Fast density peak-based clustering algorithm for multiple extended target tracking

doi:10.21629/JSEE.2019.03.01

Journal of Systems Engineering and Electronics ›› 2019, Vol. 30 ›› Issue (3): 435-447.doi: 10.21629/JSEE.2019.03.01

• Electronics Technology • Next Articles

Fast density peak-based clustering algorithm for multiple extended target tracking

Xinglin SHEN*(), Zhiyong SONG(), Hongqi FAN(), Qiang FU()

National Key Laboratory of Science and Technology on ATR, College of Electronic Science, National University of Defense Technology, Changsha 410073, China

Received:2017-09-27 Online:2019-06-01 Published:2019-07-04
Contact: Xinglin SHEN E-mail:xlshencom@163.com;zhiyongsong@163.com;fanhongqi@tsinghua.org.cn;fuqiang1962@vip.sina.com
About author:SHEN Xinglin was born in 1990. He received his B.S. degrees from South China University of Technology (SCUT) in 2012 and his M.S. degree from National University of Defense Technology (NUDT) in 2014, respectively. He is currently working toward his Ph.D. degree at National Key Laboratory of Science and Technology on ATR, College of Electronic Science, National University of Defense Technology. His research interests include radar signal processing, target detection and tracking. E-mail:xlshencom@163.com|SONG Zhiyong was born in 1983. He received his Ph.D. degrees from National University of Defense Technology (NUDT) in 2012. He is currently a lecturer at College of Electronic Science, NUDT. His research interests include radar signal processing, radar anti-jamming, and radar target recognition. E-mail:zhiyongsong@163.com|FAN Hongqi was born in 1978. He received his B.S. degree in mechanical engineering and automation from Tsinghua University in 2001, and his Ph.D. degree in information and communication engineering from National University of Defense Technology (NUDT) in 2008. He is currently an associate professor at NUDT. His research interests include radar signal processing, target tracking and information fusion, and multi-agent systems. E-mail:fanhongqi@tsinghua.org.cn|FU Qiang was born in 1962. He received his B.S. and Ph.D. degrees from National University of Defense Technology (NUDT) in 1983 and 2004, respectively. He is currently a professor, Ph.D. supervisor at NUDT. His research interests include automatic target recognition, precision guidance, and radar signal and data processing. E-mail:fuqiang1962@vip.sina.com
Supported by:
the National Natural Science Foundation of China(61401475);This work was supported by the National Natural Science Foundation of China (61401475).

Abstract

Abstract:

The key challenge of the extended target probability hypothesis density (ET-PHD) filter is to reduce the computational complexity by using a subset to approximate the full set of partitions. In this paper, the influence for the tracking results of different partitions is analyzed, and the form of the most informative partition is obtained. Then, a fast density peak-based clustering (FDPC) partitioning algorithm is applied to the measurement set partitioning. Since only one partition of the measurement set is used, the ET-PHD filter based on FDPC partitioning has lower computational complexity than the other ET-PHD filters. As FDPC partitioning is able to remove the spatially close clutter-generated measurements, the ET-PHD filter based on FDPC partitioning has good tracking performance in the scenario with more clutter-generated measurements. The simulation results show that the proposed algorithm can get the most informative partition and obviously reduce computational burden without losing tracking performance. As the number of clutter-generated measurements increased, the ET-PHD filter based on FDPC partitioning has better tracking performance than other ET-PHD filters. The FDPC algorithm will play an important role in the engineering realization of the multiple extended target tracking filter.

Key words: fast density peak-based clustering (FDPC), multiple extended target, partition, probability hypothesis density (PHD) filter, track

Xinglin SHEN, Zhiyong SONG, Hongqi FAN, Qiang FU. Fast density peak-based clustering algorithm for multiple extended target tracking[J]. Journal of Systems Engineering and Electronics, 2019, 30(3): 435-447.

Figures/Tables 8

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Fast density peak-based clustering algorithm for multiple extended target tracking

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