正交压缩采样雷达偏离网格目标时延估计技术

doi:10.3969/j.issn.0372-2112.2015.12.002

电子学报 ›› 2015, Vol. 43 ›› Issue (12): 2352-2359.DOI: 10.3969/j.issn.0372-2112.2015.12.002

正交压缩采样雷达偏离网格目标时延估计技术

陈胜垚, 席峰, 刘中

南京理工大学电子工程系, 江苏南京 210094

收稿日期:2014-05-19 修回日期:2014-10-24 出版日期:2015-12-25
- 作者简介:
- 陈胜男,1985年生于安徽庐江.南京理工大学电子工程系博士后.研究方向为压缩采样、雷达信号处理.E-mail:chen_shengyao@163.com;席峰男,1980年生于江苏宜兴.南京理工大学电子工程系副教授.研究方向为压缩采样、雷达信号处理、传感器网络.
- 基金资助:
- 国家自然科学基金 (No.61171166,No.61401210); 中国博士后科学基金 (No.2014M551597); 江苏省博士后基金 (No.1302077B)

Time-Delay Estimation of Off-Grid Targets for Quadrature Compressive Sampling Radar

CHEN Sheng-yao, XI Feng, LIU Zhong

Department of Electronic Engineering, Nanjing University of Science and Technology, Nanjing, Jiangsu 210094, China

Received:2014-05-19 Revised:2014-10-24 Online:2015-12-25 Published:2015-12-25

摘要/Abstract

摘要：

参数扰动正交匹配追踪是一种有效的偏离网格目标时延估计技术.但是,该方法在每次迭代时只搜索一个目标,计算量大.本文提出一类低计算量的偏离网格目标时延估计技术——参数扰动带排除贪婪重构算法.该算法在贪婪重构方法中引入带排除技术,用于检测多个与目标最邻近的离散网格,利用参数扰动技术来估计目标与最邻近离散网格之间的时延偏差.本文以正交压缩采样雷达为例,采用回溯自适应正交匹配追踪方法,研究参数扰动带排除贪婪重构算法性能.仿真实验表明,与已有的相关方法相比,该算法在不影响估计精度的情况下可减少一倍以上的计算时间.

关键词: 压缩采样, 雷达, 时延估计, 偏离网格目标

Abstract:

Parameter-perturbed orthogonal matching pursuit is an effective technique for estimating the time-delays of off-grid targets.However,the technique consumes large computational loading because it only searches one target at each iteration.This paper develops a kind of low complexity methods,which is called parameter-perturbed band-excluded greedy reconstruction algorithms,to estimate the off-grid targets.The proposed technique combines the band-excluded technique and the greedy reconstruction methods to detect the nearest discrete grids of several off-grid targets and exploits the parameter-perturbed technique to estimate the time-delay bias between the off-grid targets and the nearest discrete grids.Taking the quadrature compressive sampling radar as an example,this paper studies the estimation performance of the proposed technique through the backtracking adaptive orthogonal matching pursuit method.Simulation results show that in comparison with other related methods,the proposed technique reduces the computational time more than one time without affecting estimation accuracy.

Key words: compressive sampling, radar, time-delay estimation, off-grid targets

中图分类号:

TN911.7

陈胜垚, 席峰, 刘中. 正交压缩采样雷达偏离网格目标时延估计技术[J]. 电子学报, 2015, 43(12): 2352-2359.

CHEN Sheng-yao, XI Feng, LIU Zhong. Time-Delay Estimation of Off-Grid Targets for Quadrature Compressive Sampling Radar[J]. Acta Electronica Sinica, 2015, 43(12): 2352-2359.

参考文献

[1] Donoho D L.Compressed sensing[J].IEEE Transactions on Information Theory,2006,52(4):1289-1306.
[2] Candés E J,Tao T.Near-optimal signal recovery from random projections:universal encoding strategies?[J].IEEE Transactions on Information Theory,2006,52(12):5406-5425.
[3] Laska J N,Kirolos S,et al.Random sampling for analog-to-information conversion of wideband signals[A].IEEE Dallas/CAS Workshop on Design,Applications,Integration and Software[C].Dallas:IEEE,2006.119-122.
[4] Tropp J A,Wakin M B,et al.Random filters for compressive sampling and reconstruction[A].IEEE International Conference on Acoustics,Speech,and Signal Processing (ICASSP)[C].Toulouse:IEEE,2006.872-875.
[5] Tropp J A,Laska J N,et al.Beyond Nyquist:efficient sampling of sparse bandlimited signals[J].IEEE Transaction on Information Theory,2010,58(1):520-544.
[6] Mishali M,Eldar Y.From theory to practice:Sub-Nyquist sampling of sparse wideband analog signals[J].IEEE Journal of Selected Topics on Signal Processing,2010,4(2):375-391.
[7] Xi F,Chen S Y,et al.Quadrature compressive sampling for radar signals[J].IEEE Transactions on Signal Processing,2014,62(11):2787-2802.
[8] Liu C,Xi F,et al.Pulse-Doppler signal processing with quadrature compressive sampling[J].IEEE Transactions on Aerospace and Electronic Systems,2015,5(2):1216-1230.
[9] HermanM A,Strohmer T.High resolution radar via compressed sensing[J].IEEE Transactions on Signal Processing,2009,57(6):2275-2284.
[10] Zhang J D,Zhu D Y,et al.Adaptive compressed sensing radar oriented toward cognitive detection in dynamic sparse target scene[J].IEEE Transactions on Signal Processing,2012,60(4):1718-1729.
[11] Chi Y J,Scharf L L,et al.Sensitivity to basis mismatch in compressed sensing[J].IEEE Transactions on Signal Processing,2011,59(5):2182-2195.
[12] Duarte M F,Baraniuk R G.Spectral compressive sensing[J].Applied and Computational Harmonic Analysis,2013,35(1):111-129.
[13] Fannjiang A,Liao W J.Coherence pattern-guided compressive sensing with unresolved grids[J].SIAM Journal on Imaging Sciences,2012,5(1):179-202.
[14] Ekanadham C,Tranchina D,et al.Recovery of sparse translation-invariant signals with continuous basis pursuit[J].IEEE Transactions on Signal Processing,2011,59(10):4735-4744.
[15] Fyhn K,Jensen S H,et al.Compressive time delay estimation using interpolation[A].IEEE Global Conference on Signal and Information Processing (GlobalSIP)[C].Austin:IEEE,2013.624-624.
[16] Tekea O,Gurbuzb A C,et al.A robust compressive sensing based technique for reconstruction of sparse radar scenes[J].Digital Signal Processing,2014,27(1):23-32.
[17] Tekea O,Gurbuzb A C,et al.Perturbed orthogonal matching pursuit[J].IEEE Transactions on Signal Processing,2013,61(24):6220-6231.
[18] Huang H L,Makur A.Backtracking-based matching pursuit method for sparse signal reconstruction[J].IEEE Signal Processing Letters,2011,18(7):391-394.
[19] Needell D,Tropp J A.CoSaMP:Iterative signal recovery from incomplete and inaccurate samples[J].Applied and Computational Harmonic Analysis,2009,26(3):301-321.
[20] Dai W,Milenkovic O.Subspace pursuit for compressive sensing signal reconstruction[J].IEEE Transactions on Information Theory,2009,55(5):2230-2249.
[21] Bertsekas D P.Nonlinear Programming[M].Second Edition.Belmont:Athena Scientific,1999.

正交压缩采样雷达偏离网格目标时延估计技术

Time-Delay Estimation of Off-Grid Targets for Quadrature Compressive Sampling Radar

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