复合高斯杂波下距离扩展目标的OM-GLRT

广义似然比检测(Generalized Likelihood Ratio Test,GLRT)是解决复合高斯杂波下距离扩展目标检测问题的一种有效方法,而当目标速度未知时,对于毫米波等高频雷达而言,速度估计误差将造成方向矢量(steering vector)失配,从而导致GLRT性能的严重下降.此时,如何设计最佳的GLRT检测器就成为一个优化问题.本文在分析方向矢量失配对GL-RT影响的基础上提出了一种最优匹配GLRT(Optimum Matched GLRT,OM-GLRT)方法.仿真结果表明,OM-GLRT能有效地实现对速度未知目标的检测.     

非均匀环境中的分布目标自适应检测

该文研究了非均匀环境中的分布目标和多点目标的检测。其中,假设辅数据协方差矩阵服从以主数据协方差矩阵为条件的逆Wishart分布,且均值与之成比例。首先给出主数据协方差矩阵、比例因子和目标幅度的最大似然估计(MLE),然后基于贝叶斯理论和广义似然比(GLRT)判决准则提出了一种检测器。当目标只存在单个距离门时,检测器和自适应相干估计器(ACE)一致;当目标跨越多个距离门时,检测器和广义自适应子空间检测器(GASD)一致。但不同在于ACE和GASD都是基于未知的确定干扰协方差矩阵的。另外,该检测器具有恒虚警率(CFAR)特性,并且有很好的检测性能。     

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。     

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.     

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。     

局部均匀背景中距离扩展目标的GLRT检测器及性能分析

在复合高斯杂波满足局部均匀的背景下,对均匀杂波分组模型进行推广,建立了广义杂波分组模型.进一步,利用广义似然比检验（GLRT）原理,提出了距离扩展目标的基于广义杂波分组的GLRT检测器（GCC-GLRT）,并推导了检测器虚警概率与检测门限关系的表达式.GCC-GLRT有效地解决了基于均匀杂波分组的GLRT检测器（CC-GLRT）在非均匀杂波分组背景下不具备CFAR特性的问题.仿真结果还表明,对于稀疏散射点目标,在合适的杂波分组情况下,GCC-GLRT能有效克服采用NSDD-GLRT检测器出现的“坍塌损失”,改善检测器的检测性能.     

晴空飞机尾流的相参多普勒雷达探测技术

该文分析了晴空飞机尾流的相参多普勒雷达检测模型,提出了一种用于简化尾流检测器设计的方法,导出了相应的GLRT(广义似然比检验)检测器结构.推导得到了检测概率、虚警概率的解析表达式及其高斯近似形式,并将其推广应用到多个CPI(相参处理间隔)的检测性能分析.研究结果表明,该文提出的检测器可以有效增强飞机尾流的雷达探测性能.     

干扰背景下机载雷达广义似然比检测方法

针对干扰背景下机载雷达多通道检测问题,提出了一步广义似然比检测器(1S-GLRT)和两步GLRT(2S-GLRT)。分析了两种检测器的物理意义,证明了二者均具有恒虚警(CFAR)特性,并给出了检测器的工作流程。在性能评估阶段,通过计算机仿真验证了两种检测器均能有效的抑制干扰并实现目标检测。并且通过不同的参数设置分析了两种检测器的性能差别。结果表明:在低信杂噪比下,1S-GLRT的检测概率比2S-GLRT的略高,当SCNR较高时,二者的检测概率基本相同。但2S-GLRT的计算复杂度要低。     

部分均匀环境下适用于空间对称线阵的修正广义似然比检测方法

针对部分均匀高斯干扰环境下的点目标检测问题,该文基于广义似然比准则(GLRT)提出一种适用于空间对称线阵的修正GLRT检测方法。考虑到采样时存在的目标能量泄漏,在接收信号建模时采用目标能量泄漏采样模型弥补泄漏损失,并基于干扰协方差矩阵的斜对称结构降低对辅助数据的需求,最终联合待检测数据和辅助数据实现未知参数的估计,得到兼具有良好目标检测和距离估计性能的斜对称修正GLRT检测方法。仿真结果表明,该方法不仅在部分均匀环境下具有恒虚警特性,而且在辅助数据数量受限时,相比其同类型的检测方法具有1 dB以上的检测性能优势。      

