Ant clustering PHD filter for multiple-target tracking

Authors:
Benlian Xu;Huigang Xu;Jihong Zhu
Affiliations:
School of Electrical & Automatic Engineering, Changshu Institute of Technology, 215500 Changshu, China;School of Electrical & Automatic Engineering, Changshu Institute of Technology, 215500 Changshu, China;School of Automation, Nanjing University of Science & Technology, 210094 Nanjing, China
Venue:
Applied Soft Computing
Year:
2011

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Abstract

A novel ant clustering filtering algorithm, under the guidance of first-order statistic moment of posterior multiple-target state (probability hypothesis density), is investigated and applied to estimate the time-varying number of targets and their individual states in a cluttered environment. The ant clustering filtering algorithm includes two clustering steps: the first step is called rough ant clustering, which involves the stochastic selection of each ant and its state local adjustment according to the current likelihood function and posterior intensity, respectively; while the second is called fine ant clustering, which employs these ants to extract the multiple-target state. Numerical simulations verify the tracking multiple-target capability of our proposed algorithm through performance comparison with the Sequential Monte Carlo (SMC) method.