/*
* Fuse angular motion compensated optical flow rates using explicit algebraic equations generated with Matlab symbolic toolbox.
* The script file used to generate these and other equations in this filter can be found here:
* https://github.com/priseborough/InertialNav/blob/master/derivations/RotationVectorAttitudeParameterisation/GenerateNavFilterEquations.m
* Requires a valid terrain height estimate.
*/
void NavEKF2_core::FuseOptFlow()
{
Vector24 H_LOS;
Vector3f relVelSensor;
Vector14 SH_LOS;
Vector2 losPred;
// Copy required states to local variable names
float q0 = stateStruct.quat[0];
float q1 = stateStruct.quat[1];
float q2 = stateStruct.quat[2];
float q3 = stateStruct.quat[3];
float vn = stateStruct.velocity.x;
float ve = stateStruct.velocity.y;
float vd = stateStruct.velocity.z;
float pd = stateStruct.position.z;
// constrain height above ground to be above range measured on ground
float heightAboveGndEst = max((terrainState - pd), rngOnGnd);
float ptd = pd + heightAboveGndEst;
// Calculate common expressions for observation jacobians
SH_LOS[0] = sq(q0) - sq(q1) - sq(q2) + sq(q3);
SH_LOS[1] = vn*(sq(q0) + sq(q1) - sq(q2) - sq(q3)) - vd*(2*q0*q2 - 2*q1*q3) + ve*(2*q0*q3 + 2*q1*q2);
SH_LOS[2] = ve*(sq(q0) - sq(q1) + sq(q2) - sq(q3)) + vd*(2*q0*q1 + 2*q2*q3) - vn*(2*q0*q3 - 2*q1*q2);
SH_LOS[3] = 1/(pd - ptd);
SH_LOS[4] = vd*SH_LOS[0] - ve*(2*q0*q1 - 2*q2*q3) + vn*(2*q0*q2 + 2*q1*q3);
SH_LOS[5] = 2.0f*q0*q2 - 2.0f*q1*q3;
SH_LOS[6] = 2.0f*q0*q1 + 2.0f*q2*q3;
SH_LOS[7] = q0*q0;
SH_LOS[8] = q1*q1;
SH_LOS[9] = q2*q2;
SH_LOS[10] = q3*q3;
SH_LOS[11] = q0*q3*2.0f;
SH_LOS[12] = pd-ptd;
SH_LOS[13] = 1.0f/(SH_LOS[12]*SH_LOS[12]);
// Fuse X and Y axis measurements sequentially assuming observation errors are uncorrelated
for (uint8_t obsIndex=0; obsIndex<=1; obsIndex++) { // fuse X axis data first
// calculate range from ground plain to centre of sensor fov assuming flat earth
float range = constrain_float((heightAboveGndEst/Tnb_flow.c.z),rngOnGnd,1000.0f);
// calculate relative velocity in sensor frame
relVelSensor = Tnb_flow*stateStruct.velocity;
// divide velocity by range to get predicted angular LOS rates relative to X and Y axes
losPred[0] = relVelSensor.y/range;
losPred[1] = -relVelSensor.x/range;
// calculate observation jacobians and Kalman gains
memset(&H_LOS[0], 0, sizeof(H_LOS));
if (obsIndex == 0) {
H_LOS[0] = SH_LOS[3]*SH_LOS[2]*SH_LOS[6]-SH_LOS[3]*SH_LOS[0]*SH_LOS[4];
H_LOS[1] = SH_LOS[3]*SH_LOS[2]*SH_LOS[5];
H_LOS[2] = SH_LOS[3]*SH_LOS[0]*SH_LOS[1];
H_LOS[3] = SH_LOS[3]*SH_LOS[0]*(SH_LOS[11]-q1*q2*2.0f);
H_LOS[4] = -SH_LOS[3]*SH_LOS[0]*(SH_LOS[7]-SH_LOS[8]+SH_LOS[9]-SH_LOS[10]);
H_LOS[5] = -SH_LOS[3]*SH_LOS[0]*SH_LOS[6];
H_LOS[8] = SH_LOS[2]*SH_LOS[0]*SH_LOS[13];
float t2 = SH_LOS[3];
float t3 = SH_LOS[0];
float t4 = SH_LOS[2];
float t5 = SH_LOS[6];
float t100 = t2 * t3 * t5;
float t6 = SH_LOS[4];
float t7 = t2*t3*t6;
float t9 = t2*t4*t5;
float t8 = t7-t9;
float t10 = q0*q3*2.0f;
float t21 = q1*q2*2.0f;
float t11 = t10-t21;
float t101 = t2 * t3 * t11;
float t12 = pd-ptd;
float t13 = 1.0f/(t12*t12);
float t104 = t3 * t4 * t13;
float t14 = SH_LOS[5];
float t102 = t2 * t4 * t14;
float t15 = SH_LOS[1];
float t103 = t2 * t3 * t15;
float t16 = q0*q0;
float t17 = q1*q1;
float t18 = q2*q2;
float t19 = q3*q3;
float t20 = t16-t17+t18-t19;
float t105 = t2 * t3 * t20;
float t22 = P[1][1]*t102;
float t23 = P[3][0]*t101;
float t24 = P[8][0]*t104;
float t25 = P[1][0]*t102;
float t26 = P[2][0]*t103;
float t63 = P[0][0]*t8;
float t64 = P[5][0]*t100;
float t65 = P[4][0]*t105;
float t27 = t23+t24+t25+t26-t63-t64-t65;
float t28 = P[3][3]*t101;
float t29 = P[8][3]*t104;
float t30 = P[1][3]*t102;
float t31 = P[2][3]*t103;
float t67 = P[0][3]*t8;
float t68 = P[5][3]*t100;
float t69 = P[4][3]*t105;
float t32 = t28+t29+t30+t31-t67-t68-t69;
float t33 = t101*t32;
float t34 = P[3][8]*t101;
float t35 = P[8][8]*t104;
float t36 = P[1][8]*t102;
float t37 = P[2][8]*t103;
float t70 = P[0][8]*t8;
float t71 = P[5][8]*t100;
float t72 = P[4][8]*t105;
float t38 = t34+t35+t36+t37-t70-t71-t72;
float t39 = t104*t38;
float t40 = P[3][1]*t101;
float t41 = P[8][1]*t104;
float t42 = P[2][1]*t103;
float t73 = P[0][1]*t8;
float t74 = P[5][1]*t100;
float t75 = P[4][1]*t105;
float t43 = t22+t40+t41+t42-t73-t74-t75;
float t44 = t102*t43;
float t45 = P[3][2]*t101;
float t46 = P[8][2]*t104;
float t47 = P[1][2]*t102;
float t48 = P[2][2]*t103;
float t76 = P[0][2]*t8;
float t77 = P[5][2]*t100;
float t78 = P[4][2]*t105;
float t49 = t45+t46+t47+t48-t76-t77-t78;
float t50 = t103*t49;
