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| Extracting Targets’ State from Particle Approximation of the PHD |
| Tang Xu① Wei Ping① Chen Xin② |
①(School of Electronic Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China)
②(Electrical and Computer Engineering, McMaster University, Hamilton L8S2L3, Canada) |
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Abstract Probability Hypothesis Density (PHD) filter has emerged as one of powerful tools for multi-target tracking. In the Sequential Monte Carlo (SMC) implementation of it, the filter’s output is particle approximation of PHD, so some special algorithm is needed to extract the target states from those particles. In this paper, an improved algorithm is proposed. Firstly particles are clustered by their positions using the k-means algorithm, and then the positions with maximum of particles’ weight are searched and estimated in each cluster as the targets’ positions. Because the information of both particles’ weight and spatial distribution are utilized, confirmed by simulation results, the new algorithm can provide estimation of the targets states more accurately.
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Received: 11 December 2009
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Corresponding Authors:
Tang Xu
E-mail: tangxu@uestc.edu.cn
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2010, Vol. 32 
