基于联合稀疏恢复的宽带雷达动目标检测方法

方明; 戴奉周; 刘宏伟; 王小谟; 秦童

doi:10.11999/JEIT150442

基于联合稀疏恢复的宽带雷达动目标检测方法

doi: 10.11999/JEIT150442

1.
(西安电子科技大学雷达信号处理国家重点实验室西安 710071) ②(中国电子科学研究院北京 100041)

基金项目:

国家自然科学基金(61271291，61201285)，新世纪优秀人才支持计划(NCET-09-0630)，全国优秀博士学位论文作者专项资金(FANEDD-201156)和中央高校基本科研业务费专项资金

计量
- 文章访问数: 1398
- HTML全文浏览量: 125
- PDF下载量: 640
- 被引次数: 0
出版历程
- 收稿日期: 2015-04-20
- 修回日期: 2015-07-13
- 刊出日期: 2015-12-19

Detection of Moving Targets for Wideband Radar Based on Joint-sparse Recovery

1.
(National Laboratory of Radar Signal Processing, Xidian University, Xi&rsquo

Funds:

The National Natural Science Foundation of China (61271291, 61201285)

摘要

摘要: 针对宽带雷达目标回波存在越距离单元走动的问题，该文提出一种基于联合稀疏恢复的宽带雷达动目标检测方法。该方法先在频域对回波数据进行预白化处理，抑制杂波；然后在频率-慢时间域推导出目标信号的联合行稀疏表示，并通过联合稀疏恢复算法得到目标的频域估计；最后通过逆傅里叶变换得到目标的时域估计。仿真结果验证了该方法的有效性。
- 宽带雷达 /
- 目标检测 /
- 越距离单元走动 /
- 杂波抑制 /
- 联合稀疏恢复
Abstract: Range migration is the basic and troublesome problem in moving target detection for wideband radar. To solve this problem, a detection algorithm of moving targets based on joint-sparse recovery is proposed for wideband radar. Firstly, a prewhitening processing is performed to filter the clutter. Then, a jointly row sparse representation of the wideband signals is derived in frequency/slow-time domain，thus the detection problem is solved via joint-sparse recovery. Finally, by using the inverse Fourier transform, the estimation of the targets scenario is achieved. Numerical results are presented to demonstrate the effectiveness of the proposed algorithm.
- Wideband radar /
- Target detection /
- Range migration /
- Clutter suppression /
- Joint-sparse recovery

HTML全文

参考文献(29)

洪灵, 戴奉周, 刘宏伟. 一种基于二维运动重构的旋转对称目标拟规则进动参数估计方法[J]. 电子与信息学报, 2014, 36(7): 1538-1544.

Hong Ling, Dai Feng-zhou, and Liu Hong-wei. An approach for quasi-regularized precession parameters estimation of rotation symmetric object based on two-dimesional motion reconstruction[J]. Journal of Electronics Information Technology, 2014, 36(7): 1538-1544.

刘博, 常文革. 步进调频宽带雷达距离扩展目标频域检测算法[J]. 电子与信息学报, 2013, 35(10): 2481-2486.

Liu Bo and Chang Wen-ge. Range-spread target detection of stepped chirp modulated radar in the frequency domain[J]. Journal of Electronics Information Technology, 2013, 35(10): 2481-2486.

黄培康, 殷红成, 许晓剑. 雷达目标特性[M]. 北京: 电子工业出版社, 2005: 255-256.

Huang Pei-kang, Yin Hong-cheng, and Xu Xiao-jian. Radar Target Characteristics[M]. Beijing: Publishing House of Electronics Industry, 2005: 255-256.

Wehner D R. High-Resolution Radar[M]. Second Edition, Boston, MA: Artech House, 1995, Ch.1.

James D and Taylor P E. Ultra-Wideband Radar Technology [M]. New York: CRC Press, 2001, Ch.1.

