基于块稀疏信号重构的高分辨率ISAR成像算法
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摘要
为实现快速高分辨率逆合成孔径雷达(Inverse synthetic aperture radar,ISAR)成像,充分利用目标的内在块稀疏结构信息,提出一种块平滑l_0范数稀疏重构ISAR成像算法.首先,将ISAR稀疏成像转化为块l_0范数的优化问题,采用一阶负指数函数趋近块l_0范数.其次,采用单循环步骤代替平滑l_0范数算法中的双循环结构,减小控制参数的间隔,实现对块稀疏信号的优化重构.该算法能够在块稀疏度未知时利用ISAR目标固有的内在结构特征进行高分辨率成像.仿真实验结果证实该算法的成像质量高且快于其它算法.
To achieve fast high resolution inverse synthetic aperture radar(ISAR)imaging,a block sparse signal recovery ISAR imaging algorithm based on smoothed l_0 norm is proposed by utilizing the block structure of the scatters.Firstly,the ISAR sparse imaging problem is mathematically converted into block l_0 norm sparse optimization problem,one negative exponential function sequence approaches the block l_0 norm.Then,a single loop structure is proposed to instead of the double loop layers in the smoothed l_0 algorithm to solve the sparse signal recovery problem,the interval of controlling parameter decreases,the block sparse signal can be recovery precisely.The proposed method can be applied to ISAR imaging by exploiting the underlying block sparse structures,which doesn't need the information of the number of the blocks.Simulation and real data experiments verify that the quality of the ISAR image using the algorithm of this paper is higher and the speed is faster than other algorithms.
To achieve fast high resolution inverse synthetic aperture radar(ISAR)imaging,a block sparse signal recovery ISAR imaging algorithm based on smoothed l_0 norm is proposed by utilizing the block structure of the scatters.Firstly,the ISAR sparse imaging problem is mathematically converted into block l_0 norm sparse optimization problem,one negative exponential function sequence approaches the block l_0 norm.Then,a single loop structure is proposed to instead of the double loop layers in the smoothed l_0 algorithm to solve the sparse signal recovery problem,the interval of controlling parameter decreases,the block sparse signal can be recovery precisely.The proposed method can be applied to ISAR imaging by exploiting the underlying block sparse structures,which doesn't need the information of the number of the blocks.Simulation and real data experiments verify that the quality of the ISAR image using the algorithm of this paper is higher and the speed is faster than other algorithms.
引文
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[10]DUAN H,ZHANG L,FANG J,et al.Pattern-Coupled Sparse Bayesian Learning for Inverse Synthetic Aperture Radar Imaging[J].IEEE Signal Processing Letters,2015,22(11):1995-1999.
[11]MOHIMANI H,BABAIE-ZADEH M,JUTTEN C.A Fast Approach for Overcomplete Sparse Decomposition Based on Smoothed Norm[J].IEEE Transactions on Signal Processing,2009,57(1):289-301.
[12]ELDAR Y C,KUPPINGER P,BOLCSKEI H.Block-Sparse Signals:Uncertainty Relations and Efficient Recovery[J].IEEE Transactions on Signal Processing,2010,58(6):3042-3054.
[13]BARANIUK R G,CEVHER V,DUARTE M F,et al.Model-Based Compressive Sensing[J].IEEE Transactions on Information Theory,2010,56(4):1982-2001.
[14]GHALEHJEGH H S,BABAIE-ZADEH M,JUTTEN C.Fast Block-sparse Decomposition Based on SL0[C]//Proceedings of the 9th International Conference on Latent Variable Analysis and Signal Separation:Berlin,Germany:Springer 2010:426-433.
[15]VAN DEN B,FRIEDLANDER M P.Sparse Optimization with Least-squares Constraints[J].SIAM Journal on Optimization,2011,21(4):1201-1229.
[16]WIPF D P,RAO B D.An Empirical Bayesian Strategy for Solving the Simultaneous Sparse Approximation Problem[J].IEEE Transactions on Signal Processing,2007,55(7):3704-3716.
[2] CHEN Y J,ZHANG Q,YUAN N,et al.An Adaptive ISAR Imaging Considered Task Scheduling Algorithm for MultiFunction Phased Array Radars[J].IEEE Trans on Image Process,2015,63(19):5096-5110.
[3]彭立,杨武年,黄瑾.川西高原多时相干涉雷达土地覆盖分类研究[J].西南大学学报(自然科学版),2016,38(5):125-132.
[4] CHARTRAND R.Exact Reconstruction of Sparse Signals Via Nonconvex Minimization[J].IEEE Signal Process Letters,2007,14(10):707-710.
[5] NEEDELL D,VERSHYNIN R.Uniform Uncertainty Principle and Signal Recovery Via Regularized Orthogonal Matching Pursuit[J].Foundations of Computational Mathematics,2009,9(3):317-334.
[6] ELDAR Y C,MISHALI M.Robust Recovery of Signals from A Structured Union of Subspaces[J].IEEE Transactions on Information Theory,2009,55(11):5302-5316.
[7] CETIN M,STOJANOVIC I,ONHON N,et al.Sparsity-Driven Synthetic Aperture Radar Imaging:Reconstruction,Autofocusing,Moving Targets,and Compressed Sensing[J].IEEE Signal Processing Magazine,2014,31(4):27-40.
[8] ZHAO L F,WANG L,BI G,et al.An Autofocus Technique for High-resolution Inverse Synthetic Aperture Radar Imagery[J].IEEE Transactions on Geoscience and Remote Sensing,2014,52(10):6392-6403.
[9]顾福飞,池龙,张群,等.基于压缩感知的稀疏阵列MIMO雷达成像方法[J].电子与信息学报,2011,33(10):2452-2457.
[10]DUAN H,ZHANG L,FANG J,et al.Pattern-Coupled Sparse Bayesian Learning for Inverse Synthetic Aperture Radar Imaging[J].IEEE Signal Processing Letters,2015,22(11):1995-1999.
[11]MOHIMANI H,BABAIE-ZADEH M,JUTTEN C.A Fast Approach for Overcomplete Sparse Decomposition Based on Smoothed Norm[J].IEEE Transactions on Signal Processing,2009,57(1):289-301.
[12]ELDAR Y C,KUPPINGER P,BOLCSKEI H.Block-Sparse Signals:Uncertainty Relations and Efficient Recovery[J].IEEE Transactions on Signal Processing,2010,58(6):3042-3054.
[13]BARANIUK R G,CEVHER V,DUARTE M F,et al.Model-Based Compressive Sensing[J].IEEE Transactions on Information Theory,2010,56(4):1982-2001.
[14]GHALEHJEGH H S,BABAIE-ZADEH M,JUTTEN C.Fast Block-sparse Decomposition Based on SL0[C]//Proceedings of the 9th International Conference on Latent Variable Analysis and Signal Separation:Berlin,Germany:Springer 2010:426-433.
[15]VAN DEN B,FRIEDLANDER M P.Sparse Optimization with Least-squares Constraints[J].SIAM Journal on Optimization,2011,21(4):1201-1229.
[16]WIPF D P,RAO B D.An Empirical Bayesian Strategy for Solving the Simultaneous Sparse Approximation Problem[J].IEEE Transactions on Signal Processing,2007,55(7):3704-3716.