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[1]张 磊,柏业超*,张兴敢.压缩感知在宽带雷达信号处理中的应用[J].南京大学学报(自然科学),2014,50(1):35.[doi:DOI:10.13232/j.cnki.jnju.2014.01.006]
 Zhang Lei,Bai Yechao,Zhang Xinggan.Compressed sensing application in wideband radar signal processing[J].Journal of Nanjing University(Natural Sciences),2014,50(1):35.[doi:DOI:10.13232/j.cnki.jnju.2014.01.006]
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压缩感知在宽带雷达信号处理中的应用()
     

《南京大学学报(自然科学)》[ISSN:0469-5097/CN:32-1169/N]

卷:
50
期数:
2014年第1期
页码:
35
栏目:
出版日期:
2014-01-31

文章信息/Info

Title:
Compressed sensing application in wideband radar signal processing
作者:
张 磊1柏业超1*张兴敢1
南京大学电子科学与工程学院,南京,210093
Author(s):
Zhang Lei1 Bai Yechao1 Zhang Xinggan1
School of Electronic Science and Engineering, Nanjing University, Nanjing, 210093, China
关键词:
压缩感知稀疏表示观测矩阵信号重构
Keywords:
compressed sensing sparse representation measurement matrix signal reconstruction
分类号:
-
DOI:
DOI:10.13232/j.cnki.jnju.2014.01.006
文献标志码:
-
摘要:
高分辨雷达成像系统在当今的军事和民用方面都有着广泛的需求,高分辨率成像需要发射宽带的雷达信号,然而根据奈奎斯特采样定理,信号带宽的增加又使得雷达系统面临高采样率、高传输率、大数据量存储以及信号实时快速处理等问题。压缩感知(CS)理论通过构造非相关测量矩阵,以远低于奈奎斯特采样率的速率获得一组测量值,通过重构算法对信号进行精确的重构。压缩感知理论应用的前提是信号的稀疏性,关键是测量矩阵和稀疏度之间的关系,重要支撑是重构算法。本文对压缩感知原理进行了简要介绍并针对雷达常用的线性调频信号提出一种稀疏基构造方案。同时,利用matlab构造了线性调频信号模型并对压缩感知处理线性调频信号的采样重建过程及应用于二维成像的过程进行了仿真。本文也研究了不同重建算法并进行了各个算法间的效果比较。仿真结果表明,在宽带雷达回波信号的处理过程中,压缩感知能通过降低采样率有效缓解回波数据的存储和传输的压力,这一点在宽带雷达目标检测中应用前景广阔。
Abstract:
High-resolution radar imaging system is widely used in both military and civil fields. Wide-band radar signals are necessary in high-resolution radar imaging, while these signals introduce high-sampling rate, high-transmission rate, large data storage and high difficulties to real time signal processing in radar system. These problems are resulted from Nyquist sampling theorem which requires the sampling rate to be more than?two times of the?bandwidth of the signal. As a consequence, searching for new signal processing and data acquisition methods is in urgent requirement. When dealing with some signals with the property of sparsity, the theory of compressed sensing which is different from the?Nyquist sampling?theorem, gets a group of numerical values through noncorrelation-measurement and the number of these measurements is much less than that of points sampled according to Nyquist sampling theorem. Then, we can reconstruct original-signal accurately by reconstruction-algorithms. The premise of using the theory of compressed sensing is the signal’s sparse property and the key to the theorem is the relationship between the measurement matrix and sparse degree. Meanwhile, the important support is the reconstruction algorithm. As we all know, response function of?radar observations of the?scene is usually?sparse?and this property leads to the?sparsity of wide-band radar echo in some?form. Based on this property, the application of?the theory of compressed sensing in radar signal processing becomes possible. In this paper, the principle of signal sampling and reconstructing according to the theory of compressed sensing has been introduced briefly and a sparse matrix structure scheme for linear frequency modulation signal (LFM) commonly used in radar is proposed with the help of the emission signal. At the same time, the LFM signal model is structured with the help of MATLAB. Then, the processes of sampling and reconstruction of LFM and 2D imaging with the theory of compressed sensing are also simulated. Besides, this paper also studied the?different reconstruction?algorithms and makes the algorithm?effectiveness comparison. The results show that during the process of broadband radar echo signal processing, the theory of compressed sensing can effectively relieve the pressure on the echo data storage and transmission through reducing sampling rate. This advantage can be widely used in wide-band radar target detection.

参考文献/References:

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备注/Memo

备注/Memo:
江苏省产学研前瞻性联合研究项目(BY2012187)
更新日期/Last Update: 2014-01-16