www.pudn.com > OpenCV-Intel.zip > cvkalman.cpp
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#include "_cv.h"
CV_IMPL CvKalman*
cvCreateKalman( int DP, int MP, int CP )
{
CvKalman *kalman = 0;
CV_FUNCNAME( "cvCreateKalman" );
__BEGIN__;
if( DP <= 0 || MP <= 0 )
CV_ERROR( CV_StsOutOfRange,
"state and measurement vectors must have positive number of dimensions" );
if( CP < 0 )
CP = DP;
/* allocating memory for the structure */
CV_CALL( kalman = (CvKalman *)cvAlloc( sizeof( CvKalman )));
memset( kalman, 0, sizeof(*kalman));
kalman->DP = DP;
kalman->MP = MP;
kalman->CP = CP;
CV_CALL( kalman->state_pre = cvCreateMat( DP, 1, CV_32FC1 ));
cvZero( kalman->state_pre );
CV_CALL( kalman->state_post = cvCreateMat( DP, 1, CV_32FC1 ));
cvZero( kalman->state_post );
CV_CALL( kalman->transition_matrix = cvCreateMat( DP, DP, CV_32FC1 ));
cvSetIdentity( kalman->transition_matrix );
CV_CALL( kalman->process_noise_cov = cvCreateMat( DP, DP, CV_32FC1 ));
cvSetIdentity( kalman->process_noise_cov );
CV_CALL( kalman->measurement_matrix = cvCreateMat( MP, DP, CV_32FC1 ));
cvZero( kalman->measurement_matrix );
CV_CALL( kalman->measurement_noise_cov = cvCreateMat( MP, MP, CV_32FC1 ));
cvSetIdentity( kalman->process_noise_cov );
CV_CALL( kalman->error_cov_pre = cvCreateMat( DP, DP, CV_32FC1 ));
CV_CALL( kalman->error_cov_post = cvCreateMat( DP, DP, CV_32FC1 ));
cvZero( kalman->error_cov_post );
CV_CALL( kalman->gain = cvCreateMat( DP, MP, CV_32FC1 ));
if( CP > 0 )
{
CV_CALL( kalman->control_matrix = cvCreateMat( DP, CP, CV_32FC1 ));
cvZero( kalman->control_matrix );
}
CV_CALL( kalman->temp1 = cvCreateMat( DP, DP, CV_32FC1 ));
CV_CALL( kalman->temp2 = cvCreateMat( MP, DP, CV_32FC1 ));
CV_CALL( kalman->temp3 = cvCreateMat( MP, MP, CV_32FC1 ));
CV_CALL( kalman->temp4 = cvCreateMat( MP, DP, CV_32FC1 ));
CV_CALL( kalman->temp5 = cvCreateMat( MP, 1, CV_32FC1 ));
#if 1
kalman->PosterState = kalman->state_pre->data.fl;
kalman->PriorState = kalman->state_post->data.fl;
kalman->DynamMatr = kalman->transition_matrix->data.fl;
kalman->MeasurementMatr = kalman->measurement_matrix->data.fl;
kalman->MNCovariance = kalman->measurement_noise_cov->data.fl;
kalman->PNCovariance = kalman->process_noise_cov->data.fl;
kalman->KalmGainMatr = kalman->gain->data.fl;
kalman->PriorErrorCovariance = kalman->error_cov_pre->data.fl;
kalman->PosterErrorCovariance = kalman->error_cov_post->data.fl;
#endif
__END__;
if( cvGetErrStatus() < 0 )
cvReleaseKalman( &kalman );
return kalman;
}
CV_IMPL void
cvReleaseKalman( CvKalman** _kalman )
{
CvKalman *kalman;
CV_FUNCNAME( "cvReleaseKalman" );
__BEGIN__;
if( !_kalman )
CV_ERROR( CV_StsNullPtr, "" );
kalman = *_kalman;
/* freeing the memory */
cvReleaseMat( &kalman->state_pre );
cvReleaseMat( &kalman->state_post );
cvReleaseMat( &kalman->transition_matrix );
cvReleaseMat( &kalman->control_matrix );
cvReleaseMat( &kalman->measurement_matrix );
cvReleaseMat( &kalman->process_noise_cov );
cvReleaseMat( &kalman->measurement_noise_cov );
cvReleaseMat( &kalman->error_cov_pre );
cvReleaseMat( &kalman->gain );
cvReleaseMat( &kalman->error_cov_post );
cvReleaseMat( &kalman->temp1 );
cvReleaseMat( &kalman->temp2 );
cvReleaseMat( &kalman->temp3 );
cvReleaseMat( &kalman->temp4 );
cvReleaseMat( &kalman->temp5 );
memset( kalman, 0, sizeof(*kalman));
/* deallocating the structure */
cvFree( (void**)_kalman );
__END__;
}
CV_IMPL const CvMat*
cvKalmanPredict( CvKalman* kalman, const CvMat* control )
{
CvMat* result = 0;
CV_FUNCNAME( "cvKalmanPredict" );
__BEGIN__;
if( !kalman )
CV_ERROR( CV_StsNullPtr, "" );
/* update the state */
/* x'(k) = A*x(k) */
CV_CALL( cvMatMulAdd( kalman->transition_matrix, kalman->state_post, 0, kalman->state_pre ));
if( control && kalman->CP > 0 )
/* x'(k) = x'(k) + B*u(k) */
CV_CALL( cvMatMulAdd( kalman->control_matrix, control, kalman->state_pre, kalman->state_pre ));
/* update error covariance matrices */
/* temp1 = A*P(k) */
CV_CALL( cvMatMulAdd( kalman->transition_matrix, kalman->error_cov_post, 0, kalman->temp1 ));
/* P'(k) = temp1*At + Q */
CV_CALL( cvGEMM( kalman->temp1, kalman->transition_matrix, 1, kalman->process_noise_cov, 1,
kalman->error_cov_pre, CV_GEMM_B_T ));
result = kalman->state_pre;
__END__;
return result;
}
CV_IMPL const CvMat*
cvKalmanCorrect( CvKalman* kalman, const CvMat* measurement )
{
CvMat* result = 0;
CV_FUNCNAME( "cvKalmanCorrect" );
__BEGIN__;
if( !kalman || !measurement )
CV_ERROR( CV_StsNullPtr, "" );
/* temp2 = H*P'(k) */
CV_CALL( cvMatMulAdd( kalman->measurement_matrix,
kalman->error_cov_pre, 0, kalman->temp2 ));
/* temp3 = temp2*Ht + R */
CV_CALL( cvGEMM( kalman->temp2, kalman->measurement_matrix, 1,
kalman->measurement_noise_cov, 1, kalman->temp3, CV_GEMM_B_T ));
/* temp4 = inv(temp3)*temp2 = Kt(k) */
CV_CALL( cvSolve( kalman->temp3, kalman->temp2, kalman->temp4, CV_SVD ));
/* K(k) */
CV_CALL( cvTranspose( kalman->temp4, kalman->gain ));
/* temp5 = z(k) - H*x'(k) */
CV_CALL( cvGEMM( kalman->measurement_matrix, kalman->state_pre, -1, measurement, 1, kalman->temp5 ));
/* x(k) = x'(k) + K(k)*temp5 */
CV_CALL( cvMatMulAdd( kalman->gain, kalman->temp5, kalman->state_pre, kalman->state_post ));
/* P(k) = P'(k) - K(k)*temp2 */
CV_CALL( cvGEMM( kalman->gain, kalman->temp2, -1, kalman->error_cov_pre, 1,
kalman->error_cov_post, 0 ));
result = kalman->state_post;
__END__;
return result;
}