基于压缩感知的认知雷达多目标跟踪方法

杨军; 张群; 罗迎; 邓冬虎

doi:10.12000/JR14107

基于压缩感知的认知雷达多目标跟踪方法

DOI: 10.12000/JR14107

1.
(空军工程大学信息与导航学院西安 710077)
2.
(复旦大学电磁波信息科学教育部重点实验室上海 200433)

基金项目:

国家自然科学基金(61172169, 61201369)和陕西省自然科学基础研究计划项目(2013JQ8008)

详细信息

作者简介:
杨军(1990-),男,安徽芜湖人,现为空军工程大学信息与导航学院硕士研究生。研究方向为雷达信号处理。E-mail:yangjunah@163.com张群(1964-),男,陕西合阳人,现为空军工程大学信息与导航学院教授,博士生导师,IEEESeniorMember,中国电子学会无线电定位技术分会委员,发表学术论文150余篇,其中SCI、EI检索80余次。研究方向为雷达信号处理、雷达成像和电子对抗。E-mail:zhangqunnus@gmail.com罗迎(1984-),男,湖南益阳人,现为空军工程大学信息与导航学院讲师,IEEEMember,中国电子学会会员,中国图象图形学会会员,发表学术论文70余篇,其中SCI、EI检索40余次。研究方向为雷达信号处理、雷达成像。E-mail:luoying2002521@163.com

通讯作者:
杨军yangjunah@163.com

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出版历程
- 收稿日期: 2014-08-25
- 修回日期: 2014-10-31
- 网络出版日期: 2016-02-28

Method for Multiple Targets Tracking in Cognitive Radar Based on Compressed Sensing

1.
(Information and Navigation Institute, Air Force Engineering University, Xi'an 710077, China)
2.
(Key Laboratory for Information Science of Electromagnetic Waves (Ministry of Education), Fudan University, Shanghai 200433, China)

Funds:

The National Natural Science Foundation of China (61172169, 61201369), The Natural Science Foundation Research Project of Shaanxi Province (2013JQ8008)

摘要

摘要: 该文提出了一种基于压缩感知的认知雷达跟踪方法,该方法将压缩感知理论引入到认知雷达跟踪的问题中。通过对回波信号的稀疏表示,完成稀疏变换矩阵和观测矩阵的设计,实现了降采样条件下量测信号的重构。在系统的接收端,考虑到传统的粒子滤波容易陷入局部最优,对粒子数目要求大等问题,采用了粒子群优化的粒子滤波来对目标状态进行实时估计。在系统的发射端,采用优化后验克拉美罗界(Posterior Cramr-Rao Bounds, PCRB)设计了雷达发射波形参数,降低了对目标跟踪精度的PCRB。仿真表明,相比于传统跟踪方法,该文所提跟踪方法不仅有效地减少了雷达的数据量,而且较大地提高了目标的跟踪性能。
- 认知雷达 /
- 压缩感知 /
- 多目标跟踪 /
- 波形设计 /
- 粒子滤波
Abstract: A multiple targets cognitive radar tracking method based on Compressed Sensing (CS) is proposed. In this method, the theory of CS is introduced to the case of cognitive radar tracking process in multiple targets scenario. The echo signal is sparsely expressed. The designs of sparse matrix and measurement matrix are accomplished by expressing the echo signal sparsely, and subsequently, the restruction of measurement signal under the down-sampling condition is realized. On the receiving end, after considering that the problems that traditional particle filter suffers from degeneracy, and require a large number of particles, the particle swarm optimization particle filter is used to track the targets. On the transmitting end, the Posterior Cramr-Rao Bounds (PCRB) of the tracking accuracy is deduced, and the radar waveform parameters are further cognitively designed using PCRB. Simulation results show that the proposed method can not only reduce the data quantity, but also provide a better tracking performance compared with traditional method.
- Cognitive radar /
- Compressed Sensing (CS) /
- Multiple target tracking /
- Waveform design /
- Particle Filter (PF)

