Adaptive imaging for forward-looking ground penetrating radar

Most of the current forward-looking ground-penetrating radar (FLGPR) systems use conventional delay-and-sum (DAS) based methods to form radar images for detection of the target (such as a landmine). However, DAS is a data-independent approach which is known to suffer from low resolution and poor interference and clutter rejection capability. We present a data-adaptive imaging approach for FLGPR image formation based on APES (amplitude and phase estimation) and rank-deficient RCB (robust Capon beamforming). Due to the data-adaptive nature of both APES and RCB, our approach has better resolution and much better interference and clutter rejection capability than the standard DAS-based imaging methods. The excellent performance of the proposed method is demonstrated using experimental data collected via two FLGPR systems recently developed by PSI (Planning Systems, Inc.) and SRI (Stanford Research Institute).     

一种MIMO雷达幅相误差估计方法

 针对单基地相关多输入多输出(MIMO)雷达中存在的阵列幅相误差问题进行了研究。给出了单基地相关MIMO雷达的阵列模型,并提出了一种MIMO雷达幅相误差估计方法。利用发射正交信号对阵列接收信号进行匹配滤波,可分离得到类似传统阵列的"虚拟阵列",利用分时信源数据将该阵列中真实导向矢量中信源波达方向(DOA)引起的相位与幅相误差分离开,通过构造代价函数得到波达方向估计值,进而分别得到发射阵与接收阵的幅相误差的估计值,同时给出了误差引入量分析。最后通过仿真验证了该方法的有效性。本文介绍的方法简单可行,适用于任意构型MIMO雷达的幅相误差估计。     

Cumulant-based blind optimum beamforming

Sensor response, location uncertainty, and use of sample statistics can severely degrade the performance of optimum beamformers. We propose blind estimation of the source steering vector in the presence of multiple, directional, correlated or coherent Gaussian interferers via higher order statistics. In this way, we employ the statistical characteristics of the desired signal to make the necessary discrimination, without any a-priori knowledge of array manifold and direction-of-arrival (DOA) information about the desired signal. We then improve our method to utilize the data in a more efficient manner. In any application, only sample statistics are available, so we propose a robust beamforming approach that employs the steering vector estimate obtained by cumulant-based signal processing. We further propose a method that employs both covariance and cumulant information to combat finite sample effects. We analyze the effects of multipath propagation on the reception of the desired signal. We show that even in the presence of coherence, cumulant-based beamformer still behaves as the optimum beamformer that maximizes the signal-to-interference-plus-noise ratio (SINR). Finally, we propose an adaptive version of our algorithm simulations demonstrate the excellent performance of our approach in a wide variety of situations     

鲁棒成形极化敏感阵列波束的方法及极化估计

基于极化敏感阵列,提出了一种鲁棒成形阵列波束的方法。该方法首先将阵列的数据模型进行了重新描述,从而获得了信号波达角(DOA)和极化解耦的模型。借助于该模型并对信号的两个极化方向分别进行鲁棒约束,设计出了一个新的鲁棒空域波束空间成形矩阵,利用该矩阵可以获得信号两个极化分量的鲁棒估计。基于特征值分解的方法,最后给出了估计信号极化参数的方法。分析和数值仿真实验均表明:提出的方法,在对DOA估计误差以及阵列位置误差等造成的阵列失配具有较强鲁棒性的同时,也能有效抑制干扰和噪声,进而提升了极化参数估计的性能。     

基于三迭代与二阶锥规划的机载MIMO雷达稳健降维STAP方法

针对机载多输入多输出(MIMO)雷达空时自适应处理(STAP)中期望目标导向矢量的失配问题,提出一种基于三迭代(TRIA)与二阶锥规划(SOCP)的稳健降维MIMO-STAP方法。首先将MIMO-STAP权矢量分解为发射、接收、多普勒3个低维权矢量的Kronecker积,然后分别限定实际发射、接收、多普勒导向矢量与假定导向矢量之间的误差范数边界,通过优化最差性能,利用SOCP对各个低维权矢量进行三迭代求解,最后进行权矢量合成。该方法在保证机载MIMO雷达获取稳健STAP性能的同时,通过三迭代降维处理,能够有效降低训练样本数需求与运算复杂度,因此更具有实际应用价值。仿真结果验证了算法的有效性。     

