DOA estimation method for wideband signals by sparse recovery in frequency domain

The traditional super-resolution direction finding methods based on sparse recovery need to divide the estimation space into several discrete angle grids, which will bring the final result some error. To this problem, a novel method for wideband signals by sparse recovery in the frequency domain is proposed. The optimization functions are found and solved by the received data at every frequency, on this basis, the sparse support set is obtained, then the direction of arrival (DOA) is acquired by integrating the information of all frequency bins, and the initial signal can also be recovered. This method avoids the error caused by sparse recovery methods based on grid division, and the degree of freedom is also expanded by array transformation, especially it has a preferable performance under the circumstances of a small number of snapshots and a low signal to noise ratio (SNR).

New regularization method and iteratively reweighted algorithm for sparse vector recovery

Motivated by the study of regularization for sparse problems,we propose a new regularization method for sparse vector recovery.We derive sufficient conditions on the well-posedness of the new regularization,and design an iterative algorithm,namely the iteratively reweighted algorithm(IR-algorithm),for efficiently computing the sparse solutions to the proposed regularization model.The convergence of the IR-algorithm and the setting of the regularization parameters are analyzed at length.Finally,we present numerical examples to illustrate the features of the new regularization and algorithm.

DIRECTION-OF-ARRIVAL ESTIMATION IN THE PRESENCE OF MUTUAL COUPLING BASED ON JOINT SPARSE RECOVERY

A novel Direction-Of-Arrival (DOA) estimation method is proposed in the presence of mutual coupling using the joint sparse recovery. In the proposed method, the eigenvector corresponding to the maximum eigenvalue of covariance matrix of array measurement is viewed as the signal to be represented. By exploiting the geometrical property in steering vectors and the symmetric Toeplitz structure of Mutual Coupling Matrix (MCM), the redundant dictionaries containing the DOA information are constructed. Consequently, the optimization model based on joint sparse recovery is built and then is solved through Second Order Cone Program (SOCP) and Interior Point Method (IPM). The DOA estimates are gotten according to the positions of nonzeros elements. At last, computer simulations demonstrate the excellent performance of the proposed method.

Proximity point algorithm for low-rank matrix recovery from sparse noise corrupted data

The method of recovering a low-rank matrix with an unknown fraction whose entries are arbitrarily corrupted is known as the robust principal component analysis （RPCA）. This RPCA problem, under some conditions, can be exactly solved via convex optimization by minimizing a combination of the nuclear norm and the 11 norm. In this paper, an algorithm based on the Douglas-Rachford splitting method is proposed for solving the RPCA problem. First, the convex optimization problem is solved by canceling the constraint of the variables, and ~hen the proximity operators of the objective function are computed alternately. The new algorithm can exactly recover the low-rank and sparse components simultaneously, and it is proved to be convergent. Numerical simulations demonstrate the practical utility of the proposed algorithm.

Sparse Recovery of Linear Time-Varying Channel in OFDM System

In order to improve the performance of linear time-varying（LTV）channel estimation,based on the sparsity of channel taps in time domain,a sparse recovery method of LTV channel in orthogonal frequency division multiplexing（OFDM）system is proposed.Firstly,based on the compressive sensing theory,the average of the channel taps over one symbol duration in the LTV channel model is estimated.Secondly,in order to deal with the inter-carrier interference（ICI）,the group-pilot design criterion is used based on the minimization of mutual coherence of the measurement.Finally,an efficient pilot pattern optimization algorithm is proposed by a dual layer loops iteration.The simulation results show that the new method uses less pilots,has a smaller bit error ratio（BER）,and greater ability to deal with Doppler frequency shift than the traditional method does.

A NEW SUFFICIENT CONDITION FOR SPARSE RECOVERY WITH MULTIPLE ORTHOGONAL LEAST SQUARES

A greedy algorithm used for the recovery of sparse signals,multiple orthogonal least squares(MOLS)have recently attracted quite a big of attention.In this paper,we consider the number of iterations required for the MOLS algorithm for recovery of a K-sparse signal x∈R^(n).We show that MOLS provides stable reconstruction of all K-sparse signals x from y=Ax+w in|6K/ M|iterations when the matrix A satisfies the restricted isometry property(RIP)with isometry constantδ_(7K)≤0.094.Compared with the existing results,our sufficient condition is not related to the sparsity level K.

