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EKF: Make PR comply with project convention for indenting

This commit is contained in:
Paul Riseborough 2016-02-08 15:12:38 +11:00
parent cdc42c1de0
commit 47ab5ebcdd
5 changed files with 148 additions and 144 deletions
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18
EKF/common.h
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@ -168,15 +168,15 @@ struct stateSample {
}; };
struct fault_status_t { struct fault_status_t {
bool bad_mag_x: 1; // true if the fusion of the magnetometer X-axis has encountered a numerical error bool bad_mag_x: 1; // true if the fusion of the magnetometer X-axis has encountered a numerical error
bool bad_mag_y: 1; // true if the fusion of the magnetometer Y-axis has encountered a numerical error bool bad_mag_y: 1; // true if the fusion of the magnetometer Y-axis has encountered a numerical error
bool bad_mag_z: 1; // true if the fusion of the magnetometer Z-axis has encountered a numerical error bool bad_mag_z: 1; // true if the fusion of the magnetometer Z-axis has encountered a numerical error
bool bad_mag_hdg: 1; // true if the fusion of the magnetic heading has encountered a numerical error bool bad_mag_hdg: 1; // true if the fusion of the magnetic heading has encountered a numerical error
bool bad_mag_decl: 1; // true if the fusion of the magnetic declination has encountered a numerical error bool bad_mag_decl: 1; // true if the fusion of the magnetic declination has encountered a numerical error
bool bad_airspeed: 1; // true if fusion of the airspeed has encountered a numerical error bool bad_airspeed: 1; // true if fusion of the airspeed has encountered a numerical error
bool bad_sideslip: 1; // true if fusion of the synthetic sideslip constraint has encountered a numerical error 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_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_optflow_Y: 1; // true if fusion of the optical flow Y axis has encountered a numerical error
}; };
// publish the status of various GPS quality checks // publish the status of various GPS quality checks

13
EKF/control.cpp
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@ -110,12 +110,13 @@ void Ekf::controlFusionModes()
} }
} }
// if we are using 3-axis magnetometer fusion, but without external aiding, then the declination needs to be fused as an observation to prevent long term heading drift // if we are using 3-axis magnetometer fusion, but without external aiding, then the declination needs to be fused as an observation to prevent long term heading drift
if(_control_status.flags.mag_3D && _control_status.flags.gps) { if (_control_status.flags.mag_3D && _control_status.flags.gps) {
_control_status.flags.mag_dec = false; _control_status.flags.mag_dec = false;
} else {
_control_status.flags.mag_dec = true; } else {
} _control_status.flags.mag_dec = true;
}
} }
void Ekf::calculateVehicleStatus() void Ekf::calculateVehicleStatus()

8
EKF/ekf.cpp
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@ -146,9 +146,11 @@ bool Ekf::update()
if (_mag_buffer.pop_first_older_than(_imu_sample_delayed.time_us, &_mag_sample_delayed)) { if (_mag_buffer.pop_first_older_than(_imu_sample_delayed.time_us, &_mag_sample_delayed)) {
if (_control_status.flags.mag_3D && _control_status.flags.angle_align) { if (_control_status.flags.mag_3D && _control_status.flags.angle_align) {
fuseMag(); fuseMag();
if (_control_status.flags.mag_dec) {
fuseDeclination(); if (_control_status.flags.mag_dec) {
} fuseDeclination();
}
} else if (_control_status.flags.mag_hdg && _control_status.flags.angle_align) { } else if (_control_status.flags.mag_hdg && _control_status.flags.angle_align) {
fuseHeading(); fuseHeading();
} }

2
EKF/ekf.h
View File

@ -162,7 +162,7 @@ private:
void fuseHeading(); void fuseHeading();
void fuseDeclination(); void fuseDeclination();
void fuseAirspeed(); void fuseAirspeed();

251
EKF/mag_fusion.cpp
