Angle estimation for bistatic MIMO radar with unknown mutual coupling based on three-way compressive sensing

The problem of angle estimation for bistatic multiple-input multiple-output radar in the present of unknown mutual coupling (MC) is investigated, and a three-way compressive sensing (TWCS) estimation algorithm is developed. To exploit the inherent multi-dimensional structure of received data, a trilinear tensor model is firstly formulated. Then the de-coupling operation is followed. Thereafter, the high-order singular value decomposition is applied to compress the high dimensional tensor to a much smaller one. The estimation of the compressed direction matrices are linked to the compressed trilinear model, and finally two over-complete dictionaries are constructed for angle estimation. Also, Cramer-Rao bounds for angle and MC estimation are derived. The proposed TWCS algorithm is effective from the perspective of estimation accuracy as well as the computational complexity, and it can achieve automatically paired angle estimation. Simulation results show that the proposed method has much better estimation accuracy than the existing algorithms in the low signal-to-noise ratio scenario, and its estimation performance is very close to the parallel factor analysis (PARAFAC) algorithm at the high SNR regions. ? 2017 Beijing Institute of Aerospace Information.

Airborne sparse flight array SAR 3D imaging based on compressed sensing in frequency domain

In airborne array synthetic aperture radar(SAR), the three-dimensional(3D) imaging performance and cross-track resolution depends on the length of the equivalent array. In this paper, Barker sequence criterion is used for sparse flight sampling of airborne array SAR, in order to obtain high cross-track resolution in as few times of flights as possible. Under each flight, the imaging algorithm of back projection(BP) and the data extraction method based on modified uniformly redundant arrays(MURAs) are utilized to obtain complex 3D image pairs. To solve the side-lobe noise in images, the interferometry between each image pair is implemented, and compressed sensing(CS) reconstruction is adopted in the frequency domain. Furthermore, to restore the geometrical relationship between each flight, the phase information corresponding to negative MURA is compensated on each single-pass image reconstructed by CS. Finally,by coherent accumulation of each complex image, the high resolution in cross-track direction is obtained. Simulations and experiments in X-band verify the availability.

Root imaging from ground penetrating radar data by CPSO-OMP compressed sensing

As the amount of data produced by ground penetrating radar （GPR） for roots is large, the transmission and the storage of data consumes great resources. To alleviate this problem, we propose here a root imaging algorithm using chaotic particle swarm optimal （CPSO） compressed sensing based on GPR data according to the sparsity of root space. Radar data are decomposed, observed, measured and represented in sparse manner, so roots image can be reconstructed with limited data. Firstly, radar signal measurement and sparse representation are implemented, and the solution space is established by wavelet basis and Gauss random matrix; secondly, the matching function is considered as the fitness function, and the best fitness value is found by a PSO algorithm; then, a chaotic search was used to obtain the global optimal operator; finally, the root image is reconstructed by the optimal operators. A-scan data, B-scan data, and complex data from American GSSI GPR is used, respectively, in the experimental test. For B-scan data, the computation time was reduced 60 % and PSNR was improved 5.539 dB; for actual root data imaging, the reconstruction PSNR was 26.300 dB, and total computation time was only 67.210 s. The CPSO-OMP algorithm overcomes the problem of local optimum trapping and comprehensively enhances the precision during reconstruction.

Novel imaging methods of stepped frequency radar based on compressed sensing

The theory of compressed sensing （CS） provides a new chance to reduce the data acquisition time and improve the data usage factor of the stepped frequency radar system. In light of the sparsity of radar target reflectivity, two imaging methods based on CS, termed the CS-based 2D joint imaging algorithm and the CS-based 2D decoupled imaging algorithm, are proposed. These methods incorporate the coherent mixing operation into the sparse dictionary, and take random measurements in both range and azimuth directions to get high resolution radar images, thus can remarkably reduce the data rate and simplify the hardware design of the radar system while maintaining imaging quality. Ex- periments from both simulated data and measured data in the anechoic chamber show that the proposed imaging methods can get more focused images than the traditional fast Fourier trans- form method. Wherein the joint algorithm has stronger robustness and can provide clearer inverse synthetic aperture radar images, while the decoupled algorithm is computationally more efficient but has slightly degraded imaging quality, which can be improved by increasing measurements or using a robuster recovery algorithm nevertheless.

