - HKZ11*P(17,18) + HKZ12*P(3,18) + HKZ13*P(0,18) - HKZ14*P(1,18) + HKZ15*P(2,18) + HKZ9*P(18,18) + P(18,21);
-			const float HKZ18 = HKZ10*P(16,17) - HKZ11*P(17,17) + HKZ12*P(3,17) + HKZ13*P(0,17) - HKZ14*P(1,17) + HKZ15*P(2,17) + HKZ9*P(17,18) + P(17,21);
-			const float HKZ19 = HKZ10*P(1,16) - HKZ11*P(1,17) + HKZ12*P(1,3) + HKZ13*P(0,1) - HKZ14*P(1,1) + HKZ15*P(1,2) + HKZ9*P(1,18) + P(1,21);
-			const float HKZ20 = HKZ10*P(16,16) - HKZ11*P(16,17) + HKZ12*P(3,16) + HKZ13*P(0,16) - HKZ14*P(1,16) + HKZ15*P(2,16) + HKZ9*P(16,18) + P(16,21);
-			const float HKZ21 = HKZ10*P(3,16) - HKZ11*P(3,17) + HKZ12*P(3,3) + HKZ13*P(0,3) - HKZ14*P(1,3) + HKZ15*P(2,3) + HKZ9*P(3,18) + P(3,21);
-			const float HKZ22 = HKZ10*P(2,16) - HKZ11*P(2,17) + HKZ12*P(2,3) + HKZ13*P(0,2) - HKZ14*P(1,2) + HKZ15*P(2,2) + HKZ9*P(2,18) + P(2,21);
-			const float HKZ23 = HKZ10*P(16,21) - HKZ11*P(17,21) + HKZ12*P(3,21) + HKZ13*P(0,21) - HKZ14*P(1,21) + HKZ15*P(2,21) + HKZ9*P(18,21) + P(21,21);
-
-			_mag_innov_var(2) = (HKZ10*HKZ20 - HKZ11*HKZ18 + HKZ12*HKZ21 + HKZ13*HKZ16 - HKZ14*HKZ19 + HKZ15*HKZ22 + HKZ17*HKZ9 + HKZ23 + R_MAG);
-
-			if (_mag_innov_var(2) < R_MAG) {
-				// the innovation variance contribution from the state covariances is negative which means the covariance matrix is badly conditioned
-				_fault_status.flags.bad_mag_z = true;
-
-				// we need to re-initialise covariances and abort this fusion step
-				resetMagRelatedCovariances();
-				ECL_ERR("magZ %s", numerical_error_covariance_reset_string);
-				return;
-			}
-
-			const float HKZ24 = 1.0F/_mag_innov_var(2);
-
-			// calculate Z axis observation jacobians
-			Hfusion.setZero();
-			Hfusion.at<0>() = 2*HKZ0;
-			Hfusion.at<1>() = 2*HKZ1 - 2*HKZ2 - 2*HKZ3;
-			Hfusion.at<2>() = 2*HKZ4;
-			Hfusion.at<3>() = 2*HKZ5;
-			Hfusion.at<16>() = 2*HKZ6;
-			Hfusion.at<17>() = 2*HKZ7 - 2*HKZ8;
-			Hfusion.at<18>() = HKZ9;
-			Hfusion.at<21>() = 1;
-
-			// Calculate Z axis Kalman gains
-			if (update_all_states) {
-				Kfusion(0) = HKZ16*HKZ24;
-				Kfusion(1) = HKZ19*HKZ24;
-				Kfusion(2) = HKZ22*HKZ24;
-				Kfusion(3) = HKZ21*HKZ24;
-
-				for (unsigned row = 4; row <= 15; row++) {
-					Kfusion(row) = HKZ24*(HKZ10*P(row,16) - HKZ11*P(row,17) + HKZ12*P(3,row) + HKZ13*P(0,row) - HKZ14*P(1,row) + HKZ15*P(2,row) + HKZ9*P(row,18) + P(row,21));
-				}
-
-				for (unsigned row = 22; row <= 23; row++) {
-					Kfusion(row) = HKZ24*(HKZ10*P(16,row) - HKZ11*P(17,row) + HKZ12*P(3,row) + HKZ13*P(0,row) - HKZ14*P(1,row) + HKZ15*P(2,row) + HKZ9*P(18,row) + P(21,row));
-				}
-			}
-
-			Kfusion(16) = HKZ20*HKZ24;
-			Kfusion(17) = HKZ18*HKZ24;
-			Kfusion(18) = HKZ17*HKZ24;
-
-			for (unsigned row = 19; row <= 20; row++) {
-				Kfusion(row) = HKZ24*(HKZ10*P(16,row) - HKZ11*P(17,row) + HKZ12*P(3,row) + HKZ13*P(0,row) - HKZ14*P(1,row) + HKZ15*P(2,row) + HKZ9*P(18,row) + P(row,21));
-			}
-
-			Kfusion(21) = HKZ23*HKZ24;
+		for (unsigned row = 4; row <= 15; row++) {
+			Kfusion(row) = HKX24*(HKX10*P(row,17) - HKX11*P(row,18) + HKX12*P(1,row) + HKX13*P(0,row) - HKX14*P(2,row) + HKX15*P(3,row) + HKX6*P(row,16) + P(row,19));
 		}
 