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@ -506,10 +506,10 @@ void Ekf::fuseHeading()
float t30 = t26 * t26; float t30 = t26 * t26;
float t31 = t30 + 1.0f; float t31 = t30 + 1.0f;
float H_HDG[3] = {}; float H_HDG[3] = {};
H_HDG[0] = -t31 * (t20 * (magZ * t16 + magY * t18) + t25 * t27 * (magY * t8 + magZ * t10)); H_HDG[0] = -t31 * (t20 * (magZ * t16 + magY * t18) + t25 * t27 * (magY * t8 + magZ * t10));
H_HDG[1] = t31 * (t20 * (magX * t18 + magZ * t22) + t25 * t27 * (magX * t8 - magZ * t11)); H_HDG[1] = t31 * (t20 * (magX * t18 + magZ * t22) + t25 * t27 * (magX * t8 - magZ * t11));
H_HDG[2] = t31 * (t20 * (magX * t16 - magY * t22) + t25 * t27 * (magX * t10 + magY * t11)); H_HDG[2] = t31 * (t20 * (magX * t16 - magY * t22) + t25 * t27 * (magX * t10 + magY * t11));
// calculate innovation // calculate innovation
matrix::Dcm<float> R_to_earth(_state.quat_nominal); matrix::Dcm<float> R_to_earth(_state.quat_nominal);
@ -528,19 +528,19 @@ void Ekf::fuseHeading()
for (unsigned row = 0; row < 3; row++) { for (unsigned row = 0; row < 3; row++) {
for (unsigned column = 0; column < 3; column++) { for (unsigned column = 0; column < 3; column++) {
PH[row] += P[row][column] * H_HDG[column]; PH[row] += P[row][column] * H_HDG[column];
} }
innovation_var += H_HDG[row] * PH[row]; innovation_var += H_HDG[row] * PH[row];
} }
if (innovation_var >= R_mag) { if (innovation_var >= R_mag) {
// the innovation variance contribution from the state covariances is not negative, no fault // the innovation variance contribution from the state covariances is not negative, no fault
_fault_status.bad_mag_hdg = false; _fault_status.bad_mag_hdg = false;
} else { } else {
// the innovation variance contribution from the state covariances is negative which means the covariance matrix is badly conditioned // the innovation variance contribution from the state covariances is negative which means the covariance matrix is badly conditioned
_fault_status.bad_mag_hdg = true; _fault_status.bad_mag_hdg = true;
// we reinitialise the covariance matrix and abort this fusion step // we reinitialise the covariance matrix and abort this fusion step
initialiseCovariance(); initialiseCovariance();
@ -554,7 +554,7 @@ void Ekf::fuseHeading()
for (unsigned row = 0; row < _k_num_states; row++) { for (unsigned row = 0; row < _k_num_states; row++) {
for (unsigned column = 0; column < 3; column++) { for (unsigned column = 0; column < 3; column++) {
Kfusion[row] += P[row][column] * H_HDG[column]; Kfusion[row] += P[row][column] * H_HDG[column];
} }
Kfusion[row] *= innovation_var_inv; Kfusion[row] *= innovation_var_inv;
@ -591,7 +591,7 @@ void Ekf::fuseHeading()
for (unsigned column = 0; column < _k_num_states; column++) { for (unsigned column = 0; column < _k_num_states; column++) {
for (unsigned row = 0; row < 3; row++) { for (unsigned row = 0; row < 3; row++) {
HP[column] += H_HDG[row] * P[row][column]; HP[column] += H_HDG[row] * P[row][column];
} }
} }
@ -607,135 +607,136 @@ void Ekf::fuseHeading()
void Ekf::fuseDeclination() void Ekf::fuseDeclination()
{ {
// assign intermediate state variables // assign intermediate state variables
float magN = _state.mag_I(0); float magN = _state.mag_I(0);
float magE = _state.mag_I(1); float magE = _state.mag_I(1);
float R_DECL = sq(0.5f); float R_DECL = sq(0.5f);
// Calculate intermediate variables // Calculate intermediate variables
// if the horizontal magnetic field is too small, this calculation will be badly conditioned // if the horizontal magnetic field is too small, this calculation will be badly conditioned
if (fabsf(magN) < 0.001f) { if (fabsf(magN) < 0.001f) {
return; return;
} }
float t2 = 1.0f/magN;
float t4 = magE*t2;
float t3 = tanf(t4);
float t5 = t3*t3;
float t6 = t5+1.0f;
float t25 = t2*t6;
float t7 = 1.0f/(magN*magN);
float t26 = magE*t6*t7;
float t8 = P[17][17]*t25;
float t15 = P[16][17]*t26;
float t9 = t8-t15;
float t10 = t25*t9;
float t11 = P[17][16]*t25;
float t16 = P[16][16]*t26;
float t12 = t11-t16;
float t17 = t26*t12;
