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AP_NavEKF: Fix one frame delay in processing yaw estimator velocity data

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This commit is contained in:
Paul Riseborough 2020-04-25 07:52:25 +10:00 committed by Peter Barker
parent ed14ab84ce
commit 0ce4dd457d
2 changed files with 52 additions and 56 deletions
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101
libraries/AP_NavEKF/EKFGSF_yaw.cpp
View File

@ -98,47 +98,7 @@ void EKFGSF_yaw::update(const Vector3f &delAng,
predict(mdl_idx);
}
// The 3-state EKF models only run when flying to avoid corrupted estimates due to operator handling and GPS interference
if (vel_data_updated && run_ekf_gsf) {
if (!vel_fuse_running) {
// Perform in-flight alignment
for (uint8_t mdl_idx = 0; mdl_idx < N_MODELS_EKFGSF; mdl_idx ++) {
// Use the firstGPS measurement to set the velocities and corresponding variances
EKF[mdl_idx].X[0] = vel_NE[0];
EKF[mdl_idx].X[1] = vel_NE[1];
EKF[mdl_idx].P[0][0] = sq(fmaxf(vel_accuracy, 0.5f));
EKF[mdl_idx].P[1][1] = EKF[mdl_idx].P[0][0];
}
alignYaw();
vel_fuse_running = true;
} else {
float total_w = 0.0f;
float newWeight[(uint8_t)N_MODELS_EKFGSF];
bool state_update_failed = false;
for (uint8_t mdl_idx = 0; mdl_idx < N_MODELS_EKFGSF; mdl_idx ++) {
// Update states and covariances using GPS NE velocity measurements fused as direct state observations
if (!correct(mdl_idx)) {
state_update_failed = true;
}
}
if (!state_update_failed) {
// Calculate weighting for each model assuming a normal error distribution
for (uint8_t mdl_idx = 0; mdl_idx < N_MODELS_EKFGSF; mdl_idx ++) {
newWeight[mdl_idx]= fmaxf(gaussianDensity(mdl_idx) * GSF.weights[mdl_idx], 0.0f);
total_w += newWeight[mdl_idx];
}
// Normalise the sum of weights to unity
if (vel_fuse_running && is_positive(total_w)) {
float total_w_inv = 1.0f / total_w;
for (uint8_t mdl_idx = 0; mdl_idx < N_MODELS_EKFGSF; mdl_idx ++) {
GSF.weights[mdl_idx] = newWeight[mdl_idx] * total_w_inv;
}
}
}
}
} else if (vel_fuse_running && !run_ekf_gsf) {
if (vel_fuse_running && !run_ekf_gsf) {
// We have landed so reset EKF-GSF states and wait to fly again
// Do not reset the AHRS as it continues to run when on ground
initialise();
@ -176,16 +136,55 @@ void EKFGSF_yaw::update(const Vector3f &delAng,
float yawDelta = wrap_PI(EKF[mdl_idx].X[2] - GSF.yaw);
GSF.yaw_variance += GSF.weights[mdl_idx] * (EKF[mdl_idx].P[2][2] + sq(yawDelta));
}
// prevent the same velocity data being used again
vel_data_updated = false;
}
void EKFGSF_yaw::pushVelData(Vector2f vel, float velAcc)
void EKFGSF_yaw::fuseVelData(Vector2f vel, float velAcc)
{
// set class variables
velObsVar = sq(fmaxf(velAcc, 0.5f));
vel_NE = vel;
vel_accuracy = velAcc;
vel_data_updated = true;
// The 3-state EKF models only run when flying to avoid corrupted estimates due to operator handling and GPS interference
if (run_ekf_gsf) {
if (!vel_fuse_running) {
// Perform in-flight alignment
for (uint8_t mdl_idx = 0; mdl_idx < N_MODELS_EKFGSF; mdl_idx ++) {
// Use the firstGPS measurement to set the velocities and corresponding variances
EKF[mdl_idx].X[0] = vel_NE[0];
EKF[mdl_idx].X[1] = vel_NE[1];
EKF[mdl_idx].P[0][0] = velObsVar;
EKF[mdl_idx].P[1][1] = velObsVar;
}
alignYaw();
vel_fuse_running = true;
} else {
float total_w = 0.0f;
float newWeight[(uint8_t)N_MODELS_EKFGSF];
bool state_update_failed = false;
for (uint8_t mdl_idx = 0; mdl_idx < N_MODELS_EKFGSF; mdl_idx ++) {
// Update states and covariances using GPS NE velocity measurements fused as direct state observations
if (!correct(mdl_idx)) {
state_update_failed = true;
}
}
if (!state_update_failed) {
// Calculate weighting for each model assuming a normal error distribution
for (uint8_t mdl_idx = 0; mdl_idx < N_MODELS_EKFGSF; mdl_idx ++) {
newWeight[mdl_idx]= fmaxf(gaussianDensity(mdl_idx) * GSF.weights[mdl_idx], 0.0f);
total_w += newWeight[mdl_idx];
}
// Normalise the sum of weights to unity
if (vel_fuse_running && is_positive(total_w)) {
float total_w_inv = 1.0f / total_w;
for (uint8_t mdl_idx = 0; mdl_idx < N_MODELS_EKFGSF; mdl_idx ++) {
GSF.weights[mdl_idx] = newWeight[mdl_idx] * total_w_inv;
}
}
}
}
}
}
void EKFGSF_yaw::predictAHRS(const uint8_t mdl_idx)
@ -386,9 +385,6 @@ void EKFGSF_yaw::predict(const uint8_t mdl_idx)
// Returns false if the sttae and covariance correction failed
bool EKFGSF_yaw::correct(const uint8_t mdl_idx)
{
// set observation variance from accuracy estimate supplied by GPS and apply a sanity check minimum
const float velObsVar = sq(fmaxf(vel_accuracy, 0.5f));
// calculate velocity observation innovations
EKF[mdl_idx].innov[0] = EKF[mdl_idx].X[0] - vel_NE[0];
EKF[mdl_idx].innov[1] = EKF[mdl_idx].X[1] - vel_NE[1];
@ -539,7 +535,6 @@ void EKFGSF_yaw::initialise()
{
memset(&GSF, 0, sizeof(GSF));
vel_fuse_running = false;
vel_data_updated = false;
run_ekf_gsf = false;
memset(&EKF, 0, sizeof(EKF));
@ -554,8 +549,8 @@ void EKFGSF_yaw::initialise()
// Take state and variance estimates direct from velocity sensor
EKF[mdl_idx].X[0] = vel_NE[0];
EKF[mdl_idx].X[1] = vel_NE[1];
EKF[mdl_idx].P[0][0] = sq(fmaxf(vel_accuracy, 0.5f));
EKF[mdl_idx].P[1][1] = EKF[mdl_idx].P[0][0];
EKF[mdl_idx].P[0][0] = velObsVar;
EKF[mdl_idx].P[1][1] = velObsVar;
// Use half yaw interval for yaw uncertainty as that is the maximum that the best model can be away from truth
GSF.yaw_variance = sq(0.5f * yaw_increment);

