#include #include "AP_NavEKF2.h" #include "AP_NavEKF2_core.h" #include #include #include extern const AP_HAL::HAL& hal; /******************************************************** * RESET FUNCTIONS * ********************************************************/ /******************************************************** * FUSE MEASURED_DATA * ********************************************************/ // select fusion of optical flow measurements void NavEKF2_core::SelectFlowFusion() { // Check if the magnetometer has been fused on that time step and the filter is running at faster than 200 Hz // If so, don't fuse measurements on this time step to reduce frame over-runs // Only allow one time slip to prevent high rate magnetometer data preventing fusion of other measurements if (magFusePerformed && dtIMUavg < 0.005f && !optFlowFusionDelayed) { optFlowFusionDelayed = true; return; } else { optFlowFusionDelayed = false; } // start performance timer hal.util->perf_begin(_perf_FuseOptFlow); // Perform Data Checks // Check if the optical flow data is still valid flowDataValid = ((imuSampleTime_ms - flowValidMeaTime_ms) < 1000); // check is the terrain offset estimate is still valid - if we are using range finder as the main height reference, the ground is assumed to be at 0 gndOffsetValid = ((imuSampleTime_ms - gndHgtValidTime_ms) < 5000) || (activeHgtSource == HGT_SOURCE_RNG); // Perform tilt check bool tiltOK = (prevTnb.c.z > frontend->DCM33FlowMin); // Constrain measurements to zero if takeoff is not detected and the height above ground // is insuffient to achieve acceptable focus. This allows the vehicle to be picked up // and carried to test optical flow operation if (!takeOffDetected && ((terrainState - stateStruct.position.z) < 0.5f)) { ofDataDelayed.flowRadXYcomp.zero(); ofDataDelayed.flowRadXY.zero(); flowDataValid = true; } // if have valid flow or range measurements, fuse data into a 1-state EKF to estimate terrain height if (((flowDataToFuse && (frontend->_flowUse == FLOW_USE_TERRAIN)) || rangeDataToFuse) && tiltOK) { // Estimate the terrain offset (runs a one state EKF) EstimateTerrainOffset(); } // Fuse optical flow data into the main filter if (flowDataToFuse && tiltOK) { if (frontend->_flowUse == FLOW_USE_NAV) { // Set the flow noise used by the fusion processes R_LOS = sq(MAX(frontend->_flowNoise, 0.05f)); // Fuse the optical flow X and Y axis data into the main filter sequentially FuseOptFlow(); } // reset flag to indicate that no new flow data is available for fusion flowDataToFuse = false; } // stop the performance timer hal.util->perf_end(_perf_FuseOptFlow); } /* Estimation of terrain offset using a single state EKF The filter can fuse motion compensated optical flow rates and range finder measurements Equations generated using https://github.com/PX4/ecl/tree/master/EKF/matlab/scripts/Terrain%20Estimator */ void NavEKF2_core::EstimateTerrainOffset() { // start performance timer hal.util->perf_begin(_perf_TerrainOffset); // horizontal velocity squared float velHorizSq = sq(stateStruct.velocity.x) + sq(stateStruct.velocity.y); // don't fuse flow data if LOS rate is misaligned, without GPS, or insufficient velocity, as it is poorly observable // don't fuse flow data if it exceeds validity limits // don't update terrain offset if grpund is being used as the zero height datum in the main filter bool cantFuseFlowData = ((frontend->_flowUse != FLOW_USE_TERRAIN) || gpsNotAvailable || PV_AidingMode == AID_RELATIVE || velHorizSq < 25.0f || (MAX(ofDataDelayed.flowRadXY[0],ofDataDelayed.flowRadXY[1]) > frontend->_maxFlowRate)); if ((!rangeDataToFuse && cantFuseFlowData) || (activeHgtSource == HGT_SOURCE_RNG)) { // skip update inhibitGndState = true; } else { inhibitGndState = false; // record the time we last updated the terrain offset state gndHgtValidTime_ms = imuSampleTime_ms; // propagate ground position state noise each time this is called using the difference in position since the last observations and an RMS gradient assumption // limit