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ekf2: use more efficient covariance update computation

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KSK' is faster to compute than KHP and is mathematically equivalent
This commit is contained in:
bresch
2022-12-12 12:31:54 -05:00
committed by Daniel Agar
parent 61fa28c0d6
commit 41abf695a8
8 changed files with 21 additions and 49 deletions
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src/modules/ekf2/EKF/mag_fusion.cpp
+3 -5
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@@ -200,7 +200,7 @@ bool Ekf::fuseMag(const Vector3f &mag, estimator_aid_source3d_s &aid_src_mag, bo
}
}
if (measurementUpdate(Kfusion, Hfusion, aid_src_mag.innovation[index])) {
if (measurementUpdate(Kfusion, aid_src_mag.innovation_variance[index], aid_src_mag.innovation[index])) {
fused[index] = true;
limitDeclination();
@@ -312,9 +312,7 @@ bool Ekf::fuseYaw(const float innovation, const float variance, estimator_aid_so
_innov_check_fail_status.flags.reject_yaw = false;
}
SparseVector24f<0,1,2,3> Hfusion(H_YAW);
if (measurementUpdate(Kfusion, Hfusion, aid_src_status.innovation)) {
if (measurementUpdate(Kfusion, aid_src_status.innovation_variance, aid_src_status.innovation)) {
_time_last_heading_fuse = _imu_sample_delayed.time_us;
@@ -353,7 +351,7 @@ bool Ekf::fuseDeclination(float decl_sigma)
// Calculate the Kalman gains
Vector24f Kfusion = P * Hfusion / innovation_variance;
const bool is_fused = measurementUpdate(Kfusion, Hfusion, innovation);
const bool is_fused = measurementUpdate(Kfusion, innovation_variance, innovation);
_fault_status.flags.bad_mag_decl = !is_fused;