Particle filter for state estimation of jump Markov nonlinear system with application to multi-targets tracking
Hua Han, Yongsheng Ding, Kuangrong Hao, Liangjian Hu · International Journal of Systems Science · 2012
In this article, we first introduce the problem of state estimation of jump Markov nonlinear systems (JMNSs). Since the density evolution method for predictor equations satisfies Fokker–Planck–Kolmogorov equation (FPKE) in Bayes estimation, the FPKE in conjunction with Bayes’ conditional density update formula can provide optimal estimation for a general continuous-discrete nonlinear filtering problem. It is well known that the analytical solution of the FPKE and Bayes’ formula is extremely difficult to obtain except a few special cases. Hence, we try to design a particle filter to achieve Bayes estimation of the JMNSs. In order to test the viability of our algorithm, we apply it to multiple targets tracking in video surveillance. Before starting simulation, we introduce the ‘birth’ and ‘death’ description of targets, targets’ transitional probability model, and observation probability. The experiment results show good performance of our proposed filter for multiple targets tracking.