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PX4-Autopilot/src/modules/ekf2/EKF/vel_pos_fusion.cpp
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/**
* @file vel_pos_fusion.cpp
* Function for fusing gps and baro measurements/
*
* @author Roman Bast <bapstroman@gmail.com>
* @author Siddharth Bharat Purohit <siddharthbharatpurohit@gmail.com>
* @author Paul Riseborough <p_riseborough@live.com.au>
*
*/
#include <mathlib/mathlib.h>
#include "ekf.h"
bool Ekf::fuseHorizontalVelocity(const Vector3f &innov, const float innov_gate, const Vector3f &obs_var,
Vector3f &innov_var, Vector2f &test_ratio)
{
innov_var(0) = P(4, 4) + obs_var(0);
innov_var(1) = P(5, 5) + obs_var(1);
test_ratio(0) = fmaxf(sq(innov(0)) / (sq(innov_gate) * innov_var(0)),
sq(innov(1)) / (sq(innov_gate) * innov_var(1)));
const bool innov_check_pass = (test_ratio(0) <= 1.0f);
if (innov_check_pass) {
_innov_check_fail_status.flags.reject_hor_vel = false;
bool fuse_vx = fuseVelPosHeight(innov(0), innov_var(0), 0);
bool fuse_vy = fuseVelPosHeight(innov(1), innov_var(1), 1);
return fuse_vx && fuse_vy;
} else {
_innov_check_fail_status.flags.reject_hor_vel = true;
return false;
}
}
bool Ekf::fuseVerticalVelocity(const Vector3f &innov, const float innov_gate, const Vector3f &obs_var,
Vector3f &innov_var, Vector2f &test_ratio)
{
innov_var(2) = P(6, 6) + obs_var(2);
test_ratio(1) = sq(innov(2)) / (sq(innov_gate) * innov_var(2));
_vert_vel_innov_ratio = innov(2) / sqrtf(innov_var(2));
_vert_vel_fuse_time_us = _time_last_imu;
bool innov_check_pass = (test_ratio(1) <= 1.0f);
// if there is bad vertical acceleration data, then don't reject measurement,
// but limit innovation to prevent spikes that could destabilise the filter
float innovation;
if (_fault_status.flags.bad_acc_vertical && !innov_check_pass) {
const float innov_limit = innov_gate * sqrtf(innov_var(2));
innovation = math::constrain(innov(2), -innov_limit, innov_limit);
innov_check_pass = true;
} else {
innovation = innov(2);
}
if (innov_check_pass) {
_innov_check_fail_status.flags.reject_ver_vel = false;
return fuseVelPosHeight(innovation, innov_var(2), 2);
} else {
_innov_check_fail_status.flags.reject_ver_vel = true;
return false;
}
}
bool Ekf::fuseHorizontalPosition(const Vector3f &innov, const float innov_gate, const Vector3f &obs_var,
Vector3f &innov_var, Vector2f &test_ratio)
{
innov_var(0) = P(7, 7) + obs_var(0);
innov_var(1) = P(8, 8) + obs_var(1);
test_ratio(0) = fmaxf(sq(innov(0)) / (sq(innov_gate) * innov_var(0)),
sq(innov(1)) / (sq(innov_gate) * innov_var(1)));
const bool innov_check_pass = test_ratio(0) <= 1.0f;
if (innov_check_pass) {
_innov_check_fail_status.flags.reject_hor_pos = false;
bool fuse_x = fuseVelPosHeight(innov(0), innov_var(0), 3);
bool fuse_y = fuseVelPosHeight(innov(1), innov_var(1), 4);
return fuse_x && fuse_y;
} else {
_innov_check_fail_status.flags.reject_hor_pos = true;
return false;
}
}
bool Ekf::fuseVerticalPosition(const float innov, const float innov_gate, const float obs_var,
float &innov_var, float &test_ratio)
{
innov_var = P(9, 9) + obs_var;
test_ratio = sq(innov) / (sq(innov_gate) * innov_var);
_vert_pos_innov_ratio = innov / sqrtf(innov_var);
_vert_pos_fuse_attempt_time_us = _time_last_imu;
bool innov_check_pass = test_ratio <= 1.0f;
// if there is bad vertical acceleration data, then don't reject measurement,
// but limit innovation to prevent spikes that could destabilise the filter
float innovation;
if (_fault_status.flags.bad_acc_vertical && !innov_check_pass) {
const float innov_limit = innov_gate * sqrtf(innov_var);
innovation = math::constrain(innov, -innov_limit, innov_limit);
innov_check_pass = true;
} else {
innovation = innov;
}
if (innov_check_pass) {
_innov_check_fail_status.flags.reject_ver_pos = false;
return fuseVelPosHeight(innovation, innov_var, 5);
} else {
_innov_check_fail_status.flags.reject_ver_pos = true;
return false;
}
}
// Helper function that fuses a single velocity or position measurement
bool Ekf::fuseVelPosHeight(const float innov, const float innov_var, const int obs_index)
{
Vector24f Kfusion; // Kalman gain vector for any single observation - sequential fusion is used.
const unsigned state_index = obs_index + 4; // we start with vx and this is the 4. state
// calculate kalman gain K = PHS, where S = 1/innovation variance
for (int row = 0; row < _k_num_states; row++) {
Kfusion(row) = P(row, state_index) / innov_var;
}
SquareMatrix24f KHP;
for (unsigned row = 0; row < _k_num_states; row++) {
for (unsigned column = 0; column < _k_num_states; column++) {
KHP(row, column) = Kfusion(row) * P(state_index, column);
}
}
// if the covariance correction will result in a negative variance, then
// the covariance matrix is unhealthy and must be corrected
bool healthy = true;
for (int i = 0; i < _k_num_states; i++) {
if (P(i, i) < KHP(i, i)) {
// zero rows and columns
P.uncorrelateCovarianceSetVariance<1>(i, 0.0f);
healthy = false;
}
}
setVelPosStatus(obs_index, healthy);
if (healthy) {
// apply the covariance corrections
P -= KHP;
fixCovarianceErrors(true);
// apply the state corrections
fuse(Kfusion, innov);
return true;
}
return false;
}
void Ekf::setVelPosStatus(const int index, const bool healthy)
{
switch (index) {
case 0:
if (healthy) {
_fault_status.flags.bad_vel_N = false;
_time_last_hor_vel_fuse = _time_last_imu;
} else {
_fault_status.flags.bad_vel_N = true;
}
break;
case 1:
if (healthy) {
_fault_status.flags.bad_vel_E = false;
_time_last_hor_vel_fuse = _time_last_imu;
} else {
_fault_status.flags.bad_vel_E = true;
}
break;
case 2:
if (healthy) {
_fault_status.flags.bad_vel_D = false;
_time_last_ver_vel_fuse = _time_last_imu;
} else {
_fault_status.flags.bad_vel_D = true;
}
break;
case 3:
if (healthy) {
_fault_status.flags.bad_pos_N = false;
_time_last_hor_pos_fuse = _time_last_imu;
} else {
_fault_status.flags.bad_pos_N = true;
}
break;
case 4:
if (healthy) {
_fault_status.flags.bad_pos_E = false;
_time_last_hor_pos_fuse = _time_last_imu;
} else {
_fault_status.flags.bad_pos_E = true;
}
break;
case 5:
if (healthy) {
_fault_status.flags.bad_pos_D = false;
_time_last_hgt_fuse = _time_last_imu;
} else {
_fault_status.flags.bad_pos_D = true;
}
break;
}
}