-		const bool is_fused = measurementUpdate(Kfusion, Hfusion, _mag_innov(index));
-
-		switch (index) {
-		case 0:
-			_fault_status.flags.bad_mag_x = !is_fused;
-			break;
-
-		case 1:
-			_fault_status.flags.bad_mag_y = !is_fused;
-			break;
-
-		case 2:
-			_fault_status.flags.bad_mag_z = !is_fused;
-			break;
+		for (unsigned row = 22; row <= 23; row++) {
+			Kfusion(row) = HKX24*(HKX10*P(17,row) - HKX11*P(18,row) + HKX12*P(1,row) + HKX13*P(0,row) - HKX14*P(2,row) + HKX15*P(3,row) + HKX6*P(16,row) + P(19,row));
 		}
+	}
 
-		if (is_fused) {
+	Kfusion(16) = HKX17*HKX24;
+	Kfusion(17) = HKX20*HKX24;
+	Kfusion(18) = HKX18*HKX24;
+	Kfusion(19) = HKX23*HKX24;
+
+	for (unsigned row = 20; row <= 21; row++) {
+		Kfusion(row) = HKX24*(HKX10*P(17,row) - HKX11*P(18,row) + HKX12*P(1,row) + HKX13*P(0,row) - HKX14*P(2,row) + HKX15*P(3,row) + HKX6*P(16,row) + P(19,row));
+	}
+
+
+	aid_src_mag.fusion_enabled[0] = _control_status.flags.mag_3D;
+
+	if (aid_src_mag.innovation_variance[0] < R_MAG) {
+		// the innovation variance contribution from the state covariances is negative which means the covariance matrix is badly conditioned
+		_fault_status.flags.bad_mag_x = true;
+
+		// we need to re-initialise covariances and abort this fusion step
+		resetMagRelatedCovariances();
+		ECL_ERR("magX %s", numerical_error_covariance_reset_string);
+		return;
+
+	} else {
+		if (measurementUpdate(Kfusion, Hfusion, aid_src_mag.innovation[0])) {
+			aid_src_mag.fused[0] = true;
+			aid_src_mag.time_last_fuse[0] = _time_last_imu;
+			_fault_status.flags.bad_mag_x = false;
 			limitDeclination();
+		} else {
+			aid_src_mag.fused[0] = false;
+			_fault_status.flags.bad_mag_x = true;
+		}
+	}
+
+
+	// Y
+	// recalculate innovation variance because states and covariances have changed due to previous fusion
+	const float HKY0 = magD*q1 + magE*q0 - magN*q3;
+	const float HKY1 = magD*q0 - magE*q1 + magN*q2;
+	const float HKY2 = magD*q3 + magE*q2 + magN*q1;
+	const float HKY3 = magD*q2;
+	const float HKY4 = magE*q3;
+	const float HKY5 = magN*q0;
+	const float HKY6 = q1*q2;
+	const float HKY7 = q0*q3;
+	const float HKY8 = ecl::powf(q0, 2) - ecl::powf(q1, 2) + ecl::powf(q2, 2) - ecl::powf(q3, 2);
+	const float HKY9 = q0*q1 + q2*q3;
+	const float HKY10 = 2*HKY9;
+	const float HKY11 = -2*HKY6 + 2*HKY7;
+	const float HKY12 = 2*HKY2;
+	const float HKY13 = 2*HKY0;
+	const float HKY14 = 2*HKY1;
+	const float HKY15 = -2*HKY3 + 2*HKY4 + 2*HKY5;
+	const float HKY16 = HKY10*P(0,18) - HKY11*P(0,16) + HKY12*P(0,2) + HKY13*P(0,0) + HKY14*P(0,1) - HKY15*P(0,3) + HKY8*P(0,17) + P(0,20);
