#include #include #include #include "../../../../../matrix/matrix/math.hpp" typedef matrix::Vector Vector24f; typedef matrix::SquareMatrix SquareMatrix24f; float sq(float in) { return in * in; } int main() { // Compare calculation of observation Jacobians and Kalman gains for sympy and matlab generated equations float Hfusion[24]; Vector24f H_MAG; Vector24f Kfusion; float mag_innov_var; // quaternion inputs must be normalised float q0 = 2.0f * ((float)rand() - 0.5f); float q1 = 2.0f * ((float)rand() - 0.5f); float q2 = 2.0f * ((float)rand() - 0.5f); float q3 = 2.0f * ((float)rand() - 0.5f); const float length = sqrtf(sq(q0) + sq(q1) + sq(q2) + sq(q3)); q0 /= length; q1 /= length; q2 /= length; q3 /= length; const float magN = 2.0f * ((float)rand() - 0.5f); const float magE = 2.0f * ((float)rand() - 0.5f); const float magD = 2.0f * ((float)rand() - 0.5f); const float R_MAG = sq(0.05f); const bool update_all_states = true; // create a symmetrical positive dfinite matrix with off diagonals between -1 and 1 and diagonals between 0 and 1 SquareMatrix24f P; for (int col=0; col<=23; col++) { for (int row=0; row<=col; row++) { if (row == col) { P(row,col) = (float)rand(); } else { P(col,row) = P(row,col) = 2.0f * ((float)rand() - 0.5f); } } } // common expressions used by sympy generated equations // calculate intermediate variables used for X axis innovation variance, observation Jacobians and Kalman gainss const float HKX0 = -magD*q2 + magE*q3 + magN*q0; const float HKX1 = magD*q3 + magE*q2 + magN*q1; const float HKX2 = magE*q1; const float HKX3 = magD*q0; const float HKX4 = magN*q2; const float HKX5 = magD*q1 + magE*q0 - magN*q3; const float HKX6 = powf(q0, 2) + powf(q1, 2) - powf(q2, 2) - powf(q3, 2); const float HKX7 = q0*q3 + q1*q2; const float HKX8 = q1*q3; const float HKX9 = q0*q2; const float HKX10 = 2*HKX7; const float HKX11 = -2*HKX8 + 2*HKX9; const float HKX12 = 2*HKX1; const float HKX13 = 2*HKX0; const float HKX14 = -2*HKX2 + 2*HKX3 + 2*HKX4; const float HKX15 = 2*HKX5; const float HKX16 = HKX10*P(0,17) - HKX11*P(0,18) + HKX12*P(0,1) + HKX13*P(0,0) - HKX14*P(0,2) + HKX15*P(0,3) + HKX6*P(0,16) + P(0,19); const float HKX17 = HKX10*P(16,17) - HKX11*P(16,18) + HKX12*P(1,16) + HKX13*P(0,16) - HKX14*P(2,16) + HKX15*P(3,16) + HKX6*P(16,16) + P(16,19); const float HKX18 = HKX10*P(17,18) - HKX11*P(18,18) + HKX12*P(1,18) + HKX13*P(0,18) - HKX14*P(2,18) + HKX15*P(3,18) + HKX6*P(16,18) + P(18,19); const float HKX19 = HKX10*P(2,17) - HKX11*P(2,18) + HKX12*P(1,2) + HKX13*P(0,2) - HKX14*P(2,2) + HKX15*P(2,3) + HKX6*P(2,16) + P(2,19); const float HKX20 = HKX10*P(17,17) - HKX11*P(17,18) + HKX12*P(1,17) + HKX13*P(0,17) - HKX14*P(2,17) + HKX15*P(3,17) + HKX6*P(16,17) + P(17,19); const float HKX21 = HKX10*P(3,17) - HKX11*P(3,18) + HKX12*P(1,3) + HKX13*P(0,3) - HKX14*P(2,3) + HKX15*P(3,3) + HKX6*P(3,16) + P(3,19); const float HKX22 = HKX10*P(1,17) - HKX11*P(1,18) + HKX12*P(1,1) + HKX13*P(0,1) - HKX14*P(1,2) + HKX15*P(1,3) + HKX6*P(1,16) + P(1,19); const float HKX23 = HKX10*P(17,19) - HKX11*P(18,19) + HKX12*P(1,19) + HKX13*P(0,19) - HKX14*P(2,19) + HKX15*P(3,19) + HKX6*P(16,19) + P(19,19); const float HKX24 = 1.0F/(HKX10*HKX20 - HKX11*HKX18 + HKX12*HKX22 + HKX13*HKX16 - HKX14*HKX19 + HKX15*HKX21 + HKX17*HKX6 + HKX23 + R_MAG); // common expressions used by matlab generated equations float SH_MAG[9]; SH_MAG[0] = 2.0f*magD*q3 + 2.0f*magE*q2 + 2.0f*magN*q1; SH_MAG[1] = 2.0f*magD*q0 - 2.0f*magE*q1 + 2.0f*magN*q2; SH_MAG[2] = 2.0f*magD*q1 + 2.0f*magE*q0 - 2.0f*magN*q3; SH_MAG[3] = sq(q3); SH_MAG[4] = sq(q2); SH_MAG[5] = sq(q1); SH_MAG[6] = sq(q0); SH_MAG[7] = 2.0f*magN*q0; SH_MAG[8] = 2.0f*magE*q3; // Compare X axis equations { mag_innov_var = (HKX10*HKX20 - HKX11*HKX18 + HKX12*HKX22 + HKX13*HKX16 - HKX14*HKX19 + HKX15*HKX21 + HKX17*HKX6 + HKX23 + R_MAG); float HK50 = 1.0F/mag_innov_var; // Calculate X axis observation jacobians memset(Hfusion, 0, sizeof(Hfusion)); Hfusion[0] = 2*HKX0; Hfusion[1] = 2*HKX1; Hfusion[2] = 2*HKX2 - 2*HKX3 - 2*HKX4; Hfusion[3] = 2*HKX5; Hfusion[16] = HKX6; Hfusion[17] = 2*HKX7; Hfusion[18] = 2*HKX8 - 2*HKX9; Hfusion[19] = 1; // Calculate X axis Kalman gains if (update_all_states) { Kfusion(0) = HKX16*HKX24; Kfusion(1) = HKX22*HKX24; Kfusion(2) = HKX19*HKX24; Kfusion(3) = HKX21*HKX24; for (unsigned row = 4; row <= 15; row++) { Kfusion(row) = HKX24*(HKX10*P(row,17) - HKX11*P(row,18) + HKX12*P(1,row) + HKX13*P(0,row) - HKX14*P(2,row) + HKX15*P(3,row) + HKX6*P(row,16) + P(row,19)); } for (unsigned row = 22; row <= 23; row++) { Kfusion(row) = HKX24*(HKX10*P(17,row) - HKX11*P(18,row) + HKX12*P(1,row) + HKX13*P(0,row) - HKX14*P(2,row) + HKX15*P(3,row) + HKX6*P(16,row) + P(19,row)); } } Kfusion(16) = HKX17*HKX24; Kfusion(17) = HKX20*HKX24; Kfusion(18) = HKX18*HKX24; Kfusion(19) = HKX23*HKX24; for (unsigned row = 20; row <= 21; row++) { Kfusion(row) = HKX24*(HKX10*P(17,row) - HKX11*P(18,row) + HKX12*P(1,row) + HKX13*P(0,row) - HKX14*P(2,row) + HKX15*P(3,row) + HKX6*P(16,row) + P(19,row)); } // save output and repeat calculation using legacy matlab generated code float Hfusion_sympy[24]; Vector24f Kfusion_sympy; for (int row=0; row<24; row++) { Hfusion_sympy[row] = Hfusion[row]; Kfusion_sympy(row) = Kfusion(row); } // repeat calculation using matlab generated equations // X axis innovation variance mag_innov_var = (P(19,19) + R_MAG + P(1,19)*SH_MAG[0] - P(2,19)*SH_MAG[1] + P(3,19)*SH_MAG[2] - P(16,19)*(SH_MAG[3] + SH_MAG[4] - SH_MAG[5] - SH_MAG[6]) + (2.0f*q0*q3 + 2.0f*q1*q2)*(P(19,17) + P(1,17)*SH_MAG[0] - P(2,17)*SH_MAG[1] + P(3,17)*SH_MAG[2] - P(16,17)*(SH_MAG[3] + SH_MAG[4] - SH_MAG[5] - SH_MAG[6]) + P(17,17)*(2.0f*q0*q3 + 2.0f*q1*q2) - P(18,17)*(2.0f*q0*q2 - 2.0f*q1*q3) + P(0,17)*(SH_MAG[7] + SH_MAG[8] - 2.0f*magD*q2)) - (2.0f*q0*q2 - 2.0f*q1*q3)*(P(19,18) + P(1,18)*SH_MAG[0] - P(2,18)*SH_MAG[1] + P(3,18)*SH_MAG[2] - P(16,18)*(SH_MAG[3] + SH_MAG[4] - SH_MAG[5] - SH_MAG[6]) + P(17,18)*(2.0f*q0*q3 + 2.0f*q1*q2) - P(18,18)*(2.0f*q0*q2 - 2.0f*q1*q3) + P(0,18)*(SH_MAG[7] + SH_MAG[8] - 2.0f*magD*q2)) + (SH_MAG[7] + SH_MAG[8] - 2.0f*magD*q2)*(P(19,0) + P(1,0)*SH_MAG[0] - P(2,0)*SH_MAG[1] + P(3,0)*SH_MAG[2] - P(16,0)*(SH_MAG[3] + SH_MAG[4] - SH_MAG[5] - SH_MAG[6]) + P(17,0)*(2.0f*q0*q3 + 2.0f*q1*q2) - P(18,0)*(2.0f*q0*q2 - 2.0f*q1*q3) + P(0,0)*(SH_MAG[7] + SH_MAG[8] - 2.0f*magD*q2)) + P(17,19)*(2.0f*q0*q3 + 2.0f*q1*q2) - P(18,19)*(2.0f*q0*q2 - 2.0f*q1*q3) + SH_MAG[0]*(P(19,1) + P(1,1)*SH_MAG[0] - P(2,1)*SH_MAG[1] + P(3,1)*SH_MAG[2] - P(16,1)*(SH_MAG[3] + SH_MAG[4] - SH_MAG[5] - SH_MAG[6]) + P(17,1)*(2.0f*q0*q3 + 2.0f*q1*q2) - P(18,1)*(2.0f*q0*q2 - 2.0f*q1*q3) + P(0,1)*(SH_MAG[7] + SH_MAG[8] - 2.0f*magD*q2)) - SH_MAG[1]*(P(19,2) + P(1,2)*SH_MAG[0] - P(2,2)*SH_MAG[1] + P(3,2)*SH_MAG[2] - P(16,2)*(SH_MAG[3] + SH_MAG[4] - SH_MAG[5] - SH_MAG[6]) + P(17,2)*(2.0f*q0*q3 + 2.0f*q1*q2) - P(18,2)*(2.0f*q0*q2 - 2.0f*q1*q3) + P(0,2)*(SH_MAG[7] + SH_MAG[8] - 2.0f*magD*q2)) + SH_MAG[2]*(P(19,3) + P(1,3)*SH_MAG[0] - P(2,3)*SH_MAG[1] + P(3,3)*SH_MAG[2] - P(16,3)*(SH_MAG[3] + SH_MAG[4] - SH_MAG[5] - SH_MAG[6]) + P(17,3)*(2.0f*q0*q3 + 2.0f*q1*q2) - P(18,3)*(2.0f*q0*q2 - 2.0f*q1*q3) + P(0,3)*(SH_MAG[7] + SH_MAG[8] - 2.0f*magD*q2)) - (SH_MAG[3] + SH_MAG[4] - SH_MAG[5] - SH_MAG[6])*(P(19,16) + P(1,16)*SH_MAG[0] - P(2,16)*SH_MAG[1] + P(3,16)*SH_MAG[2] - P(16,16)*(SH_MAG[3] + SH_MAG[4] - SH_MAG[5] - SH_MAG[6]) + P(17,16)*(2.0f*q0*q3 + 2.0f*q1*q2) - P(18,16)*(2.0f*q0*q2 - 2.0f*q1*q3) + P(0,16)*(SH_MAG[7] + SH_MAG[8] - 2.0f*magD*q2)) + P(0,19)*(SH_MAG[7] + SH_MAG[8] - 2.0f*magD*q2)); // Calculate X axis observation jacobians H_MAG.setZero(); H_MAG(0) = SH_MAG[7] + SH_MAG[8] - 2.0f*magD*q2; H_MAG(1) = SH_MAG[0]; H_MAG(2) = -SH_MAG[1]; H_MAG(3) = SH_MAG[2]; H_MAG(16) = SH_MAG[5] - SH_MAG[4] - SH_MAG[3] + SH_MAG[6]; H_MAG(17) = 2.0f*q0*q3 + 2.0f*q1*q2; H_MAG(18) = 2.0f*q1*q3 - 2.0f*q0*q2; H_MAG(19) = 1.0f; // Calculate X axis Kalman gains float SK_MX[5]; SK_MX[0] = 1.0f / mag_innov_var; SK_MX[1] = SH_MAG[3] + SH_MAG[4] - SH_MAG[5] - SH_MAG[6]; SK_MX[2] = SH_MAG[7] + SH_MAG[8] - 2.0f*magD*q2; SK_MX[3] = 2.0f*q0*q2 - 2.0f*q1*q3; SK_MX[4] = 2.0f*q0*q3 + 2.0f*q1*q2; if (update_all_states) { Kfusion(0) = SK_MX[0]*(P(0,19) + P(0,1)*SH_MAG[0] - P(0,2)*SH_MAG[1] + P(0,3)*SH_MAG[2] + P(0,0)*SK_MX[2] - P(0,16)*SK_MX[1] + P(0,17)*SK_MX[4] - P(0,18)*SK_MX[3]); Kfusion(1) = SK_MX[0]*(P(1,19) + P(1,1)*SH_MAG[0] - P(1,2)*SH_MAG[1] + P(1,3)*SH_MAG[2] + P(1,0)*SK_MX[2] - P(1,16)*SK_MX[1] + P(1,17)*SK_MX[4] - P(1,18)*SK_MX[3]); Kfusion(2) = SK_MX[0]*(P(2,19) + P(2,1)*SH_MAG[0] - P(2,2)*SH_MAG[1] + P(2,3)*SH_MAG[2] + P(2,0)*SK_MX[2] - P(2,16)*SK_MX[1] + P(2,17)*SK_MX[4] - P(2,18)*SK_MX[3]); Kfusion(3) = SK_MX[0]*(P(3,19) + P(3,1)*SH_MAG[0] - P(3,2)*SH_MAG[1] + P(3,3)*SH_MAG[2] + P(3,0)*SK_MX[2] - P(3,16)*SK_MX[1] + P(3,17)*SK_MX[4] - P(3,18)*SK_MX[3]); Kfusion(4) = SK_MX[0]*(P(4,19) + P(4,1)*SH_MAG[0] - P(4,2)*SH_MAG[1] + P(4,3)*SH_MAG[2] + P(4,0)*SK_MX[2] - P(4,16)*SK_MX[1] + P(4,17)*SK_MX[4] - P(4,18)*SK_MX[3]); Kfusion(5) = SK_MX[0]*(P(5,19) + P(5,1)*SH_MAG[0] - P(5,2)*SH_MAG[1] + P(5,3)*SH_MAG[2] + P(5,0)*SK_MX[2] - P(5,16)*SK_MX[1] + P(5,17)*SK_MX[4] - P(5,18)*SK_MX[3]); Kfusion(6) = SK_MX[0]*(P(6,19) + P(6,1)*SH_MAG[0] - P(6,2)*SH_MAG[1] + P(6,3)*SH_MAG[2] + P(6,0)*SK_MX[2] - P(6,16)*SK_MX[1] + P(6,17)*SK_MX[4] - P(6,18)*SK_MX[3]); Kfusion(7) = SK_MX[0]*(P(7,19) + P(7,1)*SH_MAG[0] - P(7,2)*SH_MAG[1] + P(7,3)*SH_MAG[2] + P(7,0)*SK_MX[2] - P(7,16)*SK_MX[1] + P(7,17)*SK_MX[4] - P(7,18)*SK_MX[3]); Kfusion(8) = SK_MX[0]*(P(8,19) + P(8,1)*SH_MAG[0] - P(8,2)*SH_MAG[1] + P(8,3)*SH_MAG[2] + P(8,0)*SK_MX[2] - P(8,16)*SK_MX[1] + P(8,17