基于对角加载的机载MIMO雷达GLRT检测器

本文研究了基于GLRT的机载MIMO雷达自适应目标检测问题.针对参考单元数据不足对GLRT性能的影响,提出了基于对角加载的MIMO雷达GLRT(MIMO-LGLRT)检测器,并对其检测性能进行了分析,给出了虚警概率和检测概率的闭合表达式.该检测器充分利用机载MIMO雷达杂波子空间低秩的特点和对角加载技术,解决了因参考数据不足所引起的矩阵病态问题.为了减少MIMO-LGLRT检测器的运算量,在分析杂波协方差矩阵块对角化特性的基础上,给出一种简化MIMO-LGLRT检测器.理论分析和仿真结果表明,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,检测性能优于普通GLRT检测器.     

一种简单的相干雷达极化恒虚警检测算法

该文提出了高斯杂波背景下一种结构简单的相干雷达极化恒虚警检测算法。利用雷达回波的极化信息来提高检测性能,推导出了虚警概率表达式。研究结果表明,该相干雷达极化恒虚警检测算法相对于杂波协方差矩阵有恒虚警的性质。该检测算法比极化广义似然比检测算法计算量小,易于工程实现,适合于强信杂比和目标信号导向矢量未知的情况。同时仿真分析了不同极化状态时的检测性能。     

零训练样本的集中式MIMO雷达斜对称检测器

传统集中式多输入多输出(MIMO)雷达自适应检测器虽然不需要训练样本即可实现目标检测,但在波形采样数较少时检测性能下降明显。该文利用集中式MIMO雷达噪声协方差矩阵的斜对称结构,基于广义似然比(Generalized Likelihood Ratio Test, GLRT)准则和Wald准则,提出了相应的斜对称检测器,并给出了统计分布特性及检测概率和虚警概率的解析表达式。仿真结果表明所提检测器在波形采样数较少时仍可获得较好的检测性能,同时证明了理论分析的正确性。     

集中式O FDM-MIMO雷达自适应检测算法

针对频率分集条件下，集中式OFDM-MIMO雷达在未知杂波环境中的目标检测问题，首先分析了OFDM-MIMO雷达回波数据模型，由于OFDM-MIMO雷达的频率分集特性，不同频率通道回波数据相互独立，在此基础上，分别基于一步和两步广义最大似然比准则，给出了集中式OFDM-MIMO雷达GLRT和OFDM-MIMO雷达AMF两种检测器，并分析了这两种检测器的恒虚警特性。两种检测器有效利用集中式OFDM-MIMO雷达频率分集特性，提升目标检测性能，同时降低了矩阵求逆维数，以及参考单元数目的要求，并且具有恒虚警性能。计算机仿真验证了算法的有效性。     

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。     

分布式目标的子空间双门限GLRT CFAR检测

 研究了分布式目标在球不变随机变量杂波中的检测问题,提出了一种具有恒虚警特性的双门限广义似然比检测器。分布式目标建模为子空间信号,在距离维和多普勒频率维同时扩展.第一门限的作用是筛选信杂比高的待检测距离单元.将选出的距离单元进行能量积累并与第二门限进行比较做出判决.假设杂波协方差矩阵已知,构造了双门限检测器,并通过推导检测器虚警概率说明其具有恒虚警特性.将基于辅助通道数据的杂波协方差矩阵的估计值替换假设已知的杂波协方差矩阵,得到一个自适应检测器.通过Monte Carlo仿真进行性能分析,说明检测器的有效性和鲁棒性.     

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.     

非均匀杂波背景下双基地MIMO雷达距离扩展目标的GLRT检测

利用球不变随机矢量(Spherically Invariant Random Vector, SIRV)描述非均匀杂波, 建立了双基地多输入多输出(Multiple-Input Multiple-Qutput, MIMO)雷达距离扩展目标的信号检测模型, 提出了距离扩展目标的两步广义似然比检测(Generalized Likelihood Ratio Test, GLRT)算法.首先, 根据目标散射系数的两种假设模型, 分别推导确定型目标、高斯型目标GLRT检测器的解析表达式, 然后利用固定点迭代算法估计杂波协方差矩阵, 获得自适应GLRT(AD-GLRT和AG-GLRT)检测器.仿真实验表明:AD-GLRT和AG-GLRT检测器的检测性能均优于非均匀杂波背景、高斯杂波背景下点目标的检测性能, 且两者的检测性能相当, 并且虚拟阵元数、目标分布的距离单元数, 以及信杂比越大, 两者的检测性能越好.     