float t51 = P[3][5]*t101;
float t52 = P[8][5]*t104;
float t53 = P[1][5]*t102;
float t54 = P[2][5]*t103;
float t79 = P[0][5]*t8;
float t80 = P[5][5]*t100;
float t81 = P[4][5]*t105;
float t55 = t51+t52+t53+t54-t79-t80-t81;
float t56 = P[3][4]*t101;
float t57 = P[8][4]*t104;
float t58 = P[1][4]*t102;
float t59 = P[2][4]*t103;
float t83 = P[0][4]*t8;
float t84 = P[5][4]*t100;
float t85 = P[4][4]*t105;
float t60 = t56+t57+t58+t59-t83-t84-t85;
float t66 = t8*t27;
float t82 = t100*t55;
float t86 = t105*t60;
float t61 = R_LOS+t33+t39+t44+t50-t66-t82-t86;
float t62 = 1.0f/t61;
// calculate innovation variance for X axis observation and protect against a badly conditioned calculation
if (t61 > R_LOS) {
t62 = 1.0f/t61;
} else {
t61 = 0.0f;
t62 = 1.0f/R_LOS;
}
varInnovOptFlow[0] = t61;
// calculate innovation for X axis observation
innovOptFlow[0] = losPred[0] - ofDataDelayed.flowRadXYcomp.x;
// calculate Kalman gains for X-axis observation
Kfusion[0] = t62*(-P[0][0]*t8-P[0][5]*t100+P[0][3]*t101+P[0][1]*t102+P[0][2]*t103+P[0][8]*t104-P[0][4]*t105);
Kfusion[1] = t62*(t22-P[1][0]*t8-P[1][5]*t100+P[1][3]*t101+P[1][2]*t103+P[1][8]*t104-P[1][4]*t105);
Kfusion[2] = t62*(t48-P[2][0]*t8-P[2][5]*t100+P[2][3]*t101+P[2][1]*t102+P[2][8]*t104-P[2][4]*t105);
Kfusion[3] = t62*(t28-P[3][0]*t8-P[3][5]*t100+P[3][1]*t102+P[3][2]*t103+P[3][8]*t104-P[3][4]*t105);
Kfusion[4] = t62*(-t85-P[4][0]*t8-P[4][5]*t100+P[4][3]*t101+P[4][1]*t102+P[4][2]*t103+P[4][8]*t104);
Kfusion[5] = t62*(-t80-P[5][0]*t8+P[5][3]*t101+P[5][1]*t102+P[5][2]*t103+P[5][8]*t104-P[5][4]*t105);
Kfusion[6] = t62*(-P[6][0]*t8-P[6][5]*t100+P[6][3]*t101+P[6][1]*t102+P[6][2]*t103+P[6][8]*t104-P[6][4]*t105);
Kfusion[7] = t62*(-P[7][0]*t8-P[7][5]*t100+P[7][3]*t101+P[7][1]*t102+P[7][2]*t103+P[7][8]*t104-P[7][4]*t105);
Kfusion[8] = t62*(t35-P[8][0]*t8-P[8][5]*t100+P[8][3]*t101+P[8][1]*t102+P[8][2]*t103-P[8][4]*t105);
Kfusion[9] = t62*(-P[9][0]*t8-P[9][5]*t100+P[9][3]*t101+P[9][1]*t102+P[9][2]*t103+P[9][8]*t104-P[9][4]*t105);
Kfusion[10] = t62*(-P[10][0]*t8-P[10][5]*t100+P[10][3]*t101+P[10][1]*t102+P[10][2]*t103+P[10][8]*t104-P[10][4]*t105);
Kfusion[11] = t62*(-P[11][0]*t8-P[11][5]*t100+P[11][3]*t101+P[11][1]*t102+P[11][2]*t103+P[11][8]*t104-P[11][4]*t105);
Kfusion[12] = t62*(-P[12][0]*t8-P[12][5]*t100+P[12][3]*t101+P[12][1]*t102+P[12][2]*t103+P[12][8]*t104-P[12][4]*t105);
Kfusion[13] = t62*(-P[13][0]*t8-P[13][5]*t100+P[13][3]*t101+P[13][1]*t102+P[13][2]*t103+P[13][8]*t104-P[13][4]*t105);
Kfusion[14] = t62*(-P[14][0]*t8-P[14][5]*t100+P[14][3]*t101+P[14][1]*t102+P[14][2]*t103+P[14][8]*t104-P[14][4]*t105);
Kfusion[15] = t62*(-P[15][0]*t8-P[15][5]*t100+P[15][3]*t101+P[15][1]*t102+P[15][2]*t103+P[15][8]*t104-P[15][4]*t105);
if (!inhibitWindStates) {
Kfusion[22] = t62*(-P[22][0]*t8-P[22][5]*t100+P[22][3]*t101+P[22][1]*t102+P[22][2]*t103+P[22][8]*t104-P[22][4]*t105);
Kfusion[23] = t62*(-P[23][0]*t8-P[23][5]*t100+P[23][3]*t101+P[23][1]*t102+P[23][2]*t103+P[23][8]*t104-P[23][4]*t105);
} else {
Kfusion[22] = 0.0f;
Kfusion[23] = 0.0f;
}
if (!inhibitMagStates) {
Kfusion[16] = t62*(-P[16][0]*t8-P[16][5]*t100+P[16][3]*t101+P[16][1]*t102+P[16][2]*t103+P[16][8]*t104-P[16][4]*t105);
Kfusion[17] = t62*(-P[17][0]*t8-P[17][5]*t100+P[17][3]*t101+P[17][1]*t102+P[17][2]*t103+P[17][8]*t104-P[17][4]*t105);
Kfusion[18] = t62*(-P[18][0]*t8-P[18][5]*t100+P[18][3]*t101+P[18][1]*t102+P[18][2]*t103+P[18][8]*t104-P[18][4]*t105);
Kfusion[19] = t62*(-P[19][0]*t8-P[19][5]*t100+P[19][3]*t101+P[19][1]*t102+P[19][2]*t103+P[19][8]*t104-P[19][4]*t105);
Kfusion[20] = t62*(-P[20][0]*t8-P[20][5]*t100+P[20][3]*t101+P[20][1]*t102+P[20][2]*t103+P[20][8]*t104-P[20][4]*t105);
Kfusion[21] = t62*(-P[21][0]*t8-P[21][5]*t100+P[21][3]*t101+P[21][1]*t102+P[21][2]*t103+P[21][8]*t104-P[21][4]*t105);
} else {
for (uint8_t i = 16; i <= 21; i++) {
Kfusion[i] = 0.0f;
}
}
} else {
H_LOS[0] = -SH_LOS[3]*SH_LOS[6]*SH_LOS[1];
H_LOS[1] = -SH_LOS[3]*SH_LOS[0]*SH_LOS[4]-SH_LOS[3]*SH_LOS[1]*SH_LOS[5];
H_LOS[2] = SH_LOS[3]*SH_LOS[2]*SH_LOS[0];
H_LOS[3] = SH_LOS[3]*SH_LOS[0]*(SH_LOS[7]+SH_LOS[8]-SH_LOS[9]-SH_LOS[10]);
H_LOS[4] = SH_LOS[3]*SH_LOS[0]*(SH_LOS[11]+q1*q2*2.0f);
H_LOS[5] = -SH_LOS[3]*SH_LOS[0]*SH_LOS[5];
H_LOS[8] = -SH_LOS[0]*SH_LOS[1]*SH_LOS[13];
float t2 = SH_LOS[3];
float t3 = SH_LOS[0];
float t4 = SH_LOS[1];
float t5 = SH_LOS[5];
float t100 = t2 * t3 * t5;
float t6 = SH_LOS[4];
float t7 = t2*t3*t6;
float t8 = t2*t4*t5;
float t9 = t7+t8;
float t10 = q0*q3*2.0f;
float t11 = q1*q2*2.0f;
float t12 = t10+t11;
float t101 = t2 * t3 * t12;
float t13 = pd-ptd;
