MIMO Radar Moving Target Detection Against Compound-Gaussian Clutter

This paper focuses on the moving target detection (MTD) problem for multiple-input multiple-output (MIMO) radar in the presence of compound-Gaussian clutter. By exploiting the spatial and velocity diversities, we devise two different generalized likelihood ratio tests (GLRTs) according to the centralized and distributed processing schemes of MIMO radar systems, respectively. Then, we investigate the fully adaptive detectors, where the covariance matrix is replaced by a suitable estimator based on the secondary data. Finally, we provide several numerical simulations with typical parameters, and the results illustrate that the newly proposed detectors can provide much better detection performance in spikier clutter for moving targets than the phased-array counterpart and those obtained in Gaussian environment.     

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。      

严重拖尾复合高斯杂波中目标的自适应极化检测

该文研究极化高分辨雷达在动态变化的杂波场景中自适应检测小目标的问题.将统计特性严重拖尾的杂波建模为纹理分量为逆伽马分布的复合高斯过程,借助于广义似然比检验和辅助数据得到了自适应极化检测器,并推导了该检测器的虚警概率表达式,证明了该检测器对协方差矩阵结构具有恒虚警特性.最后,利用仿真杂波数据验证了检测器检测性能的有效性.     

GLRT-based Detection Algorithm for Polarimetric MIMO Radar Against SIRV Clutter

This paper mainly deals with target detecting problem using polarimetric Multiple Input Multiple Output (MIMO) radar against Spherically Invariant Random Vector (SIRV) clutter. First, we develop the MIMO signal model to two polarimetric channels and SIRV clutter-dominated scenario, and then the Generalized Likelihood Ratio Test (GLRT) is derived with known covariance structure. Meanwhile, three estimation strategies of covariance, such that Sampled Covariance Matrix (SCM), Normalized Sampled Covariance Matrix (NSCM) and Fixed Point Estimation (FPE) matrix, are introduced to make derived receiver fully adaptive. A thorough performance assessment is given by several numerical examples, and the results show that the polarimetric diversity and the spatial diversity can be exploited to improve the detection performance, and it outperforms the conventional polarimetric phased-array counterpart. Meanwhile, the FPE strategy is more suitable to implement the adaptive detection algorithm, the adaptive loss of which is completely acceptable in practical applications.     

机载MIMO雷达广义最大似然检测器

该文针对机载MIMO雷达在未知统计特性的杂波中目标检测问题,首先给出广义最大似然(GLRT)检测器(MIMO-GLRT),利用MIMO雷达的空间分集特性提高检测性能,并推导出检测概率和虚警概率表达式。然后,基于MIMO雷达杂波协方差矩阵的块对角特性,给出一种简化MIMO-GLRT检测器,大大减小算法的复杂度,同时降低对参考单元数目的要求,并在只有两个接收雷达单元的情形下,推导出简化GLRT检测性能的表达式。结果表明,上述两种检测器相对于杂波协方差矩阵都具有恒虚警特性,能够在未知杂波背景下有效地检测目标。     

复合高斯杂波中距离扩展目标的参数化Wald检测

本文研究复合高斯杂波环境中的距离扩展目标的自适应检测问题。有色杂波采用参数未知的自回归（AR）过程描述。结合Wald检测准则,仅需对H1假设条件下的未知参数进行最大似然估计,给出了一种新的基于参数化模型的扩展目标检测器——参数化Wald检测器。该检测器的检验统计量可解释为首先针对各个待测单元分别计算检验统计量,然后将所有待测单元的输出进行非相参累加,其对杂波的随机功率起伏具有恒虚警率(CFAR)特性。相比于常规的基于协方差矩阵的检测方法,参数化检测算法的执行过程不需要依赖辅助数据,仅利用待测扩展目标数据即可实现自适应处理,有效缓解了训练压力并降低了计算量。仿真实验表明,所提出的参数化Wald检测器的检测性能优于之前提出的参数化广义似然比检测器的性能。      

Performance Analysis of Colocated MIMO Radars with Randomly Distributed Arrays in Compound-Gaussian Clutter

This paper focuses on the target detection problem for colocated MIMO radar with randomly distributed arrays in the presence of compound-Gaussian clutter with unknown Power Spectral Density (PSD). First, we derive the Generalized Likelihood Ratio Test (GLRT) assuming known covariance structure, and then, three different covariance estimation strategies, i.e., Sampled Covariance Matrix (SCM), Normalized Sampled Covariance Matrix (NSCM), and Fixed Point Estimation (FPE) Matrix, are introduced in place of the exact one to make the derived GLRT fully adaptive. Thorough numerical evaluations of the detection performance are provided and discussed.     

