卡尔曼滤波算法实现代码C++实现代码如下： ============================kalman.h================= =============== // kalman.h: interface for the kalman class. // ////////////////////////////////////////////////////////////////////// #if !defined(AFX_KALMAN_H__ED3D740F_01D2_4616_8B74_8BF57636F2C0__IN CLUDED_) #define AFX_KALMAN_H__ED3D740F_01D2_4616_8B74_8BF57636F2C0__INCLU DED_#if _MSC_VER > 1000 #pragma once #endif // _MSC_VER > 1000#include <math.h> #include "cv.h"class kalman { public: void init_kalman(int x,int xv,int y,int yv); CvKalman* cvkalman; CvMat* state; CvMat* process_noise; CvMat* measurement; const CvMat* prediction; CvPoint2D32f get_predict(float x, float y);kalman(int x=0,int xv=0,int y=0,int yv=0); //virtual ~kalman();};#endif // !defined(AFX_KALMAN_H__ED3D740F_01D2_4616_8B74_8BF57636F2C 0__INCLUDED_)============================kalman.cpp=============== =================#include "kalman.h" #include <stdio.h>/* tester de printer toutes les valeurs des vecteurs */ /* tester de changer les matrices du noises */ /* replace state by cvkalman->state_post ??? */CvRandState rng; const double T = 0.1; kalman::kalman(int x,int xv,int y,int yv) {cvkalman = cvCreateKalman( 4, 4, 0 ); state = cvCreateMat( 4, 1, CV_32FC1 ); process_noise = cvCreateMat( 4, 1, CV_32FC1 ); measurement = cvCreateMat( 4, 1, CV_32FC1 ); int code = -1;/* create matrix data */ const float A[] = { 1, T, 0, 0, 0, 1, 0, 0, 0, 0, 1, T, 0, 0, 0, 1 };const float H[] = { 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0 };const float P[] = { pow(320,2), pow(320,2)/T, 0, 0, pow(320,2)/T, pow(320,2)/pow(T,2), 0, 0, 0, 0, pow(240,2), pow(240,2)/T, 0, 0, pow(240,2)/T, pow(240,2)/pow(T,2) };const float Q[] = { pow(T,3)/3, pow(T,2)/2, 0, 0, pow(T,2)/2, T, 0, 0, 0, 0, pow(T,3)/3, pow(T,2)/2, 0, 0, pow(T,2)/2, T };const float R[] = { 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0 }; cvRandInit( &rng, 0, 1, -1, CV_RAND_UNI );cvZero( measurement );cvRandSetRange( &rng, 0, 0.1, 0 ); rng.disttype = CV_RAND_NORMAL;cvRand( &rng, state );memcpy( cvkalman->transition_matrix->data.fl, A, sizeof(A)); memcpy( cvkalman->measurement_matrix->data.fl, H, sizeof(H)); memcpy( cvkalman->process_noise_cov->data.fl, Q, sizeof(Q)); memcpy( cvkalman->error_cov_post->data.fl, P, sizeof(P)); memcpy( cvkalman->measurement_noise_cov->data.fl, R, sizeof(R)); //cvSetIdentity( cvkalman->process_noise_cov, cvRealScalar(1e-5) ); //cvSetIdentity( cvkalman->error_cov_post, cvRealScalar(1)); //cvSetIdentity( cvkalman->measurement_noise_cov, cvRealScalar(1e-1) );/* choose initial state */state->data.fl[0]=x; state->data.fl[1]=xv; state->data.fl[2]=y;state->data.fl[3]=yv; cvkalman->state_post->data.fl[0]=x; cvkalman->state_post->data.fl[1]=xv; cvkalman->state_post->data.fl[2]=y; cvkalman->state_post->data.fl[3]=yv;cvRandSetRange( &rng, 0, sqrt(cvkalman->process_noise_cov->data.fl[0]), 0 ); cvRand( &rng, process_noise ); }CvPoint2D32f kalman::get_predict(float x, float y) {/* update state with current position */ state->data.fl[0]=x; state->data.fl[2]=y;/* predict point position */ /* x'k=A 鈥 k+B 鈥 kP'k=A 鈥 k-1*AT + Q */ cvRandSetRange( &rng, 0, sqrt(cvkalman->measurement_noise_cov->data.fl [0]), 0 ); cvRand( &rng, measurement );/* xk=A?xk-1+B?uk+wk */ cvMatMulAdd( cvkalman->transition_matrix, state, process_noise, cvkalman-> state_post );/* zk=H?xk+vk */cvMatMulAdd( cvkalman->measurement_matrix, cvkalman->state_post, meas urement, measurement );cvKalmanCorrect( cvkalman, measurement ); float measured_value_x = measurement->data.fl[0]; float measured_value_y = measurement->data.fl[2];const CvMat* prediction = cvKalmanPredict( cvkalman, 0 ); float predict_value_x = prediction->data.fl[0]; float predict_value_y = prediction->data.fl[2];return(cvPoint2D32f(predict_value_x,predict_value_y)); }void kalman::init_kalman(int x,int xv,int y,int yv) { state->data.fl[0]=x;state->data.fl[1]=xv; state->data.fl[2]=y; state->data.fl[3]=yv; cvkalman->state_post->data.fl[0]=x; cvkalman->state_post->data.fl[1]=xv; cvkalman->state_post->data.fl[2]=y; cvkalman->state_post->data.fl[3]=yv; }c 语言实现代码如下：#include "stdlib.h" #include "rinv.c" int lman(n,m,k,f,q,r,h,y,x,p,g) int n,m,k; double f[],q[],r[],h[],y[],x[],p[],g[]; { int i,j,kk,ii,l,jj,js; double *e,*a,*b; e=malloc(m*m*sizeof(double)); l=m; if (l<n) l=n; a=malloc(l*l*sizeof(double)); b=malloc(l*l*sizeof(double)); for (i=0; i<=n-1; i++) for (j=0; j<=n-1; j++) { ii=i*l+j; a[ii]=0.0; for (kk=0; kk<=n-1; kk++) a[ii]=a[ii]+p[i*n+kk]*f[j*n+kk]; } for (i=0; i<=n-1; i++) for (j=0; j<=n-1; j++) { ii=i*n+j; p[ii]=q[ii]; for (kk=0; kk<=n-1; kk++) p[ii]=p[ii]+f[i*n+kk]*a[kk*l+j]; } for (ii=2; ii<=k; ii++) { for (i=0; i<=n-1; i++) for (j=0; j<=m-1; j++) { jj=i*l+j; a[jj]=0.0;for (kk=0; kk<=n-1; kk++) a[jj]=a[jj]+p[i*n+kk]*h[j*n+kk];} for (i=0; i<=m-1; i++) for (j=0; j<=m-1; j++){ jj=i*m+j; e[jj]=r[jj]; for (kk=0; kk<=n-1; kk++)e[jj]=e[jj]+h[i*n+kk]*a[kk*l+j]; } js=rinv(e,m); if (js==0){ free(e); free(a); free(b); return(js);} for (i=0; i<=n-1; i++) for (j=0; j<=m-1; j++){ jj=i*m+j; g[jj]=0.0; for (kk=0; kk<=m-1; kk++)g[jj]=g[jj]+a[i*l+kk]*e[j*m+kk]; } for (i=0; i<=n-1; i++){ jj=(ii-1)*n+i; x[jj]=0.0; for (j=0; j<=n-1; j++)x[jj]=x[jj]+f[i*n+j]*x[(ii-2)*n+j]; } for (i=0; i<=m-1; i++){ jj=i*l; b[jj]=y[(ii-1)*m+i]; for (j=0; j<=n-1; j++) b[jj]=b[jj]-h[i*n+j]*x[(ii-1)*n+j]; } for (i=0; i<=n-1; i++) { jj=(ii-1)*n+i;for (j=0; j<=m-1; j++) x[jj]=x[jj]+g[i*m+j]*b[j*l];} if (ii<k){ for (i=0; i<=n-1; i++) for (j=0; j<=n-1; j++){ jj=i*l+j; a[jj]=0.0; for (kk=0; kk<=m-1; kk++)a[jj]=a[jj]-g[i*m+kk]*h[kk*n+j]; if (i==j) a[jj]=1.0+a[jj]; } for (i=0; i<=n-1; i++) for (j=0; j<=n-1; j++) { jj=i*l+j; b[jj]=0.0; for (kk=0; kk<=n-1; kk++) b[jj]=b[jj]+a[i*l+kk]*p[kk*n+j]; } for (i=0; i<=n-1; i++) for (j=0; j<=n-1; j++) { jj=i*l+j; a[jj]=0.0; for (kk=0; kk<=n-1; kk++) a[jj]=a[jj]+b[i*l+kk]*f[j*n+kk]; } for (i=0; i<=n-1; i++) for (j=0; j<=n-1; j++) { jj=i*n+j; p[jj]=q[jj]; for (kk=0; kk<=n-1; kk++) p[jj]=p[jj]+f[i*n+kk]*a[j*l+kk]; } }} free(e); free(a); free(b); return(js);}。