float t14 = 1.0f/(t13*t13);
float t104 = t3 * t4 * t14;
float t15 = SH_LOS[6];
float t105 = t2 * t4 * t15;
float t16 = SH_LOS[2];
float t102 = t2 * t3 * t16;
float t17 = q0*q0;
float t18 = q1*q1;
float t19 = q2*q2;
float t20 = q3*q3;
float t21 = t17+t18-t19-t20;
float t103 = t2 * t3 * t21;
float t22 = P[0][0]*t105;
float t23 = P[1][1]*t9;
float t24 = P[8][1]*t104;
float t25 = P[0][1]*t105;
float t26 = P[5][1]*t100;
float t64 = P[4][1]*t101;
float t65 = P[2][1]*t102;
float t66 = P[3][1]*t103;
float t27 = t23+t24+t25+t26-t64-t65-t66;
float t28 = t9*t27;
float t29 = P[1][4]*t9;
float t30 = P[8][4]*t104;
float t31 = P[0][4]*t105;
float t32 = P[5][4]*t100;
float t67 = P[4][4]*t101;
float t68 = P[2][4]*t102;
float t69 = P[3][4]*t103;
float t33 = t29+t30+t31+t32-t67-t68-t69;
float t34 = P[1][8]*t9;
float t35 = P[8][8]*t104;
float t36 = P[0][8]*t105;
float t37 = P[5][8]*t100;
float t71 = P[4][8]*t101;
float t72 = P[2][8]*t102;
float t73 = P[3][8]*t103;
float t38 = t34+t35+t36+t37-t71-t72-t73;
float t39 = t104*t38;
float t40 = P[1][0]*t9;
float t41 = P[8][0]*t104;
float t42 = P[5][0]*t100;
float t74 = P[4][0]*t101;
float t75 = P[2][0]*t102;
float t76 = P[3][0]*t103;
float t43 = t22+t40+t41+t42-t74-t75-t76;
float t44 = t105*t43;
float t45 = P[1][2]*t9;
float t46 = P[8][2]*t104;
float t47 = P[0][2]*t105;
float t48 = P[5][2]*t100;
float t63 = P[2][2]*t102;
float t77 = P[4][2]*t101;
float t78 = P[3][2]*t103;
float t49 = t45+t46+t47+t48-t63-t77-t78;
float t50 = P[1][5]*t9;
float t51 = P[8][5]*t104;
float t52 = P[0][5]*t105;
float t53 = P[5][5]*t100;
float t80 = P[4][5]*t101;
float t81 = P[2][5]*t102;
float t82 = P[3][5]*t103;
float t54 = t50+t51+t52+t53-t80-t81-t82;
float t55 = t100*t54;
float t56 = P[1][3]*t9;
float t57 = P[8][3]*t104;
float t58 = P[0][3]*t105;
float t59 = P[5][3]*t100;
float t83 = P[4][3]*t101;
float t84 = P[2][3]*t102;
float t85 = P[3][3]*t103;
float t60 = t56+t57+t58+t59-t83-t84-t85;
float t70 = t101*t33;
float t79 = t102*t49;
float t86 = t103*t60;
float t61 = R_LOS+t28+t39+t44+t55-t70-t79-t86;
float t62 = 1.0f/t61;
// calculate innovation variance for X axis observation and protect against a badly conditioned calculation
if (t61 > R_LOS) {
t62 = 1.0f/t61;
} else {
t61 = 0.0f;
t62 = 1.0f/R_LOS;
}
varInnovOptFlow[1] = t61;
// calculate innovation for Y observation
innovOptFlow[1] = losPred[1] - ofDataDelayed.flowRadXYcomp.y;
// calculate Kalman gains for the Y-axis observation
Kfusion[0] = -t62*(t22+P[0][1]*t9+P[0][5]*t100-P[0][4]*t101-P[0][2]*t102-P[0][3]*t103+P[0][8]*t104);
Kfusion[1] = -t62*(t23+P[1][5]*t100+P[1][0]*t105-P[1][4]*t101-P[1][2]*t102-P[1][3]*t103+P[1][8]*t104);
Kfusion[2] = -t62*(-t63+P[2][1]*t9+P[2][5]*t100+P[2][0]*t105-P[2][4]*t101-P[2][3]*t103+P[2][8]*t104);
Kfusion[3] = -t62*(-t85+P[3][1]*t9+P[3][5]*t100+P[3][0]*t105-P[3][4]*t101-P[3][2]*t102+P[3][8]*t104);
Kfusion[4] = -t62*(-t67+P[4][1]*t9+P[4][5]*t100+P[4][0]*t105-P[4][2]*t102-P[4][3]*t103+P[4][8]*t104);
Kfusion[5] = -t62*(t53+P[5][1]*t9+P[5][0]*t105-P[5][4]*t101-P[5][2]*t102-P[5][3]*t103+P[5][8]*t104);
Kfusion[6] = -t62*(P[6][1]*t9+P[6][5]*t100+P[6][0]*t105-P[6][4]*t101-P[6][2]*t102-P[6][3]*t103+P[6][8]*t104);
Kfusion[7] = -t62*(P[7][1]*t9+P[7][5]*t100+P[7][0]*t105-P[7][4]*t101-P[7][2]*t102-P[7][3]*t103+P[7][8]*t104);
Kfusion[8] = -t62*(t35+P[8][1]*t9+P[8][5]*t100+P[8][0]*t105-P[8][4]*t101-P[8][2]*t102-P[8][3]*t103);
Kfusion[9] = -t62*(P[9][1]*t9+P[9][5]*t100+P[9][0]*t105-P[9][4]*t101-P[9][2]*t102-P[9][3]*t103+P[9][8]*t104);
Kfusion[10] = -t62*(P[10][1]*t9+P[10][5]*t100+P[10][0]*t105-P[10][4]*t101-P[10][2]*t102-P[10][3]*t103+P[10][8]*t104);
Kfusion[11] = -t62*(P[11][1]*t9+P[11][5]*t100+P[11][0]*t105-P[11][4]*t101-P[11][2]*t102-P[11][3]*t103+P[11][8]*t104);
Kfusion[12] = -t62*(P[12][1]*t9+P[12][5]*t100+P[12][0]*t105-P[12][4]*t101-P[12][2]*t102-P[12][3]*t103+P[12][8]*t104);
Kfusion[13] = -t62*(P[13][1]*t9+P[13][5]*t100+P[13][0]*t105-P[13][4]*t101-P[13][2]*t102-P[13][3]*t103+P[13][8]*t104);
Kfusion[14] = -t62*(P[14][1]*t9+P[14][5]*t100+P[14][0]*t105-P[14][4]*t101-P[14][2]*t102-P[14][3]*t103+P[14][8]*t104);
Kfusion[15] = -t62*(P[15][1]*t9+P[15][5]*t100+P[15][0]*t105-P[15][4]*t101-P[15][2]*t102-P[15][3]*t103+P[15][8]*t104);
if (!inhibitWindStates) {
Kfusion[22] = -t62*(P[22][1]*t9+P[22][5]*t100+P[22][0]*t105-P[22][4]*t101-P[22][2]*t102-P[22][3]*t103+P[22][8]*t104);
Kfusion[23] = -t62*(P[23][1]*t9+P[23][5]*t100+P[23][0]*t105-P[23][4]*t101-P[23][2]*t102-P[23][3]*t103+P[23][8]*t104);
} else {
Kfusion[22] = 0.0f;
Kfusion[23] = 0.0f;
}
if (!inhibitMagStates) {
