卡尔曼滤波算法(C--C++两种实现代码)(11页).doc
-卡尔曼滤波算法(C-C+两种实现代码)-第 11 页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 <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.fl0=x; state->data.fl1=xv; state->data.fl2=y; state->data.fl3=yv; cvkalman->state_post->data.fl0=x; cvkalman->state_post->data.fl1=xv; cvkalman->state_post->data.fl2=y; cvkalman->state_post->data.fl3=yv; cvRandSetRange( &rng, 0, sqrt(cvkalman->process_noise_cov->data.fl0), 0 ); cvRand( &rng, process_noise ); CvPoint2D32f kalman:get_predict(float x, float y) /* update state with current position */ state->data.fl0=x; state->data.fl2=y; /* predict point position */ /* x'k=A鈥k+B鈥k P'k=A鈥k-1*AT + Q */ cvRandSetRange( &rng, 0, sqrt(cvkalman->measurement_noise_cov->data.fl0), 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, measurement, measurement ); cvKalmanCorrect( cvkalman, measurement ); float measured_value_x = measurement->data.fl0; float measured_value_y = measurement->data.fl2; const CvMat* prediction = cvKalmanPredict( cvkalman, 0 ); float predict_value_x = prediction->data.fl0; float predict_value_y = prediction->data.fl2; return(cvPoint2D32f(predict_value_x,predict_value_y);void kalman:init_kalman(int x,int xv,int y,int yv) state->data.fl0=x; state->data.fl1=xv; state->data.fl2=y; state->data.fl3=yv; cvkalman->state_post->data.fl0=x; cvkalman->state_post->data.fl1=xv; cvkalman->state_post->data.fl2=y; cvkalman->state_post->data.fl3=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; aii=0.0; for (kk=0; kk<=n-1; kk+) aii=aii+pi*n+kk*fj*n+kk; for (i=0; i<=n-1; i+) for (j=0; j<=n-1; j+) ii=i*n+j; pii=qii; for (kk=0; kk<=n-1; kk+) pii=pii+fi*n+kk*akk*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; ajj=0.0; for (kk=0; kk<=n-1; kk+) ajj=ajj+pi*n+kk*hj*n+kk; for (i=0; i<=m-1; i+) for (j=0; j<=m-1; j+) jj=i*m+j; ejj=rjj; for (kk=0; kk<=n-1; kk+) ejj=ejj+hi*n+kk*akk*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; gjj=0.0; for (kk=0; kk<=m-1; kk+) gjj=gjj+ai*l+kk*ej*m+kk; for (i=0; i<=n-1; i+) jj=(ii-1)*n+i; xjj=0.0; for (j=0; j<=n-1; j+) xjj=xjj+fi*n+j*x(ii-2)*n+j; for (i=0; i<=m-1; i+) jj=i*l; bjj=y(ii-1)*m+i; for (j=0; j<=n-1; j+) bjj=bjj-hi*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+) xjj=xjj+gi*m+j*bj*l; if (ii<k) for (i=0; i<=n-1; i+) for (j=0; j<=n-1; j+) jj=i*l+j; ajj=0.0; for (kk=0; kk<=m-1; kk+) ajj=ajj-gi*m+kk*hkk*n+j; if (i=j) ajj=1.0+ajj; for (i=0; i<=n-1; i+) for (j=0; j<=n-1; j+) jj=i*l+j; bjj=0.0; for (kk=0; kk<=n-1; kk+) bjj=bjj+ai*l+kk*pkk*n+j; for (i=0; i<=n-1; i+) for (j=0; j<=n-1; j+) jj=i*l+j; ajj=0.0; for (kk=0; kk<=n-1; kk+) ajj=ajj+bi*l+kk*fj*n+kk; for (i=0; i<=n-1; i+) for (j=0; j<=n-1; j+) jj=i*n+j; pjj=qjj; for (kk=0; kk<=n-1; kk+) pjj=pjj+fi*n+kk*aj*l+kk; free(e); free(a); free(b); return(js);
