基于连续稀疏恢复循环平稳信号的DOA估计

针对现有稀疏恢复算法在到达角(DOA)估计中存在的网格失配问题（即off-grid问题）,提出基于连续稀疏恢复循环平稳信号的DOA估计。首先,对传统的谱相关信号子空间拟合算法进行分析研究;然后,在循环域利用连续稀疏恢复的思想来构造循环平稳信号的稀疏恢复模型。与传统Cyclic MUSIC算法和现有基于离散稀疏恢复算法相比,文中算法能够克服off-grid问题,具有较高的稀疏恢复精度和较好的稀疏恢复性能;同时,也适用于信号个数多于阵元个数的场合。理论分析和仿真实验证明了算法的有效性。     

低信噪比下双基地ISAR一维距离成像分辨率增强方法

针对双基地角使双基地ISAR(B-ISAR) 1维距离成像分辨率下降的问题,该文提出基于多量测向量(MMV)模型的复数近似消息传递(MCAMP)的B-ISAR 1维距离成像分辨率增强算法。首先,建立距离联合稀疏模型。然后,通过向量化处理将联合稀疏问题转换为块稀疏复数基追踪去噪问题,利用Kronecker积提出MCAMP算法进行求解,以得到不受双基地角影响的1维距离像。最后,通过快速傅里叶变换(FFT)代替矩阵与矩阵相乘进一步减少了计算复杂度,进而提高了算法的实现效率。仿真成像结果验证了所提方法在重构精度和重构时间方面的优势。     

基于协方差矩阵重构的离网格DOA估计方法

针对稀疏表示模型中网格失配导致波达方向角(DOA)估计存在较大估计误差的问题,该文提出一种基于协方差矩阵重构的离网格(Off-Grid)DOA估计方法(OGCMR)。首先,将DOA与网格点之间偏移量包含进所构建接收数据空域离散稀疏表示模型;而后基于重构信号协方差矩阵建立关于DOA估计的稀疏表示凸优化问题;再构建采样协方差矩阵估计误差凸模型,并将此凸集显式包含进稀疏表示模型以改善稀疏信号重构性能;最后采用交替迭代方法求解所得联合优化问题以获得网格偏移参数及离网格DOA估计。数值仿真表明,与传统多重信号分类(MUSIC)、L1-SVD及基于稀疏和低秩恢复的稳健MVDR (SLRD-RMVDR)等估计算法相比,所提算法具有较好的角度分辨力以及较高的DOA估计精度。     

基于压缩感知的多光谱图像去马赛克算法

针对目前多光谱图像去马赛克算法存在计算量大、效率低的缺点,本文提出一种基于压缩感知的多光谱图像去马赛克算法。首先,分析去马赛克与压缩感知问题的等价性,建立基于压缩感知的去马赛克模型;然后,采用离散余弦变换构建压缩感知的稀疏基,将去马赛克问题转化为压缩感知的信号重构问题;最后,采用改进的光滑0范数和修正牛顿法的重构算法求解去马赛克问题,得到重构的多光谱图像。仿真实验表明,相对于基于克罗内克压缩感知和组稀疏两种算法,本文算法提高了重构的多光谱图像的峰值信噪比,能有效减少对比算法重构多光谱图像中出现的锯齿现象,改善了重构图像具有更好的视觉效果。实验结果验证了本文算法的有效性。     

InSAR通道联合稀疏贝叶斯特征化成像

针对干涉合成孔径雷达(InSAR)成像,该文提出了一种通道联合结构化稀疏的贝叶斯成像算法,可实现图像稀疏特征化增强,以提升干涉相位噪声滤波和相干斑抑制性能。基于贝叶斯准则,利用多层级统计模型建立稀疏成像模型,结构化稀疏表示InSAR图像。在稀疏成像求解中,利用最大期望(EM)算法进行图像重构和多层级统计参数估计。由于能够联合利用通道稀疏统计特性,所提算法能够有效提升InSAR幅度和相位噪声滤波性能。最后,通过实验分析进一步验证该文算法的有效性。      

