FRFT域LFM雷达回波信号的压缩采样模型

doi:10.3969/j.issn.1001-2400.2015.01.032

J4 ›› 2015, Vol. 42 ›› Issue (1): 200-206.doi: 10.3969/j.issn.1001-2400.2015.01.032

FRFT域LFM雷达回波信号的压缩采样模型

方标^1,2;黄高明¹;高俊¹;左炜¹

(1. 海军工程大学电子工程学院，湖北武汉 430033;
2. 中国人民解放军92723部队，北京 100841)

收稿日期:2013-07-23 出版日期:2015-02-20 发布日期:2015-04-14
通讯作者: 方标
作者简介:方标(1986-)，男，海军工程大学博士研究生，E-mail：allan_fb@163.com．
基金资助:
国家863计划资助项目(2013AA7014061)

Compressive sensing of linear frequency modulated echo signals in fractional Fourier domains

FANG Biao^1,2;HUANG Gaoming¹;GAO Jun¹;ZUO Wei¹

(1. College of Electronic Engineering, Naval Univ. of Engineering, Wuhan 430033, China;
2. The Unit 92723 of the PLA, Beijing 100841, China)

Received:2013-07-23 Online:2015-02-20 Published:2015-04-14
Contact: FANG Biao

摘要/Abstract

摘要：

针对宽带雷达线性调频信号(Linear Frequency Modulated，LFM)带宽大、采样率高、存储容量要求高的问题，设计了一种基于压缩感知的LFM雷达回波信号的欠采样整体方案．首先，利用LFM信号在分数阶傅里叶变换(FRactional Fourier Transform，FRFT)域上良好的能量聚集特点，提出了一种基于DFRFT核矩阵的正交基字典构造方法，在此基字典的基础上利用随机解调器和匹配追踪类算法构建了一个LFM雷达回波信号的压缩采样和重构系统模型．仿真结果验证了FRFT域处理LFM雷达回波信号的可行性和有效性，LFM雷达信号在该正交基字典上的稀疏表示效果要优于现有的波形匹配字典，而且具有更好的抗噪性能和自适应性能．

关键词: 压缩感知, 线性调频信号, 分数阶傅里叶变换

Abstract:

For the large bandwidth, high sampling rate, high storage capacity in linear frequency modulated(LFM) signals applied in wideband radar, this paper designs an overall solution for undersampling the radar echo of LFM signals based on compressive sensing(CS). First, by use of the good energy characteristics of LFM signals in the fractional Fourier transform(FRFT) domain, a novel orthogonal base dictionary based on the DFRFT nuclear matrix is proposed. Combining this dictionary with random demodulation and matching pursuit algorithms, an LFM radar echo signal sampling and reconstruction system model is built. Simulation results verify the feasibility and effectiveness of this scheme, and sparse representation of the LFM radar echo in the orthogonal basis dictionary is superior to the existing waveform matching dictionary, and has better anti-noise and adaptive performance.

Key words: compressive sensing, linear frequency modulate, fractional Fourier transform

中图分类号:

TP391

方标;黄高明;高俊;左炜. FRFT域LFM雷达回波信号的压缩采样模型[J]. J4, 2015, 42(1): 200-206.

FANG Biao;HUANG Gaoming;GAO Jun;ZUO Wei. Compressive sensing of linear frequency modulated echo signals in fractional Fourier domains[J]. J4, 2015, 42(1): 200-206.

参考文献

［1］ Eldar Y C, Kutyniok G. Compressed Sensing: Theory and Applications ［M］. Cambridge: Cambridge University Press, 2012.
［2］ Candes E, Romberg J, Tao T. Robust Uncertainty Principles: Exact Signal Reconstruction from Highly Incomplete Frequency Information ［J］. IEEE Transactions on Information Theory, 2006, 52(2): 489-509.
［3］ Donoho D. Compressed Sensing ［J］. IEEE Transactions on Information Theory, 2006, 52(4): 1289-1306.
［4］ Shi Guangming, Lin Jie, Chen Xuyang, et al. UWB Echo Signal Detection with Ultra-Low Rate Sampling Based on Compressed Sensing ［J］. IEEE Transactions on Circuits and Systems Ⅱ: Express Briefs, 2008, 55(4): 379-383.
［5］ Namias V. The Fractional Fourier Transform and Its Application in Quantum Mechanics ［J］. Journal of the Institute of Mathematics and Its Applications, 1980, 25(3): 241-265.
［6］陶然, 邓兵, 王越. 分数阶傅里叶变换及其应用［M］. 北京: 清华大学出版社, 2009.
［7］ Kirolos S, Laska J, Wakin M, et al. Analog-to-Information Conversion via Random Demodulation ［C］//Proceedings of the IEEE Dallas Circuits and System Workshop on Design, Applications, Integration and Software. Piscataway: IEEE, 2006: 1183-1186.
［8］ Tropp J A, Laska J, Duarte M, et al. Beyond Nyquist: Efficient Sampling of Sparse Bandlimited Signals ［J］. IEEE Transactions on Information Theory, 2010, 56(1): 520-544.
［9］ Mishali M, Eldar Y C. Sub-Nyquist Sampling: Bridging Theory and Practice ［J］. IEEE Signal Processing Magazine , 2011, 28(6): 98-124.
［10］ Baransky E, Itzhak G, Shmuel I, et al. A Sub-Nyquist Radar Prototype: Hardware and Algorithms ［DB/OL］. ［2013-06-05］. arXiv.org/abs/1208.2515.
［11］ Needell D, Vershynin R. Signal Recovery from Incomplete and Inaccurate Measurements via Regularized Orthogonal Matching Pursuit ［J］. IEEE Journal on Selected Topics in Signal Processing, 2010, 4(2): 310-316.
［12］甘伟, 许录平, 张华, 等. 一种贪婪自适应压缩感知重构［J］. 西安电子科技大学学报, 2012, 39(3): 50-57.
Gan Wei, Xu Luping, Zhang Hua, et al. Greedy Adaptive Recovery Algorithm for Compressed Sensing ［J］. Journal of Xidian University, 2012, 39(3): 50-57.
［13］陶然, 邓兵, 王越. 分数阶Fourier变换在信号处理领域的研究进展［J］. 中国科学E辑信息科学, 2006, 36(2): 113-136.
Tao Ran, Deng Bing, Wang Yue. Fractional Fourier Transform and Its Evolution in Signal Processing ［J］. Science in China Serize E Information Sciences, 2006, 36(2): 113-136.
［14］ Pei S C, Ding J J. Closed-form Discrete Fractional and Affine Fourier Transform ［J］. IEEE Transactions on Signal Processing, 2000, 48(5): 1338-1353.

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FRFT域LFM雷达回波信号的压缩采样模型

Compressive sensing of linear frequency modulated echo signals in fractional Fourier domains

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