Low sidelobe robust imaging in random frequency-hopping wideband radar based on compressed sensing

High resolution range imaging with correlation processing suffers from high sidelobe pedestal in random frequency-hopping wideband radar.After the factors which affect the sidelobe pedestal being analyzed,a compressed sensing based algorithm for high resolution range imaging and a new minimized l 1-norm criterion for motion compensation are proposed.The random hopping of the transmitted carrier frequency is converted to restricted isometry property of the observing matrix.Then practical problems of imaging model solution and signal parameter design are resolved.Due to the particularity of the proposed algorithm,two new indicators of range profile,i.e.,average signal to sidelobe ratio and local similarity,are defined.The chamber measured data are adopted to testify the validity of the proposed algorithm,and simulations are performed to analyze the precision of velocity measurement as well as the performance of motion compensation.The simulation results show that the proposed algorithm has such advantages as high precision velocity measurement,low sidelobe and short period imaging,which ensure robust imaging for moving targets when signal-to-noise ratio is above 10 dB.

THE HIGH RESOLUTION MIMO RADAR SYSTEM BASED ON MINIMIZING THE STATISTICAL COHERENCE OF COMPRESSED SENSING MATRIX

Compressed Sensing (CS) theory is a great breakthrough of the traditional Nyquist sampling theory. It can accomplish compressive sampling and signal recovery based on the sparsity of interested signal, the randomness of measurement matrix and nonlinear optimization method of signal recovery. Firstly, the CS principle is reviewed. Then the ambiguity function of Multiple-Input Multiple-Output (MIMO) radar is deduced. After that, combined with CS theory, the ambiguity function of MIMO radar is analyzed and simulated in detail. At last, the resolutions of coherent and non-coherent MIMO radars on the CS theory are discussed. Simulation results show that the coherent MIMO radar has better resolution performance than the non-coherent. But the coherent ambiguity function has higher side lobes, which caused a deterioration in radar target detection performances. The stochastic embattling method of sparse array based on minimizing the statistical coherence of sensing matrix is proposed. And simulation results show that it could effectively suppress side lobes of the ambiguity function and improve the capability of weak target detection.

Imaging algorithm of multi-ship motion target based on compressed sensing

An imaging algorithm based on compressed sensing（CS） for the multi-ship motion target is presented. In order to reduce the quantity of data transmission in searching the ships on a large sea area, both range and azimuth of the moving ship targets are converted into sparse representation under certain signal basis. The signal reconstruction algorithm based on CS at a distant calculation station, and the Keystone and fractional Fourier transform（FRFT） algorithm are used to compensate range migration and obtain Doppler frequency. When the sea ships satisfy the sparsity, the algorithm can obtain higher resolution in both range and azimuth than the conventional imaging algorithm. Some simulations are performed to verify the reliability and stability.

Sub-Nyquist radar receiver based on photonics-assisted compressed sensing and cascaded dictionaries

A sub-Nyquist radar receiver based on photonics-assisted compressed sensing is proposed.Cascaded dictionaries are applied to extract the delay and the Doppler frequency of the echo signals,which do not need to accumulate multiple echo periods and can achieve better Doppler accuracy.An experiment is performed.Radar echoes with different delays and Doppler frequencies are undersampled and successfully reconstructed to obtain the delay and Doppler information of the targets.Experimental results show that the average reconstruction error of the Doppler frequency is 5.33 kHz using an 8-μs radar signal under the compression ratio of 5.The proposed method provides a promising solution for the sub-Nyquist radar receiver.

Compressive Sensing Inverse Synthetic Aperture Radar Imaging Based on Gini Index Regularization

In compressive sensing(CS) based inverse synthetic aperture radar(ISAR) imaging approaches, the quality of final image significantly depends on the number of measurements and the noise level. In this paper, we propose an improved version of CSbased method for inverse synthetic aperture radar(ISAR) imaging. Different from the traditional l1 norm based CS ISAR imaging method, our method explores the use of Gini index to measure the sparsity of ISAR images to improve the imaging quality. Instead of simultaneous perturbation stochastic approximation(SPSA), we use weighted l1 norm as the surrogate functional and successfully develop an iteratively re-weighted algorithm to reconstruct ISAR images from compressed echo samples. Experimental results show that our approach significantly reduces the number of measurements needed for exact reconstruction and effectively suppresses the noise. Both the peak sidelobe ratio(PSLR) and the reconstruction relative error(RE) indicate that the proposed method outperforms the l1 norm based method.

基于频率分集阵列小埋体检测三维形貌成像方法

频率分集阵列(Frequency Diverse Array,简称FDA)在埋体管线的探测识别与成像中具有很大优势,利用其灵活的波束控制和信号处理性能,能够摆脱传统阵列发射信号限制,灵活接收和处理复杂信号。通过发出窄带信号进而获得宽带信号探测参数,大大降低操作成本,实现高效率、高精度、高性价比三维立体成像。现如今埋体管线探测成为城市发展中不可避免的痛点,小埋藏体检测成像更是难点问题。文章提出一种基于多进多出技术(Multiple-Input Multiple-Output,简称MIMO)的频率分集阵列三维合成孔径雷达(3D-FDA-MAR)成像方法,并将MIMO阵列引入频率分集阵列实现三维成像,建立了MIMO-FDA三维形貌成像模型。该多进多出频率分集阵列在三维空间中能够随平台运动而运动,在沿航向处得到综合孔径,根据切航向阵列能够获得仿真频率分集阵列平面,从而得到目标物成像的三维立体效果,实现精准定位,全空间透视探测,智能3D成像,小埋藏体的精准检测诊断。