+	const float HKY17 = HKY10*P(17,18) - HKY11*P(16,17) + HKY12*P(2,17) + HKY13*P(0,17) + HKY14*P(1,17) - HKY15*P(3,17) + HKY8*P(17,17) + P(17,20);
+	const float HKY18 = HKY10*P(16,18) - HKY11*P(16,16) + HKY12*P(2,16) + HKY13*P(0,16) + HKY14*P(1,16) - HKY15*P(3,16) + HKY8*P(16,17) + P(16,20);
+	const float HKY19 = HKY10*P(3,18) - HKY11*P(3,16) + HKY12*P(2,3) + HKY13*P(0,3) + HKY14*P(1,3) - HKY15*P(3,3) + HKY8*P(3,17) + P(3,20);
+	const float HKY20 = HKY10*P(18,18) - HKY11*P(16,18) + HKY12*P(2,18) + HKY13*P(0,18) + HKY14*P(1,18) - HKY15*P(3,18) + HKY8*P(17,18) + P(18,20);
+	const float HKY21 = HKY10*P(1,18) - HKY11*P(1,16) + HKY12*P(1,2) + HKY13*P(0,1) + HKY14*P(1,1) - HKY15*P(1,3) + HKY8*P(1,17) + P(1,20);
+	const float HKY22 = HKY10*P(2,18) - HKY11*P(2,16) + HKY12*P(2,2) + HKY13*P(0,2) + HKY14*P(1,2) - HKY15*P(2,3) + HKY8*P(2,17) + P(2,20);
+	const float HKY23 = HKY10*P(18,20) - HKY11*P(16,20) + HKY12*P(2,20) + HKY13*P(0,20) + HKY14*P(1,20) - HKY15*P(3,20) + HKY8*P(17,20) + P(20,20);
+
+	aid_src_mag.innovation_variance[1] = (HKY10*HKY20 - HKY11*HKY18 + HKY12*HKY22 + HKY13*HKY16 + HKY14*HKY21 - HKY15*HKY19 + HKY17*HKY8 + HKY23 + R_MAG);
+
+	const float HKY24 = 1.f / aid_src_mag.innovation_variance[1];
+
+	// Calculate Y axis observation jacobians
+	Hfusion.setZero();
+	Hfusion.at<0>() = 2*HKY0;
+	Hfusion.at<1>() = 2*HKY1;
+	Hfusion.at<2>() = 2*HKY2;
+	Hfusion.at<3>() = 2*HKY3 - 2*HKY4 - 2*HKY5;
+	Hfusion.at<16>() = 2*HKY6 - 2*HKY7;
+	Hfusion.at<17>() = HKY8;
+	Hfusion.at<18>() = 2*HKY9;
+	Hfusion.at<20>() = 1;
+
+	// Calculate Y axis Kalman gains
+	if (update_all_states) {
+		Kfusion(0) = HKY16*HKY24;
+		Kfusion(1) = HKY21*HKY24;
+		Kfusion(2) = HKY22*HKY24;
+		Kfusion(3) = HKY19*HKY24;
+
+		for (unsigned row = 4; row <= 15; row++) {
+			Kfusion(row) = HKY24*(HKY10*P(row,18) - HKY11*P(row,16) + HKY12*P(2,row) + HKY13*P(0,row) + HKY14*P(1,row) - HKY15*P(3,row) + HKY8*P(row,17) + P(row,20));
+		}
+
+		for (unsigned row = 22; row <= 23; row++) {
+			Kfusion(row) = HKY24*(HKY10*P(18,row) - HKY11*P(16,row) + HKY12*P(2,row) + HKY13*P(0,row) + HKY14*P(1,row) - HKY15*P(3,row) + HKY8*P(17,row) + P(20,row));
+		}
+	}
+
+	Kfusion(16) = HKY18*HKY24;
+	Kfusion(17) = HKY17*HKY24;
+	Kfusion(18) = HKY20*HKY24;
+	Kfusion(19) = HKY24*(HKY10*P(18,19) - HKY11*P(16,19) + HKY12*P(2,19) + HKY13*P(0,19) + HKY14*P(1,19) - HKY15*P(3,19) + HKY8*P(17,19) + P(19,20));
+	Kfusion(20) = HKY23*HKY24;
+	Kfusion(21) = HKY24*(HKY10*P(18,21) - HKY11*P(16,21) + HKY12*P(2,21) + HKY13*P(0,21) + HKY14*P(1,21) - HKY15*P(3,21) + HKY8*P(17,21) + P(20,21));
+
+	// fuse y
+	aid_src_mag.fusion_enabled[1] = _control_status.flags.mag_3D;
+
+
+	if (aid_src_mag.innovation_variance[1] < R_MAG) {