基于杂波模型的相干雷达极化自适应恒虚警检测算法

该文研究了高斯杂波背景下相干雷达极化自适应检测问题。利用雷达回波的极化信息来提高检测性能。提出了一种基于杂波模型的极化自适应恒虚警检测算法,该算法比极化自适应匹配滤波器算法有更小的估计损失,并推导出了虚警概率表达式。仿真结果表明,所提出的检测算法相对于杂波协方差矩阵都有恒虚警的性质。     

复合高斯杂波环境中分布性目标相干雷达恒虚警检测算法研究

本文研究了在未知统计特性的复合高斯杂波环境中对分布性目标检测的问题。利用Toeplitz矩阵的次对称性提出了一种新的杂波协方差矩阵结构的估计方法,并用此估计方法来实现基于广义似然比检验的恒虚警检测算法。该算法能够在不需要杂波功率谱密度对称的情况下作到对杂波的结构分量和协方差矩阵都有恒虚警的性质,而且该实现方法的检测性能优于先前提出的实现方法。     

Subspace detection for range and Doppler distributed targets with Rao and Wald tests

For the detection problem caused by the point target broken up into the distributed target in the high-range resolution radar system, adaptive detection algorithms based on the Rao and the Wald tests are proposed. The distributed target, which is modeled as a subspace random signal, may be distributed both in range and also in Doppler frequency axes. The clutter is modeled as a spherically invariant random process (SIRP). The unknown parameters are estimated by maximum likelihood estimation only under hypothesis H₀ in Rao test, while only under hypothesis H₁ in Wald test. Interestingly the proposed non-adaptive detectors from different deriving progresses have the same final form, which perform an incoherent processing over all the cells under test and ensure CFAR property with respect to the unknown statistics of the clutter texture component. In the end, performances of the proposed adaptive detectors are assessed through Monte Carlo simulations and are shown to have better detection performance compared with existing similar detectors.     

非高斯海杂波背景最优相干检测性能分析

非高斯海杂波背景下雷达目标检测一直是研究的重点。海杂波分布参数形式的多样化使得检测器对杂波平均功率的恒虚警特性无法保证,因此文中对描述非高斯海杂波的K分布、广义Pareto分布和逆高斯纹理复合高斯分布的参数形式进行了规范化,得到了规范化后的三种杂波分布对应的最优相干检测器。理论分析和实验验证了三个自适应检测器分别对杂波平均功率、目标多普勒频率和散斑协方差矩阵具有恒虚警特性。仿真实验表明:散斑协方差矩阵估计方法和辅助数据的数量均影响着自适应检测器的检测概率和虚警概率。     

恒虚警极化自适应子空间检测器

本文研究了在未知统计特性的局部均匀高斯杂波环境中的相干雷达极化自适应目标检测问题.基于广义似然比检验提出极化自适应子空间检测器,并推导出虚警概率和检测概率的理论表达式.研究结果证明该算法在均匀杂波环境和局部均匀杂波环境都有恒虚警的性质.比较了均匀杂波环境下极化自适应子空间检测器、极化广义似然比检验和极化自适应匹配滤波器算法的检测性能,仿真分析了不同极化状态局部均匀杂波环境下极化自适应子空间检测器算法在的检测性能.     

复合高斯杂波中MIMO雷达DOA估计的克拉美-罗下限

该文研究了复合高斯杂波下MIMO雷达DOA估计的平均克拉美-罗下限(Average CRB, ACRB)。首先,给出了MIMO雷达信号与杂波模型;然后,推导出目标DOA估计ACRB的一般关系式,在此基础上给出当杂波texture分量满足反Gamma分布时,ACRB的闭合表达式;接着,研究了目标DOA估计的中断CRB(Outage CRB),克服了一个发射单元时ACRB发散的问题;最后进行计算机仿真并给出结论。结果表明MIMO雷达的空间分集能有效地降低其DOA估计ACRB,且ACRB随发射单元数量的提高而减小;另外在相同条件下,MIMO雷达在复合高斯背景下DOA估计的ACRB大于相应高斯背景下的ACRB。     

Polarimetric Detection of Targets in Heavy Inhomogeneous Clutter

Polarization diversity has proved to be a useful tool for radar detection, especially when discrimination by Doppler effect is not possible. In this paper, we address the problem of improving the performance of polarimetric detectors for targets in heavy inhomogeneous clutter. First, we introduce a new polarimetric radar model that includes the realistic dependence of the clutter reflections on the transmitted signal. Then, we develop a polarimetric detection test that is robust to inhomogeneous clutter. We run this polarimetric test against synthetic and real data to assess its performance in comparison with existing polarimetric detectors. Finally, we propose a polarimetric waveform-design algorithm to further improve the target-detection performance. A numerical analysis is presented to demonstrate the potential performance improvement that can be achieved with this algorithm.     