天波超视距雷达非均匀采样信号频谱重构

受瞬态干扰影响和空海同时探测的需求,在长相参积累时间条件下,天波超视距雷达(over-the-horizon radar,OTHR)回波信号的有效采样点往往缺损且非均匀,严重影响目标检测性能。针对此问题,提出了一种基于压缩感知的OTHR频谱重构方法。首先,建立了OTHR频域信号的稀疏模型;然后,提出了快速自适应复近似消息传递(fast adaptive complex approximate message passing,FACAMP)频谱重构算法并给出了算法实现步骤;最后,利用FACAMP算法实现了OTHR频谱重构并分析了重构性能。与现有重构算法相比,FACAMP算法具有重构精度高、运算复杂度低、可自适应调整参数和保留背景噪声高斯性的优势。理论分析和仿真实验均验证了所提算法的有效性。

基于压缩感知的被动雷达导引头信源数估计方法

随着雷达反摧毁技术的发展,被动雷达导引头面临着在雷达和诱饵形成的不完全重合信号中进行超分辨测向的挑战,需要单快拍测向技术。现有测向技术大多假设信源数已知,而传统的信源数估计方法在单快拍时性能大幅下降甚至失效。为解决这一问题,提出了一种基于压缩感知的单快拍信源数估计方法。该方法首先将被动雷达导引头阵列测向模型稀疏表示,其次通过子空间算法和压缩感知算法相结合进行降阶和解相干处理,然后提出一种改进的正交匹配追踪算法,研究残差的变化规律,最后构造目标函数估计信源数。仿真实验表明,该方法在单快拍、阵元数少、信号相干的极端情况下估计成功率较高。

一种幅相联合调制雷达波形设计与处理方法

随着脉内特征识别、信号分选等电子侦察技术的发展,雷达波形设计正面临严峻的挑战。幅度调制作为一种新型脉冲调制方式,能够增加信号时域的复杂性,提升波形的反识别能力。本文提出一种幅相联合编码雷达波形,通过幅度相位联合调制提升雷达波形的复杂度,具有良好的反侦察潜力;利用幅度上的稀疏采样特点,提出匹配滤波与压缩感知相结合的回波信号处理方法处理此信号,有效提升低信噪比条件下的检测概率。最后通过仿真实验证明了所提幅相联合调制雷达波形设计与处理方法的有效性。

基于稀疏恢复的雷达信号处理研究综述

随着雷达目标探测需求的增加,基于压缩感知(CS)模型的稀疏恢复(SR)技术被广泛应用于雷达信号处理领域。该文首先对压缩感知的基本理论进行梳理;接着从场景稀疏以及稀疏观测两个角度介绍了雷达信号处理中的稀疏特性;然后基于稀疏特性,从空域处理、脉冲压缩、相参处理、雷达成像以及目标检测等角度概述了压缩感知技术在雷达信号处理中的应用。最后,对压缩感知技术在雷达信号处理中的应用进行了总结。

基于曲波域凸集投影算法的缺失GPR信号高精度重建

受采集环境和仪器性能的影响,实测探地雷达(GPR)剖面中不可避免会出现部分信号缺失和坏道现象,易造成目标体产生的反射波和绕射波同相轴不连续,严重降低后续处理与成像精度和分辨率。为此,将图像处理中广泛应用的凸集投影(POCS)算法与具有良好稀疏特性的曲波变换相结合,提出了一种基于曲波域POCS算法的缺失GPR信号高精度重建方法。从压缩感知理论出发,建立了离散曲波变换基下缺失信号重建的目标函数,并采用POCS算法详细推导了缺失GPR信号重建的时间域迭代公式。其中,线性和指数迭代阈值模型用于更新曲波变换系数,从而高精度重建时间域缺失信号;平均绝对误差、信噪比、峰值信噪比用于定量评价GPR信号重建精度。模拟与实测GPR信号的重建试验表明:POCS算法可有效重建GPR剖面中的缺失信号;与线性阈值模型的POCS算法相比,指数阈值模型的POCS算法重建精度更高;与指数阈值模型的频率域POCS算法相比,指数阈值模型的曲波域POCS算法用于重建GPR剖面中连续多道缺失信号的误差更小、纵向伪影能量更弱,且对复杂GPR结构模型的缺失信号重建具有较强的适用性;与线性和指数阈值模型的频率域POCS算法、线性阈值模型的曲波域POCS算法相比,指数阈值模型的曲波域POCS重建方法的重建精度更高、平均绝对误差下降45%~99%、信噪比和峰值信噪比提高1~20 dB,其重建结果可为后续处理与解释提供高质量GPR信号。