distance to prevent intialisation afer bad gps causing bad numerical conditioning float distanceTravelledSq = sq(stateStruct.position[0] - prevPosN) + sq(stateStruct.position[1] - prevPosE); distanceTravelledSq = MIN(distanceTravelledSq, 100.0f); prevPosN = stateStruct.position[0]; prevPosE = stateStruct.position[1]; // in addition to a terrain gradient error model, we also have the growth in uncertainty due to the copters vertical velocity float timeLapsed = MIN(0.001f * (imuSampleTime_ms - timeAtLastAuxEKF_ms), 1.0f); float Pincrement = (distanceTravelledSq * sq(frontend->_terrGradMax)) + sq(timeLapsed)*P[5][5]; Popt += Pincrement; timeAtLastAuxEKF_ms = imuSampleTime_ms; // fuse range finder data if (rangeDataToFuse) { // predict range float predRngMeas = MAX((terrainState - stateStruct.position[2]),rngOnGnd) / prevTnb.c.z; // Copy required states to local variable names float q0 = stateStruct.quat[0]; // quaternion at optical flow measurement time float q1 = stateStruct.quat[1]; // quaternion at optical flow measurement time float q2 = stateStruct.quat[2]; // quaternion at optical flow measurement time float q3 = stateStruct.quat[3]; // quaternion at optical flow measurement time // Set range finder measurement noise variance. TODO make this a function of range and tilt to allow for sensor, alignment and AHRS errors float R_RNG = frontend->_rngNoise; // calculate Kalman gain float SK_RNG = sq(q0) - sq(q1) - sq(q2) + sq(q3); float K_RNG = Popt/(SK_RNG*(R_RNG + Popt/sq(SK_RNG))); // Calculate the innovation variance for data logging varInnovRng = (R_RNG + Popt/sq(SK_RNG)); // constrain terrain height to be below the vehicle terrainState = MAX(terrainState, stateStruct.position[2] + rngOnGnd); // Calculate the measurement innovation innovRng = predRngMeas - rangeDataDelayed.rng; // calculate the innovation consistency test ratio auxRngTestRatio = sq(innovRng) / (sq(MAX(0.01f * (float)frontend->_rngInnovGate, 1.0f)) * varInnovRng); // Check the innovation test ratio and don't fuse if too large if (auxRngTestRatio < 1.0f) { // correct the state terrainState -= K_RNG * innovRng; // constrain the state terrainState = MAX(terrainState, stateStruct.position[2] + rngOnGnd); // correct the covariance Popt = Popt - sq(Popt)/(SK_RNG*(R_RNG + Popt/sq(SK_RNG))*(sq(q0) - sq(q1) - sq(q2) + sq(q3))); // prevent the state variance from becoming negative Popt = MAX(Popt,0.0f); } } if (!cantFuseFlowData) { Vector3f relVelSensor; // velocity of sensor relative to ground in sensor axes Vector2f losPred; // predicted optical flow angular rate measurement float q0 = stateStruct.quat[0]; // quaternion at optical flow measurement time float q1 = stateStruct.quat[1]; // quaternion at optical flow measurement time float q2 = stateStruct.quat[2]; // quaternion at optical flow measurement time float q3 = stateStruct.quat[3]; // quaternion at optical flow measurement time float K_OPT; float H_OPT; Vector2f auxFlowObsInnovVar; // predict range to centre of image float flowRngPred = MAX((terrainState - stateStruct.position.z),rngOnGnd) / prevTnb.c.z; // constrain terrain height to be below the vehicle terrainState = MAX(terrainState, stateStruct.position.z + rngOnGnd); // calculate relative velocity in sensor frame relVelSensor = prevTnb*stateStruct.velocity; // divide velocity by range, subtract body rates and apply scale factor to // get predicted sensed angular optical rates relative to X and Y sensor axes losPred.x = relVelSensor.y / flowRngPred; losPred.y = - relVelSensor.x / flowRngPred; // calculate innovations auxFlowObsInnov = losPred - ofDataDelayed.flowRadXYcomp; // calculate observation jacobians float t2 = q0*q0; float t3 = q1*q1; float t4 = q2*q2; float t5 = q3*q3; float t6 = stateStruct.position.z - terrainState; float t7 = 1.0f / (t6*t6); float t8 = q0*q3*2.0f; float t9 = t2-t3-t4+t5; // prevent the state variances from becoming badly conditioned Popt = MAX(Popt,1E-6f); // calculate observation noise variance from parameter float flow_noise_variance = sq(MAX(frontend->_flowNoise, 0.05f)); // Fuse Y axis data // Calculate observation partial derivative H_OPT = t7*t9*(-stateStruct.velocity.z*(q0*q2*2.0-q1*q3*2.0)+stateStruct.velocity.x*(t2+t3-t4-t5)+stateStruct.velocity.y*(t8+q1*q2*2.0)); // calculate innovation variance auxFlowObsInnovVar.y = H_OPT * Popt * H_OPT + flow_noise_variance; // calculate Kalman gain K_OPT = Popt * H_OPT / auxFlowObsInnovVar.y; // calculate the innovation consistency test ratio auxFlowTestRatio.y = sq(auxFlowObsInnov.y) / (sq(MAX(0.01f * (float)frontend->_flowInnovGate, 1.0f)) * auxFlowObsInnovVar.y); // don't fuse if optical flow data is outside valid range if (auxFlowTestRatio.y < 1.0f) { // correct the state terrainState -= K_OPT * auxFlowObsInnov.y; // constrain the state terrainState = MAX(terrainState, stateStruct.position.z + rngOnGnd); // update intermediate variables used when fusing the X axis t6 = stateStruct.position.z - terrainState; t7 = 1.0f / (t6*t6); // correct the covariance Popt = Popt - K_OPT * H_OPT * Popt; // prevent the state variances from becoming badly conditioned Popt = MAX(Popt,1E-6f); } // fuse X axis data H_OPT = -t7*t9*(stateStruct.velocity.z*(q0*q1*2.0+q2*q3*2.0)+stateStruct.velocity.y*(t2-t3+t4-t5)-stateStruct.velocity.x*(t8-q1*q2*2.0)); // calculate innovation variances auxFlowObsInnovVar.x = H_OPT * Popt * H_OPT + flow_noise_variance; // calculate Kalman gain K_OPT = Popt * H_OPT / auxFlowObsInnovVar.x; // calculate the innovation consistency test ratio auxFlowTestRatio.x = sq(auxFlowObsInnov.x) / (sq(MAX(0.01f * (float)frontend->_flowInnovGate, 1.0f)) * auxFlowObsInnovVar.x); // don't fuse if optical flow data is outside valid range if (auxFlowTestRatio.x < 1.0f) { // correct the state terrainState -= K_OPT * auxFlowObsInnov.x; // constrain the state terrainState = MAX(terrainState, stateStruct.position.z + rngOnGnd); // correct the covariance Popt = Popt - K_OPT * H_OPT * Popt; // prevent the state variances from becoming badly conditioned Popt = MAX(Popt,1E-6f); } } } // stop the performance timer hal.util->perf_end(_perf_TerrainOffset); } /* * 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; Vector28 Kfusion {}; // 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/prevTnb.c.z),rngOnGnd,1000.0f); // correct range for flow sensor offset body frame position offset // the corrected value is the predicted range from the sensor focal point to the // centre of the image on the ground assuming flat terrain Vector3f posOffsetBody = (*ofDataDelayed.body_offset) - accelPosOffset; if (!posOffsetBody.is_zero()) { Vector3f posOffsetEarth = prevTnb.mul_transpose(posOffsetBody); range -= posOffsetEarth.z / prevTnb.c.z; } // calculate relative velocity in sensor frame including the relative motion due to rotation relVelSensor = prevTnb*stateStruct.velocity + ofDataDelayed.bodyRadXYZ % posOffsetBody; // 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; faultStatus.bad_yflow = false; } else { t61 = 0.0f; t62 = 1.0f/R_LOS; faultStatus.bad_yflow = true; return; } 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 Y axis observation and protect against a badly conditioned calculation if (t61 > R_LOS) { t62 = 1.0f/t61; faultStatus.bad_yflow = false; } else { t61 = 0.0f; t62 = 1.0f/R_LOS; faultStatus.bad_yflow = true; return; } 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(MAX(0.01f * (float)frontend->_flowInnovGate, 1.0f)) * 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; // 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; } } // 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(); // zero the attitude error state - by definition it is assumed to be zero before each observation 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); } else { // record bad axis if (obsIndex == 0) { faultStatus.bad_xflow = true; } else if (obsIndex == 1) { faultStatus.bad_yflow = true; } } } } } /******************************************************** * MISC FUNCTIONS * ********************************************************/