基于协方差矩阵加权的非理想情况下自适应阵列干扰自由度估计

 在均匀线阵条件下,证明了干扰特征值渐近为周期延拓后的干扰空间谱这一性质.据此提出了一种非理想情况下估计干扰自由度,即大干扰特征值个数的方法.该方法首先利用协方差矩阵加权得到非理想情况下的干扰协方差函数和对应的空间谱,然后根据周期延拓后的干扰空间谱估计干扰自由度.仿真结果验证了该方法的有效性.     

相干积累检测器中协方差矩阵估计的新算法

针对传统协方差矩阵估计方法忽略了海杂波的统计一致性随雷达距离分辨率的变化而变化以及可积累脉冲受参考样本数量的限制这两个问题,提出了一种基于对角加载的协方差矩阵估计算法,其加载系数随着杂波向量的一致性变化而自适应地变化.改进的估计矩阵相对误差小,条件数好,加载系数为近似最优.实测海杂波数据实验表明,使用改进的估计算法使检测器的性能有明显改善.     

非均匀非高斯杂波的协方差矩阵估计

在雷达目标检测中,杂波的协方差矩阵估计利用了待检测单元附近的杂波数据。本文考虑一种非均匀环境中,非高斯杂波下的杂波协方差矩阵估计问题,即假定待检测单元与参考单元的杂波协方差矩阵之间满足某种统计关系,并假定杂波数据满足复合高斯统计分布模型。在这种场景下,常规的杂波协方差矩阵估计方法会导致信号检测性能的下降。采用共轭先验分布作为非均匀非高斯场景的统计分布模型,利用贝叶斯方法,本文给出了基于Gibbs抽样的杂波协方差最小均方误差估计方法。计算机仿真结果表明,与常规的杂波协方差矩阵估计方法相比较,本文所给出的杂波协方差矩阵的估计算法能够在参考数据较少,累积脉冲个数较少时,非均匀场景中获得较好的检测性能。本文还分析了先验分布模型参数误差对检测性能的影响。      

复合高斯杂波协方差矩阵估计的失配性能分析

在球不变随机向量的复合高斯杂波满足局部均匀的背景下,当估计的杂波分组大小与实际情况失配时,分析了自适应归一化匹配滤波器(ANMF)的恒虚警率(CFAR)特性和检测性能.理论分析表明,当杂波实际分组大小是估计分组大小的整数倍时,ANMF检测器对协方差矩阵结构和杂波功率水平均具有CFAR特性;而在其它的情况下ANMF检测器...     

改进的杂波协方差矩阵结构估计方法

在球不变随机向量建模的非高斯杂波背景下,针对现有协方差矩阵估结构计方法对杂波不具备完全自适应性以及计算复杂度高等问题,本文利用经验信息,提出了基于经验的自适应估计方法(E-AE),并将其作为初始化矩阵,再次利用经验信息进行迭代估计,得到了基于经验的自适应迭代估计方法(E-ARE)。E-AE和E-ARE只需要进行实数运算,减小了计算复杂度。从理论上证明了所提方法对应的ANMF对杂波纹理分量和协方差矩阵结构都具有CFAR特性,并通过仿真实验对其有效性进行了验证。最后,利用匹配滤波器的输出信杂比损失及相应的ANMF检测性能,对所提方法和已有方法进行效果评估,结果表明,本文方法具有收敛速度快、所需的辅助单元数少、信杂比损失小和对应的ANMF检测性能优等特点。      

基于几何方法的结构化协方差矩阵估计

针对雷达在非均匀环境下难以获得足够数量样本估计协方差矩阵,致使干扰抑制性能下降的问题,遵循几何范式,本文提出了两种结构化干扰协方差矩阵（ICM）估计算法。具体而言,算法从一组训练数据出发,考虑不同的协方差矩阵结构信息（Persymmetric或Toeplitz结构）,构造了两种结构样本协方差矩阵（SSCMs）,通过利用正定矩阵空间特性并施加条件数上限约束,以ICM和SSCMs之间的Frobenius范数作为目标函数建立极小化问题,并对该问题进行转化,最终得到估计器的闭式解。在两种场景下的仿真结果表明了所提算法可更加精确地估计ICM,有效地提高了干扰抑制性能,且在小样本情况下的优化效果更为显著。     

部分均匀杂波环境中极化自适应Rao检测器

研究了机载极化雷达在部分均匀杂波背景下对于低慢小目标的自适应检测问题。基于Rao准则设计得到了极化自适应Rao检测器,分析了极化自适应Rao检测器的性能,推导得到了虚警概率和检测概率的解析表达式,证明了极化自适应Rao检测器对于杂波协方差矩阵和杂波功率比因子具有恒虚警率特性。理论分析和数值仿真表明,与基于广义似然比检验准则得到的自适应子空间检测器和自适应匹配检测器相比,极化自适应Rao检测器具有更好的检测性能和更低的计算复杂度。      