不需要训练数据的分布式MIMO雷达距离扩展目标检测器

在非均匀杂波环境中,研究了高分辨率分布式多输入多输出(MIMO)雷达的距离扩展目标检测问题。由于分布式MIMO雷达观测到的杂波是非均匀的,无法获得足够的独立同分布的均匀训练数据来估计检测单元的杂波协方差矩阵。采用复逆Wishart分布对杂波协方差矩阵建模,基于该杂波模型设计了一种不需要训练数据的分布式MIMO雷达距离扩展目标广义似然比检测器。数值仿真结果表明:在非均匀杂波环境中,所设计的检测器的性能比用训练数据的协方差矩阵类检测器有明显的改进。     

均匀和部分均匀杂波中子空间目标的斜对称自适应检测方法

在雷达目标的自适应检测领域, 当参考单元数不足时, 充分挖掘协方差矩阵的结构信息是有效提高检测性能的途径之一。为此, 针对多维子空间目标的检测问题, 该文在协方差矩阵关于次对角线具有斜对称结构的约束下, 分别基于一步和两步广义似然比(GLRT), 推导了均匀和部分均匀杂波中的斜对称自适应检测器。由于检测器在设计阶段利用了协方差矩阵的结构信息, 仿真结果表明, 与已有检测器相比, 在参考单元数不足时, 斜对称自适应检测器可明显改善检测性能。此外, 分别从协方差估计方法的影响、目标子空间维数的影响、目标子空间失配性能以及目标起伏的影响4个方面对检测性能进行了仿真分析。      

非均匀环境下基于知识辅助的扩展目标Wald检测器

针对分布式多输入多输出（Multiple Input Multiple Output, MIMO）雷达中运动扩展目标的检测问题,本文首先假设每个发射-接收天线组的干扰信号协方差矩阵为互不相同的随机矩阵,以模拟实际的非均匀工作环境。然后引入知识辅助模型,建立先验信息矩阵,描述非均匀环境下的干扰信号特性,其中所有发射-接收天线组的干扰协方差矩阵服从以先验信息矩阵为基础的逆Wishart分布。在此基础上,设计了一种基于知识辅助的Wald（KA-Wald）检测器。仿真实验表明,在小样本的情况下,本文设计的KA-Wald检测器在检测性能上优于传统Wald检测器。而与已有的基于知识辅助的广义似然比检验（KA-GLRT）检测器相比,检测性能相近,但是计算效率更高。      

Performance analysis of GLRT-based adaptive detector for distributed targets in compound-Gaussian clutter

The problem of adaptive detection for spatially distributed targets in compound-Gaussian clutter is studied. We first derive the optimum NP detector and suboptimum two-step GLRT detector. For the two-step detection strategy, we also introduce three covariance matrix estimation strategies and evaluate their CFAR properties and complexity issues. Next, the numerical results are presented by means of Monte Carlo simulation strategy. In particular, the simulation results highlight that the performance loss due to adaptively estimating the texture is negligible, and that the loss due to adaptively estimating covariance matrix largely depends on the estimation algorithm, the number of the secondary data vectors and the number of the scatterers.     

Knowledge-aided Bayesian radar adaptive detection in heterogeneous environment: GLRT, Rao and Wald tests

Adaptive filtering is an effective method for clutter suppression and radar detection. However, the performances degrade severely if the environment is heterogeneous. To solve this problem, we resort to a Bayesian framework and design knowledge-aided detectors under partially homogeneous model assumption, which outperform their conventional counterparts in heterogeneous environment. It is also proved that the proposed Bayesian generalized likelihood ratio test (GLRT) coincides with the Bayesian Rao and Wald tests, under the assumption that the covariance matrix of the cell under test is proportional to that of the training data.     

复合高斯杂波加热噪声中基于Rao检验的分布式目标检测

随着雷达分辨率的不断提高,每个距离单元中分布的杂波能量逐渐减少,当杂噪比低于10dB时,热噪声对检测性能的影响是不可以忽略的。针对低杂噪比的情况,在复合高斯杂波加热噪声的背景中研究了分布式目标的检测问题。首先假设内部热噪声和外部杂波统计独立,在给定杂波纹理分量τ的前提下,将白高斯热噪声加上由球不变随机向量表示的复合高斯杂波之后的总干扰近似等效处理成一个新的复合高斯杂波,只是将其参数做了适当调整。然后将分布式目标建模为在距离维和Doppler频率维同时扩展的子空间模型,基于Rao检验构造了N-Rao检测器。通过对N-Rao检测器虚警概率的计算表明,在不存在目标的假设下,虚警概率只由脉冲重复数N、分布式目标占据的实际距离单元数H、每个距离单元内目标散射点总数目Nt来决定,即N-RAO检测器具有恒虚警率特性。最后通过Monte Carlo仿真实验表明,杂波形状参数v的减少与CNR的增加都会使N-RAO检测器的检测性能有所提高,且在低杂噪比的情况下,N-RAO检测器有很好的检测性能。