fly316/PX4-Autopilot
7
0
Fork 0
mirror of https://gitee.com/mirrors_PX4/PX4-Autopilot.git synced 2026-10-16 15:03:33 +08:00
Code Issues Packages Projects Releases Wiki Activity

EKF: Use more generic variable name for bad yaw fusion flag

Browse Source 
This flag now reports on  fusion of data that is not from a magnetometer.
This commit is contained in:
Paul Riseborough
2018-09-19 06:32:43 +10:00
committed by Paul Riseborough
parent eb52f7c410
commit 0220f469b0
3 changed files with 9 additions and 9 deletions
Download Patch File Download Diff File Expand all files Collapse all files
EKF/common.h
+1 -1
View File
@@ -367,7 +367,7 @@ union fault_status_u {
bool bad_mag_x: 1; ///< 0 - true if the fusion of the magnetometer X-axis has encountered a numerical error
bool bad_mag_y: 1; ///< 1 - true if the fusion of the magnetometer Y-axis has encountered a numerical error
bool bad_mag_z: 1; ///< 2 - true if the fusion of the magnetometer Z-axis has encountered a numerical error
bool bad_mag_hdg: 1; ///< 3 - true if the fusion of the magnetic heading has encountered a numerical error
bool bad_hdg: 1; ///< 3 - true if the fusion of the heading angle has encountered a numerical error
bool bad_mag_decl: 1; ///< 4 - true if the fusion of the magnetic declination has encountered a numerical error
bool bad_airspeed: 1; ///< 5 - true if fusion of the airspeed has encountered a numerical error
bool bad_sideslip: 1; ///< 6 - true if fusion of the synthetic sideslip constraint has encountered a numerical error
EKF/gps_yaw_fusion.cpp
+4 -4
View File
@@ -165,12 +165,12 @@ void Ekf::fuseGpsAntYaw()
// check if the innovation variance calculation is badly conditioned
if (_heading_innov_var >= R_YAW) {
// the innovation variance contribution from the state covariances is not negative, no fault
_fault_status.flags.bad_mag_hdg = false;
_fault_status.flags.bad_hdg = false;
heading_innov_var_inv = 1.0f / _heading_innov_var;
} else {
// the innovation variance contribution from the state covariances is negative which means the covariance matrix is badly conditioned
_fault_status.flags.bad_mag_hdg = true;
_fault_status.flags.bad_hdg = true;
// we reinitialise the covariance matrix and abort this fusion step
initialiseCovariance();
@@ -255,7 +255,7 @@ void Ekf::fuseGpsAntYaw()
// if the covariance correction will result in a negative variance, then
// the covariance marix is unhealthy and must be corrected
bool healthy = true;
_fault_status.flags.bad_mag_hdg = false;
_fault_status.flags.bad_hdg = false;
for (int i = 0; i < _k_num_states; i++) {
if (P[i][i] < KHP[i][i]) {
@@ -267,7 +267,7 @@ void Ekf::fuseGpsAntYaw()
healthy = false;
// update individual measurement health status
_fault_status.flags.bad_mag_hdg = true;
_fault_status.flags.bad_hdg = true;
}
}
EKF/mag_fusion.cpp
+4 -4
View File
@@ -666,12 +666,12 @@ void Ekf::fuseHeading()
// check if the innovation variance calculation is badly conditioned
if (_heading_innov_var >= R_YAW) {
// the innovation variance contribution from the state covariances is not negative, no fault
_fault_status.flags.bad_mag_hdg = false;
_fault_status.flags.bad_hdg = false;
heading_innov_var_inv = 1.0f / _heading_innov_var;
} else {
// the innovation variance contribution from the state covariances is negative which means the covariance matrix is badly conditioned
_fault_status.flags.bad_mag_hdg = true;
_fault_status.flags.bad_hdg = true;
// we reinitialise the covariance matrix and abort this fusion step
initialiseCovariance();
@@ -756,7 +756,7 @@ void Ekf::fuseHeading()
// if the covariance correction will result in a negative variance, then
// the covariance marix is unhealthy and must be corrected
bool healthy = true;
_fault_status.flags.bad_mag_hdg = false;
_fault_status.flags.bad_hdg = false;
for (int i = 0; i < _k_num_states; i++) {
if (P[i][i] < KHP[i][i]) {
@@ -768,7 +768,7 @@ void Ekf::fuseHeading()
healthy = false;
// update individual measurement health status
_fault_status.flags.bad_mag_hdg = true;
_fault_status.flags.bad_hdg = true;
}
}