一种改进的泊松点过程概率多假设跟踪方法

doi:10.19665/j.issn1001-2400.2021.02.004

西安电子科技大学学报 ›› 2021, Vol. 48 ›› Issue (2): 27-34.doi: 10.19665/j.issn1001-2400.2021.02.004

一种改进的泊松点过程概率多假设跟踪方法

张逸宸(),水鹏朗()

西安电子科技大学雷达信号处理国家重点实验室,陕西西安 710071

收稿日期:2020-10-01 修回日期:2020-12-09 出版日期:2021-04-20 发布日期:2021-04-28
通讯作者: 水鹏朗
作者简介:张逸宸(1995—),男,西安电子科技大学博士研究生,E-mail: yichenzhang@stu.xidian.edu.cn|张逸宸(1995—),男,西安电子科技大学博士研究生,E-mail: yichenzhang@stu.xidian.edu.cn
基金资助:
国家自然科学基金(62071346);国家自然科学基金(61871303);国家自然科学基金(62071346);国家自然科学基金(61871303)

Improved probabilistic multi-hypothesis tracker via the Poisson point process

ZHANG Yichen(),SHUI Penglang()

National Laboratory of Radar Signal Processing,Xidian University,Xi’an 710071,China

Received:2020-10-01 Revised:2020-12-09 Online:2021-04-20 Published:2021-04-28
Contact: Penglang SHUI

摘要/Abstract

摘要：

多目标跟踪方法的核心是获取量测点与目标间的最优关联。传统的多目标跟踪方法从穷举出的所有可能关联中估计出最优关联,随目标数目指数倍增的关联复杂度制约了雷达跟踪多目标的能力。因此提出了一种低运算复杂度、高跟踪精度的多目标跟踪算法和与其配套的航迹管理方法,目标和杂波产生的量测点被建模为泊松点过程,以量测的来源为缺失信息,通过EM(Expectation Maximisation)算法迭代求解目标状态,独立的量测关联和目标混合概率大大降低了算法的复杂度。此外,额外的回波多普勒信息作为目标特征被引入关联和滤波环节辅助跟踪,提升了算法区分量测来源的能力,获得了更高的跟踪精度。实验结果表明,所提方法可以实现稳健的目标跟踪,且运算时间随目标数目线性增加。

关键词: 概率多假设跟踪, 目标特征辅助跟踪, 多目标跟踪, 目标跟踪, 跟踪系统, 概率多假设跟踪, 目标特征辅助跟踪, 多目标跟踪, 目标跟踪, 跟踪系统

Abstract:

Optimal data association is the main task of multi-target tracking due to the similarity of the tracker’s filtering parts.Traditional Multi-target tracking methods pick up the optimal data association from all possible associations that account for the complexity exponentially increasing with the number of targets and limiting the maximum number of targets which can be stably tracked.This paper proposes an efficient and accurate method where the measurement points raised by targets and clutter are modeled as the Poisson point process and the expectation maximisation algorithm is utilized to estimate the target states recursively.Independent data association and mixing probability decrease the computational complexity.Furthermore,Doppler information refers to the fact that the target feature has been used in association and filtering stage to improve tracking performance without adding complexity.The experiment with simulation data show that the performance of the proposed method is better than that of the traditional method with a shorter operation time.

Key words: probabilistic multi-hypothesis tracker, feature-aided tracking, multi-target tracking, target tracking, tracking system, probabilistic multi-hypothesis tracker, feature-aided tracking, multi-target tracking, target tracking, tracking system

中图分类号:

TN820.4

张逸宸,水鹏朗. 一种改进的泊松点过程概率多假设跟踪方法[J]. 西安电子科技大学学报, 2021, 48(2): 27-34.

ZHANG Yichen,SHUI Penglang. Improved probabilistic multi-hypothesis tracker via the Poisson point process[J]. Journal of Xidian University, 2021, 48(2): 27-34.