基于截断核范数正则化的协方差矩阵估计

非均匀杂波环境导致机载雷达用于协方差矩阵估计的样本不足。本文提出了一种基于截断核范数正则化（Truncated Nuclear Norm Regularization, TNNR）的协方差矩阵估计算法以满足小样本条件下机载雷达空时自适应处理（Space Time Adaptive Processing, STAP）应用。本文利用TNNR确保所估计杂波协方差矩阵（Clutter Covariance Matrix, CCM）的低秩特性,并将NP-hard问题转换为凸优化问题。不同于常规的秩最小化算法,如核范数松弛方法,本文所提出的TNNR算法仅最小化与矩阵的秩无关的较小奇异值的和,可以更加准确地约束矩阵的秩。在此基础上本文还利用CCM的块Toeplitz结构先验信息,可确保在连续域上进行信号建模,有效避免网格点失配问题。仿真结果表明本文所提出的算法在小样本条件下可更加准确地估计CCM且STAP性能更优。     

基于最大似然的双基地STAP杂波协方差矩阵估计方法

在分析现有的双基地STAP杂波协方差矩阵估计方法的基础上,从杂波建模的角度,建立了不同距离门的杂波协方差矩阵的联系,并在此基础上提出了基于最大似然的双基地STAP杂波协方差矩阵估计方法,数据仿真和分析表明该方法不仅具有较优的处理性能而且具有较好的适用性和扩展性.     

一种稳健的非均匀杂波协方差矩阵估计方法

在非均匀环境下,针对传统样本挑选、样本加权等方法由于数据利用率低导致独立同分布训练样本不足的问题,该文提出一种在空时2维谱平面联合距离维逐空-时频点谱估计与滤波的协方差矩阵估计方法。该方法根据杂波和目标在距离-空时2维谱平面的分布特性,逐点频估计待检测单元杂波谱,并采用中值滤波方式消除目标污染对地物杂波谱估计的干扰;最后重构无空时孔径损失的杂波协方差矩阵。仿真结果表明,相比于传统非均匀统计STAP方法,所提的距离-空时2维谱滤波方法能够在样本数不足时有效缓解目标信号污染、离散地形杂波或孤立干扰引起的STAP性能下降问题。     

On the second-order statistics of the weighted sample covariance matrix

The second-order statistics of the sample covariance are encountered in many covariance based processing algorithms. This paper derives closed-form expressions for the covariance of the weighted sample covariance matrix with an arbitrary weight for both a real system and complex system. Given a system model, the results explicitly rely on the second-order and fourth-order statistics of the channel noise and inputs. They are shown to coincide with the existing results when the channel inputs and noise are Gaussian distributed. Our results can be directly applied to analyze the statistical properties of subspace-based channel estimation methods for single-input multiple-output (SIMO) systems and code-division multiple access (CDMA) systems. Numerical examples are provided to further verify analyses.     

An alternate derivation of the maximum likelihood estimator of a covariance matrix

An alternate derivation of the maximum likelihood estimator of a covariance matrix is given. The derivation is based upon the eigenvalue properties of the product of the inverse of the covariance matrix and the sample covariance matrix.     

A new model for sea clutter

First-order (small roughness amplitude) scattering theory is applied to obtain sea clutter cross sections in terms of mean-squared height spectrum of the sea surface. The results are in remarkably good agreement with observations for vertical polarization atP-, L-, C-, andX-bands. Modification of the calculation to take into account the larger scale structure of the sea surface yields reasonable agreement for horizontal polarization atP- andL-bands but is less successful for the cross-polarized and horizontal cross sections atC- andX-bands.     

Small-target detection in sea clutter

Sea clutter in marine surveillance radar makes the task of detecting small targets a very challenging problem. In this paper, a set of three signal processing techniques designed to suppress unwanted sea clutter radar echo and achieve target detection with no prior knowledge of the ocean and environmental conditions is presented. These include signal averaging, time-frequency representation, and morphological filtering. Datasets from real marine radar operating in staring mode are used to illustrate the performance of the new approaches.     

AR-based growler detection in sea clutter

A technique that performs better than conventional, noncoherent marine radars in detecting small pieces of glacial ice (growlers) in the sea is reported. A coherent detector based on an autoregressive (AR) parameterization of received radar echoes that distinguishes growler returns from the background sea clutter returns is considered. A conventional noncoherent detector is also evaluated as a basis for comparison. The AR-based detector is shown to offer significant improvements in detecting growlers. The results reported are based on real X-band radar data     

Sparse spectrum fitting algorithm using signal covariance matrix reconstruction and weighted sparse constraint

Multidimensional Systems and Signal Processing - Although sparse spectrum fitting algorithm has good multi-target resolution, the performance is decreased under the influence of strong...     

Analysis of sea spikes in radar sea clutter data

In this paper three sets of high-resolution, coherent, and polarimetric radar sea clutter data are analyzed and compared with radar sea clutter models. The nature of the data allows a thorough analysis of the power, polarization and velocity of the sea clutter. It is shown that these quantities, especially the velocity, are good measures of many physical properties of the ocean surface. Furthermore, it is shown that these physical properties match well with the sea clutter models. Sea clutter is found to consist of two components, a diffuse background, characterized by low values of backscattered power, HH/VV polarization ratio and Doppler velocity, and a number of spiking events, which possess higher power, polarization ratio and velocity. The background is reasonably well modeled by tilt-modulated Bragg scattering, whereas the spikes may be associated with the scattering on steepened and/or breaking waves. Moreover, it is shown that the influence of microbreakers has to be taken into account to explain the relatively high polarization ratio. A breaking wave origin for the spikes is supported in two ways. First, by a detailed analysis of the temporal behavior of individual spike backscatter properties, and second, by a statistical analysis of the entire population of spikes.