ET-GM-PHD Filtering Algorithm Based on Dynamic Grid Density SNN Clustering

PENG  Cong; WANG  Jiegui; ZHU  Kefan

doi:10.15892/j.cnki.djzdxb.2019.02.035

Journal of Projectiles, Rockets, Missiles and Guidance >

2019 , Vol. 39 >Issue 2: 152 - 158

DOI: https://doi.org/10.15892/j.cnki.djzdxb.2019.02.035

ET-GM-PHD Filtering Algorithm Based on Dynamic Grid Density SNN Clustering

PENG Cong ,
WANG Jiegui ,
ZHU Kefan

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Electronic Countermeasures College, National University of Defense Technology, Hefei 230037, China

Received date: 2018-05-17

Online published: 2025-05-20

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Abstract

In view of the large difference of measurement density produced by different extended targets, the problem of the multi extended target Gauss mixture probability hypothesis density (ET-GM-PHD) measurement set is difficult and the computational complexity is heavy, and a SNN similarity measurement division algorithm based on dynamic grid density is proposed. First, the dynamic grid technology is used to preprocess the measured data, and the clutter interference in the measurement is reduced, and then the shared nearest neighbor (SNN) similarity is used to measure the measured values. The simulation results show that the proposed algorithm reduces the running time and improves the tracking stability compared with the traditional algorithm.

Key words： extended target; ET-GM-PHD filter; dynamic grid density; SNN similarity; measurement set partition

Cite this article

PENG Cong , WANG Jiegui , ZHU Kefan . ET-GM-PHD Filtering Algorithm Based on Dynamic Grid Density SNN Clustering[J]. Journal of Projectiles, Rockets, Missiles and Guidance, 2019 , 39(2) : 152 -158 . DOI: 10.15892/j.cnki.djzdxb.2019.02.035

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References

[1]	FORTMANNTE, BAR-SHALOM Y, SCHEFFE M. Multi-target tracking using joint probabilistic data association[C]// Proceedings of the 19th IEEE Conference on Decision and Control including the Symposium on Adaptive Processes. [S.l.]: IEEE, 1980: 807-812.
[2]	MAHLER R. PHD filters for nonstandard targets: I Extended targets [C]// Proceedings of 12th International Conference on Information Fusion. S. l.]: IEEE, 2009: 915-921.
[3]	GRANSTROM K, LUNDQUIST C, ORGUNER O. Extended target tracking using a Gaussian-mixture PHD filter [J]. IEEE Transactions on Aerospace and Electronic Systems, 2012, 48(4): 3268-3286.
[4]	GRANSTROM K, LUNDQUIST C, ORGUNER U. A gaussian mixture PHD filter for extended target tracking [C]// 13th Conference on Information Fusion, 2010: 1-8.
[5]	韩玉兰, 任重义, 韩崇昭. 多扩展目标滤波器的量测集划分算法.压电与声光,2015,37(4): 603-608.
[6]	韩玉兰, 朱洪艳, 韩崇昭,等. 多扩展目标的高斯混合概率假设密度滤波器[J].西安交通大学学报,2014, 48(4):95-101.
[7]	郑学弢. 复杂环境下基于随机集的多目标跟踪算法研究[D]. 西安:西安电子科技大学,2013.
[8]	刘妹琴, 兰剑. 目标跟踪前沿理论与应用[M].北京:科学出版社,2015: 159 – 161.
[9]	孔云波, 冯新喜, 危璋. 利用高斯混合概率假设密度滤波器对扩展目标量测集进行划分[J].西安交通大学学报,2015,49(7): 126-133.
[10]	王晓峰, 张国毅, 戴杰. 一种新的未知雷达信号聚类分选方法.电子信息对抗技术,2012,27(1):19 – 22.
[11]	王军, 张冰. 基于动态网格密度聚类的雷达信号分选算法.现代电子技术,2013,36(21):1-4.
[12]	郑盈盈, 倪志伟, 吴姗,等. 基于移动网格和密度的数据流聚类算法[J]. 计算机工程与应用,2009,45(8): 129 – 131.
[13]	JARVIS RA, PATRICK E A. Clustering using a similarity measure based on shared nearest neighbors [J]. IEEE Transactions on Computers, 1973, C-22(11): 1025-1034.
[14]	冯晓磊, 于洪涛. 密度不敏感的近邻传播聚类算法研究[J]. 计算机工程,2012,38(2):159 - 162.
[15]	SCHUHMACHER D, VO BT, VO BN. A consistent metric for performance evaluation of multi-object filters [J]. IEEE Transactions on Signal Processing, 2008, 56(8): 3447-3457.

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