基于贝叶斯压缩感知的CSR稳健参数估计方法

针对"完全扰动"情况下压缩感知雷达(compressed sensing radar,CSR)观测矢量和感知矩阵严重失配,进而引起参数估计性能急剧下降的问题,提出了一种基于贝叶斯压缩感知(Bayesian compressed sensing,BCS)的稳健参数估计方法。首先构造"完全扰动"情况下CSR参数估计的稀疏线性模型,并从稀疏矢量的最大后验概率(maximum a posteriori,MAP)出发,推导了完全扰动矩阵服从柯西分布时的优化目标函数;随后通过稀疏矢量和尺度参数的交替迭代,求得稀疏矢量的最优解。与现有重构算法及其改进算法相比,该方法能够有效改善CSR系统应对失配误差的稳健性,提高目标成功检测的概率和参数估计的精度。计算机仿真实验验证了该方法的有效性和鲁棒性。

冲击噪声下基于混合LL_2-L_1优化求解的CSR参数估计

针对冲击噪声环境下压缩感知雷达参数估计性能急剧下降的问题,提出一种新的鲁棒性参数估计方法。首先,根据压缩感知雷达参数估计的稀疏线性模型,基于Lorentzian范数和L1范数稀疏正则化构造冲击噪声背景下稀疏重构的混合LL2-L1范数优化模型;然后,利用迭代加权最小二乘法和阈值收缩函数推导上述模型优化求解的一步迭代公式;最后,从理论上对文中算法的收敛性进行证明,并给出算法计算复杂度的定量分析。计算机仿真实验表明,文中算法在冲击噪声下支撑集的重构更精确、重构信号的精度更高、重构的计算量更小。

Joint compressed sensing imaging and phase adjustment via an iterative method for multistatic passive radar

The resolution of the multistatic passive radar imaging system(MPRIS)is poor due to the narrow bandwidth of the signal transmitted by illuminators of opportunity.Moreover,the inaccuracies caused by the inaccurate tracking system or the error position measurement of illuminators or receivers can deteriorate the quality of an image.To improve the performance of an MPRIS,an imaging method based on the tomographic imaging principle is presented.Then the compressed sensing technique is extended to the MPRIS to realize high-resolution imaging.Furthermore,a phase correction technique is developed for compensating for phase errors in an MPRIS.Phase errors can be estimated by iteratively solving an equation that is derived by minimizing the mean recovery error of the reconstructed image based on the principle of fixed-point iteration technique.The technique is nonparametric and can be used to estimate phase errors of any form.The effectiveness and convergence of the technique are confirmed by numerical simulations.

Distributed inverse synthetic aperture radar imaging of ship target with complex motion

For ship targets with complex motion,it is difficult for the traditional monostatic inverse synthetic aperture radar(ISAR)imaging to improve the cross-range resolution by increasing of accumulation time.In this paper,a distributed ISAR imaging algorithm is proposed to improve the cross-range resolution for the ship target.Multiple stations are used to observe the target in a short time,thereby the effect of incoherence caused by the complex motion of the ship can be reduced.The signal model of ship target with three-dimensional(3-D)rotation is constructed firstly.Then detailed analysis about the improvement of crossrange resolution is presented.Afterward,we propose the methods of parameters estimation to solve the problem of the overlap or gap,which will cause a loss of resolution and is necessary for subsequent processing.Besides,the compressed sensing(CS)method is applied to reconstruct the echoes with gaps.Finally,numerical simulations are presented to verify the effectiveness and the robustness of the proposed algorithm.

Aliasing-free high resolution imaging of fast rotating targets with narrowband radar

Narrowband radar has been successfully used for high resolution imaging of fast rotating targets by exploiting their micro-motion features.In some practical situations,however,the target image may suffer from aliasing due to the fixed pulse repetition interval(PRI)of traditional radar scheme.In this work,the random PRI signal associated with compressed sensing(CS)theory was introduced for aliasing reduction to obtain high resolution images of fast rotating targets.To circumvent the large-scale dictionary and high computational complexity problem arising from direct application of CS theory,the low resolution image was firstly generated by applying a modified generalized Radon transform on the time-frequency domain,and then the dictionary was scaled down by random undersampling as well as the atoms extraction according to those strong scattering areas of the low resolution image.The scale-down-dictionary CS(SDD-CS)processing scheme was detailed and simulation results show that the SDD-CS scheme for narrowband radar can achieve preferable images with no aliasing as well as acceptable computational cost.