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1、卡尔曼滤波算法实现代码卡尔曼滤波算法实现代码 其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 (ln) l=n; a=malloc(l*l*sizeof(double); b=malloc(l*l*sizeof(double); for (i=0; i=n-1; i+
2、) 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-
3、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+a
4、i*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 (iik) for (i=0
5、; 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 (
6、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 1000 #pragma once #endif / _MSC_VER 1000 #include #include cv.h class kalman public: void init_kalman(int x,int xv,int y,int yv); CvKalman* cvkalman; CvMat* state; CvMat* process_no
7、ise; 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 /* tester de printer tout
8、es 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 = cvCre
9、ateMat( 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)/
10、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(
11、&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.
12、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_nois
13、e_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-p
14、rocess_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 */ /* xk=A鈥k+B鈥k Pk=A鈥k-1*AT + Q */ cvRandSetRange( &rng, 0, sqrt(cvkalman-measurem
15、ent_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 ); /* adjust Kalman filter
16、 state */ /* Kk=Pk鈥T鈥?H鈥k鈥T+R)-1 xk=xk+Kk鈥?zk-H鈥k) Pk=(I-Kk鈥)鈥k */ 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
17、.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;