Robust adaptive beamforming for HF surface wave over-the-horizon radar

Adaptive beamforming is used to enhance the detection of target echoes received by high frequency (HF) surface wave (HFSW) over-the-horizon (OTH) radars in the presence of spatially structured interference. External interference from natural and man-made sources typically masks the entire range-Doppler search space and is characterized by a spatial covariance matrix that is time-varying or nonstationary over the coherent processing interval (CPI). Adaptive beamformers that update the spatial filtering weight vector within the CPI are likely to suppress such interference most effectively, but the intra-CPI antenna pattern fluctuations result in temporal decorrelation of the clutter which severely degrades subclutter visibility after Doppler processing. A robust adaptive beamformer that effectively suppresses spatially nonstationary interference without degrading subclutter visibility is proposed here. The proposed algorithm is computationally efficient and suitable for practical implementation. Its operational performance is evaluated using experimental data recorded by the Iluka HFSW OTH radar, located near Darwin in far north Australia.     

Efficient implementation of Capon and APES for spectral estimation

Both the Capon and APES estimators can be shown to belong to the class of matched-filterbank spectral estimators and can be used to obtain complex spectral estimates that have more narrow spectral peaks and lower sidelobe levels than the fast Fourier transform (FFT) methods. It can also be shown that APES has better statistical performance than Capon. In this paper, we address the issue of how to efficiently implement Capon and APES for spectral estimation     

Systolic robust adaptive beamforming with an adjustable constraint

This paper presents a novel, fully parallel algorithm for robust linearly constrained minimum variance (LCMV) beamforming. The robustness results from the use of an adjustable constraint to compensate for the unavoidable small mismatch between the nominal and the actual steering vector of the signal-of-interest. The need for parallelism follows from the large computational demands of beamforming algorithms and by the high data rates which are typically present in communications applications. The algorithm exhibits good numerical properties as it is based on orthogonal transformations only. Two systolic architectures are presented on which the throughput is independent of the size of the beamformer     

基于杂波子空间估计的MIMO雷达降维STAP研究

 多输入多输出(MIMO)雷达是近年来出现的一种新体制雷达,针对MIMO体制的机载雷达开展空时自适应处理（STAP）技术研究是值得进一步努力的方向。本文研究了机载MIMO雷达STAP技术的降维算法,通过对STAP技术杂波抑制原理进行分析,推导并得到一种基于杂波子空间的降维算法。结合扁长椭球波函数（PSWF）的特点,提出了一种基于杂波子空间估计的降维算法,并与若干降维算法的杂波抑制性能进行比较。结果表明,当存在阵元幅相误差时,该算法在保持杂波抑制性能的同时能够有效地降低STAP算法的运算量。     

基于时空结构的双基MIMO雷达多维参数联合估计

为了充分利用目标的时域特性来改善双基多输入多输出(MIMO)雷达波达方向(DOA)和离波方向(DOD)的估计性能,探讨了双基MIMO雷达在时-空模型下的多维参数联合估计问题。针对慢动目标,基于多维旋转不变技术信号参数估计(ESPRIT)算法,同时利用时域相邻两级延迟包含的时间相位差、发射阵列相邻阵元包含的空间相位差以及接收阵列相邻阵元包含的空间相位差作为旋转因子,形成旋转不变子空间,再根据矩阵、矩阵的特征值及特征值对应的特征向量三者之间的关系解决了参数配对问题,实现了三维参数联合估计。该方法在信噪比低、积累脉冲数少时,目标的DOA与DOD估计性能比不利用时域特性时有明显改善。仿真结果验证了本方法的有效性。     

