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| Improved CPHD Filter for Multitarget Tracking |
| Ouyang Cheng Ji Hong-bing Zhang Jun-gen |
| School of Electronic Engineering, Xidian University, Xi’an 710071, China |
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Abstract The Cardinalized Probability Hypothesis Density (CPHD) fiter is a recursive Bayesian algorithm for estimating multiple target states with varying target number in clutter. Due to the fact that there is a missed detection problem in the CPHD filter, an improved algorithm is proposed, which provides a closed-form solution under Gaussian mixture assumptions. Firstly, the estimate to track association is made by labeling each Gaussian component, and then the weights of Gaussian components having been pruned and merged are reassigned twice. At first, the Gaussian components’ weights exceeding the detection threshold are reassigned, which can solve the missed detection issue effectively, and then the second distribution is made based on the fact that a target can only have one measurement, which improves the performance when the targets cross each other. Simulation results show that the improved CPHD filter has advantages over the ordinary one in both the aspects of multi-target state estimation and track maintenance.
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Received: 08 September 2009
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Corresponding Authors:
Ouyang Cheng
E-mail: ouoyc@yahoo.com.cn
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2010, Vol. 32 