Kfusion[16] = -t62*(P[16][1]*t9+P[16][5]*t100+P[16][0]*t105-P[16][4]*t101-P[16][2]*t102-P[16][3]*t103+P[16][8]*t104);
Kfusion[17] = -t62*(P[17][1]*t9+P[17][5]*t100+P[17][0]*t105-P[17][4]*t101-P[17][2]*t102-P[17][3]*t103+P[17][8]*t104);
Kfusion[18] = -t62*(P[18][1]*t9+P[18][5]*t100+P[18][0]*t105-P[18][4]*t101-P[18][2]*t102-P[18][3]*t103+P[18][8]*t104);
Kfusion[19] = -t62*(P[19][1]*t9+P[19][5]*t100+P[19][0]*t105-P[19][4]*t101-P[19][2]*t102-P[19][3]*t103+P[19][8]*t104);
Kfusion[20] = -t62*(P[20][1]*t9+P[20][5]*t100+P[20][0]*t105-P[20][4]*t101-P[20][2]*t102-P[20][3]*t103+P[20][8]*t104);
Kfusion[21] = -t62*(P[21][1]*t9+P[21][5]*t100+P[21][0]*t105-P[21][4]*t101-P[21][2]*t102-P[21][3]*t103+P[21][8]*t104);
} else {
for (uint8_t i = 16; i <= 21; i++) {
Kfusion[i] = 0.0f;
}
}
}
// calculate the innovation consistency test ratio
flowTestRatio[obsIndex] = sq(innovOptFlow[obsIndex]) / (sq(frontend._flowInnovGate) * varInnovOptFlow[obsIndex]);
// Check the innovation for consistency and don't fuse if out of bounds or flow is too fast to be reliable
if ((flowTestRatio[obsIndex]) < 1.0f && (ofDataDelayed.flowRadXY.x < frontend._maxFlowRate) && (ofDataDelayed.flowRadXY.y < frontend._maxFlowRate)) {
// record the last time observations were accepted for fusion
prevFlowFuseTime_ms = imuSampleTime_ms;
// zero the attitude error state - by definition it is assumed to be zero before each observaton fusion
stateStruct.angErr.zero();
// correct the state vector
for (uint8_t j= 0; j<=stateIndexLim; j++) {
statesArray[j] = statesArray[j] - Kfusion[j] * innovOptFlow[obsIndex];
}
// the first 3 states represent the angular misalignment vector. This is
// is used to correct the estimated quaternion on the current time step
stateStruct.quat.rotate(stateStruct.angErr);
// correct the covariance P = (I - K*H)*P
// take advantage of the empty columns in KH to reduce the
// number of operations
for (unsigned i = 0; i<=stateIndexLim; i++) {
for (unsigned j = 0; j<=5; j++) {
KH[i][j] = Kfusion[i] * H_LOS[j];
}
for (unsigned j = 6; j<=7; j++) {
KH[i][j] = 0.0f;
}
KH[i][8] = Kfusion[i] * H_LOS[8];
for (unsigned j = 9; j<=23; j++) {
KH[i][j] = 0.0f;
}
}
for (unsigned j = 0; j<=stateIndexLim; j++) {
for (unsigned i = 0; i<=stateIndexLim; i++) {
ftype res = 0;
res += KH[i][0] * P[0][j];
res += KH[i][1] * P[1][j];
res += KH[i][2] * P[2][j];
res += KH[i][3] * P[3][j];
res += KH[i][4] * P[4][j];
res += KH[i][5] * P[5][j];
res += KH[i][8] * P[8][j];
KHP[i][j] = res;
}
}
for (unsigned i = 0; i<=stateIndexLim; i++) {
for (unsigned j = 0; j<=stateIndexLim; j++) {
P[i][j] = P[i][j] - KHP[i][j];
}
}
}
// fix basic numerical errors
ForceSymmetry();
ConstrainVariances();
}
}
// fuse selected position, velocity and height measurements
void NavEKF2_core::FuseVelPosNED()
{
// start performance timer
hal.util->perf_begin(_perf_FuseVelPosNED);
// health is set bad until test passed
velHealth = false;
posHealth = false;
hgtHealth = false;
// declare variables used to check measurement errors
Vector3f velInnov;
// declare variables used to control access to arrays
bool fuseData[6] = {false,false,false,false,false,false};
uint8_t stateIndex;
uint8_t obsIndex;
// declare variables used by state and covariance update calculations
float posErr;
Vector6 R_OBS; // Measurement variances used for fusion
Vector6 R_OBS_DATA_CHECKS; // Measurement variances used for data checks only
Vector6 observation;
float SK;
// perform sequential fusion of GPS measurements. This assumes that the
// errors in the different velocity and position components are
// uncorrelated which is not true, however in the absence of covariance
// data from the GPS receiver it is the only assumption we can make
// so we might as well take advantage of the computational efficiencies
// associated with sequential fusion
if (fuseVelData || fusePosData || fuseHgtData) {
// set the GPS data timeout depending on whether airspeed data is present
uint32_t gpsRetryTime;
if (useAirspeed()) gpsRetryTime = frontend->gpsRetryTimeUseTAS_ms;
else gpsRetryTime = frontend->gpsRetryTimeNoTAS_ms;
// form the observation vector
observation[0] = gpsDataDelayed.vel.x;
observation[1] = gpsDataDelayed.vel.y;
observation[2] = gpsDataDelayed.vel.z;
observation[3] = gpsDataDelayed.pos.x;
observation[4] = gpsDataDelayed.pos.y;
observation[5] = -hgtMea;
// calculate additional error in GPS position caused by manoeuvring
posErr = frontend->gpsPosVarAccScale * accNavMag;
// estimate the GPS Velocity, GPS horiz position and height measurement variances.