+		// the innovation variance contribution from the state covariances is negtive which means the covariance matrix is badly conditioned
+		_fault_status.flags.bad_mag_y = true;
+
+		// we need to re-initialise covariances and abort this fusion step
+		resetMagRelatedCovariances();
+		ECL_ERR("magY %s", numerical_error_covariance_reset_string);
+		return;
+
+	} else {
+		if (measurementUpdate(Kfusion, Hfusion, aid_src_mag.innovation[1])) {
+			aid_src_mag.fused[1] = true;
+			aid_src_mag.time_last_fuse[1] = _time_last_imu;
+			_fault_status.flags.bad_mag_y = false;
+			limitDeclination();
+		} else {
+			aid_src_mag.fused[1] = false;
+			_fault_status.flags.bad_mag_y = true;
+		}
+	}
+
+	// Z
+	if (!_control_status.flags.synthetic_mag_z) {
+		// we do not fuse synthesized magnetomter measurements when doing 3D fusion
+
+		// recalculate innovation variance becasue states and covariances have changed due to previous fusion
+		const float HKZ0 = magD*q0 - magE*q1 + magN*q2;
+		const float HKZ1 = magN*q3;
+		const float HKZ2 = magD*q1;
+		const float HKZ3 = magE*q0;
+		const float HKZ4 = -magD*q2 + magE*q3 + magN*q0;
+		const float HKZ5 = magD*q3 + magE*q2 + magN*q1;
+		const float HKZ6 = q0*q2 + q1*q3;
+		const float HKZ7 = q2*q3;
+		const float HKZ8 = q0*q1;
+		const float HKZ9 = ecl::powf(q0, 2) - ecl::powf(q1, 2) - ecl::powf(q2, 2) + ecl::powf(q3, 2);
+		const float HKZ10 = 2*HKZ6;
+		const float HKZ11 = -2*HKZ7 + 2*HKZ8;
+		const float HKZ12 = 2*HKZ5;
+		const float HKZ13 = 2*HKZ0;
+		const float HKZ14 = -2*HKZ1 + 2*HKZ2 + 2*HKZ3;
+		const float HKZ15 = 2*HKZ4;
+		const float HKZ16 = HKZ10*P(0,16) - HKZ11*P(0,17) + HKZ12*P(0,3) + HKZ13*P(0,0) - HKZ14*P(0,1) + HKZ15*P(0,2) + HKZ9*P(0,18) + P(0,21);
+		const float HKZ17 = HKZ10*P(16,18) - HKZ11*P(17,18) + HKZ12*P(3,18) + HKZ13*P(0,18) - HKZ14*P(1,18) + HKZ15*P(2,18) + HKZ9*P(18,18) + P(18,21);
+		const float HKZ18 = HKZ10*P(16,17) - HKZ11*P(17,17) + HKZ12*P(3,17) + HKZ13*P(0,17) - HKZ14*P(1,17) + HKZ15*P(2,17) + HKZ9*P(17,18) + P(17,21);
+		const float HKZ19 = HKZ10*P(1,16) - HKZ11*P(1,17) + HKZ12*P(1,3) + HKZ13*P(0,1) - HKZ14*P(1,1) + HKZ15*P(1,2) + HKZ9*P(1,18) + P(1,21);
+		const float HKZ20 = HKZ10*P(16,16) - HKZ11*P(16,17) + HKZ12*P(3,16) + HKZ13*P(0,16) - HKZ14*P(1,16) + HKZ15*P(2,16) + HKZ9*P(16,18) + P(16,21);
+		const float HKZ21 = HKZ10*P(3,16) - HKZ11*P(3,17) + HKZ12*P(3,3) + HKZ13*P(0,3) - HKZ14*P(1,3) + HKZ15*P(2,3) + HKZ9*P(3,18) + P(3,21);
+		const float HKZ22 = HKZ10*P(2,16) - HKZ11*P(2,17) + HKZ12*P(2,3) + HKZ13*P(0,2) - HKZ14*P(1,2) + HKZ15*P(2,2) + HKZ9*P(2,18) + P(2,21);
