Paper Info
Title
A GM-PHD algorithm for multiple target tracking based on false alarm detection with irregular window
Abstract
Probability hypothesis density (PHD) filter is a suboptimal Bayesian multi-target filter based on random finite set. Gaussian mixture is an approximation scheme to obtain the closed solution of the PHD filter, which is only suitable for linear Gaussian case. However, when targets are moving closely to each other, GM-PHD filter cannot correctly estimate the number of targets and their states. Especially, the estimation accuracy of both target number and their states is rather difficult when targets born and disappear in closely spaced target tracking scenarios. To solve these problems, a novel multiple target tracking algorithm is proposed in this paper. For one hand, when the targets are close, a novel weight redistribution scheme of targets is proposed, which can appropriately modify the weights of the closely spaced targets so that the higher precision of state estimates can be obtained. On the other hand, we propose a false alarm detection method by using an irregular window, in which the multi-scan measurement information is considered to reduce the disturbance of clutter. In numerical experiments, the results demonstrate that the proposed approach can achieve better performance compared to the other existing methods.
Year
DOI
Venue
2016
10.1016/j.sigpro.2015.10.007
Signal Processing
Keywords
Field
DocType
Multiple target tracking,Gaussian mixture PHD,Random finite set,False alarm detection,Irregular window
Probability hypothesis density filter,Mathematical optimization,False alarm,Finite set,Clutter,Algorithm,Gaussian,Merge (version control),Mathematics,Bayesian probability
Journal
Volume
ISSN
Citations 
120
0165-1684
6
PageRank 
References 
Authors
0.46
12
4
Name
Order
Citations
PageRank
Huanqing Zhang181.17
Hong-Wei Ge2405.85
Jinlong Yang3278.07
Yun-Hao Yuan423522.18