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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.
This work is supported by national natural science foundation of China (No.60804068) and by national science foundation of Jiangsu province (No.BK2010261) and by cooperation innovation of industry, education and academy of Jiangsu province (No.BY2010126).
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Authors and Affiliations
School of Automation, NanJing University of Science & Technology, NanJing, 210094, China
Jihong Zhu & Qiquan Wang
School of Electric and Automatic Engineering, ChangShu Institute of Technology, ChangShu, 215500, China
Benlian Xu & Fei Wang
- Jihong Zhu
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- Benlian Xu
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- Fei Wang
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Editors and Affiliations
Key Laboratory of Machine Perception, Department of Machine Intelligence, School of Electronics Engineering and Computer Science, Peking University, 100871, Beijing, China
Ying Tan
Department of Electrical and Electronic Engineering, Xi’an Jiaotong-Liverpool University, 215123, Suzhou, China
Yuhui Shi
Automation College, Chongqing University, 400030, Chongqing, China
Yi Chai
Institute of Computer Science and Technology, Chongqing University of Posts and Telecommunications, 400065, Chongqing, P.R. China
Guoyin Wang
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Zhu, J., Xu, B., Wang, F., Wang, Q. (2011). A New Method Based on Ant Colony Optimization for the Probability Hypothesis Density Filter. In: Tan, Y., Shi, Y., Chai, Y., Wang, G. (eds) Advances in Swarm Intelligence. ICSI 2011. Lecture Notes in Computer Science, vol 6729. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21524-7_66
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