// if the GPS is able to report a speed error, we use it to adjust the observation noise for GPS velocity
// otherwise we scale it using manoeuvre acceleration
// Use different errors if flying without GPS using synthetic position and velocity data
if (PV_AidingMode == AID_NONE && inFlight) {
// Assume the vehicle will be flown with velocity changes less than 10 m/s in this mode (realistic for indoor use)
// This is a compromise between corrections for gyro errors and reducing angular errors due to maneouvres
R_OBS[0] = sq(10.0f);
R_OBS[1] = R_OBS[0];
R_OBS[2] = R_OBS[0];
// Assume a large position uncertainty so as to contrain position states in this mode but minimise angular errors due to manoeuvres
R_OBS[3] = sq(25.0f);
R_OBS[4] = R_OBS[3];
} else {
if (gpsSpdAccuracy > 0.0f) {
// use GPS receivers reported speed accuracy if available and floor at value set by gps noise parameter
R_OBS[0] = sq(constrain_float(gpsSpdAccuracy, frontend->_gpsHorizVelNoise, 50.0f));
R_OBS[2] = sq(constrain_float(gpsSpdAccuracy, frontend->_gpsVertVelNoise, 50.0f));
} else {
// calculate additional error in GPS velocity caused by manoeuvring
R_OBS[0] = sq(constrain_float(frontend->_gpsHorizVelNoise, 0.05f, 5.0f)) + sq(frontend->gpsNEVelVarAccScale * accNavMag);
R_OBS[2] = sq(constrain_float(frontend->_gpsVertVelNoise, 0.05f, 5.0f)) + sq(frontend->gpsDVelVarAccScale * accNavMag);
}
R_OBS[1] = R_OBS[0];
R_OBS[3] = sq(constrain_float(frontend->_gpsHorizPosNoise, 0.1f, 10.0f)) + sq(posErr);
R_OBS[4] = R_OBS[3];
}
R_OBS[5] = posDownObsNoise;
// For data integrity checks we use the same measurement variances as used to calculate the Kalman gains for all measurements except GPS horizontal velocity
// For horizontal GPs velocity we don't want the acceptance radius to increase with reported GPS accuracy so we use a value based on best GPs perfomrance
// plus a margin for manoeuvres. It is better to reject GPS horizontal velocity errors early
for (uint8_t i=0; i<=1; i++) R_OBS_DATA_CHECKS[i] = sq(constrain_float(frontend->_gpsHorizVelNoise, 0.05f, 5.0f)) + sq(frontend->gpsNEVelVarAccScale * accNavMag);
for (uint8_t i=2; i<=5; i++) R_OBS_DATA_CHECKS[i] = R_OBS[i];
// if vertical GPS velocity data and an independant height source is being used, check to see if the GPS vertical velocity and altimeter
// innovations have the same sign and are outside limits. If so, then it is likely aliasing is affecting
// the accelerometers and we should disable the GPS and barometer innovation consistency checks.
