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EKF: Improve protection against bad pos vel fusion

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This commit is contained in:
Paul Riseborough
2016-05-07 14:22:59 +10:00
parent da9d894441
commit 310bd97080
2 changed files with 64 additions and 8 deletions
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EKF/common.h
+7
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@@ -332,6 +332,13 @@ struct fault_status_t {
bool bad_sideslip: 1; // true if fusion of the synthetic sideslip constraint has encountered a numerical error
bool bad_optflow_X: 1; // true if fusion of the optical flow X axis has encountered a numerical error
bool bad_optflow_Y: 1; // true if fusion of the optical flow Y axis has encountered a numerical error
bool bad_vel_N: 1; // true if fusion of the North velocity has encountered a numerical error
bool bad_vel_E: 1; // true if fusion of the East velocity has encountered a numerical error
bool bad_vel_D: 1; // true if fusion of the Down velocity has encountered a numerical error
bool bad_pos_N: 1; // true if fusion of the North position has encountered a numerical error
bool bad_pos_E: 1; // true if fusion of the East position has encountered a numerical error
bool bad_pos_D: 1; // true if fusion of the Down position has encountered a numerical error
};
// publish the status of various GPS quality checks
EKF/vel_pos_fusion.cpp
+57 -8
View File
@@ -201,9 +201,6 @@ void Ekf::fuseVelPosHeight()
Kfusion[row] = P[row][state_index] / _vel_pos_innov_var[obs_index];
}
// update the states
fuse(Kfusion, _vel_pos_innov[obs_index]);
// update covarinace matrix via Pnew = (I - KH)P
for (unsigned row = 0; row < _k_num_states; row++) {
for (unsigned column = 0; column < _k_num_states; column++) {
@@ -211,13 +208,65 @@ void Ekf::fuseVelPosHeight()
}
}
for (unsigned row = 0; row < _k_num_states; row++) {
for (unsigned column = 0; column < _k_num_states; column++) {
P[row][column] = P[row][column] - KHP[row][column];
// if the covariance correction will result in a negative variance, then
// the covariance marix 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
zeroRows(P,i,i);
zeroCols(P,i,i);
//flag as unhealthy
healthy = false;
// update individual measurement health status
if (obs_index == 0) {
_fault_status.bad_vel_N = true;
} else if (obs_index == 1) {
_fault_status.bad_vel_E = true;
} else if (obs_index == 2) {
_fault_status.bad_vel_D = true;
} else if (obs_index == 3) {
_fault_status.bad_pos_N = true;
} else if (obs_index == 4) {
_fault_status.bad_pos_E = true;
} else if (obs_index == 5) {
_fault_status.bad_pos_D = true;
}
} else {
// update individual measurement health status
if (obs_index == 0) {
_fault_status.bad_vel_N = false;
} else if (obs_index == 1) {
_fault_status.bad_vel_E = false;
} else if (obs_index == 2) {
_fault_status.bad_vel_D = false;
} else if (obs_index == 3) {
_fault_status.bad_pos_N = false;
} else if (obs_index == 4) {
_fault_status.bad_pos_E = false;
} else if (obs_index == 5) {
_fault_status.bad_pos_D = false;
}
}
}
makeSymmetrical();
limitCov();
// only apply covariance and state corrrections if healthy
if (healthy) {
// apply the covariance corrections
for (unsigned row = 0; row < _k_num_states; row++) {
for (unsigned column = 0; column < _k_num_states; column++) {
P[row][column] = P[row][column] - KHP[row][column];
}
}
// correct the covariance marix for gross errors
makeSymmetrical();
limitCov();
// apply the state corrections
fuse(Kfusion, _vel_pos_innov[obs_index]);
}
}
}