两种星载高分辨宽测绘带SAR系统通道相位误差估计方法

 本文针对星载方位多通道高分辨宽测绘带(HRWS)合成孔径雷达(SAR)系统,提出了两种通道相位误差估计方法:信号子空间比较法和天线方向图法.信号子空间比较法基于信号特征向量张成的空间(即信号子空间)与真实导向矢量张成的空间相同这一特性,得到各通道间的相对相位误差,该方法适用范围广,估计精度高,对系统要求低,且运算量小.天线方向图法结合天线方向图,直接估计通道相位误差,无须特征分解,无须矩阵求逆,运算量小,主要适用于均匀分布的场景.最后利用实测地基数据验证了两种方法的有效性.     

基于通道误差校准的空域导向矢量多通道SAR-GMTI 杂波抑制方法

针对通道幅相误差和图像配准误差等非理想因素导致地面动目标检测性能下降的问题,该文结合最小方差和空域导向矢量两种杂波抑制算法,提出一种基于通道误差校准的空域导向矢量杂波抑制方法。该方法首先计算最小方差杂波抑制的权向量,通过该权向量构造配准图像,然后利用配准图像计算杂波正交补空间,最后通过正交子空间的方法实现杂波抑制。理论分析及实验结果表明,所提方法在图像配准误差和通道幅相误差较大的情况下仍具有很好的检测性能,能获得较最小方差杂波抑制方法和基于空域导向矢量的杂波抑制方法更高的信杂噪比。      

维纳滤波最优权修正导向矢量的SAR-GMTI动目标径向速度估计方法

针对通道幅相误差和图像配准误差等非理想因素导致动目标径向速度估计性能下降问题,本文提出利用维纳滤波最优权修正导向矢量的动目标径向速度估计方法.该方法利用抑制杂波的维纳滤波最优权矢量对动目标理想导向矢量加权处理获得修正导向矢量,并采用匹配滤波算法估计动目标径向速度.仿真数据和某机载多通道SAR-GMTI实测数据处理表明,所提方法对通道幅相误差和图像配准误差稳健,在图像相邻像素存在较大相关性时仍可获得较高的动目标径向速度估计精度.     

“北斗”信号重构的导向矢量实时校正

针对导航应用中阵列天线导向矢量误差导致波束合成器性能恶化甚至失效的问题,提出了一种“北斗”信号重构的导向矢量实时校正算法。该算法利用重构的本地“北斗”参考信号与阵列天线接收信号进行相关解扩处理,然后利用信号子空间与信号正交补空间正交的特性,构造代价函数对各卫星方向的阵列导向矢量进行校正。仿真结果表明,经过校正的导向矢量相位误差从-100°~100°降低到-10°~10°范围内,幅度误差从-10~10 dB降低到-4~2 dB范围内;另外,导向矢量校正后,卫星信号波达方向估计误差在0.2°以内,估计精度大大提高。     

MC-SAR改进正交子空间相位误差估计算法

方位多通道SAR用于高分辨率宽测绘带成像有着广阔的前景,然而通道失配引起的通道相位误差会较大地影响成像的质量。针对这一问题提出了一种改进正交子空间(OS)算法用于校正通道相位误差。改进OS算法的通道相位误差估计考虑了天线方向图加权,并利用全部多普勒频点信号子空间与噪声子空间的正交约束条件建立代价函数,从而得到相位误差估计的最优值。该方法有效克服常规正交子空间算法利用单个多普勒频点估计通道相位误差时的不稳健问题。基于机载实测数据的实验验证了改进OS算法的有效性和稳健性。     

多普勒频谱模糊情况下的星载方位向多通道高分宽幅SAR-GMTI杂波抑制方法

该文提出了一种在多普勒频谱模糊情况下的星载方位向多通道高分宽幅合成孔径雷达地面运动目标检测(SAR-GMTI)系统的杂波抑制方法。首先,利用方位解线性调频对方位向多通道(HRWS) SAR-GMTI系统中的回波进行处理,得到杂波和动目标的粗聚焦图像。然后,将多通道SAR系统的粗聚焦图像表示为矩阵形式,并估计出相应的协方差矩阵。之后,用杂波协方差矩阵构造杂波空间的正交矢量,即最小特征值对应的特征向量。该方法需要一个冗余的通道自由度。由于杂波空间的正交矢量与杂波空间向量是正交的,因此可以用来抑制杂波。最后,通过仿真和实测数据实验结果验证该文所提杂波抑制方法的有效性。      