)*SK_MX[4] - P(8,18)*SK_MX[3]); Kfusion(9) = SK_MX[0]*(P(9,19) + P(9,1)*SH_MAG[0] - P(9,2)*SH_MAG[1] + P(9,3)*SH_MAG[2] + P(9,0)*SK_MX[2] - P(9,16)*SK_MX[1] + P(9,17)*SK_MX[4] - P(9,18)*SK_MX[3]); Kfusion(10) = SK_MX[0]*(P(10,19) + P(10,1)*SH_MAG[0] - P(10,2)*SH_MAG[1] + P(10,3)*SH_MAG[2] + P(10,0)*SK_MX[2] - P(10,16)*SK_MX[1] + P(10,17)*SK_MX[4] - P(10,18)*SK_MX[3]); Kfusion(11) = SK_MX[0]*(P(11,19) + P(11,1)*SH_MAG[0] - P(11,2)*SH_MAG[1] + P(11,3)*SH_MAG[2] + P(11,0)*SK_MX[2] - P(11,16)*SK_MX[1] + P(11,17)*SK_MX[4] - P(11,18)*SK_MX[3]); Kfusion(12) = SK_MX[0]*(P(12,19) + P(12,1)*SH_MAG[0] - P(12,2)*SH_MAG[1] + P(12,3)*SH_MAG[2] + P(12,0)*SK_MX[2] - P(12,16)*SK_MX[1] + P(12,17)*SK_MX[4] - P(12,18)*SK_MX[3]); Kfusion(13) = SK_MX[0]*(P(13,19) + P(13,1)*SH_MAG[0] - P(13,2)*SH_MAG[1] + P(13,3)*SH_MAG[2] + P(13,0)*SK_MX[2] - P(13,16)*SK_MX[1] + P(13,17)*SK_MX[4] - P(13,18)*SK_MX[3]); Kfusion(14) = SK_MX[0]*(P(14,19) + P(14,1)*SH_MAG[0] - P(14,2)*SH_MAG[1] + P(14,3)*SH_MAG[2] + P(14,0)*SK_MX[2] - P(14,16)*SK_MX[1] + P(14,17)*SK_MX[4] - P(14,18)*SK_MX[3]); Kfusion(15) = SK_MX[0]*(P(15,19) + P(15,1)*SH_MAG[0] - P(15,2)*SH_MAG[1] + P(15,3)*SH_MAG[2] + P(15,0)*SK_MX[2] - P(15,16)*SK_MX[1] + P(15,17)*SK_MX[4] - P(15,18)*SK_MX[3]); Kfusion(22) = SK_MX[0]*(P(22,19) + P(22,1)*SH_MAG[0] - P(22,2)*SH_MAG[1] + P(22,3)*SH_MAG[2] + P(22,0)*SK_MX[2] - P(22,16)*SK_MX[1] + P(22,17)*SK_MX[4] - P(22,18)*SK_MX[3]); Kfusion(23) = SK_MX[0]*(P(23,19) + P(23,1)*SH_MAG[0] - P(23,2)*SH_MAG[1] + P(23,3)*SH_MAG[2] + P(23,0)*SK_MX[2] - P(23,16)*SK_MX[1] + P(23,17)*SK_MX[4] - P(23,18)*SK_MX[3]); } Kfusion(16) = SK_MX[0]*(P(16,19) + P(16,1)*SH_MAG[0] - P(16,2)*SH_MAG[1] + P(16,3)*SH_MAG[2] + P(16,0)*SK_MX[2] - P(16,16)*SK_MX[1] + P(16,17)*SK_MX[4] - P(16,18)*SK_MX[3]); Kfusion(17) = SK_MX[0]*(P(17,19) + P(17,1)*SH_MAG[0] - P(17,2)*SH_MAG[1] + P(17,3)*SH_MAG[2] + P(17,0)*SK_MX[2] - P(17,16)*SK_MX[1] + P(17,17)*SK_MX[4] - P(17,18)*SK_MX[3]); Kfusion(18) = SK_MX[0]*(P(18,19) + P(18,1)*SH_MAG[0] - P(18,2)*SH_MAG[1] + P(18,3)*SH_MAG[2] + P(18,0)*SK_MX[2] - P(18,16)*SK_MX[1] + P(18,17)*SK_MX[4] - P(18,18)*SK_MX[3]); Kfusion(19) = SK_MX[0]*(P(19,19) + P(19,1)*SH_MAG[0] - P(19,2)*SH_MAG[1] + P(19,3)*SH_MAG[2] + P(19,0)*SK_MX[2] - P(19,16)*SK_MX[1] + P(19,17)*SK_MX[4] - P(19,18)*SK_MX[3]); Kfusion(20) = SK_MX[0]*(P(20,19) + P(20,1)*SH_MAG[0] - P(20,2)*SH_MAG[1] + P(20,3)*SH_MAG[2] + P(20,0)*SK_MX[2] - P(20,16)*SK_MX[1] + P(20,17)*SK_MX[4] - P(20,18)*SK_MX[3]); Kfusion(21) = SK_MX[0]*(P(21,19) + P(21,1)*SH_MAG[0] - P(21,2)*SH_MAG[1] + P(21,3)*SH_MAG[2] + P(21,0)*SK_MX[2] - P(21,16)*SK_MX[1] + P(21,17)*SK_MX[4] - P(21,18)*SK_MX[3]); // find largest observation variance difference as a fraction of the matlab value float max_diff_fraction = 0.0f; int max_row; float max_old, max_new; for (int row=0; row<24; row++) { float diff_fraction; if (H_MAG(row) != 0.0f) { diff_fraction = fabsf(Hfusion_sympy[row] - H_MAG(row)) / fabsf(H_MAG(row)); } else if (Hfusion_sympy[row] != 0.0f) { diff_fraction = fabsf(Hfusion_sympy[row] - H_MAG(row)) / fabsf(Hfusion_sympy[row]); } else { diff_fraction = 0.0f; } if (diff_fraction > max_diff_fraction) { max_diff_fraction = diff_fraction; max_row = row; max_old = H_MAG(row); max_new = Hfusion_sympy[row]; } } if (max_diff_fraction > 1e-5f) { printf("Fail: Magnetomer X axis Hfusion max diff fraction = %e , old = %e , new = %e , location index = %i\n",max_diff_fraction, max_old, max_new, max_row); } else { printf("Pass: Magnetomer X axis Hfusion max diff fraction = %e\n",max_diff_fraction); } // find largest Kalman gain difference as a fraction of the matlab value max_diff_fraction = 0.0f; for (int row=0; row<4; row++) { float diff_fraction; if (Kfusion(row) != 0.0f) { diff_fraction = fabsf(Kfusion_sympy(row) - Kfusion(row)) / fabsf(Kfusion(row)); } else if (Kfusion_sympy(row) != 0.0f) { diff_fraction = fabsf(Kfusion_sympy(row) - Kfusion(row)) / fabsf(Kfusion_sympy(row)); } else { diff_fraction = 0.0f; } if (diff_fraction > max_diff_fraction) { max_diff_fraction = diff_fraction; max_row = row; max_old = Kfusion(row); max_new = Kfusion_sympy(row); } } if (max_diff_fraction > 1e-5f) { printf("Fail: Magnetomer X axis Kfusion max diff fraction = %e , old = %e , new = %e , location index = %i\n",max_diff_fraction, max_old, max_new, max_row); } else { printf("Pass: Magnetomer X axis Kfusion max diff fraction = %e\n",max_diff_fraction); } } // Compare Y axis equations { // recalculate innovation variance becasue states and covariances have changed due to previous fusion const float HKY0 = magD*q1 + magE*q0 - magN*q3; const float HKY1 = magD*q0 - magE*q1 + magN*q2; const float HKY2 = magD*q3 + magE*q2 + magN*q1; const float HKY3 = magD*q2; const float HKY4 = magE*q3; const float HKY5 = magN*q0; const float HKY6 = q1*q2; const