float t13 = R_DECL+t10-t17; // innovation variance
// check the innovation variance calculation for a badly conditioned covariance matrix float t2 = 1.0f / magN;
if (t13 >= R_DECL) { float t4 = magE * t2;
// the innovation variance contribution from the state covariances is not negative, no fault float t3 = tanf(t4);
_fault_status.bad_mag_decl = false; float t5 = t3 * t3;
float t6 = t5 + 1.0f;
float t25 = t2 * t6;
float t7 = 1.0f / (magN * magN);
float t26 = magE * t6 * t7;
float t8 = P[17][17] * t25;
float t15 = P[16][17] * t26;
float t9 = t8 - t15;
float t10 = t25 * t9;
float t11 = P[17][16] * t25;
float t16 = P[16][16] * t26;
float t12 = t11 - t16;
float t17 = t26 * t12;
float t13 = R_DECL + t10 - t17; // innovation variance
} else { // check the innovation variance calculation for a badly conditioned covariance matrix
// the innovation variance contribution from the state covariances is negtive which means the covariance matrix is badly conditioned if (t13 >= R_DECL) {
_fault_status.bad_mag_decl = true; // the innovation variance contribution from the state covariances is not negative, no fault
_fault_status.bad_mag_decl = false;
// we reinitialise the covariance matrix and abort this fusion step } else {
initialiseCovariance(); // the innovation variance contribution from the state covariances is negtive which means the covariance matrix is badly conditioned
return; _fault_status.bad_mag_decl = true;
}
float t14 = 1.0f/t13; // we reinitialise the covariance matrix and abort this fusion step
float t18 = magE; initialiseCovariance();
float t19 = magN; return;
float t21 = 1.0f/t19; }
float t22 = t18*t21;
float t20 = tanf(t22);
float t23 = t20*t20;
float t24 = t23+1.0f;
// Calculate the observation Jacobian float t14 = 1.0f / t13;
// Note only 2 terms are non-zero which can be used in matrix operations for calculation of Kalman gains and covariance update to significantly reduce cost float t18 = magE;
float H_DECL[24] = {}; float t19 = magN;
H_DECL[16] = -t18*1.0f/(t19*t19)*t24; float t21 = 1.0f / t19;
H_DECL[17] = t21*t24; float t22 = t18 * t21;
float t20 = tanf(t22);
float t23 = t20 * t20;
float t24 = t23 + 1.0f;
// Calculate the Kalman gains // Calculate the observation Jacobian
float Kfusion[_k_num_states] = {}; // Note only 2 terms are non-zero which can be used in matrix operations for calculation of Kalman gains and covariance update to significantly reduce cost
Kfusion[0] = t14*(P[0][17]*t25-P[0][16]*t26); float H_DECL[24] = {};
Kfusion[1] = t14*(P[1][17]*t25-P[1][16]*t26); H_DECL[16] = -t18 * 1.0f / (t19 * t19) * t24;
Kfusion[2] = t14*(P[2][17]*t25-P[2][16]*t26); H_DECL[17] = t21 * t24;
Kfusion[3] = t14*(P[3][17]*t25-P[3][16]*t26);
Kfusion[4] = t14*(P[4][17]*t25-P[4][16]*t26);
Kfusion[5] = t14*(P[5][17]*t25-P[5][16]*t26);
Kfusion[6] = t14*(P[6][17]*t25-P[6][16]*t26);
Kfusion[7] = t14*(P[7][17]*t25-P[7][16]*t26);
Kfusion[8] = t14*(P[8][17]*t25-P[8][16]*t26);
Kfusion[9] = t14*(P[9][17]*t25-P[9][16]*t26);
Kfusion[10] = t14*(P[10][17]*t25-P[10][16]*t26);
Kfusion[11] = t14*(P[11][17]*t25-P[11][16]*t26);
Kfusion[12] = t14*(P[12][17]*t25-P[12][16]*t26);
Kfusion[13] = t14*(P[13][17]*t25-P[13][16]*t26);
Kfusion[14] = t14*(P[14][17]*t25-P[14][16]*t26);
Kfusion[15] = t14*(P[15][17]*t25-P[15][16]*t26);
Kfusion[16] = -t14*(t16-P[16][17]*t25);
Kfusion[17] = t14*(t8-P[17][16]*t26);
Kfusion[18] = t14*(P[18][17]*t25-P[18][16]*t26);
Kfusion[19] = t14*(P[19][17]*t25-P[19][16]*t26);
Kfusion[20] = t14*(P[20][17]*t25-P[20][16]*t26);
Kfusion[21] = t14*(P[21][17]*t25-P[21][16]*t26);
Kfusion[22] = t14*(P[22][17]*t25-P[22][16]*t26);
Kfusion[23] = t14*(P[23][17]*t25-P[23][16]*t26);
// calculate innovation and constrain // Calculate the Kalman gains
float innovation = atanf(t4) - math::radians(_params.mag_declination_deg); float Kfusion[_k_num_states] = {};
innovation = math::constrain(innovation, -0.5f, 0.5f); Kfusion[0] = t14 * (P[0][17] * t25 - P[0][16] * t26);
Kfusion[1] = t14 * (P[1][17] * t25 - P[1][16] * t26);