if (useGpsVertVel && fuseVelData && (frontend->_altSource != 2)) {
// calculate innovations for height and vertical GPS vel measurements
float hgtErr = stateStruct.position.z - observation[5];
float velDErr = stateStruct.velocity.z - observation[2];
// check if they are the same sign and both more than 3-sigma out of bounds
if ((hgtErr*velDErr > 0.0f) && (sq(hgtErr) > 9.0f * (P[8][8] + R_OBS_DATA_CHECKS[5])) && (sq(velDErr) > 9.0f * (P[5][5] + R_OBS_DATA_CHECKS[2]))) {
badIMUdata = true;
} else {
badIMUdata = false;
}
}
// calculate innovations and check GPS data validity using an innovation consistency check
// test position measurements
if (fusePosData) {
// test horizontal position measurements
innovVelPos[3] = stateStruct.position.x - observation[3];
innovVelPos[4] = stateStruct.position.y - observation[4];
varInnovVelPos[3] = P[6][6] + R_OBS_DATA_CHECKS[3];
varInnovVelPos[4] = P[7][7] + R_OBS_DATA_CHECKS[4];
// apply an innovation consistency threshold test, but don't fail if bad IMU data
float maxPosInnov2 = sq(max(0.01f * (float)frontend->_gpsPosInnovGate, 1.0f))*(varInnovVelPos[3] + varInnovVelPos[4]);
posTestRatio = (sq(innovVelPos[3]) + sq(innovVelPos[4])) / maxPosInnov2;
posHealth = ((posTestRatio < 1.0f) || badIMUdata);
// declare a timeout condition if we have been too long without data or not aiding
posTimeout = (((imuSampleTime_ms - lastPosPassTime_ms) > gpsRetryTime) || PV_AidingMode == AID_NONE);
// use position data if healthy, timed out, or in constant position mode
if (posHealth || posTimeout || (PV_AidingMode == AID_NONE)) {
posHealth = true;
// only reset the failed time and do glitch timeout checks if we are doing full aiding
if (PV_AidingMode == AID_ABSOLUTE) {
lastPosPassTime_ms = imuSampleTime_ms;
// if timed out or outside the specified uncertainty radius, reset to the GPS
if (posTimeout || ((P[6][6] + P[7][7]) > sq(float(frontend->_gpsGlitchRadiusMax)))) {
// reset the position to the current GPS position
ResetPosition();
// reset the velocity to the GPS velocity
ResetVelocity();
// don't fuse GPS data on this time step
fusePosData = false;
fuseVelData = false;
// Reset the position variances and corresponding covariances to a value that will pass the checks
zeroRows(P,6,7);
zeroCols(P,6,7);
P[6][6] = sq(float(0.5f*frontend->_gpsGlitchRadiusMax));
P[7][7] = P[6][6];
// Reset the normalised innovation to avoid failing the bad fusion tests
posTestRatio = 0.0f;
velTestRatio = 0.0f;
}
}
} else {
posHealth = false;
}
}
// test velocity measurements
if (fuseVelData) {
// test velocity measurements
uint8_t imax = 2;
// Don't fuse vertical velocity observations if inhibited by the user or if we are using synthetic data
if (frontend->_fusionModeGPS >= 1 || PV_AidingMode != AID_ABSOLUTE) {
imax = 1;
}
float innovVelSumSq = 0; // sum of squares of velocity innovations
float varVelSum = 0; // sum of velocity innovation variances
for (uint8_t i = 0; i<=imax; i++) {
// velocity states start at index 3
stateIndex = i + 3;
// calculate innovations using blended and single IMU predicted states
velInnov[i] = stateStruct.velocity[i] - observation[i]; // blended
// calculate innovation variance
varInnovVelPos[i] = P[stateIndex][stateIndex] + R_OBS_DATA_CHECKS[i];
// sum the innovation and innovation variances
innovVelSumSq += sq(velInnov[i]);
varVelSum += varInnovVelPos[i];
}
// apply an innovation consistency threshold test, but don't fail if bad IMU data
// calculate the test ratio
velTestRatio = innovVelSumSq / (varVelSum * sq(max(0.01f * (float)frontend->_gpsVelInnovGate, 1.0f)));
// fail if the ratio is greater than 1
velHealth = ((velTestRatio < 1.0f) || badIMUdata);
// declare a timeout if we have not fused velocity data for too long or not aiding
velTimeout = (((imuSampleTime_ms - lastVelPassTime_ms) > gpsRetryTime) || PV_AidingMode == AID_NONE);
// use velocity data if healthy, timed out, or in constant position mode
if (velHealth || velTimeout) {
velHealth = true;
// restart the timeout count
lastVelPassTime_ms = imuSampleTime_ms;
// If we are doing full aiding and velocity fusion times out, reset to the GPS velocity
if (PV_AidingMode == AID_ABSOLUTE && velTimeout) {
// reset the velocity to the GPS velocity
ResetVelocity();
// don't fuse GPS velocity data on this time step
fuseVelData = false;
// Reset the normalised innovation to avoid failing the bad fusion tests
velTestRatio = 0.0f;
}
} else {
velHealth = false;
}
}
// test height measurements
if (fuseHgtData) {
// calculate height innovations
innovVelPos[5] = stateStruct.position.z - observation[5];
varInnovVelPos[5] = P[8][8] + R_OBS_DATA_CHECKS[5];
// calculate the innovation consistency test ratio
hgtTestRatio = sq(innovVelPos[5]) / (sq(max(0.01f * (float)frontend->_hgtInnovGate, 1.0f)) * varInnovVelPos[5]);
// fail if the ratio is > 1, but don't fail if bad IMU data
hgtHealth = ((hgtTestRatio < 1.0f) || badIMUdata);
// Fuse height data if healthy or timed out or in constant position mode
if (hgtHealth || hgtTimeout || (PV_AidingMode == AID_NONE && onGround)) {
// Calculate a filtered value to be used by pre-flight health checks
// We need to filter because wind gusts can generate significant baro noise and we want to be able to detect bias errors in the inertial solution
if (onGround) {
float dtBaro = (imuSampleTime_ms - lastHgtPassTime_ms)*1.0e-3f;
const float hgtInnovFiltTC = 2.0f;
float alpha = constrain_float(dtBaro/(dtBaro+hgtInnovFiltTC),0.0f,1.0f);
hgtInnovFiltState += (innovVelPos[5]-hgtInnovFiltState)*alpha;