float HKY7 = q0*q3; const float HKY8 = powf(q0, 2) - powf(q1, 2) + powf(q2, 2) - powf(q3, 2); const float HKY9 = q0*q1 + q2*q3; const float HKY10 = 2*HKY9; const float HKY11 = -2*HKY6 + 2*HKY7; const float HKY12 = 2*HKY2; const float HKY13 = 2*HKY0; const float HKY14 = 2*HKY1; const float HKY15 = -2*HKY3 + 2*HKY4 + 2*HKY5; const float HKY16 = HKY10*P(0,18) - HKY11*P(0,16) + HKY12*P(0,2) + HKY13*P(0,0) + HKY14*P(0,1) - HKY15*P(0,3) + HKY8*P(0,17) + P(0,20); const float HKY17 = HKY10*P(17,18) - HKY11*P(16,17) + HKY12*P(2,17) + HKY13*P(0,17) + HKY14*P(1,17) - HKY15*P(3,17) + HKY8*P(17,17) + P(17,20); const float HKY18 = HKY10*P(16,18) - HKY11*P(16,16) + HKY12*P(2,16) + HKY13*P(0,16) + HKY14*P(1,16) - HKY15*P(3,16) + HKY8*P(16,17) + P(16,20); const float HKY19 = HKY10*P(3,18) - HKY11*P(3,16) + HKY12*P(2,3) + HKY13*P(0,3) + HKY14*P(1,3) - HKY15*P(3,3) + HKY8*P(3,17) + P(3,20); const float HKY20 = HKY10*P(18,18) - HKY11*P(16,18) + HKY12*P(2,18) + HKY13*P(0,18) + HKY14*P(1,18) - HKY15*P(3,18) + HKY8*P(17,18) + P(18,20); const float HKY21 = HKY10*P(1,18) - HKY11*P(1,16) + HKY12*P(1,2) + HKY13*P(0,1) + HKY14*P(1,1) - HKY15*P(1,3) + HKY8*P(1,17) + P(1,20); const float HKY22 = HKY10*P(2,18) - HKY11*P(2,16) + HKY12*P(2,2) + HKY13*P(0,2) + HKY14*P(1,2) - HKY15*P(2,3) + HKY8*P(2,17) + P(2,20); const float HKY23 = HKY10*P(18,20) - HKY11*P(16,20) + HKY12*P(2,20) + HKY13*P(0,20) + HKY14*P(1,20) - HKY15*P(3,20) + HKY8*P(17,20) + P(20,20); const float HKY24 = 1.0F/(HKY10*HKY20 - HKY11*HKY18 + HKY12*HKY22 + HKY13*HKY16 + HKY14*HKY21 - HKY15*HKY19 + HKY17*HKY8 + HKY23 + R_MAG); // Calculate Y axis observation jacobians memset(Hfusion, 0, sizeof(Hfusion)); Hfusion[0] = 2*HKY0; Hfusion[1] = 2*HKY1; Hfusion[2] = 2*HKY2; Hfusion[3] = 2*HKY3 - 2*HKY4 - 2*HKY5; Hfusion[16] = 2*HKY6 - 2*HKY7; Hfusion[17] = HKY8; Hfusion[18] = 2*HKY9; Hfusion[20] = 1; // Calculate Y axis Kalman gains if (update_all_states) { Kfusion(0) = HKY16*HKY24; Kfusion(1) = HKY21*HKY24; Kfusion(2) = HKY22*HKY24; Kfusion(3) = HKY19*HKY24; for (unsigned row = 4; row <= 15; row++) { Kfusion(row) = HKY24*(HKY10*P(row,18) - HKY11*P(row,16) + HKY12*P(2,row) + HKY13*P(0,row) + HKY14*P(1,row) - HKY15*P(3,row) + HKY8*P(row,17) + P(row,20)); } for (unsigned row = 22; row <= 23; row++) { Kfusion(row) = HKY24*(HKY10*P(18,row) - HKY11*P(16,row) + HKY12*P(2,row) + HKY13*P(0,row) + HKY14*P(1,row) - HKY15*P(3,row) + HKY8*P(17,row) + P(20,row)); } } Kfusion(16) = HKY18*HKY24; Kfusion(17) = HKY17*HKY24; Kfusion(18) = HKY20*HKY24; Kfusion(19) = HKY24*(HKY10*P(18,19) - HKY11*P(16,19) + HKY12*P(2,19) + HKY13*P(0,19) + HKY14*P(1,19) - HKY15*P(3,19) + HKY8*P(17,19) + P(19,20)); Kfusion(20) = HKY23*HKY24; Kfusion(21) = HKY24*(HKY10*P(18,21) - HKY11*P(16,21) + HKY12*P(2,21) + HKY13*P(0,21) + HKY14*P(1,21) - HKY15*P(3,21) + HKY8*P(17,21) + P(20,21)); // save output and repeat calculation using legacy matlab generated code float Hfusion_sympy[24]; Vector24f Kfusion_sympy; for (int row=0; row<24; row++) { Hfusion_sympy[row] = Hfusion[row]; Kfusion_sympy(row) = Kfusion(row); } // repeat calculation using matlab generated equations // Y axis innovation variance mag_innov_var = (P(20,20) + R_MAG + P(0,20)*SH_MAG[2] + P(1,20)*SH_MAG[1] + P(2,20)*SH_MAG[0] - P(17,20)*(SH_MAG[3] - SH_MAG[4] + SH_MAG[5] - SH_MAG[6]) - (2.0f*q0*q3 - 2.0f*q1*q2)*(P(20,16) + P(0,16)*SH_MAG[2] + P(1,16)*SH_MAG[1] + P(2,16)*SH_MAG[0] - P(17,16)*(SH_MAG[3] - SH_MAG[4] + SH_MAG[5] - SH_MAG[6]) - P(16,16)*(2.0f*q0*q3 - 2.0f*q1*q2) + P(18,16)*(2.0f*q0*q1 + 2.0f*q2*q3) - P(3,16)*(SH_MAG[7] + SH_MAG[8] - 2.0f*magD*q2)) + (2.0f*q0*q1 + 2.0f*q2*q3)*(P(20,18) + P(0,18)*SH_MAG[2] + P(1,18)*SH_MAG[1] + P(2,18)*SH_MAG[0] - P(17,18)*(SH_MAG[3] - SH_MAG[4] + SH_MAG[5] - SH_MAG[6]) - P(16,18)*(2.0f*q0*q3 - 2.0f*q1*q2) + P(18,18)*(2.0f*q0*q1 + 2.0f*q2*q3) - P(3,18)*(SH_MAG[7] + SH_MAG[8] - 2.0f*magD*q2)) - (SH_MAG[7] + SH_MAG[8] - 2.0f*magD*q2)*(P(20,3) + P(0,3)*SH_MAG[2] + P(1,3)*SH_MAG[1] + P(2,3)*SH_MAG[0] - P(17,3)*(SH_MAG[3] - SH_MAG[4] + SH_MAG[5] - SH_MAG[6]) - P(16,3)*(2.0f*q0*q3 - 2.0f*q1*q2) + P(18,3)*(2.0f*q0*q1 + 2.0f*q2*q3) - P(3,3)*(SH_MAG[7] + SH_MAG[8] - 2.0f*magD*q2)) - P(16,20)*(2.0f*q0*q3 - 2.0f*q1*q2) + P(18,20)*(2.0f*q0*q1 + 2.0f*q2*q3) + SH_MAG[2]*(P(20,0) + P(0,0)*SH_MAG[2] + P(1,0)*SH_MAG[1] + P(2,0)*SH_MAG[0] - P(17,0)*(SH_MAG[3] - SH_MAG[4] + SH_MAG[5] - SH_MAG[6]) - P(16,0)*(2.0f*q0*q3 - 2.0f*q1*q2) + P(18,0)*(2.0f*q0*q1 + 2.0f*q2*q3) - P(3,0)*(SH_MAG[7] + SH_MAG[8] - 2.0f*magD*q2)) + SH_MAG[1]*(P(20,1) + P(0,1)*SH_MAG[2] + P(1,1)*SH_MAG[1] + P(2,1)*SH_MAG[0] - P(17,1)*(SH_MAG[3] - SH_MAG[4] + SH_MAG[5] - SH_MAG[6]) - P(16,1)*(2.0f*q0*q3 - 2.0f*q1*q2) + P(18,1)*(2.0f*q0*q1 + 2.0f*q2*q3) - P(3,1)*(SH_MAG[7] + SH_MAG[8] - 