|本期目录/Table of Contents|

[1]苏亮又,柏业超*,张兴敢,等.基于协方差准则迭代估计的雷达回波压缩重构[J].南京大学学报(自然科学),2015,51(1):31-36.[doi:10.13232/j.cnki.jnju.2015.01.006]
 Su Liangyou,Bai Yechao,Zhang Xinggan,et al.Compression and Reconstruction of Radar Echo Based on Sparse Iterative Covariance-based Estimation[J].Journal of Nanjing University(Natural Sciences),2015,51(1):31-36.[doi:10.13232/j.cnki.jnju.2015.01.006]
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基于协方差准则迭代估计的雷达回波压缩重构()
     

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

卷:
51
期数:
2015年第1期
页码:
31-36
栏目:
出版日期:
2015-01-30

文章信息/Info

Title:
Compression and Reconstruction of Radar Echo Based on Sparse Iterative Covariance-based Estimation
作者:
苏亮又柏业超*张兴敢吴琼
(南京大学电子科学与工程学院,南京,210023)
Author(s):
Su Liangyou Bai Yechao Zhang Xinggan Wu Qiong
(School of Electronic Science and Eng7ineering, Nanjing University, Nanjing, 210023, China)
关键词:
宽带雷达压缩感知SPICE稀疏字典
Keywords:
Broadband Radar Compressive Sensing SPICE Sparse Dictionary
分类号:
-
DOI:
10.13232/j.cnki.jnju.2015.01.006
文献标志码:
-
摘要:
在雷达技术领域得到高度关注的压缩感知理论,能够有效地降低高分辨率雷达成像系统的数据率,解决雷达系统中超大数据量的采集、存储与传输问题。宽带雷达回波在信号的幅度-延时基上具有稀疏表示。基于这一特性,可以使用压缩感知理论通过降维采样大大减少数据量。针对降维采样后信号重建问题,文中研究了一种基于协方差准则循环迭代的稀疏参数估计方法(SPICE)。文中首先根据雷达回波信号的特征构造了波形延时稀疏字典,再通过随机采样对数据进行压缩,最后将SPICE作为信号重构算法引入雷达回波压缩感知处理过程中。仿真结果表明利用SPICE参数估计方法,可使得压缩率降到很小的程度,且降低重建信号相对原始信号的误差。此外,SPICE算法本身具有数据自适应特性,不需要再根据信号特征选取循环结束条件。仿真结果表明,算法具有较快的收敛速度,能够在较短的时间内准确估计出雷达回波的稀疏参数。 


Abstract:
Compressive sensing (CS) theory is highly focused in radar community over the last decade. Its incoherence measurement process can effectively reduce the data rate of high-resolution imaging radar system. Consequently, compressive sensing theory can be used to release the burden of radar system on huge amount of data sampling, storage and transmission. This paper concentrates on finding a kind of proper reconstruction algorithm when dealing with the radar echo with compressive sensing theory. The broadband radar echo has a sparse representation on the amplitude-delay sparse dictionary. Compressive sensing theory can greatly reduce the amount of data through dimension reduction sampling based on this feature. After the dimension reduction sampling, we need to find a proper algorithm to reconstruct the original signal. In order to solve this problem, a kind of sparse iterative covariance-based estimation method (SPICE) is studied in this paper. SPICE is computationally quite efficient and enjoys global convergence properties. Besides, this algorithm does not require any subtle choices of user parameters and can be used in many fields, such as spectral analysis, array processing, astronautic applications and so on. In this paper, we apply this algorithm to wide-band radar, which is widely used in both military and civil fields. During the simulation, an amplitude-delay sparse dictionary was constructed according to the characteristics of the radar echo signal waveform firstly. Then, the amount of data was compressed through random sampling. Finally, SPICE, as the signal reconstruction algorithm, was introduced into the process of the radar echo based on compressive sensing theory. The simulation results show that the usage of SPICE parameter estimation makes the degree of compression ratio very small and the relative error between reconstruction signal and original signal is also small. In addition, the SPICE algorithm itself has data adaptive features, which is an important advantage different from other kinds of algorithm. As a consequence, there is no need to select end conditions according to the signal feature. At the same time, the algorithm also converges very fast, and can estimate the sparse parameters of radar echoes in a relatively short period of time. This advantage can meet the requirement of real-time for wide-band radar target detection.

参考文献/References:

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备注/Memo

备注/Memo:
江苏省产学前瞻性联合研究(BY2012187)
更新日期/Last Update: 2015-01-04