导航系统的通道自均衡空时抗干扰技术

针对导航系统由于通道之间幅相特性的不一致导致抗干扰性能下降的问题,本文提出了一种通道自均衡的空时抗干扰技术。该方法首先在无干扰的情况下,利用本地参考信号与接收数据的相关特性求出实际导向矢量,再采用波达方向估计方法估计出信号的理想波达方向,进而求出理想导向矢量,并由此实际导向矢量和理想导向矢量求得幅相误差矩阵,然后在有干扰的情况下,利用求得的幅相误差矩阵对接收数据进行阵列误差校正,再对校正后的数据进行波束形成,此方法可以改善通道失配导致干扰抑制性能下降的问题,仿真验证了其有效性和优越性。     

基于噪声空间加权重构的相干信号DOA估计

针对传统相干信号DOA估计算法在低信噪比、少快拍数以及阵列通道幅相误差下存在的角度分辨力差和测角性能不稳定问题,本文提出一种基于噪声空间加权重构的相干信号DOA估计算法。该算法首先利用共轭重构对阵列接收信号协方差矩阵作解相干处理;然后在信源大致方位内对导向矢量作定积分,计算积分矢量与整个噪声子空间正交基矢量的交空间基矢量,并以此对噪声子空间进行加权;最后运用加权重构后的噪声子空间建立算法空间谱函数,谱峰搜索估计得目标的DOA。计算机仿真结果表明,本文所提算法易实现低信噪比、少快拍数以及通道幅相误差下相干信号的DOA估计。     

矢量传感器误差校正与补偿

当阵列模型存在误差时,子空间类方法的高分辨性能会显著下降,因此必须进行有效的校正。由于矢量天线本身的复杂性,其相位误差很难精确校准且原有的基于标量天线相位误差的估计方法已经不再适应于矢量天线。该文在到达角已知情况下,给出了一种估计矢量天线阵列相位误差的新方法,并导出了各通道相位误差的计算公式。最后的数值模拟结果说明了本文方法是简单有效的。     

基于雷达回波数据相位矢量的通道盲均衡

通道均衡是实现地面运动目标测速、定位的前提条件.本文研究机载多通道雷达基于空时二维回波数据的通道均衡问题.利用杂波在沿航迹多幅多普勒锐化图像(DBS,Doppler Beam Sharpening)间的相位关系,提出一种基于数据相位矢量的通道盲均衡算法.理论分析和实测数据处理表明该算法能有效均衡通道误差,并对运动目标信号污染具有稳健性.     

智能天线中新的波束形成方法

提出了智能天线中三种利用估计得到的信号导向向量直接形成波束的新方法.根据CDMA信号模型和多径信道模型,用码滤波法估计得到用户最强信号(主径)的导向向量,利用这些导向向量形成恢复期望信号,抑制干扰的制约条件.根据制约条件可直接得到最小二乘(LS)波束形成方法;按照制约条件使输出能量最小得到广义最小方差无失真响应(GMVDR)波束形成方法;在GMVDR方法的基础上引入虚拟信号来进一步抑制旁瓣得到扩展最小方差无失真响应(EMVDR)波束形成方法.模拟表明这三种方法都能很好的恢复期望信号、抑制干扰和噪声,但通过比较看出EMVDR方法形成的波束最好.     

An angle difference of directions arrival algorithm with channel inconsistency

This article proposes a new angle difference of directions arrival (ADOA) estimation algorithm. It can estimate the values of ADOA of incidence signals even under channel incoherence environments. It performs an eigenvalue decomposition of a covariance matrix to obtain signal and noise subspace eigenvectors, then estimates the values of ADOA of signals based on the orthogonality between the noise subspace eigenvectors and steering vectors. Its main advantage is that the performance is not degenerated by array elements channel incoherence. In addition, the system complexity is also reduced. Finally, the numerical simulations have been conducted to verify the performance improvement of this algorithm.     

A robust adaptive array based on signal subspace approach

In this note, we propose a signal subspace approach that improves the performance of a beam steered adaptive array in the presence of steering errors due to look-direction error (LDE) and/or random steering error (RSE). In the method, the degrees of freedom (DOF) are reduced so as not to cancel the desired signal while preserving the optimal characteristic of the array, and thus the weights of the array are determined by a linear combination of the eigenvectors of the signal subspace. The proposed method works as far as the eigen decomposition of the input covariance matrix into signal and noise subspaces is possible. The proposed method improves noticeably the array performance of the beam steered array in the presence of steering errors and provides the optimum array performance in the absence of steering errors     

低信噪比下的信号源个数估计方法

针对低信噪比下信号源个数估计问题,改进基于特征向量与阵列流形正交性的这种目前信号源个数估计效果很好的算法。该改进主要在于引入一个更能去噪的门限准则,结合原来的盲波束形成方法,最后得到一种基于阵列信号处理的信号源个数估计算法。仿真结果证实在低信噪比下,文中所提方法在等功率信号入射时比原方法有效,而且在不等功率信号入射时效果也比原方法好。     