2.0f*magD*q2)) + SH_MAG[0]*(P(20,2) + P(0,2)*SH_MAG[2] + P(1,2)*SH_MAG[1] + P(2,2)*SH_MAG[0] - P(17,2)*(SH_MAG[3] - SH_MAG[4] + SH_MAG[5] - SH_MAG[6]) - P(16,2)*(2.0f*q0*q3 - 2.0f*q1*q2) + P(18,2)*(2.0f*q0*q1 + 2.0f*q2*q3) - P(3,2)*(SH_MAG[7] + SH_MAG[8] - 2.0f*magD*q2)) - (SH_MAG[3] - SH_MAG[4] + SH_MAG[5] - SH_MAG[6])*(P(20,17) + P(0,17)*SH_MAG[2] + P(1,17)*SH_MAG[1] + P(2,17)*SH_MAG[0] - P(17,17)*(SH_MAG[3] - SH_MAG[4] + SH_MAG[5] - SH_MAG[6]) - P(16,17)*(2.0f*q0*q3 - 2.0f*q1*q2) + P(18,17)*(2.0f*q0*q1 + 2.0f*q2*q3) - P(3,17)*(SH_MAG[7] + SH_MAG[8] - 2.0f*magD*q2)) - P(3,20)*(SH_MAG[7] + SH_MAG[8] - 2.0f*magD*q2)); // Calculate Y axis observation jacobians H_MAG.setZero(); H_MAG(0) = SH_MAG[2]; H_MAG(1) = SH_MAG[1]; H_MAG(2) = SH_MAG[0]; H_MAG(3) = 2.0f*magD*q2 - SH_MAG[8] - SH_MAG[7]; H_MAG(16) = 2.0f*q1*q2 - 2.0f*q0*q3; H_MAG(17) = SH_MAG[4] - SH_MAG[3] - SH_MAG[5] + SH_MAG[6]; H_MAG(18) = 2.0f*q0*q1 + 2.0f*q2*q3; H_MAG(20) = 1.0f; // Calculate Y axis Kalman gains float SK_MY[5]; SK_MY[0] = 1.0f / mag_innov_var; SK_MY[1] = SH_MAG[3] - SH_MAG[4] + SH_MAG[5] - SH_MAG[6]; SK_MY[2] = SH_MAG[7] + SH_MAG[8] - 2.0f*magD*q2; SK_MY[3] = 2.0f*q0*q3 - 2.0f*q1*q2; SK_MY[4] = 2.0f*q0*q1 + 2.0f*q2*q3; if (update_all_states) { Kfusion(0) = SK_MY[0]*(P(0,20) + P(0,0)*SH_MAG[2] + P(0,1)*SH_MAG[1] + P(0,2)*SH_MAG[0] - P(0,3)*SK_MY[2] - P(0,17)*SK_MY[1] - P(0,16)*SK_MY[3] + P(0,18)*SK_MY[4]); Kfusion(1) = SK_MY[0]*(P(1,20) + P(1,0)*SH_MAG[2] + P(1,1)*SH_MAG[1] + P(1,2)*SH_MAG[0] - P(1,3)*SK_MY[2] - P(1,17)*SK_MY[1] - P(1,16)*SK_MY[3] + P(1,18)*SK_MY[4]); Kfusion(2) = SK_MY[0]*(P(2,20) + P(2,0)*SH_MAG[2] + P(2,1)*SH_MAG[1] + P(2,2)*SH_MAG[0] - P(2,3)*SK_MY[2] - P(2,17)*SK_MY[1] - P(2,16)*SK_MY[3] + P(2,18)*SK_MY[4]); Kfusion(3) = SK_MY[0]*(P(3,20) + P(3,0)*SH_MAG[2] + P(3,1)*SH_MAG[1] + P(3,2)*SH_MAG[0] - P(3,3)*SK_MY[2] - P(3,17)*SK_MY[1] - P(3,16)*SK_MY[3] + P(3,18)*SK_MY[4]); Kfusion(4) = SK_MY[0]*(P(4,20) + P(4,0)*SH_MAG[2] + P(4,1)*SH_MAG[1] + P(4,2)*SH_MAG[0] - P(4,3)*SK_MY[2] - P(4,17)*SK_MY[1] - P(4,16)*SK_MY[3] + P(4,18)*SK_MY[4]); Kfusion(5) = SK_MY[0]*(P(5,20) + P(5,0)*SH_MAG[2] + P(5,1)*SH_MAG[1] + P(5,2)*SH_MAG[0] - P(5,3)*SK_MY[2] - P(5,17)*SK_MY[1] - P(5,16)*SK_MY[3] + P(5,18)*SK_MY[4]); Kfusion(6) = SK_MY[0]*(P(6,20) + P(6,0)*SH_MAG[2] + P(6,1)*SH_MAG[1] + P(6,2)*SH_MAG[0] - P(6,3)*SK_MY[2] - P(6,17)*SK_MY[1] - P(6,16)*SK_MY[3] + P(6,18)*SK_MY[4]); Kfusion(7) = SK_MY[0]*(P(7,20) + P(7,0)*SH_MAG[2] + P(7,1)*SH_MAG[1] + P(7,2)*SH_MAG[0] - P(7,3)*SK_MY[2] - P(7,17)*SK_MY[1] - P(7,16)*SK_MY[3] + P(7,18)*SK_MY[4]); Kfusion(8) = SK_MY[0]*(P(8,20) + P(8,0)*SH_MAG[2] + P(8,1)*SH_MAG[1] + P(8,2)*SH_MAG[0] - P(8,3)*SK_MY[2] - P(8,17)*SK_MY[1] - P(8,16)*SK_MY[3] + P(8,18)*SK_MY[4]); Kfusion(9) = SK_MY[0]*(P(9,20) + P(9,0)*SH_MAG[2] + P(9,1)*SH_MAG[1] + P(9,2)*SH_MAG[0] - P(9,3)*SK_MY[2] - P(9,17)*SK_MY[1] - P(9,16)*SK_MY[3] + P(9,18)*SK_MY[4]); Kfusion(10) = SK_MY[0]*(P(10,20) + P(10,0)*SH_MAG[2] + P(10,1)*SH_MAG[1] + P(10,2)*SH_MAG[0] - P(10,3)*SK_MY[2] - P(10,17)*SK_MY[1] - P(10,16)*SK_MY[3] + P(10,18)*SK_MY[4]); Kfusion(11) = SK_MY[0]*(P(11,20) + P(11,0)*SH_MAG[2] + P(11,1)*SH_MAG[1] + P(11,2)*SH_MAG[0] - P(11,3)*SK_MY[2] - P(11,17)*SK_MY[1] - P(11,16)*SK_MY[3] + P(11,18)*SK_MY[4]); Kfusion(12) = SK_MY[0]*(P(12,20) + P(12,0)*SH_MAG[2] + P(12,1)*SH_MAG[1] + P(12,2)*SH_MAG[0] - P(12,3)*SK_MY[2] - P(12,17)*SK_MY[1] - P(12,16)*SK_MY[3] + P(12,18)*SK_MY[4]); Kfusion(13) = SK_MY[0]*(P(13,20) + P(13,0)*SH_MAG[2] + P(13,1)*SH_MAG[1] + P(13,2)*SH_MAG[0] - P(13,3)*SK_MY[2] - P(13,17)*SK_MY[1] - P(13,16)*SK_MY[3] + P(13,18)*SK_MY[4]); Kfusion(14) = SK_MY[0]*(P(14,20) + P(14,0)*SH_MAG[2] + P(14,1)*SH_MAG[1] + P(14,2)*SH_MAG[0] - P(14,3)*SK_MY[2] - P(14,17)*SK_MY[1] - P(14,16)*SK_MY[3] + P(14,18)*SK_MY[4]); Kfusion(15) = SK_MY[0]*(P(15,20) + P(15,0)*SH_MAG[2] + P(15,1)*SH_MAG[1] + P(15,2)*SH_MAG[0] - P(15,3)*SK_MY[2] - P(15,17)*SK_MY[1] - P(15,16)*SK_MY[3] + P(15,18)*SK_MY[4]); Kfusion(22) = SK_MY[0]*(P(22,20) + P(22,0)*SH_MAG[2] + P(22,1)*SH_MAG[1] + P(22,2)*SH_MAG[0] - P(22,3)*SK_MY[2] - P(22,17)*SK_MY[1] - P(22,16)*SK_MY[3] + P(22,18)*SK_MY[4]); Kfusion(23) = SK_MY[0]*(P(23,20) + P(23,0)*SH_MAG[2] + P(23,1)*SH_MAG[1] + P(23,2)*SH_MAG[0] - P(23,3)*SK_MY[2] - P(23,17)*SK_MY[1] - P(23,16)*SK_MY[3] + P(23,18)*SK_MY[4]); } Kfusion(16) = SK_MY[0]*(P(16,20) + P(16,0)*SH_MAG[2] + P(16,1)*SH_MAG[1] + P(16,2)*SH_MAG[0] - P(16,3)*SK_MY[2] - P(16,17)*SK_MY[1] - P(16,16)*SK_MY[3] + P(16,18)*SK_MY[4]); Kfusion(17) = SK_MY[0]*(P(17,20) + P(17,0)*SH_MAG[2] + P(17,1)*SH_MAG[1] + P(17,2)*SH_MAG[0] - P(17,3)*SK_MY[2] - P(17,17)*SK_MY[1] - P(17,16)*SK_MY[3] + P(17,18)*SK_MY[4]); Kfusion(18) = SK_MY[0]*(P(18,20) + P(18,0)*SH_MAG[2] + P(18,1)*SH_MAG[1] + P(18,2)*SH_MAG[0] - P(18,3)*SK_MY[2] - P(18,17)*SK_MY[1] - P(18,16)*SK_MY[3] + P(18,18)*SK_MY[4]); Kfusion(19) = SK_MY[0]*(P(19,20) + P(19,0)*SH_MAG[2] + P(19,1)*SH_MAG[1] + P(19,2)*SH_MAG[0] - P(19,3)*SK_MY[2] - P(19,17)*SK_MY[1] - P(19,16)*SK_MY[3] + P(19,18)*SK_MY[4]); Kfusion(20) = SK_MY[0]*(P(20,20) + P(20,0)*SH_MAG[2] + P(20,1)*SH_MAG[1] + P(20,2)*SH_MAG[0] - P(20,3)*SK_MY[2] - P(20,17)*SK_MY[1] - P(20,16)*SK_MY[3] + P(20,18)*SK_MY[4]); Kfusion(21) = SK_MY[0]*(P(21,20) + P(21,0)*SH_MAG[2] + P(21,1)*SH_MAG[1] + P(21,2)*SH_MAG[0] - P(21,3)*SK_MY[2] - P(21,17)*SK_MY[1] - P(21,16)*SK_MY[3] + P(21,18)*SK_MY[4]); // find