kalman.txt

kalman program(c) and some examples on kalman which i will upset later.

============================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 <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_8BF57636F2C0__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