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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__INCLUDED_)

#define AFX_KALMAN_H__ED3D740F_01D2_4616_8B74_8BF57636F2C0__INCLUDED_

#if _MSC_VER > 1000 #pragma once

#endif // _MSC_VER > 1000

#include #include \

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_8BF57636F2C0__INCLUDED_)

============================kalman.cpp================================

#include \#include

/* 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�xk+B鈥�uk P'k=A鈥�Pk-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 */