7
libraries/AP_NavEKF/EKFGSF_yaw.h
View File

@ -22,7 +22,9 @@ public:
bool runEKF, // set to true when flying or movement suitable for yaw estimation
float TAS); // true airspeed used for centripetal accel compensation - set to 0 when not required.
void pushVelData(Vector2f vel, // NE velocity measurement (m/s)
// Fuse NE velocty mesurements and update the EKF's and GSF state and covariance estimates
// Should be called after update(...) whenever new velocity data is available
void fuseVelData(Vector2f vel, // NE velocity measurement (m/s)
float velAcc); // 1-sigma accuracy of velocity measurement (m/s)
// get solution data for logging
@ -100,10 +102,9 @@ private:
};
EKF_struct EKF[N_MODELS_EKFGSF];
bool vel_fuse_running; // true when the bank of EKF's has started fusing GPS velocity data
bool vel_data_updated; // true when velocity data has been updated
bool run_ekf_gsf; // true when operating condition is suitable for to run the GSF and EKF models and fuse velocity data
Vector2f vel_NE; // NE velocity observations (m/s)
float vel_accuracy; // 1-sigma accuracy of velocity observations (m/s)
float velObsVar; // variance of velocity observations (m/s)^2
// Initialises the EKF's and GSF states, but not the AHRS complementary filters
void initialise();