#ifndef __KALMAN_FILTER_H__ #define __KALMAN_FILTER_H__ #include "dclapack.h" /* * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * Legend: * xhat_k_km1 -- Predicted state at time 'k' using information available at time 'k-1' * P_k_km1 -- Predicted covariance at time 'k' using information available at time 'k-1' * F_k -- State transition model * u_k -- Control vector (i.e. external process) * B_k -- Control input model * w_k -- Gaussian White noise * H_k -- Observation model (to transform true state into measurements) * Q_k -- Covariance matrix of process noise * R_k -- Covariance of observational noise * v_k -- Gaussian white observational noise * * PREDICTION PHASE * Predicted state: xhat_k_km1 = Fk * xhat_km1_km1 + Bk * uk * Predicted covar: P_k_km1 = Fk * P_km1_km1 * Fk' + Qk * * UPDATE PHASE * Measurement residual: yhat_k = zk - Hk * xhat_k_km1 * Residual covariance: S_k = H_k * P_k_km1 * H_k' + R_k * Optimal Kalman gain: K_k = P_k_km1 * H_k' * inv(S_k) * Updated state esti: xhat_k_k = xhat_k_km1 + K_k * yhat_k * Updated covariance: P_k_k = (I - K_k * H_k) * P_k_km1 * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * */ #define KAL_STRIDE 36 #define KAL_MAT(_var) float _var[ORDER][ORDER] #define KAL_VEC(_var) float _var[ORDER][ORDER] void KalmanPredict( KAL_VEC(xhat_k_km1), /* OUTPUT: (S) Predicted state at time 'k' */ KAL_MAT(P_k_km1), /* OUTPUT: (S x S) Predicted covariance at time 'k' */ KAL_MAT(P_km1_km1), /* INPUT: (S x S) Updated covariance from time 'k-1' */ KAL_VEC(xhat_km1_km1), /* INPUT: (S) Updated state from time 'k-1' */ KAL_MAT(F_k), /* INPUT: (S x S) State transition model */ KAL_MAT(B_k), /* INPUT: (S x U) Control input model */ KAL_VEC(u_k), /* INPUT: (U) Control vector */ KAL_MAT(Q_k), /* INPUT: (S x S) Covariance of process noise */ int S, /* INPUT: Number of dimensions in state vector */ int U); /* INPUT: Size of control input vector */ void KalmanUpdate( KAL_VEC(xhat_k_k), /* (S) OUTPUT: Updated state at time 'k' */ KAL_MAT(P_k_k), /* (S x S) OUTPUT: Updated covariance at time 'k' */ KAL_VEC(xhat_k_km1), /* (S) INPUT: Predicted state at time 'k' */ KAL_MAT(P_k_km1), /* (S x S) INPUT: Predicted covariance at time 'k' */ KAL_VEC(z_k), /* (B) INPUT: Observation vector */ KAL_MAT(H_k), /* (B x S) INPUT: Observational model */ KAL_MAT(R_k), /* (S x S) INPUT: Covariance of observational noise */ int B, /* INPUT: Number of observations in observation vector */ int S); /* INPUT: Number of measurements in the state vector */ /* * * * * * * * * * * * * State vector format: * xhat[12] = { x, y, z, ix, iy, iz, jx, jy, jz, kx, ky, kz, vx, vy, vz, vix, viy, viz, vjx, vjy, vjz, vkx, vky, vkz, ax, ay, az, aix, aiy, aiz, ajx, ajy, ajz, akx, aky, akz }, * 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 * where, * i -- right direction vector * j -- forward direction vector * k -- up direction vector * * * * * * * * * * * */ /* positional state */ #define ST_X 0 #define ST_Y 1 #define ST_Z 2 #define ST_IX 3 #define ST_IY 4 #define ST_IZ 5 #define ST_JX 6 #define ST_JY 7 #define ST_JZ 8 #define ST_KX 9 #define ST_KY 10 #define ST_KZ 11 /* velocity state */ #define ST_VX 12 #define ST_VY 13 #define ST_VZ 14 #define ST_VIX 15 #define ST_VIY 16 #define ST_VIZ 17 #define ST_VJX 18 #define ST_VJY 19 #define ST_VJZ 20 #define ST_VKX 21 #define ST_VKY 22 #define ST_VKZ 23 /* acceleration state */ #define ST_AX 24 #define ST_AY 25 #define ST_AZ 26 #define ST_AIX 27 #define ST_AIY 28 #define ST_AIZ 29 #define ST_AJX 30 #define ST_AJY 31 #define ST_AJZ 32 #define ST_AKX 33 #define ST_AKY 34 #define ST_AKZ 35 /* * * * * * * * * * * * * Measurement: * * * * * * * * * * * */ #endif