+		const float HKZ23 = HKZ10*P(16,21) - HKZ11*P(17,21) + HKZ12*P(3,21) + HKZ13*P(0,21) - HKZ14*P(1,21) + HKZ15*P(2,21) + HKZ9*P(18,21) + P(21,21);
+
+		aid_src_mag.innovation_variance[2] = (HKZ10*HKZ20 - HKZ11*HKZ18 + HKZ12*HKZ21 + HKZ13*HKZ16 - HKZ14*HKZ19 + HKZ15*HKZ22 + HKZ17*HKZ9 + HKZ23 + R_MAG);
+
+		const float HKZ24 = 1.f / aid_src_mag.innovation_variance[2];
+
+		// calculate Z axis observation jacobians
+		Hfusion.setZero();
+		Hfusion.at<0>() = 2*HKZ0;
+		Hfusion.at<1>() = 2*HKZ1 - 2*HKZ2 - 2*HKZ3;
+		Hfusion.at<2>() = 2*HKZ4;
+		Hfusion.at<3>() = 2*HKZ5;
+		Hfusion.at<16>() = 2*HKZ6;
+		Hfusion.at<17>() = 2*HKZ7 - 2*HKZ8;
+		Hfusion.at<18>() = HKZ9;
+		Hfusion.at<21>() = 1;
+
+		// Calculate Z axis Kalman gains
+		if (update_all_states) {
+			Kfusion(0) = HKZ16*HKZ24;
+			Kfusion(1) = HKZ19*HKZ24;
+			Kfusion(2) = HKZ22*HKZ24;
+			Kfusion(3) = HKZ21*HKZ24;
+
+			for (unsigned row = 4; row <= 15; row++) {
+				Kfusion(row) = HKZ24*(HKZ10*P(row,16) - HKZ11*P(row,17) + HKZ12*P(3,row) + HKZ13*P(0,row) - HKZ14*P(1,row) + HKZ15*P(2,row) + HKZ9*P(row,18) + P(row,21));
+			}
+
+			for (unsigned row = 22; row <= 23; row++) {
+				Kfusion(row) = HKZ24*(HKZ10*P(16,row) - HKZ11*P(17,row) + HKZ12*P(3,row) + HKZ13*P(0,row) - HKZ14*P(1,row) + HKZ15*P(2,row) + HKZ9*P(18,row) + P(21,row));
+			}
+		}
+
+		Kfusion(16) = HKZ20*HKZ24;
+		Kfusion(17) = HKZ18*HKZ24;
+		Kfusion(18) = HKZ17*HKZ24;
+
+		for (unsigned row = 19; row <= 20; row++) {
+			Kfusion(row) = HKZ24*(HKZ10*P(16,row) - HKZ11*P(17,row) + HKZ12*P(3,row) + HKZ13*P(0,row) - HKZ14*P(1,row) + HKZ15*P(2,row) + HKZ9*P(18,row) + P(row,21));
+		}
+
+		Kfusion(21) = HKZ23*HKZ24;
+
+		if (aid_src_mag.innovation_variance[2] < R_MAG) {
+			// the innovation variance contribution from the state covariances is negative which means the covariance matrix is badly conditioned
+			_fault_status.flags.bad_mag_z = true;
+
+			// we need to re-initialise covariances and abort this fusion step
+			resetMagRelatedCovariances();
+			ECL_ERR("magZ %s", numerical_error_covariance_reset_string);
+			return;
+
+		} else {
+			// fuse z
+			aid_src_mag.fusion_enabled[2] = _control_status.flags.mag_3D;
+
+			if (measurementUpdate(Kfusion, Hfusion, aid_src_mag.innovation[2])) {
+				aid_src_mag.fused[2] = true;
+				aid_src_mag.time_last_fuse[2] = _time_last_imu;
+				_fault_status.flags.bad_mag_z = false;
+				limitDeclination();
+			} else {
+				aid_src_mag.fused[2] = false;
+				_fault_status.flags.bad_mag_z = true;
+			}
 		}
 	}
 }
@@ -651,7 +690,7 @@ bool Ekf::updateQuaternion(const float innovation, const float variance, const f
 	_yaw_test_ratio = sq(innovation) / (sq(gate_sigma) * _heading_innov_var);
 