} else {
hgtInnovFiltState = 0.0f;
}
// if timed out, reset the height
if (hgtTimeout) {
ResetHeight();
hgtTimeout = false;
}
// If we have got this far then declare the height data as healthy and reset the timeout counter
hgtHealth = true;
lastHgtPassTime_ms = imuSampleTime_ms;
}
}
// set range for sequential fusion of velocity and position measurements depending on which data is available and its health
if (fuseVelData && velHealth) {
fuseData[0] = true;
fuseData[1] = true;
if (useGpsVertVel) {
fuseData[2] = true;
}
tiltErrVec.zero();
}
if (fusePosData && posHealth) {
fuseData[3] = true;
fuseData[4] = true;
tiltErrVec.zero();
}
if (fuseHgtData && hgtHealth) {
fuseData[5] = true;
}
// fuse measurements sequentially
for (obsIndex=0; obsIndex<=5; obsIndex++) {
if (fuseData[obsIndex]) {
stateIndex = 3 + obsIndex;
// calculate the measurement innovation, using states from a different time coordinate if fusing height data
// adjust scaling on GPS measurement noise variances if not enough satellites
if (obsIndex <= 2)
{
innovVelPos[obsIndex] = stateStruct.velocity[obsIndex] - observation[obsIndex];
R_OBS[obsIndex] *= sq(gpsNoiseScaler);
}
else if (obsIndex == 3 || obsIndex == 4) {
innovVelPos[obsIndex] = stateStruct.position[obsIndex-3] - observation[obsIndex];
R_OBS[obsIndex] *= sq(gpsNoiseScaler);
} else if (obsIndex == 5) {
innovVelPos[obsIndex] = stateStruct.position[obsIndex-3] - observation[obsIndex];
const float gndMaxBaroErr = 4.0f;
const float gndBaroInnovFloor = -0.5f;
if(getTouchdownExpected()) {
// when a touchdown is expected, floor the barometer innovation at gndBaroInnovFloor
// constrain the correction between 0 and gndBaroInnovFloor+gndMaxBaroErr
// this function looks like this:
// |/
//---------|---------
// ____/|
// / |
// / |
innovVelPos[5] += constrain_float(-innovVelPos[5]+gndBaroInnovFloor, 0.0f, gndBaroInnovFloor+gndMaxBaroErr);
}
}
// calculate the Kalman gain and calculate innovation variances
varInnovVelPos[obsIndex] = P[stateIndex][stateIndex] + R_OBS[obsIndex];
SK = 1.0f/varInnovVelPos[obsIndex];
for (uint8_t i= 0; i<=15; i++) {
Kfusion[i] = P[i][stateIndex]*SK;
}
// inhibit magnetic field state estimation by setting Kalman gains to zero
if (!inhibitMagStates) {
for (uint8_t i = 16; i<=21; i++) {
Kfusion[i] = P[i][stateIndex]*SK;
}
} else {
for (uint8_t i = 16; i<=21; i++) {
Kfusion[i] = 0.0f;
}
}
// inhibit wind state estimation by setting Kalman gains to zero
if (!inhibitWindStates) {
Kfusion[22] = P[22][stateIndex]*SK;
Kfusion[23] = P[23][stateIndex]*SK;
} else {
Kfusion[22] = 0.0f;
Kfusion[23] = 0.0f;
}
// zero the attitude error state - by definition it is assumed to be zero before each observaton fusion
stateStruct.angErr.zero();
// calculate state corrections and re-normalise the quaternions for states predicted using the blended IMU data
for (uint8_t i = 0; i<=stateIndexLim; i++) {
statesArray[i] = statesArray[i] - Kfusion[i] * innovVelPos[obsIndex];
}
// the first 3 states represent the angular misalignment vector. This is
// is used to correct the estimated quaternion
stateStruct.quat.rotate(stateStruct.angErr);
// sum the attitude error from velocity and position fusion only
// used as a metric for convergence monitoring
if (obsIndex != 5) {
tiltErrVec += stateStruct.angErr;
}
// update the covariance - take advantage of direct observation of a single state at index = stateIndex to reduce computations
// this is a numerically optimised implementation of standard equation P = (I - K*H)*P;
for (uint8_t i= 0; i<=stateIndexLim; i++) {
for (uint8_t j= 0; j<=stateIndexLim; j++)
{
KHP[i][j] = Kfusion[i] * P[stateIndex][j];
}
}
for (uint8_t i= 0; i<=stateIndexLim; i++) {
for (uint8_t j= 0; j<=stateIndexLim; j++) {
P[i][j] = P[i][j] - KHP[i][j];
}
}
}
}
}
// force the covariance matrix to be symmetrical and limit the variances to prevent ill-condiioning.
ForceSymmetry();
ConstrainVariances();
// stop performance timer
hal.util->perf_end(_perf_FuseVelPosNED);
}
void NavEKF3_core::FuseRngBcn()
{
// declarations
float pn;
float pe;
float pd;
float bcn_pn;
float bcn_pe;
float bcn_pd;
const float R_BCN = sq(MAX(rngBcnDataDelayed.rngErr , 0.1f));
float rngPred;
// health is set bad until test passed
rngBcnHealth = false;
if (activeHgtSource != HGT_SOURCE_BCN) {
// calculate the vertical offset from EKF datum to beacon datum
CalcRangeBeaconPosDownOffset(R_BCN, stateStruct.position, false);
} else {
bcnPosOffsetNED.z = 0.0f;
}
// copy required states to local variable names
pn = stateStruct.position.x;
pe = stateStruct.position.y;
pd = stateStruct.position.z;
bcn_pn = rngBcnDataDelayed.beacon_posNED.x;
bcn_pe = rngBcnDataDelayed.beacon_posNED.y;
bcn_pd = rngBcnDataDelayed.beacon_posNED.z + bcnPosOffsetNED.z;
// predicted range
Vector3f deltaPosNED = stateStruct.position - rngBcnDataDelayed.beacon_posNED;
rngPred = deltaPosNED.length();
// calculate measurement innovation
innovRngBcn = rngPred - rngBcnDataDelayed.rng;
// perform fusion of range measurement
if (rngPred > 0.1f)
{
// calculate observation jacobians
float H_BCN[24];
memset(H_BCN, 0, sizeof(H_BCN));
float t2 = bcn_pd-pd;
float t3 = bcn_pe-pe;
float t4 = bcn_pn-pn;
float t5 = t2*t2;
float t6 = t3*t3;
float t7 = t4*t4;
float t8 = t5+t6+t7;
float t9 = 1.0f/sqrtf(t8);
H_BCN[7] = -t4*t9;
H_BCN[8] = -t3*t9;
// If we are not using the beacons as a height reference, we pretend that the beacons
// are at the same height as the flight vehicle when calculating the observation derivatives
