A new method based on ant colony optimization for the probability hypothesis density filter

Authors:
Jihong Zhu;Benlian Xu;Fei Wang;Qiquan Wang
Affiliations:
School of Automation, NanJing University of Science & Technology, NanJing, China;School of Electric and Automatic Engineering, ChangShu Institute of Technology, ChangShu, China;School of Electric and Automatic Engineering, ChangShu Institute of Technology, ChangShu, China;School of Automation, NanJing University of Science & Technology, NanJing, China
Venue:
ICSI'11 Proceedings of the Second international conference on Advances in swarm intelligence - Volume Part II
Year:
2011

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Abstract

A new approximating estimate method based on ant colony optimization algorithm for probability hypothesis density (PHD) filter is investigated and applied to estimate the time-varying number of targets and their states in clutter environment. Four key process phases are included: generation of candidates, initiation, extremum search and state extraction. Numerical simulations show the performance of the proposed method is closed to the sequence Monte Carlo PHD method.