Pulse Signal Recovery Method Based on Sparse Representation

Pulse signal recovery is to extract useful amplitude and time information from the pulse signal contaminated by noise. It is a great challenge to precisely recover the pulse signal in loud background noise. The conventional approaches,which are mostly based on the distribution of the pulse energy spectrum,do not well determine the locations and shapes of the pulses. In this paper,we propose a time domain method to reconstruct pulse signals. In the proposed approach,a sparse representation model is established to deal with the issue of the pulse signal recovery under noise conditions. The corresponding problem based on the sparse optimization model is solved by a matching pursuit algorithm. Simulations and experiments validate the effectiveness of the proposed approach on pulse signal recovery.

A fast decoupled ISAR high-resolution imaging method using structural sparse information under low SNR

Inverse synthetic aperture radar (ISAR) image can be represented and reconstructed by sparse recovery (SR) approaches. However, the existing SR algorithms, which are used for ISAR imaging, have suffered from high computational cost and poor imaging quality under a low signal to noise ratio (SNR) condition. This paper proposes a fast decoupled ISAR imaging method by exploiting the inherent structural sparse information of the targets. Firstly, the ISAR imaging problem is decoupled into two sub-problems. One is range direction imaging and the other is azimuth direction focusing. Secondly, an efficient two-stage SR method is proposed to obtain higher resolution range profiles by using jointly sparse information. Finally, the residual linear Bregman iteration via fast Fourier transforms (RLBI-FFT) is proposed to perform the azimuth focusing on low SNR efficiently. Theoretical analysis and simulation results show that the proposed method has better performence to efficiently implement higher-resolution ISAR imaging under the low SNR condition.

SPARSE RECOVERY BASED ON THE GENERALIZED ERROR FUNCTION

In this paper,we offer a new sparse recovery strategy based on the generalized error function.The introduced penalty function involves both the shape and the scale parameters,making it extremely flexible.For both constrained and unconstrained models,the theoretical analysis results in terms of the null space property,the spherical section property and the restricted invertibility factor are established.The practical algorithms via both the iteratively reweighted■_(1)and the difference of convex functions algorithms are presented.Numerical experiments are carried out to demonstrate the benefits of the suggested approach in a variety of circumstances.Its practical application in magnetic resonance imaging(MRI)reconstruction is also investigated.

Fast image super-resolution algorithm based on multi-resolution dictionary learning and sparse representation

Sparse representation has attracted extensive attention and performed well on image super-resolution（SR） in the last decade. However, many current image SR methods face the contradiction of detail recovery and artifact suppression. We propose a multi-resolution dictionary learning（MRDL） model to solve this contradiction, and give a fast single image SR method based on the MRDL model. To obtain the MRDL model, we first extract multi-scale patches by using our proposed adaptive patch partition method（APPM）. The APPM divides images into patches of different sizes according to their detail richness. Then, the multiresolution dictionary pairs, which contain structural primitives of various resolutions, can be trained from these multi-scale patches.Owing to the MRDL strategy, our SR algorithm not only recovers details well, with less jag and noise, but also significantly improves the computational efficiency. Experimental results validate that our algorithm performs better than other SR methods in evaluation metrics and visual perception.

Truncated sparse approximation property and truncated q-norm minimization

This paper considers approximately sparse signal and low-rank matrix’s recovery via truncated norm minimization minx∥xT∥q and minX∥XT∥Sq from noisy measurements.We first introduce truncated sparse approximation property,a more general robust null space property,and establish the stable recovery of signals and matrices under the truncated sparse approximation property.We also explore the relationship between the restricted isometry property and truncated sparse approximation property.And we also prove that if a measurement matrix A or linear map A satisfies truncated sparse approximation property of order k,then the first inequality in restricted isometry property of order k and of order 2k can hold for certain different constantsδk andδ2k,respectively.Last,we show that ifδs(k+|T^c|)<√(s-1)/s for some s≥4/3,then measurement matrix A and linear map A satisfy truncated sparse approximation property of order k.It should be pointed out that when Tc=Ф,our conclusion implies that sparse approximation property of order k is weaker than restricted isometry property of order sk.