Robust adaptive radar detection in the presence of steering vector mismatches

In this paper, we consider the problem of robust radar detection in the presence of Gaussian disturbance with unknown covariance matrix. We design and assess three new robust adaptive detectors, capable of operating in the presence of unknown discrepancies between the nominal and the actual steering vector. Remarkably the new decision rules exhibit a bounded constant false alarm rate (CFAR) behavior and allow, through the regulation of a design parameter, to trade off target sensitivity with sidelobes energy rejection. Finally, computer simulations show that the proposed detectors achieve a visible performance improvement, in many situations of practical interest, over the traditional adaptive detection algorithms, especially in the presence of severe steering vector mismatches.     

二维嵌套混合MIMO相控阵雷达接收阵列设计

针对二维混合多输入多输出（MIMO）相控阵雷达发射阵列子阵分割带来的自由度损失问题,提出一种基于二维嵌套阵列的二维混合MIMO相控阵雷达接收阵列设计新方法。首先,给出一种由稀疏阵列和密集阵列构成的嵌套阵接收阵列的二维混合MIMO相控阵雷达信号模型;其次,通过对接收信号的协方差矩阵进行Khatri-Rao乘积处理,得到阵元位置差的差异阵列,形成接收阵元数目的虚拟扩展;最后,通过空间平滑处理进行波达方向估计。仿真实验表明,与传统二维混合MIMO相控阵雷达相比,所提方法在不增加实际阵元数目的情况下可以有效扩展虚拟阵元数目,提高雷达阵列自由度,进而提高二维混合MIMO相控阵雷达波达方向估计精度。     

MIMO雷达DOD和DOA联合估计算法: RTR-ESPRIT

 针对双基地多输入多输出(MIMO)雷达多目标波离角(DOD)和波达角(DOA)的联合估计问题,提出一种接收-发射-接收(RTR)-ESPRIT算法。该算法首先利用一维接收ESPRIT(R-ESPRIT)预估计目标DOA,随后分别利用一维发射ESPRIT(T-ESPRIT)和一维接收ESPRIT得到目标的高精度DOD和DOA估计,在每两次ESPRIT算法之间分别构造正交投影算子对接收信号进行接收波束形成和发射波束形成。与传统ESPRIT算法相比,该算法大大降低了数据协方差矩阵维数和计算复杂度,无需额外的配对算法,且理论证明了该方法还可以用于相干目标和单快拍情况下DOD和DOA的联合估计。仿真结果表明了该算法的正确性及良好的估计性能。     

基于压缩感知的嵌套FDA 雷达距离- 角度联合估计

针对频率分集技术，与非均匀阵列、压缩感知理论相结合，实现目标距离- 角度的联合估计。通过 频率分集技术提高信号空间自由度，将距离参数引入导向矢量矩阵，实现距离- 角度参数的联合。并结合嵌套 阵列，显著提高雷达阵列孔径。该方法针对嵌套阵虚拟阵列流型矩阵的优化过程导致的秩亏缺问题，传统的空 间平滑算法会以牺牲部分阵列孔径为代价将单快拍问题转化为多快拍问题，采用正交匹配追踪算法可以实现单 快拍下的高精度参数估计，避免了空间平滑技术对阵列孔径的影响。并通过压缩感知理论的应用降低信号维度， 减小了计算复杂度。     

基于CRCB方法的多波束形成

针对国际上提出的相干稳健Capon波束形成(coherent robust capon beamforming．CRCB)方法，首先详细推导了其有用信号导向矢量的估计公式，并将CRCB方法应用到“多信号源多相干干扰源”情况下进行多波束形成，实验结果证明了其可行性。     

The Effect of Random Steering Vector Errors in the Applebaum Adaptive Array

The effect of random errors in the steering vector of an Applebaum adaptive array is examined. Each component of the steering vector is assumed to have a random error component uncorrelated between elements. The array output signal-to-interferenceplus-noise ratio (SINR) is computed as a function of the error variance. It is shown that the array output SINR becomes more sensitive to steering vector errors as more elements are added to the array and as the received desired signal power becomes larger. The variance of the steering vector error that may be tolerated depends on the required desired signal dynamic range. The larger the dynamic range that must be accommodated, the smaller the error variance must be.     

