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| Multi-sensor Probability Hypothesis Density Algorithm in Multi-target Filtering |
| Yang Ke① Fu Zhong-qian① Wang Jian-ting①② Lin Ri-zhao① |
①(Electronic Science and Technology Department, University of Science and Technology of China, Hefei 230027, China)
②(Shanghai Technical Physical Institute, Chinese Academy of Sciences, Shanghai 200433, China) |
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Abstract Multi-target filtering using Probability Hypothesis Density (PHD) in multi-sensor case is based on assumption model to avoid being computationally intractable. Based on describing target state space and sensor observation space by Random Finite Set (RFS) method, and on the analysis of detection probability, likelihood function and clutter distribution under the multi-sensor universal assumption model, the multi-sensor version of multi-target PHD filter is constructed by Probability Generating Functional (PGFL), the multi-sensor labeling particle Sequential Monte Carlo PHD (SMC-PHD) filtering algorithm is presented to implement this fiter with lower computational complexity. Finally, the better estimation of target number and track-valued state are obtained by simulation.
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Received: 09 September 2011
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Corresponding Authors:
Fu Zhong-qian
E-mail: zqfu@ustc.edu.cn
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2012, Vol. 34 