图/表 5

参考文献 20

[1]	BLACKMAN S, POPOLI R. Design and Analysis of Modern Tracking Systems[M]. Norwood:Artech House Publishers, 1999: 1-43.
[2]	BAR-SHALOM Y, FORTMANN T E. Tracking and Data Association[M]. Salt Lake City: Academic Press Professional, 1987: 14-52.
[3]	ZHOU B, BOSE N K. An Efficient Algorithm for Data Association in Multitarget Tracking[J]. IEEE Transactions on Aerospace and Electronic Systems, 1995,31(1):458-468.
[4]	BLACKMAN S S. Multiple Hypojournal Tracking for Multiple Target Tracking[J]. IEEE Transactions on Aerospace and Electronic Systems, 2004,19(1):5-18.
[5]	WILLETTP, RUAN Y, STREIT R. PMHT:Problems and Some Solutions[J]. IEEE Transactions on Aerospace and Electronic Systems, 2002,38(3):738-754.
[6]	DAVEYS. Detecting a Small Boat Using Histogram PMHT[J]. Journal of Advances in Information Fusion. 2011,6(2):167-186.
[7]	DAVEYS J, GRAY D A. Integrated Track Maintenance for the PMHT Via the Hysteresis Model[J]. IEEE Transactions on Aerospace and Electronic Systems, 2007,43(1):93-111.
[8]	DAVEY S. Probabilistic Multihypojournal Tracker with an Evolving Poisson Prior[J]. IEEE Transactions on Aerospace and Electronic Systems, 2015,51(1):747-759.
[9]	SCHOENECKERS, WILLETT P, BAR-SHALOM Y. The ML-PMHT Multistatic Tracker for Sharply Maneuvering Targets[J]. IEEE Transactions on Aerospace and Electronic Systems, 2013,49(4):2235-2249.
[10]	DAVEY S J, GAETJENS H X. Track-Before-Detect Using Expectation Maximisation[M]. Heidelberg:Springer, 2018: 33-57.
[11]	BREKKE E, HALLINGSTAD O, GLATTETRE J. Tracking Small Targets in Heavy-tailed Clutter Using Amplitude Information[J]. IEEE Journal of Oceanic Engineering, 2010,35(2):314-329.
[12]	宋建锋, 苗启广, 申猛, 等. 多特征融合的相关滤波红外单目标跟踪算法[J]. 西安电子科技大学学报, 2019,46(5):142-147.
	SONG Jianfeng, MIAO Qiguang, SHEN Meng, et al. Algorithm for Tracking an Infrared Single Target Based on Correlation Filtering with Multi-feature Fusion[J]. Journal of Xidian University, 2019,46(5):142-147.
[13]	SHI S N, SHUI P L. Detection of Low-velocity and Floating Small Targets in Sea Clutter Via Income-reference Particle Filters[J]. Signal Processing, 2018,148(1):78-90.
[14]	SHI S N, SHUI P L. Sea-surface Floating Small Target Detection by One-class Classifier in Time-frequency Feature Space[J]. IEEE Transactions on Geoscience and Remote Sensing, 2018,56(11):6395-6411.
[15]	SHUIP L, ZHANG K. Ship Radial Size Estimation in High-resolution Maritime Surveillance Radars Via Sparse Recovery Using Linear Programming[J]. IEEE Access, 2019,7:70673-70688.
[16]	YU H, SHUI P L, ZENG W L, et al. Bipercentile Parameter Estimators of Bias Reduction Generalized Pareto Clutter Model[J]. IET Radar,Sonar and Navigation, 2020,14(7):1105-1112.
[17]	王明杰, 姬红兵, 刘龙. 噪声野值下的学生t分布混合CPHD滤波[J]. 西安电子科技大学学报, 2019,46(5):134-141.
	WANG Mingjie, JI Hongbing, LIU Long. Student’s t Distribution Mixture CPHD Filter with Noise Outliers[J]. Journal of Xidian University, 2019,46(5):134-141.
[18]	王树亮, 毕大平, 阮怀林. 机动目标跟踪波形和检测门限自适应算法[J]. 西安电子科技大学学报, 2019,46(3):96-101.
	WANG Shuliang, BI Daping, RUAN Huailin. Waveform and Detection Threshold Self-adaption Algorithm for Maneuvering Target Tracking[J]. Journal of Xidian University, 2019,46(3):96-101.
[19]	李翠芸, 李洋, 姬红兵, 等. 一步数据关联GLMB扩展目标跟踪算法[J]. 西安电子科技大学学报, 2020,47(5):137-143.
	LI Cuiyun, LI Yang, JI Hongbing, et al. GLMB Extended Target Tracking Based on One-step Data Association[J]. Journal of Xidian University, 2020,47(5):137-143.
[20]	李翠芸, 陈东伟, 石仁政. 自适应目标新生δ广义标签多伯努利滤波算法[J]. 西安电子科技大学学报, 2019,46(2):12-16.
	LI Cuiyun, CHEN Dongwei, SHI Renzheng. Adaptive Target Birth δ-generalized Labeled Multi-Bernoulli Filtering Algorithm[J]. Journal of Xidian University, 2019,46(2):12-16.