Error Analysis of the Optimal Antenna Array Processors

The optimal weights of an antenna array processor, which maximizes the output signal-to-noise ratio (SNR) in the absence of errors, are computed using the noise-alone matrix inverse (NAMI) and the steering vector in the look direction or the signal-plus-noise matrix inverse (SPNMI) and the steering vector. In practice the estimated steering vector as well as the estimated optimal weights are corrupted by random errors. This paper has analyzed the effects of these errors on the performance of the NAMI processor and the SPNMI processor by deriving analytic expressions for the output signal power, output noise power, output SNR, and the array gain as a function of the error variance. The treatment is for a general array configuration and no assumption about a particular array geometry is made.     

Moving target localization in distributed MIMO radar with transmitter and receiver location uncertainties

In this paper, the problem of moving target localization from Bistatic Range(BR) and Bistatic Range Rate(BRR) measurements in a Multiple-Input Multiple-Output(MIMO) radar system having widely separated antennas is investigated. We consider a practically motivated scenario,where the accurate knowledge of transmitter and receiver locations is not known and only the nominal values are available for processing. With the transmitter and receiver location uncertainties,which are usually neglected in MIMO radar systems by prior studies, taken into account in the measurement model, we develop a novel algebraic solution to reduce the estimation error for moving target localization. The proposed algorithm is based on the pseudolinear set of equations and two-step weighted least squares estimation. The Cramer-Rao Lower Bound(CRLB) is derived in the presence of transmitter and receiver location uncertainties. Theoretical accuracy analysis demonstrates that the proposed solution attains the CRLB, and numerical examples show that the proposed solution achieves significant performance improvement over the existing algorithms.     

多输入多输出电磁矢量阵列雷达的DOD和DOA联合估计

研究一种基于电磁矢量传感器阵列的多输入多输出(MIMO)雷达目标波离角(DOD)和波达角(DOA)的联合估计算法.提出了一种新型MIMO雷达系统,发射阵列采用常规阵元,而接收阵列采用电磁矢量传感器,在此基础上,算法首先利用矢量传感器的内在结构特点结合子空间旋转不变性质获得目标DOA预估计,随后采用最佳加权子空间拟合算法...     

两种自适应方向图峰值副瓣控制方法比较

自适应阵列（或称自适应波束形成）目前已广泛应用到雷达、声纳和通信领域中用来抑制各种干扰（有意的干扰，杂波干扰和多用户干扰等）。在雷达应用中，为了减轻脉冲欺骗式干扰或旁瓣目标并利用单脉冲雷达来准确测量目标波达方向．要求自适应方向图具有低副瓣和稳定的主瓣形状。在实际应用中，各种失配误差将降低自适应阵列的性能．这些误差包括由于目标的波达方向不精确引起的信号指向误差，由通道失配和位置扰动引起的阵列校准误差和由小样本教引起的协方差矩阵估计误差。在此情况下，自适应波束形成的性能大大下降（干扰抑制性能变差。主瓣失真和高的副瓣）。已提出了一种基于二次约束的集成峰值副瓣控制（integrated peak sidelobe control，简称IPSC）方法。该方法可以精确地控制峰值副瓣电平并产生具有稳定的主瓣形状的自适应方向图。研究IPSC中目标信号的影响和信号消除方案以进一步提高IPSC的性能。并将IPSC方法和最新提出的基于二阶锥规划（second-order cone programming，简称SOCP）的分布式峰值副瓣控制（distfibuted peak sidelobe control，简称为DPSC）新方法在性能上进行了比较。仿真结果表明。在干扰抑制性能和方向图控制质量方面IPSC比DPSC性能优越。此外IPSC比DPSC计算高效。