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参考文献(16)

[1]	黎湘, 范梅梅. 认知雷达及其关键技术研究进展[J]. 电子学报, 2012, 40(9): 1863-1870. Li Xiang and Fan Mei-mei. Research advance on cognitive radar and its key technology[J]. Acta Electronica Sinica, 2012, 40(9): 1863-1870.
[2]	Haykin S. Cognitive radar: A way of the future[J]. IEEE Signal Processing Magazine, 2006, 23(1): 30-40．
[3]	Haykin S, Zia A, Arasaratnam I, et al.. Cognitive tracking radar[C]. Proceedings of 2010 IEEE Radar Conference, Washington DC, 2010: 1467-1470.
[4]	Chavali P and Nehorai A. Cognitive radar for target tracking in multipath scenarios[C]. Proceedings of 2010 IEEE Waveform Diversity and Design Conference, Niagara Falls, Canada, 2010: 110-114.
[5]	Chavali P and Nehorai A. Scheduling and power allocation in a cognitive radar network for multiple-target tracking[J]. IEEE Transactions on Signal Processing, 2012, 60(2): 715-729.
[6]	Candes E and Tao T. Decoding by linear programming[J]. IEEE Transactions on Information Theory, 2005, 51(12): 4203-4215.
[7]	Candes E, Romberg J, and Tao T. Stable signal recovery from incomplete and inaccurate measurements[J]. Communications on Pure and Applied Mathematics, 2006, 59(8): 1207-1223.
[8]	Candes E. Compressive sampling[C]. Proceedings of the International Conference of Mathematicians, Madrid, Spain, 2006: 1433-l452.
[9]	Sira S P. Time-varying waveform selection and configuration for agile sensors in tracking applications[C]. Proceedings of IEEE International Conference on Acoustics, Speech, and Signal Processing, Philadelphia, Pennsylvania, 2005: 880-884.
[10]	薛文虎, 张社国, 徐红华. 用于目标跟踪的自适应广义调频波形设计算法[J]. 海军工程大学学报, 2012, 24(4): 67-71. Xue Wen-hu, Zhang She-guo, and Xu Hong-hua. An adaptive waveform design algorithm for target tracking based on generalized frequency modulation[J]. Journal of Naval University of Engineering, 2012, 24(4): 67-71.
[11]	石光明, 刘丹华, 高大化, 等. 压缩感知理论及其研究进展[J]. 电子学报, 2009, 37(5): 1070-1081. Shi Guang-ming, Liu Dan-hua, Gao Da-hua, et al.. Advances in theory and application of compressed sensing[J]. Acta Electronica Sinica, 2009, 37(5): 1070-1081.
[12]	时燕, 陈迪荣. 基于小波包算法的压缩传感SAR 成像方法[J]. 雷达学报, 2013, 2(2): 218-225. Shi Yan and Chen Di-rong. A compressive sensing SAR imaging approach based on wavelet package algorithm[J]. Journal of Radars, 2013, 2(2): 218-225.
[13]	方正, 佟国峰, 徐心和. 粒子群优化粒子滤波方法[J]. 控制与决策, 2007, 22(3): 273-277. Fang Zheng, Tong Guo-feng, and Xu Xin-he. Particle swarm optimized particle filter[J]. Control and Decision, 2007, 22(3): 273-277.
[14]	朱志宇. 粒子滤波算法及其应用[M]. 北京: 科学出版社, 2010: 80-81. Zhu Zhi-yu. Particle Filter Algorithm and Its Application[M]. Beijing: Science Press, 2010: 80-81.
[15]	Kershaw D and Evans R. Optimal waveform selection for tracking systems[J]. IEEE Transactions on Information Theory, 1994, 40(9): 1536-1550.
[16]	Tichavsk P, Muravchik C H, and Nehorai A. Posterior Cramr-Rao bounds for discrete-time nonlinear filtering[J]. IEEE Transactions on Signal Processing, 1998, 46(5): 1386-1394.