 	// we are no longer using 3-axis fusion so set the reported test levels to zero
-	_mag_test_ratio.setZero();
+	memset(_aid_src_mag.test_ratio, 0, sizeof(_aid_src_mag.test_ratio)); // TODO
 
 	// set the magnetometer unhealthy if the test fails
 	if (_yaw_test_ratio > 1.0f) {

src/modules/ekf2/EKF/vel_pos_fusion.cpp

@@ -102,7 +102,7 @@ bool Ekf::fuseVerticalVelocity(const Vector3f &innov, const float innov_gate, co
 }
 
 bool Ekf::fuseHorizontalPosition(const Vector3f &innov, const float innov_gate, const Vector3f &obs_var,
-				 Vector3f &innov_var, Vector2f &test_ratio, bool inhibit_gate)
+				 Vector3f &innov_var, Vector2f &test_ratio)
 {
 
 	innov_var(0) = P(7, 7) + obs_var(0);
@@ -112,12 +112,7 @@ bool Ekf::fuseHorizontalPosition(const Vector3f &innov, const float innov_gate,
 
 	const bool innov_check_pass = test_ratio(0) <= 1.0f;
 
-	if (innov_check_pass || inhibit_gate) {
-		if (inhibit_gate && test_ratio(0) > sq(100.0f / innov_gate)) {
-			// always protect against extreme values that could result in a NaN
-			return false;
-		}
-
+	if (innov_check_pass) {
 		_innov_check_fail_status.flags.reject_hor_pos = false;
 
 		bool fuse_x = fuseVelPosHeight(innov(0), innov_var(0), 3);

src/modules/ekf2/EKF2.cpp

@@ -614,6 +614,8 @@ void EKF2::Run()
 			UpdateMagCalibration(now);
 			PublishSensorBias(now);
 
+			PublishAidSourceStatus(now);
+
 		} else {
 			// ekf no update
 			perf_set_elapsed(_ecl_ekf_update_perf, hrt_elapsed_time(&ekf_update_start));
@@ -632,6 +634,29 @@ void EKF2::Run()
 	ScheduleDelayed(100_ms);
 }
 