Human Mouth-State Recognition Based on Image Warping and Sparse Representation Combined with Homotopy

It is often necessary to recognize human mouth-states for detecting the number of audio sources and improving the speech recognition capability of an intelligent robot auditory system. A human mouth-state recognition method based on image warping and sparse representation( SR) combined with homotopy is proposed.Using properly warped training mouth-state images as atoms of the overcomplete dictionary overcomes the impact of the diversity of the mouths' scales,shapes and positions so that further improvement of the robustness can be achieved and the requirement for a large number of training samples can be relieved. The homotopy method is employed to compute the expansion coefficients effectively,i. e.,for sparse coding. The orthogonal matching pursuit( OMP) is also tested and compared with the homototy method. Experimental results and comparisons with the state-of-the-art methods have proved the effectiveness of the proposed approach.

树脂柱串联法分离地质样品中Sr-Nd-U

Sr、Nd、U等同位素体系被广泛应用于地球表生过程中年代测定及物源示踪等研究,高效地分离这些同位素体系,对于推广这些同位素方法的应用具有重要现实意义。若要同时分析地质样品中Sr、Nd、U三种元素的同位素,现有方法往往需要消解两份样品,一份用于Sr-Nd而另一份用于U的分离提纯。这种方法不但增加了样品用量,而且需要多次蒸干溶液转换介质,既延长了分离流程也增加了样品被污染的风险。为了提高样品利用率和分析效率,本文通过将树脂柱串联改进了分离流程,提出一种仅需消解一份样品,便可同时提取Sr、Nd、U三种元素的新方法。本方法中Sr的分离采用Sr特效树脂,包含Nd在内的稀土元素(REE)的分离采用AG50W-X8树脂,U的分离采用UTEVA特效树脂。实验中将三种树脂柱串联,采用3mol/L硝酸淋洗液淋洗,同步进行平衡树脂、上样、洗杂志,避免了蒸干操作。分离后的淋出液使用电感耦合等离子体质谱仪(ICP-MS)测试元素含量。结果表明:U的回收率接近99.9%,Sr的回收率超过90%,Nd的回收率超过80%;同时三种树脂柱串联的分离流程,主要基体元素(K、Ca、Na、Ba、Fe、Rb等)的去除率均超过99%,降低了对Sr、Nd、U高精度同位素分析的干扰;REE中的Sm则可以通过后续使用Ln树脂等进一步去除。此外,本文还交换了Sr特效树脂和UTEVA树脂的位置,比对两种不同串联顺序对分离结果的影响,结果表明两种树脂柱串联顺序对目标元素的分离并无显著影响。使用该方法可以有效地实现Sr、Nd、U的分离,在减少操作步骤的同时节省约一半的样品用量,提高了同位素分析效率。

Multi-static InISAR imaging for ships under sparse aperture

This paper concentrates on super-resolution imaging of the ship target under the sparse aperture situation.Firstly,a multi-static configuration is utilized to solve the coherent processing interval(CPI)problem caused by the slow-speed motion of ship targets.Then,we realize signal restoration and image reconstruction with the alternating direction method of multipliers(ADMM).Furthermore,we adopt the interferometric technique to produce the three-dimensional(3D)images of ship targets,namely interferometric inverse synthetic aperture radar(InISAR)imaging.Experiments based on the simulated data are utilized to verify the validity of the proposed method.

Hollow Fiber Supported Liquid Membrane for Separation and Recovery of ^152+154Eu and ⁹⁰Sr from Aqueous Acidic Wastes

Separation and recovery of 152+154Eu and 90Sr from radioactive waste using tracer concentration from active material from waste tank in the ET-RR1 Egypt via hollow fiber supported liquid membrane (HFSLM) were achieved. The Polypropylene was used as supporter to carrier 0.5M Cyanex301/kerosene (bis(2,4,4-trimethylpentyl)dithiophosphinic acid and 0.1MEDTA as stripping of 152+154Eu and 90Sr ions from nitrate medium at pH ~3.6. The separation factor was found to be ~4 for 152+154Eu over 90Sr. The aqueous feed of mass transfer coefficient (ki) and the organic mass transfer coefficient (km) were calculated to be (1.52 and 4.5) × 10﹣2cm/s, respectively. In addition, the mass transfer modeling was performed and the validity of the developed model from experimental data was found to join in well with the theoretical values when the Cyanex301 concentration is higher than 1% (v/v). The number of cycles evaluated for complete separation of 152+154Eu and 90Sr is five cycles.