基于DCS 的统计MIMO 雷达信号模型及参数估计

分布式压缩感知(Distributed Compressed Sensing, DCS)将单信号的压缩采样扩展到信号群的压缩采样,利用信号内相关性和互相关性对多个信号进行联合重构。统计多输入多输出(Multiple-Input Multiple-Output,MIMO)雷达系统通过多发多收配置,在发射机、目标以及接收机之间构成对目标的分布式探测系统。该文将DCS应用到统计MIMO 雷达中,通过对该场景中目标回波的延时在距离空间稀疏性的分析,提出联合所有接收信号重构目标场景的设想,建立了接收信号的联合稀疏模型,并实现了目标参数估计的联合重构算法。仿真结果表明与基于压缩感知(Compressed Sensing, CS)的算法相比,基于DCS 的算法在进一步降低采样数目的同时提高了参数估计精度,同时也验证了DCS-MIMO 雷达可以有效克服目标的雷达散射截面积(Radar Cross Section, RCS)起伏。      

基于协方差矩阵重构的离网格DOA估计方法

针对稀疏表示模型中网格失配导致波达方向角(DOA)估计存在较大估计误差的问题,该文提出一种基于协方差矩阵重构的离网格(Off-Grid)DOA估计方法(OGCMR).首先,将DOA与网格点之间偏移量包含进所构建接收数据空域离散稀疏表示模型;而后基于重构信号协方差矩阵建立关于DOA估计的稀疏表示凸优化问题;再构建采样协方差矩阵估计误差凸模型,并将此凸集显式包含进稀疏表示模型以改善稀疏信号重构性能;最后采用交替迭代方法求解所得联合优化问题以获得网格偏移参数及离网格DOA估计.数值仿真表明,与传统多重信号分类(MUSIC)、L1-SVD及基于稀疏和低秩恢复的稳健MVDR(SLRD-RMVDR)等估计算法相比,所提算法具有较好的角度分辨力以及较高的DOA估计精度.     

任意稀疏结构的复稀疏信号快速重构算法及其逆合成孔径雷达成像

为同时提高压缩感知(CS)中任意稀疏结构多量测向 量(MMV)模型的重构性能和重构速度,提出基于改进线性Bergman 迭代(LBI)的任意稀疏 结构MMV模型重构算法。首先给出了任意稀疏结构MMV模型,并对模型进行了分析,利用LBI 实现MMV模型的二维重构;然后,通过 设置预条件子的方 法对感知矩阵的条件数进行优化,以通过提高收敛速度而提高重构速度,并从理论和仿真两 个方面对算法 的收敛性和运算量进行了分析;最后通过仿真结果表明,本文算法能够高质量地重构任意稀 疏结构MMV模型, 同时在重构速度方面具有明显的优势。基于实测数据不同信噪比(SNR)条件下的逆合成孔径雷达(ISAR)成像结果,验证了算法的有效性。     

联合感知矩阵优化的穿墙MIMO阵列稀疏成像方法

采用压缩感知理论的穿墙稀疏成像恢复算法需要感知矩阵满足有限等距性质(RIP),最直接的验证方法是判定感知矩阵的相干系数是否较小。针对现有的穿墙多输入多输出(MIMO)阵列稀疏成像方法没有验证感知矩阵是否满足RIP 性质而容易出现重构失败并导致成像模糊的问题,提出一种联合感知矩阵优化的穿墙MIMO阵列稀疏成像方法。该方法首先依据配置指标将阵元配置为两端发中间收和分时复用的模式,既能使感知矩阵的相干系数最小,又能获得均匀而不冗余的等效虚拟阵元;然后,从中选取部分能够满足感知矩阵相干系数最小的虚拟阵元组合,使用可分离逼近结构稀疏恢复算法充分考虑扩展目标信号的结构稀疏先验信息对其进行稀疏成像重构;最后,选取成像性能指标较好的一组作为成像结果。仿真和实验结果表明,该方法降低了运算量和虚拟阵元间的干扰,节约了硬件成本,提高了算法的稀疏重构性能,获得了高分辨的穿墙扩展目标成像。     

基于稀疏表示的多输入多输出雷达多目标定位

针对双基地多输入多输出(Multiple-Input Multiple-Output,MIMO)雷达目标定位问题,提出一种基于稀疏表示的双基地MIMO雷达多目标定位方法.利用点目标所在的二维角度空间构造冗余字典; 通过对接收信号的协方差矩阵进行特征分解,从中选取不同数目的特征向量在该冗余字典下稀疏表示,构建以特征向量为观测信号的多重测量向量(Multiple Measurement Vectors,MMV)模型,提取的特征向量在充分包含目标的角度信息的前提下,降低了直接以接收信号为观测信号的矩阵维数,形成低维稀疏线性模型; 最后,通过特征向量的稀疏重构,得到目标的角度估计.与现有算法相比,该算法对特征向量的稀疏重构降低了重构原始接受信号的计算复杂度,且在低信噪比和低快拍下仍有较好的估计性能,仿真实验验证了算法的有效性.     