+void EKF2::PublishAidSourceStatus(const hrt_abstime &timestamp)
+{
+	// baro height
+	PublishAidSourceStatus(_ekf.aid_src_baro_hgt(), _status_baro_hgt_pub_last, _estimator_aid_src_baro_hgt_pub);
+
+	// RNG height
+	PublishAidSourceStatus(_ekf.aid_src_rng_hgt(), _status_rng_hgt_pub_last, _estimator_aid_src_rng_hgt_pub);
+
+	// fake position
+	PublishAidSourceStatus(_ekf.aid_src_fake_pos(), _status_fake_pos_pub_last, _estimator_aid_src_fake_pos_pub);
+
+	// mag heading
+	PublishAidSourceStatus(_ekf.aid_src_mag_heading(), _status_mag_heading_pub_last, _estimator_aid_src_mag_heading_pub);
+
+	// mag
+	PublishAidSourceStatus(_ekf.aid_src_mag(), _status_mag_pub_last, _estimator_aid_src_mag_pub);
+
+	// GNSS velocity & position
+	PublishAidSourceStatus(_ekf.aid_src_gnss_vel(), _status_gnss_vel_pub_last, _estimator_aid_src_gnss_vel_pub);
+	PublishAidSourceStatus(_ekf.aid_src_gnss_pos(), _status_gnss_pos_pub_last, _estimator_aid_src_gnss_pos_pub);
+
+}
+
 void EKF2::PublishAttitude(const hrt_abstime &timestamp)
 {
 	if (_ekf.attitude_valid()) {
@@ -713,6 +738,10 @@ void EKF2::PublishEventFlags(const hrt_abstime &timestamp)
 		event_flags.starting_vision_vel_fusion          = _ekf.information_event_flags().starting_vision_vel_fusion;
 		event_flags.starting_vision_yaw_fusion          = _ekf.information_event_flags().starting_vision_yaw_fusion;
 		event_flags.yaw_aligned_to_imu_gps              = _ekf.information_event_flags().yaw_aligned_to_imu_gps;
+		event_flags.reset_hgt_to_baro                   = _ekf.information_event_flags().reset_hgt_to_baro;
+		event_flags.reset_hgt_to_gps                    = _ekf.information_event_flags().reset_hgt_to_gps;
+		event_flags.reset_hgt_to_rng                    = _ekf.information_event_flags().reset_hgt_to_rng;
+		event_flags.reset_hgt_to_ev                     = _ekf.information_event_flags().reset_hgt_to_ev;
 
 		event_flags.warning_event_changes               = _filter_warning_event_changes;
 		event_flags.gps_quality_poor                    = _ekf.warning_event_flags().gps_quality_poor;
@@ -1332,9 +1361,6 @@ void EKF2::PublishStatusFlags(const hrt_abstime &timestamp)
 		status_flags.reject_ver_vel                  = _ekf.innov_check_fail_status_flags().reject_ver_vel;
 		status_flags.reject_hor_pos                  = _ekf.innov_check_fail_status_flags().reject_hor_pos;
 		status_flags.reject_ver_pos                  = _ekf.innov_check_fail_status_flags().reject_ver_pos;
-		status_flags.reject_mag_x                    = _ekf.innov_check_fail_status_flags().reject_mag_x;
-		status_flags.reject_mag_y                    = _ekf.innov_check_fail_status_flags().reject_mag_y;
-		status_flags.reject_mag_z                    = _ekf.innov_check_fail_status_flags().reject_mag_z;
 		status_flags.reject_yaw                      = _ekf.innov_check_fail_status_flags().reject_yaw;
 		status_flags.reject_airspeed                 = _ekf.innov_check_fail_status_flags().reject_airspeed;
 		status_flags.reject_sideslip                 = _ekf.innov_check_fail_status_flags().reject_sideslip;

src/modules/ekf2/EKF2.hpp

@@ -135,6 +135,7 @@ private:
 
 	void Run() override;
 
+	void PublishAidSourceStatus(const hrt_abstime &timestamp);
 	void PublishAttitude(const hrt_abstime &timestamp);
 	void PublishBaroBias(const hrt_abstime &timestamp);
 	void PublishEventFlags(const hrt_abstime &timestamp);
@@ -167,6 +168,21 @@ private:
 	void UpdateGyroCalibration(const hrt_abstime &timestamp);
 	void UpdateMagCalibration(const hrt_abstime &timestamp);
 
