Table of Contents Author Guidelines Submit a Manuscript
Mathematical Problems in Engineering
Volume 2014, Article ID 938242, 11 pages
Research Article

Unknown Clutter Estimation by FMM Approach in Multitarget Tracking Algorithm

1The Institute of Integrated Automation, MOE KLINNS Lab, School of Electronics and Information, Xi’an Jiaotong University, Xi’an 710049, China
2Xi’an Research Institute of Hi-Tech, Hongqing Town, Xi’an 710025, China

Received 28 August 2013; Accepted 19 December 2013; Published 16 March 2014

Academic Editor: Shuli Sun

Copyright © 2014 Ning Lv et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Finite mixture model (FMM) approach is a research focus in multitarget tracking field. The clutter was treated as uniform distribution previously. Aiming at severe bias caused by unknown and complex clutter, a multitarget tracking algorithm based on clutter model estimation is put forward in this paper. Multitarget likelihood function is established with FMM. In this frame, the algorithms of expectation maximum (EM) and Markov Chain Monte Carlo (MCMC) are both consulted in FMM parameters estimation. Furthermore, target number and multitarget states can be estimated precisely after the clutter model fitted. Association between target and measurement can be avoided. Simulation proved that the proposed algorithm has a good performance in dealing with unknown and complex clutter.