Kfusion[2] = t14 * (P[2][17] * t25 - P[2][16] * t26);
Kfusion[3] = t14 * (P[3][17] * t25 - P[3][16] * t26);
Kfusion[4] = t14 * (P[4][17] * t25 - P[4][16] * t26);
Kfusion[5] = t14 * (P[5][17] * t25 - P[5][16] * t26);
Kfusion[6] = t14 * (P[6][17] * t25 - P[6][16] * t26);
Kfusion[7] = t14 * (P[7][17] * t25 - P[7][16] * t26);
Kfusion[8] = t14 * (P[8][17] * t25 - P[8][16] * t26);
Kfusion[9] = t14 * (P[9][17] * t25 - P[9][16] * t26);
Kfusion[10] = t14 * (P[10][17] * t25 - P[10][16] * t26);
Kfusion[11] = t14 * (P[11][17] * t25 - P[11][16] * t26);
Kfusion[12] = t14 * (P[12][17] * t25 - P[12][16] * t26);
Kfusion[13] = t14 * (P[13][17] * t25 - P[13][16] * t26);
Kfusion[14] = t14 * (P[14][17] * t25 - P[14][16] * t26);
Kfusion[15] = t14 * (P[15][17] * t25 - P[15][16] * t26);
Kfusion[16] = -t14 * (t16 - P[16][17] * t25);
Kfusion[17] = t14 * (t8 - P[17][16] * t26);
Kfusion[18] = t14 * (P[18][17] * t25 - P[18][16] * t26);
Kfusion[19] = t14 * (P[19][17] * t25 - P[19][16] * t26);
Kfusion[20] = t14 * (P[20][17] * t25 - P[20][16] * t26);
Kfusion[21] = t14 * (P[21][17] * t25 - P[21][16] * t26);
Kfusion[22] = t14 * (P[22][17] * t25 - P[22][16] * t26);
Kfusion[23] = t14 * (P[23][17] * t25 - P[23][16] * t26);
// zero attitude error states and perform the state correction // calculate innovation and constrain
_state.ang_error.setZero(); float innovation = atanf(t4) - math::radians(_params.mag_declination_deg);
fuse(Kfusion, innovation); innovation = math::constrain(innovation, -0.5f, 0.5f);
// use the attitude error estimate to correct the quaternion // zero attitude error states and perform the state correction
Quaternion dq; _state.ang_error.setZero();
dq.from_axis_angle(_state.ang_error); fuse(Kfusion, innovation);
_state.quat_nominal = dq * _state.quat_nominal;
_state.quat_nominal.normalize();
// apply covariance correction via P_new = (I -K*H)*P // use the attitude error estimate to correct the quaternion
// first calculate expression for KHP Quaternion dq;
// then calculate P - KHP dq.from_axis_angle(_state.ang_error);
// take advantage of the empty columns in KH to reduce the number of operations _state.quat_nominal = dq * _state.quat_nominal;
float KH[_k_num_states][_k_num_states] = {}; _state.quat_nominal.normalize();
for (unsigned row = 0; row < _k_num_states; row++) { // apply covariance correction via P_new = (I -K*H)*P
for (unsigned column = 16; column < 17; column++) { // first calculate expression for KHP
KH[row][column] = Kfusion[row] * H_DECL[column]; // then calculate P - KHP
} // take advantage of the empty columns in KH to reduce the number of operations
} float KH[_k_num_states][_k_num_states] = {};
float KHP[_k_num_states][_k_num_states] = {}; for (unsigned row = 0; row < _k_num_states; row++) {
for (unsigned column = 16; column < 17; column++) {
KH[row][column] = Kfusion[row] * H_DECL[column];
}
}
for (unsigned row = 0; row < _k_num_states; row++) { float KHP[_k_num_states][_k_num_states] = {};
for (unsigned column = 0; column < _k_num_states; column++) {
float tmp = KH[row][0] * P[0][column];
tmp += KH[row][16] * P[16][column];
tmp += KH[row][17] * P[17][column];
KHP[row][column] = tmp;
}
}
for (unsigned row = 0; row < _k_num_states; row++) { for (unsigned row = 0; row < _k_num_states; row++) {
for (unsigned column = 0; column < _k_num_states; column++) { for (unsigned column = 0; column < _k_num_states; column++) {
P[row][column] -= KHP[row][column]; float tmp = KH[row][0] * P[0][column];
} tmp += KH[row][16] * P[16][column];
} tmp += KH[row][17] * P[17][column];
KHP[row][column] = tmp;
}
}
// force the covariance matrix to be symmetrical and don't allow the variances to be negative. for (unsigned row = 0; row < _k_num_states; row++) {
makeSymmetrical(); for (unsigned column = 0; column < _k_num_states; column++) {
limitCov(); P[row][column] -= KHP[row][column];
}
}
// force the covariance matrix to be symmetrical and don't allow the variances to be negative.
makeSymmetrical();
limitCov();
} }