Variational Bayesian Probabilistic Data Association Algorithm

Aiming at the problem of target tracking in clutter, this paper proposes a variational Bayesian based probabilistic data association algorithm (VB-PDA). Firstly, associated events are regarded as a random variable and modelled by the multi-nomial distribution. Then, the joint probability density function of data set, target state and associated events is constructed and the posterior probability density function of associated events is obtained by using this joint probability density function. Finally, the posterior probability density function of associated events is introduced into the framework of variational Bayesian to obtain the approximate posterior probability density function of state. Compared with the probabilistic data association algorithm, the VB-PDA algorithm obtains a state posterior probability density function with higher approximation degree based on the weight Kullback-Leibler (KL) average criterion while improving real-time performance. The simulation experiments verify the effectiveness of proposed algorithm. © 2022 Science Press. All rights reserved.