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Mathematical Problems in Engineering
Volume 2013, Article ID 483913, 7 pages
Research Article

Multitarget Tracking by Improved Particle Filter Based on Unscented Transform

The Department of Systems and Control, Beihang University (BUAA), Beijing 100191, China

Received 29 August 2013; Accepted 11 October 2013

Academic Editor: Tao Li

Copyright © 2013 Yazhao Wang. 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.


This paper considers the problem of multitarget tracking in cluttered environment. To reduce the dependency on the noise priori knowledge, an improved particle filtering (PF) data association approach is presented based on the filter (HF). This approach can achieve higher robustness in the condition that the measurement noise prior is unknown. Because of the limitations of the HF in nonlinear tracking, we first present the unscented filter (HUF) by embedding the unscented transform (UT) into the extended filter (HEF) structure. Then the HUF is incorporated into the Rao-Blackwellized particle filter (RBPF) framework to update the particles. Simulation results are provided to demonstrate the effectiveness of the proposed algorithms in linear and nonlinear multitarget tracking.