// and Kalman gains
// TODO - less hacky way of achieving this, preferably using an alternative derivation
if (activeHgtSource != HGT_SOURCE_BCN) {
t2 = 0.0f;
}
H_BCN[9] = -t2*t9;
// calculate Kalman gains
float t10 = P[9][9]*t2*t9;
float t11 = P[8][9]*t3*t9;
float t12 = P[7][9]*t4*t9;
float t13 = t10+t11+t12;
float t14 = t2*t9*t13;
float t15 = P[9][8]*t2*t9;
float t16 = P[8][8]*t3*t9;
float t17 = P[7][8]*t4*t9;
float t18 = t15+t16+t17;
float t19 = t3*t9*t18;
float t20 = P[9][7]*t2*t9;
float t21 = P[8][7]*t3*t9;
float t22 = P[7][7]*t4*t9;
float t23 = t20+t21+t22;
float t24 = t4*t9*t23;
varInnovRngBcn = R_BCN+t14+t19+t24;
float t26;
if (varInnovRngBcn >= R_BCN) {
t26 = 1.0f/varInnovRngBcn;
faultStatus.bad_rngbcn = false;
} else {
// the calculation is badly conditioned, so we cannot perform fusion on this step
// we reset the covariance matrix and try again next measurement
CovarianceInit();
faultStatus.bad_rngbcn = true;
return;
}
Kfusion[0] = -t26*(P[0][7]*t4*t9+P[0][8]*t3*t9+P[0][9]*t2*t9);
Kfusion[1] = -t26*(P[1][7]*t4*t9+P[1][8]*t3*t9+P[1][9]*t2*t9);
Kfusion[2] = -t26*(P[2][7]*t4*t9+P[2][8]*t3*t9+P[2][9]*t2*t9);
Kfusion[3] = -t26*(P[3][7]*t4*t9+P[3][8]*t3*t9+P[3][9]*t2*t9);
Kfusion[4] = -t26*(P[4][7]*t4*t9+P[4][8]*t3*t9+P[4][9]*t2*t9);
Kfusion[5] = -t26*(P[5][7]*t4*t9+P[5][8]*t3*t9+P[5][9]*t2*t9);
Kfusion[7] = -t26*(t22+P[7][8]*t3*t9+P[7][9]*t2*t9);
Kfusion[8] = -t26*(t16+P[8][7]*t4*t9+P[8][9]*t2*t9);
if (!inhibitDelAngBiasStates) {
Kfusion[10] = -t26*(P[10][7]*t4*t9+P[10][8]*t3*t9+P[10][9]*t2*t9);
Kfusion[11] = -t26*(P[11][7]*t4*t9+P[11][8]*t3*t9+P[11][9]*t2*t9);
Kfusion[12] = -t26*(P[12][7]*t4*t9+P[12][8]*t3*t9+P[12][9]*t2*t9);
} else {
// zero indexes 10 to 12 = 3*4 bytes
memset(&Kfusion[10], 0, 12);
}
if (!inhibitDelVelBiasStates) {
Kfusion[13] = -t26*(P[13][7]*t4*t9+P[13][8]*t3*t9+P[13][9]*t2*t9);
Kfusion[14] = -t26*(P[14][7]*t4*t9+P[14][8]*t3*t9+P[14][9]*t2*t9);
Kfusion[15] = -t26*(P[15][7]*t4*t9+P[15][8]*t3*t9+P[15][9]*t2*t9);
} else {
// zero indexes 13 to 15 = 3*4 bytes
memset(&Kfusion[13], 0, 12);
}
// only allow the range observations to modify the vertical states if we are using it as a height reference
if (activeHgtSource == HGT_SOURCE_BCN) {
Kfusion[6] = -t26*(P[6][7]*t4*t9+P[6][8]*t3*t9+P[6][9]*t2*t9);
Kfusion[9] = -t26*(t10+P[9][7]*t4*t9+P[9][8]*t3*t9);
} else {
Kfusion[6] = 0.0f;
Kfusion[9] = 0.0f;
}
if (!inhibitMagStates) {
Kfusion[16] = -t26*(P[16][7]*t4*t9+P[16][8]*t3*t9+P[16][9]*t2*t9);
Kfusion[17] = -t26*(P[17][7]*t4*t9+P[17][8]*t3*t9+P[17][9]*t2*t9);
Kfusion[18] = -t26*(P[18][7]*t4*t9+P[18][8]*t3*t9+P[18][9]*t2*t9);
Kfusion[19] = -t26*(P[19][7]*t4*t9+P[19][8]*t3*t9+P[19][9]*t2*t9);
Kfusion[20] = -t26*(P[20][7]*t4*t9+P[20][8]*t3*t9+P[20][9]*t2*t9);
Kfusion[21] = -t26*(P[21][7]*t4*t9+P[21][8]*t3*t9+P[21][9]*t2*t9);
} else {
// zero indexes 16 to 21 = 6*4 bytes
memset(&Kfusion[16], 0, 24);
}
if (!inhibitWindStates) {
Kfusion[22] = -t26*(P[22][7]*t4*t9+P[22][8]*t3*t9+P[22][9]*t2*t9);
Kfusion[23] = -t26*(P[23][7]*t4*t9+P[23][8]*t3*t9+P[23][9]*t2*t9);
} else {
// zero indexes 22 to 23 = 2*4 bytes
memset(&Kfusion[22], 0, 8);
}
// Calculate innovation using the selected offset value
Vector3f delta = stateStruct.position - rngBcnDataDelayed.beacon_posNED;
innovRngBcn = delta.length() - rngBcnDataDelayed.rng;
// calculate the innovation consistency test ratio
rngBcnTestRatio = sq(innovRngBcn) / (sq(MAX(0.01f * (float)frontend->_rngBcnInnovGate, 1.0f)) * varInnovRngBcn);
// fail if the ratio is > 1, but don't fail if bad IMU data
rngBcnHealth = ((rngBcnTestRatio < 1.0f) || badIMUdata);
// test the ratio before fusing data
if (rngBcnHealth) {
// restart the counter
lastRngBcnPassTime_ms = imuSampleTime_ms;
// correct the covariance P = (I - K*H)*P
// take advantage of the empty columns in KH to reduce the
// number of operations
for (unsigned i = 0; i<=stateIndexLim; i++) {
for (unsigned j = 0; j<=6; j++) {
KH[i][j] = 0.0f;
}
for (unsigned j = 7; j<=9; j++) {
KH[i][j] = Kfusion[i] * H_BCN[j];
}
for (unsigned j = 10; j<=23; j++) {
KH[i][j] = 0.0f;
}
}
for (unsigned j = 0; j<=stateIndexLim; j++) {
for (unsigned i = 0; i<=stateIndexLim; i++) {
ftype res = 0;
res += KH[i][7] * P[7][j];
res += KH[i][8] * P[8][j];
res += KH[i][9] * P[9][j];
KHP[i][j] = res;
}
}
// Check that we are not going to drive any variances negative and skip the update if so
bool healthyFusion = true;
for (uint8_t i= 0; i<=stateIndexLim; i++) {
if (KHP[i][i] > P[i][i]) {
healthyFusion = false;
}
}
if (healthyFusion) {
// update the covariance matrix
for (uint8_t i= 0; i<=stateIndexLim; i++) {
for (uint8_t j= 0; j<=stateIndexLim; j++) {
P[i][j] = P[i][j] - KHP[i][j];
}
}
// force the covariance matrix to be symmetrical and limit the variances to prevent ill-conditioning.
ForceSymmetry();
ConstrainVariances();
// correct the state vector
for (uint8_t j= 0; j<=stateIndexLim; j++) {
statesArray[j] = statesArray[j] - Kfusion[j] * innovRngBcn;
}
// record healthy fusion
faultStatus.bad_rngbcn = false;
} else {
// record bad fusion
faultStatus.bad_rngbcn = true;
}
}
// Update the fusion report
rngBcnFusionReport[rngBcnDataDelayed.beacon_ID].beaconPosNED = rngBcnDataDelayed.beacon_posNED;
rngBcnFusionReport[rngBcnDataDelayed.beacon_ID].innov = innovRngBcn;
rngBcnFusionReport[rngBcnDataDelayed.beacon_ID].innovVar = varInnovRngBcn;
rngBcnFusionReport[rngBcnDataDelayed.beacon_ID].rng = rngBcnDataDelayed.rng;
rngBcnFusionReport[rngBcnDataDelayed.beacon_ID].testRatio = rngBcnTestRatio;
}
}