一种新的最大选择恒虚警检测器

本文提出了一种基于有序统计和单元平均产生局部估计，并应用最大选择产生检测单元杂波功率水平估计Ｚ的新的恒虚警检测器。我们推导出了该检测器在ＳｗｅｒｌｉｎｇⅡ型目标假设下的虚警概率、检测概率和度量平均判决门限解析表达式。分析了它在均匀背景和强干扰环境中的检测性能。     

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。      

有源干扰条件下机载MIMO雷达STAP协方差矩阵秩的分析

该文分析了正侧阵机载MIMO雷达杂波加有源干扰协方差矩阵的结构,得到杂波加有源干扰协方差矩阵秩的上界为杂波的秩与有源干扰的秩之和减去有源干扰个数。并由此得到MIMO雷达杂波加有源干扰协方差矩阵非满秩而SIMO雷达杂波加有源干扰协方差矩阵满秩时有源干扰个数的范围。当有源干扰的数目在此范围时,SIMO雷达的理论性能严重下降,而MIMO雷达在理论上仍然有足够的自由度来抑制杂波和有源干扰,从而保证有较好的性能。通过仿真实验验证了上述结论。     

The Rao and Wald Tests Designed for Distributed Targets with Polarization MIMO Radar in Compound-Gaussian Clutter

In high resolution radars, the distributed target is usually modeled as a few isolated points referred to multiple dominant scattering centers, while the clutter is a compound-Gaussian model. Additionally, the polarimetric diversity can be exploited to enhance detection performance. Motivated by extending the detection problem of multiple-input multiple-output (MIMO) radar to such cases, this paper mainly addresses distributed targets detection problem with polarization MIMO radar against a compound-Gaussian clutter dominated scenario with unknown covariance matrix. The adaptive detectors based on Rao and Wald criteria are studied, and a two-step design procedure is adopted. Specifically, the Rao and Wald tests are derived by assuming a known covariance matrix, and then a suitable estimation of the covariance matrix based on the secondary data is inserted into the derived detectors to make them fully adaptive. Some numerical results are presented together with a polarization generalized likelihood ratio test (GLRT), showing that the derived detectors provide excellent detection performance in spiky clutter for distributed targets, and that the polarimetric diversity can be exploited to improve detection performance. Overall, the Wald test performs the best.     

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。     

MIMO Radar Space–Time Adaptive Processing Using Prolate Spheroidal Wave Functions

In the traditional transmitting beamforming radar system, the transmitting antennas send coherent waveforms which form a highly focused beam. In the multiple-input multiple-output (MIMO) radar system, the transmitter sends noncoherent (possibly orthogonal) broad (possibly omnidirectional) waveforms. These waveforms can be extracted at the receiver by a matched filterbank. The extracted signals can be used to obtain more diversity or to improve the spatial resolution for clutter. This paper focuses on space-time adaptive processing (STAP) for MIMO radar systems which improves the spatial resolution for clutter. With a slight modification, STAP methods developed originally for the single-input multiple-output (SIMO) radar (conventional radar) can also be used in MIMO radar. However, in the MIMO radar, the rank of the jammer-and-clutter subspace becomes very large, especially the jammer subspace. It affects both the complexity and the convergence of the STAP algorithm. In this paper, the clutter space and its rank in the MIMO radar are explored. By using the geometry of the problem rather than data, the clutter subspace can be represented using prolate spheroidal wave functions (PSWF). A new STAP algorithm is also proposed. It computes the clutter space using the PSWF and utilizes the block-diagonal property of the jammer covariance matrix. Because of fully utilizing the geometry and the structure of the covariance matrix, the method has very good SINR performance and low computational complexity.     

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.     

一种基于信道估计的MIMO雷达空时信号设计方法

多输入多输出雷达的新颖之处在于它利用空间分集和波形分集来改善雷达的性能。合理的波形设计可以更好地实现空间分集,该文在杂波统计特性已知的条件下,提出了一种基于信道参数估计的MIMO雷达空时信号设计方法。该方法在推导出信道参数的线性贝叶斯估计之后,通过最小化贝叶斯估计均方误差的方法来优化发射信号。该方法设计的优化信号为杂波协方差矩阵的函数,论文从信息论的角度验证了该结论的正确性。仿真结果表明MIMO雷达发射该方法设计的信号可改善目标的检测性能。     

基于贝叶斯的高斯杂波背景下MIMO雷达自适应检测算法

对于集中式多输入多输出(Multiple-Input Multiple-Output, MIMO)雷达,该文研究了高斯杂波背景下的目标检测问题。该文假设杂波的协方差矩阵是未知随机的,且服从逆复Wishart分布,基于贝叶斯方法和广义似然比检验准则设计了两种新型自适应检测器。该文提出的贝叶斯检测器具有两个显著的优点:(1)不需要训练数据;(2)杂波的先验知识体现在设计方案中,从而提高了检测性能。仿真结果显示该文提出的贝叶斯检测器的检测性能优于目前常用的非贝叶斯检测器,特别是在发射波形采样数较少时。另外,该贝叶斯检测器在参数失配条件下的性能会有一定程度下降。