参数	JPDA	PMHT	基于泊松点过程的PMHT
平均OSPA距离/m	33.2	53.4	11.9
平均运行时间/s	8.3	3.2	2.2

一种改进的泊松点过程概率多假设跟踪方法

Improved probabilistic multi-hypothesis tracker via the Poisson point process

RichHTML

PDF (PC)

赞

可视化

摘要/Abstract

引用本文

使用本文

图/表 5

参考文献 20

相关文章 15

Metrics

本文评价

推荐阅读 10

[1]	顾兆军,陈辉,王家亮,高冰. 一种小型四轴飞行器目标跟踪控制算法[J]. 西安电子科技大学学报, 2021, 48(5): 117-127.
[2]	毛昭勇,王亦晨,王鑫,沈钧戈. 面向高速公路的车辆视频监控分析系统[J]. 西安电子科技大学学报, 2021, 48(5): 178-189.
[3]	宋建锋,苗启广,王崇晓,徐浩,杨瑾. 注意力机制的多尺度单目标跟踪算法[J]. 西安电子科技大学学报, 2021, 48(5): 110-116.
[4]	王海军,张圣燕,杜玉杰. 响应差异约束的相关滤波无人机目标跟踪算法[J]. 西安电子科技大学学报, 2021, 48(5): 149-155.
[5]	逯彦,廖桂生,黄庆享. 结合稀疏表示的跨摄像头运动目标跟踪算法[J]. 西安电子科技大学学报, 2021, 48(2): 197-204.
[6]	成磊,王玥,田春娜. 一种添加残差注意力机制的视觉目标跟踪算法[J]. 西安电子科技大学学报, 2020, 47(6): 148-157.
[7]	李翠芸,李洋,姬红兵,石仁政. 一步数据关联GLMB扩展目标跟踪算法[J]. 西安电子科技大学学报, 2020, 47(5): 137-143.
[8]	余志超,张瑞红. 结合深度轮廓特征的改进孪生网络跟踪算法[J]. 西安电子科技大学学报, 2020, 47(3): 40-49.
[9]	张昀普,单甘霖,段修生,王猛. 主/被动传感器辐射控制的调度方法[J]. 西安电子科技大学学报, 2019, 46(6): 67-74.
[10]	宋建锋,苗启广,申猛,权义宁,陈毓生. 多特征融合的相关滤波红外单目标跟踪算法[J]. 西安电子科技大学学报, 2019, 46(5): 142-147.
[11]	王明杰,姬红兵,刘龙. 噪声野值下的学生t分布混合CPHD滤波[J]. 西安电子科技大学学报, 2019, 46(5): 134-141.
[12]	李翠芸,陈东伟,石仁政. 自适应目标新生δ广义标签多伯努利滤波算法[J]. 西安电子科技大学学报, 2019, 46(2): 12-16.
[13]	王海军,张圣燕. 自适应权值卷积特征的鲁棒目标跟踪算法[J]. 西安电子科技大学学报, 2019, 46(1): 117-123.
[14]	王欣远,肖嵩,李磊,焦玲玲. 融合ELM和相关滤波的鲁棒性目标跟踪算法[J]. 西安电子科技大学学报, 2019, 46(1): 57-63.
[15]	刘雨情;肖嵩;李磊. 在线判别式超像素跟踪算法[J]. 西安电子科技大学学报, 2018, 45(3): 13-17+168.