largest observation variance difference as a fraction of the matlab value float max_diff_fraction = 0.0f; int max_row; float max_old, max_new; for (int row=0; row<24; row++) { float diff_fraction; if (H_MAG(row) != 0.0f) { diff_fraction = fabsf(Hfusion_sympy[row] - H_MAG(row)) / fabsf(H_MAG(row)); } else if (Hfusion_sympy[row] != 0.0f) { diff_fraction = fabsf(Hfusion_sympy[row] - H_MAG(row)) / fabsf(Hfusion_sympy[row]); } else { diff_fraction = 0.0f; } if (diff_fraction > max_diff_fraction) { max_diff_fraction = diff_fraction; max_row = row; max_old = H_MAG(row); max_new = Hfusion_sympy[row]; } } if (max_diff_fraction > 1e-5f) { printf("Fail: Magnetomer Y axis Hfusion max diff fraction = %e , old = %e , new = %e , location index = %i\n",max_diff_fraction, max_old, max_new, max_row); } else { printf("Pass: Magnetomer Y axis Hfusion max diff fraction = %e\n",max_diff_fraction); } // find largest Kalman gain difference as a fraction of the matlab value max_diff_fraction = 0.0f; for (int row=0; row<4; row++) { float diff_fraction; if (Kfusion(row) != 0.0f) { diff_fraction = fabsf(Kfusion_sympy(row) - Kfusion(row)) / fabsf(Kfusion(row)); } else if (Kfusion_sympy(row) != 0.0f) { diff_fraction = fabsf(Kfusion_sympy(row) - Kfusion(row)) / fabsf(Kfusion_sympy(row)); } else { diff_fraction = 0.0f; } if (diff_fraction > max_diff_fraction) { max_diff_fraction = diff_fraction; max_row = row; max_old = Kfusion(row); max_new = Kfusion_sympy(row); } } if (max_diff_fraction > 1e-5f) { printf("Fail: Magnetomer Y axis Kfusion max diff fraction = %e , old = %e , new = %e , location index = %i\n",max_diff_fraction, max_old, max_new, max_row); } else { printf("Pass: Magnetomer Y axis Kfusion max diff fraction = %e\n",max_diff_fraction); } } // Compare Z axis equations { // recalculate innovation variance becasue states and covariances have changed due to previous fusion const float HKZ0 = magD*q0 - magE*q1 + magN*q2; const float HKZ1 = magN*q3; const float HKZ2 = magD*q1; const float HKZ3 = magE*q0; const float HKZ4 = -magD*q2 + magE*q3 + magN*q0; const float HKZ5 = magD*q3 + magE*q2 + magN*q1; const float HKZ6 = q0*q2 + q1*q3; const float HKZ7 = q2*q3; const float HKZ8 = q0*q1; const float HKZ9 = powf(q0, 2) - powf(q1, 2) - powf(q2, 2) + powf(q3, 2); const float HKZ10 = 2*HKZ6; const float HKZ11 = -2*HKZ7 + 2*HKZ8; const float HKZ12 = 2*HKZ5; const float HKZ13 = 2*HKZ0; const float HKZ14 = -2*HKZ1 + 2*HKZ2 + 2*HKZ3; const float HKZ15 = 2*HKZ4; const float HKZ16 = HKZ10*P(0,16) - HKZ11*P(0,17) + HKZ12*P(0,3) + HKZ13*P(0,0) - HKZ14*P(0,1) + HKZ15*P(0,2) + HKZ9*P(0,18) + P(0,21); const float HKZ17 = HKZ10*P(16,18) - HKZ11*P(17,18) + HKZ12*P(3,18) + HKZ13*P(0,18) - HKZ14*P(1,18) + HKZ15*P(2,18) + HKZ9*P(18,18) + P(18,21); const float HKZ18 = HKZ10*P(16,17) - HKZ11*P(17,17) + HKZ12*P(3,17) + HKZ13*P(0,17) - HKZ14*P(1,17) + HKZ15*P(2,17) + HKZ9*P(17,18) + P(17,21); const float HKZ19 = HKZ10*P(1,16) - HKZ11*P(1,17) + HKZ12*P(1,3) + HKZ13*P(0,1) - HKZ14*P(1,1) + HKZ15*P(1,2) + HKZ9*P(1,18) + P(1,21); const float HKZ20 = HKZ10*P(16,16) - HKZ11*P(16,17) + HKZ12*P(3,16) + HKZ13*P(0,16) - HKZ14*P(1,16) + HKZ15*P(2,16) + HKZ9*P(16,18) + P(16,21); const float HKZ21 = HKZ10*P(3,16) - HKZ11*P(3,17) + HKZ12*P(3,3) + HKZ13*P(0,3) - HKZ14*P(1,3) + HKZ15*P(2,3) + HKZ9*P(3,18) + P(3,21); const float HKZ22 = HKZ10*P(2,16) - HKZ11*P(2,17) + HKZ12*P(2,3) + HKZ13*P(0,2) - HKZ14*P(1,2) + HKZ15*P(2,2) + HKZ9*P(2,18) + P(2,21); const float HKZ23 = HKZ10*P(16,21) - HKZ11*P(17,21) + HKZ12*P(3,21) + HKZ13*P(0,21) - HKZ14*P(1,21) + HKZ15*P(2,21) + HKZ9*P(18,21) + P(21,21); const float HKZ24 = 1.0F/(HKZ10*HKZ20 - HKZ11*HKZ18 + HKZ12*HKZ21 + HKZ13*HKZ16 - HKZ14*HKZ19 + HKZ15*HKZ22 + HKZ17*HKZ9 + HKZ23 + R_MAG); // calculate Z axis observation jacobians memset(Hfusion, 0, sizeof(Hfusion)); Hfusion[0] = 2*HKZ0; Hfusion[1] = 2*HKZ1 - 2*HKZ2 - 2*HKZ3; Hfusion[2] = 2*HKZ4; Hfusion[3] = 2*HKZ5; Hfusion[16] = 2*HKZ6; Hfusion[17] = 2*HKZ7 - 2*HKZ8; Hfusion[18] = HKZ9; Hfusion[21] = 1; // Calculate Z axis Kalman gains if (update_all_states) { Kfusion(0) = HKZ16*HKZ24; Kfusion(1) = HKZ19*HKZ24; Kfusion(2) = HKZ22*HKZ24; Kfusion(3) = HKZ21*HKZ24; for (unsigned row = 4; row <= 15; row++) { Kfusion(row) = HKZ24*(HKZ10*P(row,16) - HKZ11*P(row,17) + HKZ12*P(3,row) + HKZ13*P(0,row) - HKZ14*P(1,row) + HKZ15*P(2,row) + HKZ9*P(row,18) + P(row,21)); } for (unsigned row = 22; row <= 23; row++) { Kfusion(row) = HKZ24*(HKZ10*P(16,row) - HKZ11*P(17,row) + HKZ12*P(3,row) + HKZ13*P(0,row) - HKZ14*P(1,row) + HKZ15*P(2,row) + HKZ9*P(18,row) + P(21,row)); } } Kfusion(16) = HKZ20*HKZ24; Kfusion(17) = HKZ18*HKZ24; Kfusion(18) = HKZ17*HKZ24; for (unsigned row = 19; row <= 20; row++) { Kfusion(row) = HKZ24*(HKZ10*P(16,row) - HKZ11*P(17,row) + HKZ12*P(3,row) + HKZ13*P(0,row) - HKZ14*P(1,row) + HKZ15*P(2,row) + HKZ9*P(18,row) + P(row,21)); } Kfusion(21) = HKZ23*HKZ24; // save output and repeat calculation using legacy matlab generated code float Hfusion_sympy[24]; Vector24f Kfusion_sympy; for (int row=0; row<24; row++) { Hfusion_sympy[row] = Hfusion[row]; Kfusion_sympy(row) = Kfusion(row); } // repeat calculation using matlab generated equations // Z axis innovation variance mag_innov_var = (P(21,21) + R_MAG + P(0,21)*SH_MAG[1] - P(1,21)*SH_MAG[2] + P(3,21)*SH_MAG[0] + P(18,21)*(SH_MAG[3] - SH_MAG[4] - SH_MAG[5] + SH_MAG[6]) + (2.0f*q0*q2 + 2.0f*q1*q3)*(P(21,16) + P(0,16)*SH_MAG[1] - P(1,16)*SH_MAG[2] + P(3,16)*SH_MAG[0] + P(18,16)*(SH_MAG[3] - SH_MAG[4] - SH_MAG[5] + SH_MAG[6]) + P(16,16)*(2.0f*q0*q2 + 2.0f*q1*q3) - P(17,16)*(2.0f*q0*q1 - 2.0f*q2*q3) + P(2,16)*(SH_MAG[7] + SH_MAG[8] - 2.0f*magD*q2)) - (2.0f*q0*q1 - 2.0f*q2*q3)*(P(21,17) + P(0,17)*SH_MAG[1] - P(1,17)*SH_MAG[2] + P(3,17)*SH_MAG[0] + P(18,17)*(SH_MAG[3] - SH_MAG[4] - SH_MAG[5] + SH_MAG[6]) + P(16,17)*(2.0f*q0*q2 + 2.0f*q1*q3) - P(17,17)*(2.0f*q0*q1 - 2.0f*q2*q3) + P(2,17)*(SH_MAG[7] + SH_MAG[8] - 2.0f*magD*q2)) + (SH_MAG[7] + SH_MAG[8] - 2.0f*magD*q2)*(P(21,2) + P(0,2)*SH_MAG[1] - P(1,2)*SH_MAG[2] + P(3,2)*SH_MAG[0] + P(18,2)*(SH_MAG[3] - SH_MAG[4] - SH_MAG[5] + SH_MAG[6]) + P(16,2)*(2.0f*q0*q2 + 2.0f*q1*q3) - P(17,2)*(2.0f*q0*q1 - 2.0f*q2*q3) + P(2,2)*(SH_MAG[7] + SH_MAG[8] - 2.0f*magD*q2)) + P(16,21)*(2.0f*q0*q2 + 2.0f*q1*q3) - P(17,21)*(2.0f*q0*q1 - 2.0f*q2*q3) + SH_MAG[1]*(P(21,0) + P(0,0)*SH_MAG[1] - P(1,0)*SH_MAG[2] + P(3,0)*SH_MAG[0] + P(18,0)*(SH_MAG[3] - SH_MAG[4] - SH_MAG[5] + SH_MAG[6]) + P(16,0)*(2.0f*q0*q2 + 2.0f*q1*q3) - P(17,0)*(2.0f*q0*q1 - 2.0f*q2*q3) + P(2,0)*(SH_MAG[7] + SH_MAG[8] - 2.0f*magD*q2)) - SH_MAG[2]*(P(21,1) + P(0,1)*SH_MAG[1] - P(1,1)*SH_MAG[2] + P(3,1)*SH_MAG[0] + P(18,1)*(SH_MAG[3] - SH_MAG[4] - SH_MAG[5] + SH_MAG[6]) + P(16,1)*(2.0f*q0*q2 + 2.0f*q1*q3) - P(17,1)*(2.0f*q0*q1 - 2.0f*q2*q3) + P(2,1)*(SH_MAG[7] + SH_MAG[8] - 2.0f*magD*q2)) + SH_MAG[0]*(P(21,3) + P(0,3)*SH_MAG[1] - P(1,3)*SH_MAG[2] + P(3,3)*SH_MAG[0] + P(18,3)*(SH_MAG[3] - SH_MAG[4] - SH_MAG[5] + SH_MAG[6]) + P(16,3)*(2.0f*q0*q2 + 2.0f*q1*q3) - P(17,3)*(2.0f*q0*q1 - 2.0f*q2*q3) + P(2,3)*(SH_MAG[7] + SH_MAG[8] - 2.0f*magD*q2)) + (SH_MAG[3] - SH_MAG[4] - SH_MAG[5] + SH_MAG[6])*(P(21,18) + P(0,18)*SH_MAG[1] - P(1,18)*SH_MAG[2] + P(3,18)*SH_MAG[0] + P(18,18)*(SH_MAG[3] - SH_MAG[4] - SH_MAG[5] + SH_MAG[6]) + P(16,18)*(2.0f*q0*q2 + 2.0f*q1*q3) - P(17,18)*(2.0f*q0*q1 - 2.0f*q2*q3) + P(2,18)*(SH_MAG[7] + SH_MAG[8] - 2.0f*magD*q2)) + P(2,21)*(SH_MAG[7] + SH_MAG[8] - 2.0f*magD*q2)); // calculate Z axis observation jacobians H_MAG.setZero(); H_MAG(0) = SH_MAG[1]; H_MAG(1) = -SH_MAG[2]; H_MAG(2) = SH_MAG[7] + SH_MAG[8] - 2.0f*magD*q2; H_MAG(3) = SH_MAG[0]; H_MAG(16) = 2.0f*q0*q2 + 2.0f*q1*q3; H_MAG(17) = 2.0f*q2*q3 - 2.0f*q0*q1; H_MAG(18) = SH_MAG[3] - SH_MAG[4] - SH_MAG[5] + SH_MAG[6]; H_MAG(21) = 1.0f; // Calculate Z axis Kalman gains float SK_MZ[5]; SK_MZ[0] = 1.0f / mag_innov_var; SK_MZ[1] = SH_MAG[3] - SH_MAG[4] - SH_MAG[5] + SH_MAG[6]; SK_MZ[2] = SH_MAG[7] + SH_MAG[8] - 2.0f*magD*q2; SK_MZ[3] = 2.0f*q0*q1 - 2.0f*q2*q3; SK_MZ[4] = 2.0f*q0*q2 + 2.0f*q1*q3; if (update_all_states) { Kfusion(0) = SK_MZ[0]*(P(0,21) + P(0,0)*SH_MAG[1] - P(0,1)*SH_MAG[2] + P(0,3)*SH_MAG[0] + P(0,2)*SK_MZ[2] + P(0,18)*SK_MZ[1] + P(0,16)*SK_MZ[4] - P(0,17)*SK_MZ[3]); Kfusion(1) = SK_MZ[0]*(P(1,21) + P(1,0)*SH_MAG[1] - P(1,1)*SH_MAG[2] + P(1,3)*SH_MAG[0] + P(1,2)*SK_MZ[2] + P(1,18)*SK_MZ[1] + P(1,16)*SK_MZ[4] - P(1,17)*SK_MZ[3]); Kfusion(2) = SK_MZ[0]*(P(2,21) + P(2,0)*SH_MAG[1] - P(2,1)*SH_MAG[2] + P(2,3)*SH_MAG[0] + P(2,2)*SK_MZ[2] + P(2,18)*SK_MZ[1] + P(2,16)*SK_MZ[4] - P(2,17)*SK_MZ[3]); Kfusion(3) = SK_MZ[0]*(P(3,21) + P(3,0)*SH_MAG[1] - P(3,1)*SH_MAG[2] + P(3,3)*SH_MAG[0] + P(3,2)*SK_MZ[2] + P(3,18)*SK_MZ[1] + P(3,16)*SK_MZ[4] - P(3,17)*SK_MZ[3]); Kfusion(4) = SK_MZ[0]*(P(4,21) + P(4,0)*SH_MAG[1] - P(4,1)*SH_MAG[2] + P(4,3)*SH_MAG[0] + P(4,2)*SK_MZ[2] + P(4,18)*SK_MZ[1] + P(4,16)*SK_MZ[4] - P(4,17)*SK_MZ[3]); Kfusion(5) = SK_MZ[0]*(P(5,21) + P(5,0)*SH_MAG[1] - P(5,1)*SH_MAG[2] + P(5,3)*SH_MAG[0] + P(5,2)*SK_MZ[2] + P(5,18)*SK_MZ[1] + P(5,16)*SK_MZ[4] - P(5,17)*SK_MZ[3]); Kfusion(6) = SK_MZ[0]*(P(6,21) + P(6,0)*SH_MAG[1] - P(6,1)*SH_MAG[2] + P(6,3)*SH_MAG[0] + P(6,2)*SK_MZ[2] + P(6,18)*SK_MZ[1] + P(6,16)*SK_MZ[4] - P(6,17)*SK_MZ[3]); Kfusion(7) = SK_MZ[0]*(P(7,21) + P(7,0)*SH_MAG[1] - P(7,1)*SH_MAG[2] + P(7,3)*SH_MAG[0] + P(7,2)*SK_MZ[2] + P(7,18)*SK_MZ[1] + P(7,16)*SK_MZ[4] - P(7,17)*SK_MZ[3]); Kfusion(8) = SK_MZ[0]*(P(8,21) + P(8,0)*SH_MAG[1] - P(8,1)*SH_MAG[2] + P(8,3)*SH_MAG[0] + P(8,2)*SK_MZ[2] + P(8,18)*SK_MZ[1] + P(8,16)*SK_MZ[4] - P(8,17)*SK_MZ[3]); Kfusion(9) = SK_MZ[0]*(P(9,21) + P(9,0)*SH_MAG[1] - P(9,1)*SH_MAG[2] + P(9,3)*SH_MAG[0] + P(9,2)*SK_MZ[2] + P(9,18)*SK_MZ[1] + P(9,16)*SK_MZ[4] - P(9,17)*SK_MZ[3]); Kfusion(10) = SK_MZ[0]*(P(10,21) + P(10,0)*SH_MAG[1] - P(10,1)*SH_MAG[2] + P(10,3)*SH_MAG[0] + P(10,2)*SK_MZ[2] + P(10,18)*SK_MZ[1] + P(10,16)*SK_MZ[4] - P(10,17)*SK_MZ[3]); Kfusion(11) = SK_MZ[0]*(P(11,21) + P(11,0)*SH_MAG[1] - P(11,1)*SH_MAG[2] + P(11,3)*SH_MAG[0] + P(11,2)*SK_MZ[2] + P(11,18)*SK_MZ[1] + P(11,16)*SK_MZ[4] - P(11,17)*SK_MZ[3]); Kfusion(12) = SK_MZ[0]*(P(12,21) + P(12,0)*SH_MAG[1] - P(12,1)*SH_MAG[2] + P(12,3)*SH_MAG[0] + P(12,2)*SK_MZ[2] + P(12,18)*SK_MZ[1] + P(12,16)*SK_MZ[4] - P(12,17)*SK_MZ[3]); Kfusion(13) = SK_MZ[0]*(P(13,21) + P(13,0)*SH_MAG[1] - P(13,1)*SH_MAG[2] + P(13,3)*SH_MAG[0] + P(13,2)*SK_MZ[2] + P(13,18)*SK_MZ[1] + P(13,16)*SK_MZ[4] - P(13,17)*SK_MZ[3]); Kfusion(14) = SK_MZ[0]*(P(14,21) + P(14,0)*SH_MAG[1] - P(14,1)*SH_MAG[2] + P(14,3)*SH_MAG[0] + P(14,2)*SK_MZ[2] + P(14,18)*SK_MZ[1] + P(14,16)*SK_MZ[4] - P(14,17)*SK_MZ[3]); Kfusion(15) = SK_MZ[0]*(P(15,21) + P(15,0)*SH_MAG[1] - P(15,1)*SH_MAG[2] + P(15,3)*SH_MAG[0] + P(15,2)*SK_MZ[2] + P(15,18)*SK_MZ[1] + P(15,16)*SK_MZ[4] - P(15,17)*SK_MZ[3]); Kfusion(22) = SK_MZ[0]*(P(22,21) + P(22,0)*SH_MAG[1] - P(22,1)*SH_MAG[2] + P(22,3)*SH_MAG[0] + P(22,2)*SK_MZ[2] + P(22,18)*SK_MZ[1] + P(22,16)*SK_MZ[4] - P(22,17)*SK_MZ[3]); Kfusion(23) = SK_MZ[0]*(P(23,21) + P(23,0)*SH_MAG[1] - P(23,1)*SH_MAG[2] + P(23,3)*SH_MAG[0] + P(23,2)*SK_MZ[2] + P(23,18)*SK_MZ[1] + P(23,16)*SK_MZ[4] - P(23,17)*SK_MZ[3]); } Kfusion(16) = SK_MZ[0]*(P(16,21) + P(16,0)*SH_MAG[1] - P(16,1)*SH_MAG[2] + P(16,3)*SH_MAG[0] + P(16,2)*SK_MZ[2] + P(16,18)*SK_MZ[1] + P(16,16)*SK_MZ[4] - P(16,17)*SK_MZ[3]); Kfusion(17) = SK_MZ[0]*(P(17,21) + P(17,0)*SH_MAG[1] - P(17,1)*SH_MAG[2] + P(17,3)*SH_MAG[0] + P(17,2)*SK_MZ[2] + P(17,18)*SK_MZ[1] + P(17,16)*SK_MZ[4] - P(17,17)*SK_MZ[3]); Kfusion(18) = SK_MZ[0]*(P(18,21) + P(18,0)*SH_MAG[1] - P(18,1)*SH_MAG[2] + P(18,3)*SH_MAG[0] + P(18,2)*SK_MZ[2] + P(18,18)*SK_MZ[1] + P(18,16)*SK_MZ[4] - P(18,17)*SK_MZ[3]); Kfusion(19) = SK_MZ[0]*(P(19,21) + P(19,0)*SH_MAG[1] - P(19,1)*SH_MAG[2] + P(19,3)*SH_MAG[0] + P(19,2)*SK_MZ[2] + P(19,18)*SK_MZ[1] + P(19,16)*SK_MZ[4] - P(19,17)*SK_MZ[3]); Kfusion(20) = SK_MZ[0]*(P(20,21) + P(20,0)*SH_MAG[1] - P(20,1)*SH_MAG[2] + P(20,3)*SH_MAG[0] + P(20,2)*SK_MZ[2] + P(20,18)*SK_MZ[1] + P(20,16)*SK_MZ[4] - P(20,17)*SK_MZ[3]); Kfusion(21) = SK_MZ[0]*(P(21,21) + P(21,0)*SH_MAG[1] - P(21,1)*SH_MAG[2] + P(21,3)*SH_MAG[0] + P(21,2)*SK_MZ[2] + P(21,18)*SK_MZ[1] + P(21,16)*SK_MZ[4] - P(21,17)*SK_MZ[3]); // find largest observation variance difference as a fraction of the matlab value float max_diff_fraction = 0.0f; int max_row; float max_old, max_new; for (int row=0; row<24; row++) { float diff_fraction; if (H_MAG(row) != 0.0f) { diff_fraction = fabsf(Hfusion_sympy[row] - H_MAG(row)) / fabsf(H_MAG(row)); } else if (Hfusion_sympy[row] != 0.0f) { diff_fraction = fabsf(Hfusion_sympy[row] - H_MAG(row)) / fabsf(Hfusion_sympy[row]); } else { diff_fraction = 0.0f; } if (diff_fraction > max_diff_fraction) { max_diff_fraction = diff_fraction; max_row = row; max_old = H_MAG(row); max_new = Hfusion_sympy[row]; } } if (max_diff_fraction > 1e-5f) { printf("Fail: Magnetomer Z axis Hfusion max diff fraction = %e , old = %e , new = %e , location index = %i\n",max_diff_fraction, max_old, max_new, max_row); } else { printf("Pass: Magnetomer Z axis Hfusion max diff fraction = %e\n",max_diff_fraction); } // find largest Kalman gain difference as a fraction of the matlab value max_diff_fraction = 0.0f; for (int row=0; row<4; row++) { float diff_fraction; if (Kfusion(row) != 0.0f) { diff_fraction = fabsf(Kfusion_sympy(row) - Kfusion(row)) / fabsf(Kfusion(row)); } else if (Kfusion_sympy(row) != 0.0f) { diff_fraction = fabsf(Kfusion_sympy(row) - Kfusion(row)) / fabsf(Kfusion_sympy(row)); } else { diff_fraction = 0.0f; } if (diff_fraction > max_diff_fraction) { max_diff_fraction = diff_fraction; max_row = row; max_old = Kfusion(row); max_new = Kfusion_sympy(row); } } if (max_diff_fraction > 1e-5f) { printf("Fail: Magnetomer Z axis Kfusion max diff fraction = %e , old = %e , new = %e , location index = %i\n",max_diff_fraction, max_old, max_new, max_row); } else { printf("Pass: Magnetomer Z axis Kfusion max diff fraction = %e\n",max_diff_fraction); } } return 0; }