Zhu Huan, Chen Yi, and Wang Ning. A novel method of wideband radar signal detection[C]. IEEE 7th International Congress on Image and Signal Processing, Dalian, 2014: 847-851.

Li X, Qiao D, and Li Y. Macro-motion detection using ultra-wideband impulse radar[C]. IEEE 36th Annual International Conference on Engineering in Medicine and Biology Society (EMBS), Chicago, IL, 2014: 2237-2240.

Sakamoto T and Sato T. Exploiting multipath echoes with Capon method for high-resolution ultra-wideband radar imaging using a single omni-directional antenna[C]. IEEE Conference on Antenna Measurements and Applications (CAMA), Antibes Juan-les-Pins, 2014: 16-19.

Conte E, Maio A D, and Ricci G. GLRT-based adaptive detection algorithms for range-spread targets[J]. IEEE Transactions on Signal Processing, 2001, 49(7): 1336-1348.

Perry P R, Dipietro R C, and Fante R L. Imaging of moving target[J]. IEEE Transactions on Aerospace and Electronic Systems, 1999, 35(1): 188-200.

高玉祥, 张兴敢, 柏业超. 基于Keystone变换的高速运动目标检测方法研究[J]. 南京大学学报(自然科学版), 2014, 50(1): 30-34.

Gao Yu-xiang, Zhang Xing-gan, and Bai Ye-chao. Research on high-velocity targets detection based on Keystone transform[J]. Journal of Nanjing University (Natural Science), 2014, 50(1): 30-34.

侯庆禹, 刘宏伟, 保铮. 基于Keystone变换的宽带目标识别雷达杂波抑制[J]. 系统工程与电子技术, 2009, 31(1): 49-53.

Hou Qing-yu, Liu Hong-wei, and Bao Zheng. Clutter suppression of wideband target recognition radars based on Keystone transformation[J]. Systems Engineering and Electronics, 2009, 31(1): 49-53.

保铮, 邢孟道, 王彤. 雷达成像技术[M]. 北京: 电子工业出版社, 2010: 20-45.

Bao Zheng, Xing Meng-dao, and Wang Tong. Radar Imaging Technique[M]. Beijing: Publishing House of Electronics Industry, 2010: 20-45.

Bidon S, Tourneret J Y, Savy L, et al.. Bayesian sparse estimation of migrating targets for wideband radar[J]. IEEE Transactions on Aerospace and Electronic Systems, 2014, 50(2): 871-886.

杨万海. 雷达系统建模与仿真[M]. 西安: 西安电子科技大学出版社, 2007: 48-73.

Yang Wan-hai. Modelling and Simulation of Radar Systems[M]. Xian: Xidian University Publishing House, 2007: 48-73.

Rangaswamy M, Weiner D, and Ozturk A. Non-Gaussian random vector identification using spherically invariant random process[J]. IEEE Transactions on Aerospace and Electronic Systems, 1993, 29(1): 111-124.

戴奉周. 宽带雷达信号处理-检测、杂波抑制与认知跟踪[D]. [博士论文], 西安电子科技大学, 2010.

Dai Feng-zhou. Wideband radar signal processing-detection, clutter suppression and cognitive tracking[D]. [Ph.D. dissertation], Xidian University, 2010.

Donoho D L. Compressed sensing[J]. IEEE Transactions on Information Theory, 2006, 52(4): 1289-1306.

Ziniel J and Schniter P. Dynamic compressive sensing of time-varying signals via approximate message passing[J]. IEEE Transactions on Signal Processing, 2013, 61(21): 5270-5284.

Angelosante D, Giannakis G B, and Grossi E. Compressed sensing of time-varying signals[C]. IEEE 16th International Conference on Digital Signal Processing, Santorini-Hellas, 2009: 1-8.

Tan X, Roberts W, Li J, et al.. Sparse learning via iterative minimization with application to MIMO radar imaging[J]. IEEE Transactions on Signal Processing, 2011, 59(3): 1088-1101.

施引文献

资源附件(0)

访问统计