Markov-based eigenanalysis method for frequency estimation

This paper proposes an eigenanalysis-based method for estimating the frequencies of complex-valued sine waves. The basic idea behind this method consists of using a set of linearly independent vectors that are orthogonal to the signal subspace spanned by the principal eigenvectors of the data covariance matrix. Exploiting that orthogonality condition gives an overdetermined system of linear equations, the unknown parameters of which are uniquely related to the frequencies. Analytical expressions are derived for the covariances of the equation errors in the sample version of the aforementioned linear system of equations. Based on these expressions a Markov-like estimate of the unknown parameters is introduced, which asymptotically (with respect to either the number of data samples or the signal-to-noise ratio) provides the minimum variance frequency estimates in a fairly large class of consistent estimators. The paper includes Monte-Carlo simulations that support the theoretical analysis results and show that those results may apply to scenarios with rather low values of the number of data samples and the signal-to-noise ratio     

Vector coding for partial response channels

A linear technique for combining equalization and coset codes on partial response channels with additive white Gaussian noise is developed. The technique, vector coding, uses a set of transmit filters or `vectors' to partition the channel into an independent set of parallel intersymbol interference (ISI)-free channels for any given finite (or infinite) block length. The optimal transmit vectors for such channel partitioning are shown to be the eigenvectors of the channel covariance matrix for the specified block length, and the gains of the individual channels are the eigenvalues. An optimal bit allocation and energy distribution, are derived for the set of parallel channels, under an accurate extension of the continuous approximation for power in optimal multidimensional signal sets for constellations with unequal signal spacing in different dimensions. Examples are presented that demonstrate performance advantages with respect to zero-forcing decision feedback methods that use the same coset code on the same partial response channel. Only resampling the channel at an optimal rate and assuming no errors in the feedback path will bring the performance of the decision feedback methods up to the level of the vector coded system     

基于多途模型匹配的浅海噪声源聚焦定位方法

 本文在建立符合水声相干多途信道特点的阵列信号模型的基础上,提出了基于多途模型匹配的浅海噪声源聚焦定位方法.该方法在本质上利用了基于模型和数据匹配的相干处理思想,充分利用了多途信道信息,生成与实际声传播特性相匹配的空间聚焦导向矢量,从而有效克服了多途效应的影响,提高了定位精度.进而结合基于最差性能优化的稳健聚焦处理器设计方法,对空间聚焦导向矢量实施约束,并划归为具有单一非线性约束的二次最小化问题,求解最优权矢量,修正得到新的空间谱形式.通过对空间谱、定位误差、-3dB波束宽度及主峰与最大旁瓣比性能进行对比分析,讨论了约束参数选取与失配误差程度之间的关系,并证明了新算法在存在失配误差的情况下,可有效提高MVDR(Minimum Variance Distortionless Response)高分辨算法的稳健性,保持较尖锐的谱峰及较强的背景起伏抑制能力.     

Robust adaptive beamforming using worst-case performance optimization: a solution to the signal mismatch problem

Adaptive beamforming methods are known to degrade if some of underlying assumptions on the environment, sources, or sensor array become violated. In particular, if the desired signal is present in training snapshots, the adaptive array performance may be quite sensitive even to slight mismatches between the presumed and actual signal steering vectors (spatial signatures). Such mismatches can occur as a result of environmental nonstationarities, look direction errors, imperfect array calibration, distorted antenna shape, as well as distortions caused by medium inhomogeneities, near-far mismatch, source spreading, and local scattering. The similar type of performance degradation can occur when the signal steering vector is known exactly but the training sample size is small. In this paper, we develop a new approach to robust adaptive beamforming in the presence of an arbitrary unknown signal steering vector mismatch. Our approach is based on the optimization of worst-case performance. It turns out that the natural formulation of this adaptive beamforming problem involves minimization of a quadratic function subject to infinitely many nonconvex quadratic constraints. We show that this (originally intractable) problem can be reformulated in a convex form as the so-called second-order cone (SOC) program and solved efficiently (in polynomial time) using the well-established interior point method. It is also shown that the proposed technique can be interpreted in terms of diagonal loading where the optimal value of the diagonal loading factor is computed based on the known level of uncertainty of the signal steering vector. Computer simulations with several frequently encountered types of signal steering vector mismatches show better performance of our robust beamformer as compared with existing adaptive beamforming algorithms.     

基于时空极化导向矢量的阵列信号多维参数联合估计

通过对极化敏感阵列的时域采样构造了具有旋转不变结构的矩阵对,该矩阵对分解的特征值与对应特征向量分别是旋转矩阵和阵列的时空导向矢量,利用该导向矢量和旋转矩阵直接得到信号的频率、二维波达方向和极化的联合估计,该多维参数计算方法具有虚拟阵元和较高的估计精度。简述了多维ESPRIT方法的基本原理,其配对方法采用了多个矩阵束具有相同的特征向量,两种方法均利用了的相同信息-矩阵束的特征值和特征向量,仿真实验验证和比较了两种方法的性能。