+	// publish helper for estimator_aid_source topics
+	template <typename T>
+	void PublishAidSourceStatus(const T &status, hrt_abstime &status_publish_last, uORB::PublicationMulti<T> &pub)
+	{
+		if (status.timestamp_sample > status_publish_last) {
+			// publish if updated
+			T status_out{status};
+			status_out.estimator_instance = _instance;
+			status_out.timestamp = hrt_absolute_time();
+			pub.publish(status_out);
+
+			// record timestamp sample
+			status_publish_last = status.timestamp_sample;
+		}
+	}
 
 	/*
 	 * Calculate filtered WGS84 height from estimated AMSL height
@@ -239,6 +255,17 @@ private:
 	hrt_abstime _last_sensor_bias_published{0};
 	hrt_abstime _last_gps_status_published{0};
 
+	hrt_abstime _status_baro_hgt_pub_last{0};
+	hrt_abstime _status_rng_hgt_pub_last{0};
+
+	hrt_abstime _status_fake_pos_pub_last{0};
+
+	hrt_abstime _status_mag_heading_pub_last{0};
+	hrt_abstime _status_mag_pub_last{0};
+
+	hrt_abstime _status_gnss_vel_pub_last{0};
+	hrt_abstime _status_gnss_pos_pub_last{0};
+
 	float _last_baro_bias_published{};
 
 	float _airspeed_scale_factor{1.0f}; ///< scale factor correction applied to airspeed measurements
@@ -298,6 +325,17 @@ private:
 	uORB::PublicationMulti<vehicle_odometry_s>           _estimator_visual_odometry_aligned_pub{ORB_ID(estimator_visual_odometry_aligned)};
 	uORB::PublicationMulti<yaw_estimator_status_s>       _yaw_est_pub{ORB_ID(yaw_estimator_status)};
 
+	uORB::PublicationMulti<estimator_aid_source_1d_s> _estimator_aid_src_baro_hgt_pub{ORB_ID(estimator_aid_src_baro_hgt)};
+	uORB::PublicationMulti<estimator_aid_source_1d_s> _estimator_aid_src_rng_hgt_pub{ORB_ID(estimator_aid_src_rng_hgt)};
+
+	uORB::PublicationMulti<estimator_aid_source_2d_s> _estimator_aid_src_fake_pos_pub{ORB_ID(estimator_aid_src_fake_pos)};
+
+	uORB::PublicationMulti<estimator_aid_source_1d_s> _estimator_aid_src_mag_heading_pub{ORB_ID(estimator_aid_src_mag_heading)};
+	uORB::PublicationMulti<estimator_aid_source_3d_s> _estimator_aid_src_mag_pub{ORB_ID(estimator_aid_src_mag)};
+
+	uORB::PublicationMulti<estimator_aid_source_3d_s> _estimator_aid_src_gnss_vel_pub{ORB_ID(estimator_aid_src_gnss_vel)};
+	uORB::PublicationMulti<estimator_aid_source_3d_s> _estimator_aid_src_gnss_pos_pub{ORB_ID(estimator_aid_src_gnss_pos)};
+
 	// publications with topic dependent on multi-mode
 	uORB::PublicationMulti<vehicle_attitude_s>           _attitude_pub;
 	uORB::PublicationMulti<vehicle_local_position_s>     _local_position_pub;

src/modules/ekf2/test/change_indication/ekf_gsf_reset.csv

@@ -58,26 +58,26 @@ Timestamp,state[0],state[1],state[2],state[3],state[4],state[5],state[6],state[7
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src/modules/ekf2/test/change_indication/iris_gps.csv

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src/modules/ekf2/test/test_EKF_fusionLogic.cpp

@@ -157,7 +157,7 @@ TEST_F(EkfFusionLogicTest, rejectGpsSignalJump)
 	EXPECT_TRUE(matrix::isEqual(accel_bias_new, accel_bias_old, 0.01f));
 
 	// BUT THEN: GPS fusion should reset after a while
-	// (it takes some time beacuse vel fusion is still good)
+	// (it takes some time because vel fusion is still good)
 	_sensor_simulator.runSeconds(14);
 	pos_new = _ekf->getPosition();
 	vel_new = _ekf->getVelocity();