Variational Bayesian Target Tracking Algorithm in Clutter Interference Environment
To improve the target tracking accuracy in clutter,this paper combines probabilistic data association with variational Bayesian and proposes the variational Bayesian probabilistic data association Kalman filter(PDA-VB-KF)tracking algorithm.Firstly,the effective measurement of the target is obtained by using probability data associa-tion(PDA),and then the adaptive factor is designed based on gamma distribution to correct the effective measurement noise covariance.Finally,the joint estimation of adaptive factor and state is realized under the framework of variational Bayesian(VB).In order to verify the tracking performance of proposed filter,this paper tests it in two simulation ca-ses.The simulation results show that the proposed filter has higher tracking accuracy than the nearest neighbor Kal-man filter(NN-KF),the k-nearest neighbor Kalman filter(KNN-KF)and the probability data association Kalman filter(PDA-KF).
ClutterProbabilistic data associationVariational BayesianKalman filterAdaptive factor
万方数据
维普