雷达海上预警的快速SR-STAP海杂波抑制方法

针对稀疏重构空时自适应处理技术在低样本量时的非均匀海杂波抑制中存在大维度杂波测量矩阵和复杂迭代机制中的运算负担较大的问题,提出了一种快速SR-STAP海杂波抑制方法.通过回波空时解耦的方式降低了测量矩阵维度,减小了高维矩阵的运算负担;基于快速傅里叶变换获取频谱分布支撑集并构造先验矩阵因子,改进稀疏优化问题并通过调整因子权值加快收敛,以实现杂波频谱快速恢复.仿真结果验证了所提方法的低复杂度优势与可靠的海上目标检测性能.

基于自适应字典校正的稀疏恢复STAP算法

空时自适应处理(Space-Time Adaptive Processing,STAP)技术在时间维(脉冲维)和空间维(阵元维)联合进行信号处理,以实现动目标检测功能。但是,传统STAP技术的计算复杂度非常高,而且在优化处理信号过程中需要大样本的支撑,在实际的工作场景中,杂波环境复杂易变,不易获取足够多的独立同分布样本,因此杂波抑制效果较差。稀疏恢复空时自适应处理(Sparse Recovery Space-Time Adaptive Processing,SR-STAP)算法可以利用很少的训练样本实现杂波抑制,但大多数SR-STAP算法的计算量巨大,运行速度慢,算法实时性不高。此外,SRSTAP算法需要对连续空时二维平面进行离散化处理,将空时二维平面划分为很多细小的网格,由于真实的杂波在空时平面上是连续分布的,同时考虑雷达接收信号中噪声、系统参数误差等因素的影响,真实杂波点与离散化网格点之间一定存在着偏差,会造成网格失配现象,导致SR-STAP算法杂波抑制性能下降。针对此问题,本文提出了基于自适应字典校正的稀疏恢复STAP算法。该算法首先通过子空间投影法筛选出与杂波最相关的原子;然后围绕选定原子由粗到细进行自适应局部网格划分,按照局部网格迭代选优准则,不断调整选择局域内的最优原子,直到满足迭代终止条件,以匹配真实的杂波点;最后利用选定的最优原子对应的空时导向矢量构造杂波子空间,更新噪声子空间上与杂波子空间正交的投影矩阵得到STAP权值。仿真实验表明,所提算法与传统SR-STAP算法相比,具有更高的稀疏恢复精度,更快的运行速度,改善了STAP性能。

基于稀疏增强重加权与掩码块张量的红外弱小目标检测

高度异构的复杂背景破坏了场景的低秩性,现有算法难以利用低秩稀疏恢复方法从背景中分离出小目标。为了解决上述问题,本文将小目标检测问题转化为张量模型的凸优化函数求解问题,提出基于稀疏增强重加权与掩码块张量的检测模型。首先,将掩码块图像以堆叠方式扩展至张量空间,并构建掩码块张量模型以筛选候选目标。在此基础上,利用结构张量构建稀疏增强重加权模型以抑制背景杂波,克服凸优化函数求解过程中设定加权参数的缺陷。实验表明本文检测算法在背景抑制因子及信杂比增益两方面都优于新近代表性算法,证明该算法的有效性。

基于稀疏恢复的双基地机载雷达杂波抑制方法

基于杂波谱稀疏恢复(sparse recovery,SR)的空时自适应处理(space-time adaptive processing,STAP)技术对训练样本个数要求低,适用于杂波非均匀环境。然而在双基地配置下,杂波脊不落在均匀网格点上,这将造成网格失配,严重影响SR-STAP方法性能。针对双基地机载雷达杂波的网格失配问题,提出了一种利用杂波脊先验信息划分非均匀网格的SR-STAP方法,所提方法首先由导航系统或参数估计方法获得待检测单元的杂波空时轨迹,然后根据杂波空时轨迹和均匀划分的空间频率确定初始归一化多普勒频率集合,接着根据可调参数更新归一化多普勒频率集合,最后构造对应的非均匀字典。所提方法可适用于任意双基地配置情况。仿真结果表明,应用非均匀字典的SR-STAP方法的杂波抑制性能较传统均匀字典有较大提升,并能在非理想条件下表现稳健。