基于分布式压缩感知的宽带欠定信号DOA估计

为解决基于稀疏阵列的宽带欠定信号到达角(DOA)估计问题,该文提出基于分布式压缩感知(DCS)的宽带DOA估计算法。首先,对稀疏阵列宽带信号处理模型进行理论推导与分析,将宽带信号DOA估计建模成DCS问题;其次,利用经典DCS算法实现稀疏阵列上的宽带欠定信号DOA估计;最后,引入网格失配误差,建立包含网格失配参数的DCS模型,并进行迭代求解,实现对DOA和网格失配参数的联合估计。仿真结果表明,该算法能够实现宽带欠定信号DOA估计,较现有成果而言,在保证测向精度的同时,具备分辨率高、运算速度快的优点。     

Fast super-resolution estimation of DOA and DOD in bistatic MIMO Radar with off-grid targets

In this paper, we focus on the problem of joint DOA and DOD estimation in Bistatic MIMO Radar using sparse reconstruction method. In traditional ways, we usually convert the 2D parameter estimation problem into 1D parameter estimation problem by Kronecker product which will enlarge the scale of the parameter estimation problem and bring more computational burden. Furthermore, it requires that the targets must fall on the predefined grids. In this paper, a 2D-off-grid model is built which can solve the grid mismatch problem of 2D parameters estimation. Then in order to solve the joint 2D sparse reconstruction problem directly and efficiently, three kinds of fast joint sparse matrix reconstruction methods are proposed which are Joint-2D-OMP algorithm, Joint-2D-SL0 algorithm and Joint-2D-SOONE algorithm. Simulation results demonstrate that our methods not only can improve the 2D parameter estimation accuracy but also reduce the computational complexity compared with the traditional Kronecker Compressed Sensing method.     

协方差矩阵稀疏表示在网格失配波达方向估计中的应用

该文利用了入射信号在空域的稀疏性,将波达方向（DOA）估计问题描述为在网格划分的空间协方差矩阵稀疏表示模型,并将其松弛为一个凸问题,从而提出了一种网格匹配下的交替迭代方法（AIEGM）。传统的基于稀疏重构的波达方向估计算法由于其模型的局限性,一旦入射角不在预先设定的离散化网格上,就会造成估计性能的急剧恶化。针对这个问题,该算法可以在离散化网格比较粗糙的前提下,通过交替迭代的方法求解一系列基追踪去噪（BPDN）问题,对于不在网格上的真实角度估计值进行修正,从而达到更精确的波达方向估计。仿真结果证明了AIEGM算法的有效性。      

Off-grid direction-of-arrival estimation for wideband noncircular sources

Researchers have recently shown an increased interest in estimating the direction-of-arrival (DOA) of wideband noncircular sources, but existing studies have been restricted to subspace-based methods. An off-grid sparse recovery-based algorithm is proposed in this paper to improve the accuracy of existing algorithms in low signal-to-noise ratio situations. The covariance and pseudo covariance matrices can be jointly represented subject to block sparsity constraints by taking advantage of the joint sparsity between signal components and bias. Furthermore, the estimation problem is transformed into a single measurement vector problem utilizing the focused operation, resulting in a significant reduction in computational complexity. The proposed algorithm's error threshold and the Cramer–Rao bound for wideband noncircular DOA estimation are deduced in detail. The proposed algorithm's effectiveness and feasibility are demonstrated by simulation results.     

