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Mathematical Problems in Engineering
Volume 2017, Article ID 8742897, 9 pages
Research Article

Efficient Approximation of the Labeled Multi-Bernoulli Filter for Online Multitarget Tracking

College of Mechanical and Electrical Engineering, Harbin Engineering University, Harbin, Heilongjiang, China

Correspondence should be addressed to Liang Ma; moc.liamtoh@01116110xllm

Received 4 March 2017; Revised 26 May 2017; Accepted 1 June 2017; Published 20 July 2017

Academic Editor: Huanqing Wang

Copyright © 2017 Ping Wang 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.


Online tracking time-varying number of targets is a challenging issue due to measurement noise, target birth or death, and association uncertainty, especially when target number is large. In this paper, we propose an efficient approximation of the Labeled Multi-Bernoulli (LMB) filter to perform online multitarget state estimation and track maintenance efficiently. On the basis of the original LMB filer, we propose a target posterior approximation technique to use a weighted single Gaussian component representing each individual target. Moreover, we present the Gaussian mixture implementation of the proposed efficient approximation of the LMB filter under linear, Gaussian assumptions on the target dynamic model and measurement model. Numerical results verify that our proposed efficient approximation of the LMB filer achieves accurate tracking performance and runs several times faster than the original LMB filer.