Journal Article

Sequential Monte Carlo implementation and state extraction of the group probability hypothesis density filter for partly unresolvable group targets-tracking problem

Yan, X.-X., Lian, F., Han, C.-Z., Liu, W.-F., Zhou, H.-Y.

2010

Overview

This study proposes a sequential Monte Carlo (SMC) implementation to the Mahler's group probability hypothesis density filter (PHDF) for partly unresolvable group targets tracking problem. Therefore, the authors have to jointly extract the group number and states from the proposed group SMC-PHDF at each time step. The authors propose to fit the resampled particles of the group SMC-PHDF by application of Gaussian mixture models with unknown component number. The Markov chain Monte Carlo (MCMC) algorithm is proposed to estimate the component parameters of the mixture. The estimate of component number of the mixture can be derived by a component management strategy. In simulation, the proposed group SMC-PHDF with the expectation maximum (EM) and MCMC extractions are, respectively, used to detect and track the groups. Hundreds of Monte Carlo simulation results show that the latter outperforms the former a lot in estimating the group number and states, although the computational requirement of the MCMC extraction is more expensive than the EM extraction.

• Publisher: Institution of Engineering and Technology,The Institution of Engineering & Technology
• Subject: Applied sciences / Computer simulation / Density / Estimates / Exact sciences and technology / Extraction / Information, signal and communications theory / Monte Carlo methods / Navigation / Signal and communications theory / Signal representation. Spectral analysis / Signal, noise / Sonar / Strategy / Target tracking / Telecommunications and information theory