Off-grid DOA estimation algorithm based on unitary transform and sparse Bayesian learning

A rapid off-grid DOA estimating method of RV-OGSBL was raised based on unitary transformation,against the problem of traditional sparse Bayesian learning (SBL) algorithm in solving effectiveness of signal’s DOA estimation under condition of lower signal noise ratio (SNR).Actual received signal of uniform linear array was generated through constructing augment matrix as the processing signal used by DOA estimation.Then,estimation model was transformed from complex value to real value by using unitary transformation.In the next step,off-grid model and sparse Bayesian learning algorithm were combined together to process the realization of DOA estimation iteratively.The accuracy of estimation could made relatively high.The simulation result demonstrates that the RV-OGSBL method not only maintains the performance of traditional SBL algorithm,but also reduces the computational complexity significantly.Under the situation of lower signal noise ratio (SNR) and low number of snapshots,the running time of algorithm is reduced about 50%.This shows the RV-OGSBL method is a rapid DOA estimation algorithm.     

Video compressed sensing reconstruction based on structural group sparsity and successive approximation estimation model

The existing video compressed sensing (CS) algorithms for inconsistent sampling ignore the joint correlations of video signals in space and time, and their reconstruction quality and speed need further improvement. To balance reconstruction quality with computational complexity, we introduce a structural group sparsity model for use in the initial reconstruction phase and propose a weight-based group sparse optimization algorithm acting in joint domains. Then, a coarse-to-fine optical flow estimation model with successive approximation is introduced for use in the interframe prediction stage to recover non-key frames through alternating optical flow estimation and residual sparse reconstruction. Experimental results show that, compared with the existing algorithms, the proposed algorithm achieves a peak signal-to-noise ratio gain of 1–3 dB and a multi-scale structural similarity gain of 0.01–0.03 at a low time complexity, and the reconstructed frames not only have good edge contours but also retain textural details.     

低信噪比条件下宽带欠定信号高精度DOA估计

为提高低信噪比条件下宽带欠定信号DOA估计精度,该文提出基于网格失配迭代最小化稀疏学习的宽带DOA估计方法。该方法首先对频域协方差矩阵进行矢量化处理实现虚拟阵列扩展,将欠定信号转换为超定信号。其次利用线性变换滤除含有噪声项的虚拟阵元,并对协方差估计误差进行了白化处理,抑制了信号中的干扰项。最后建立了包含不同频点联合稀疏参数和网格失配参数的贝叶斯层次架构,推导了联合稀疏参数、网格失配参数的最小稀疏表达式并进行了迭代学习。较传统方法,该方法不依赖任何先验信息,更好地抑制了虚拟阵元中的噪声和干扰,降低了网格失配对DOA估计的影响,在低信噪比条件下具有更高的DOA估计精度和分辨率。仿真实验验证了该方法的有效性。     

Joint frequency and DOA estimation using spatial-temporal co-prime sampling with off-grid sources

A method of joint frequency and direction of arrival (DOA) estimation using spatial-temporal co-prime sam-pling with off-grid sources was proposed to solve the problem of two-dimensional (2D) mismatches of signals.Firstly,the received signal was sampled by spatio-temporal co-prime sampling technique to construct 2D sparse reconstruction model with the filled coarray.Then the frequency mismatch term and angle mismatch term were incorporated into the re-ceived data model.Based on the above modified model,an improved greedy algorithm was presented to obtain the fre-quency mismatch and angle mismatch by estimating the spectrum leakage terms jointly.So the 2D off-grid sources can be corrected to accurate positions.The method can increase the degrees of freedom (DOF) in the space-frequency domain.Moreover,the method has reduced the amount of computation,and it can improve the frequency and DOA estimation accuracy in the meantime.Simulation results demonstrate the correctness and effectiveness of the proposed approach.     

Off-Grid DOA Estimation Via Real-Valued Sparse Bayesian Method in Compressed Sensing

A novel real-valued sparse Bayesian method for the off-grid direction-of-arrival (DOA) estimation is proposed in compressed sensing (CS). The off-grid model is reformulated by the second-order Taylor expansion to reduce modeling error caused by mismatch. To apply the Bayesian perspective in CS conveniently, complex data are addressed to yield a real-valued problem by utilizing a unitary transformation. By assuming that sources among snapshots are independent and share the same sparse prior, joint sparsity is exploited for DOA estimation. Specifically, a full posterior density function can be provided in the Bayesian framework. The convergence rate and convergence stability of the proposed method can be guaranteed in the iterative procedure. Simulation results show superior performance of the proposed method as compared with existing methods.