XIA Chaoyu,GAO Yuxiang,XIE Jianfeng,et al.Generalized Orthogonal Matching Pursuit CS Radar Imaging based on Constraints[J].Journal of Chengdu University of Information Technology,2020,35(04):400-405.[doi:10.16836/j.cnki.jcuit.2020.04.006]
基于约束条件的广义正交匹配追踪CS雷达成像算法
- Title:
- Generalized Orthogonal Matching Pursuit CS Radar Imaging based on Constraints
- 文章编号:
- 2096-1618(2020)04-0400-06
- Keywords:
- radar imaging; compressed sensing; noise suppression; generalized orthogonal matching pursuit
- 分类号:
- TN951
- 文献标志码:
- A
- 摘要:
- 强高斯噪声破坏了成像区域的稀疏性,造成传统压缩感知(CS)雷达B-scan像中出现若干虚假目标。针对以上问题提出一种基于约束条件的广义正交匹配追踪(C-gOMP)改进算法,可以显著提高CS雷达在强高斯杂波背景下的成像性能。首先,该算法将回波数据进行贪婪迭代; 然后,使用代价函数对迭代后的系数施加更深层次的约束以保证整个函数的收敛性,即在重构过程中针对高斯分量进行抑制。仿真结果表明,在相同实验条件下,C-gOMP获得的距离向分辨率为传统匹配滤波法的2倍。在SNR为0 dB时,成像成功率比gOMP高出20%,得到的二维B-scan像的MFML系数约为gOMP的2倍
- Abstract:
- Gaussian noise will destroy the sparsity of the imaging region, which causes the traditional compressed sensing(CS)radar b-scan image to produce some fake targets. Aiming at the above problems, an improved generalized orthogonal matching pursuit(C-gOMP)algorithm based on constraints condition is proposed in this paper, which can significantly improve the imaging performance of CS radar under the background of strong Gaussian clutter. Firstly, this arithmetic carry out greedy iteration of echo data, then, the cost function is used to impose a deeper constraint on the iterated coefficients to ensure the convergence of the whole function. Namely, Gauss components are restrained during signal reconstruction. The simulation results show that under the same experimental conditions, the range resolution of the C-gOMP algorithm is twice than that of the traditional matched filtering method. When SNR=0 dB, the positioning success rate is up to 20% higher than that of the gOMP algorithm, and the image coefficients ofMFML obtained by C-gOMP are about twice than that of gOMP algorithm
参考文献/References:
[1] Donoho D L.Compressed sensing [J].IEEE Transactions on information theory,2006,52(4):1289-1306.
[2] Tropp J A,Gilbert A C.Signal recovery from random measurements via orthogonal matching pursuit[J].IEEE Transactions on information theory,2007,53(12):4655-4666.
[3] Liu J,Han C Z,Yao X H,et al.Compressed sensing based track before detect algorithm for airborne radars[J].Progress In Electromagnetics Research,2013,138:433-451.
[4] Gribonval R,Vandergheynst P.On the exponential convergence of matching pursuits in quasi-incoherent dictionaries [J].IEEE Transactions on Information Theory,2005,52(1):255-261.
[5] Blumensath T,Davies M E.Iterative hard thresholding for compressed sensing[J].Applied & Computational HarmonicAnalysis,2008,27(3):265-274.
[6] Xia C Y,Gao Y X,Yu J,et al.Block-sparse signal recovery based on orthogonal matching pursuit via stage-wise weak selection[J].Signal,Image and Video Processing,2019:1-9.
[7] Ji S,Xue Y,Carin L.Bayesian compressive sensing.IEEE Transactions on signal processing,2008,56(6),2346.
[8] Kwon S,Wang J,Shim B.Multipath matching pursuit[J].IEEE Transactions on Information Theory,2014,60(5):2986-3001.
[9] Sturm B L,Christensen M G.Comparison of orthogonal matching pursuit implementations[C].2012 Proceedings of the 20th European Signal Processing Conference(EUSIPCO).Bucharest:IEEE,2012:220-224.
[10] Jun L,Mengdao X,Shunjun W. Application of compressed sensing in sparse aperture imaging of radar[C].2009 2nd Asian-Pacific Conference on Synthetic Aperture Radar.Shanxi:IEEE,2009:651-655.
[11] Wang J,Kwon S,Shim B. Generalized orthogonal matching pursuit[J].IEEE Transactions on signal processing,2012,60(12):6202-6216.
[12] Yan H,Xu J,Zhang X.Compressed sensing radar imaging of off-grid sparse targets[C].2015 IEEE Radar Conference(RadarCon).Arlington:EEE, 2015:690-693.
[13] Li Y,Lin C,Huang P.An improved OMP method based on memory effect and its application[J].2015.
[14] Li B,Petropulu A.RIP analysis of the measurement matrix for compressive sensing-based MIMO radars[C].2014 IEEE 8th Sensor Array and Multichannel Signal Processing Workshop(SAM).Coruna:IEEE,2014:497-500.
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备注/Memo
收稿日期:2019-11- 08
基金项目:四川省教育厅重点资助项目(2017Z032)