自适应能量检测器及在失配信号检测中的应用(英文)

自适应能量检测器(AED)是一种信号导向矢量完全未知时有效的检测器, 最早根据广义似然比(GLRT)准则得到。该文首先分析了AED一些重要特性。指出AED与根据Rao准则和Wald准则得到的检测器相同, 并得到了其精确的统计分布, 根据该统计分布进而较容易地推导出AED解析的检测概率(PD)和虚警概率(PFA)。其次利用AED设计了一种信号失配情形下的参数可调检测器。与现有的参数可调检测器相比, 新检测器对失配信号的检测具有更好的灵活度。此外, 对于匹配信号, 新检测器具有更高的检测概率。该可调检测器的这些功能通过调节两个标量参数实现, 两个参数被称为可调参数。      

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.     

基于时频融合的分布式目标的恒虚警率检测

从双门限检测理论出发，将时域、频域双门限检测法结合起来，推导出一种基于时频融合的分布式目标的恒虚警率检测方法。以同一目标的两个不同角域的距离像检测为例，通过仿真验证了时频融合检测法具有较好的检测性能，并将其与时域、频域双门限检测法和广义似然比检测法进行了性能比较。结果表明：时频融合检测法优于广义似然比检测法；而与时域、频域双门限检测法相比，时频融合检测法对不同角域距离像具有良好的稳健性。     

The heuristic, GLRT, and MAP detectors for double differential Modulation are identical

We consider the relationship between several single-symbol detectors for double differential modulation. We have shown in a previous paper that a simple heuristic detector is identical to the generalized likelihood ratio test (GLRT) detector. In this correspondence, we introduce a maximum a posteriori probability (MAP) detector and show that it is identical to the heuristic and GLRT detectors. Consequently, neither GLRT nor MAP can offer any gain over the simple heuristic detector, which means that the latter should be the detector of choice.     

AR-model-based adaptive detection of range-spread targets in compound Gaussian clutter

In this paper, we consider the problem of adaptive detection for range-spread targets with known Doppler and unknown complex amplitude in compound Gaussian clutter. The speckle component of the clutter is modeled as an autoregressive (AR) process. By using the generalized likelihood ratio test (GLRT) approach, we will first estimate the AR parameters and the unknown complex amplitude, and then propose an adaptive AR-based GLR detector. The performance assessments are presented too. The computer simulations show that the proposed detector, without a priori information of the covariance matrix, has the same asymptotical performances as the two-step GLR-based detector with known covariance matrix.     

Detection of random transient signals via hyperparameter estimation

Difficulties arise with the generalized likelihood ratio test (GLRT) in situations where one or more of the unknown signal parameters requires an enumeration that is computationally intractable. In the transient signal detection problem, the frequency characteristics of the signal are typically unknown; therefore, even if an aggregate signal bandwidth is assumed, the estimation problem intrinsic to the GLRT requires an enumeration of all possible sets of signal locations within the monitored band. In this paper, a prior distribution is imposed over those portions of the signal parameter space that traditionally require enumeration. By replacing intractable enumeration over possible signal characteristics with an a priori signal distribution and by estimating the “hyperparameters” (of the prior distribution) jointly with other signal parameters, it is possible to obtain a new formulation of the GLRT that avoids enumeration and is computationally feasible. The GLRT philosophy is not changed by this approach-what is different from the original GLRT is the underlying signal model. The performance of this new approach appears to be competitive with that of a scheme of emerging acceptance: the “power-law” detector     

Novel GLRT packet-data receivers

In this paper we design novel generalized likelihood ratio test (GLRT)-type packet-data detectors for general multiaccess/multiuser digital communication systems and we develop analytical performance evaluation tools for finite data packet sizes. For the known channel case, we derive a coherent GLRT packet-data detector while for the unknown channel case we derive both a coherent pilot assisted GLRT packet-data detector and a differential phase-shift-keying (DPSK) GLRT packet-data detector. Efficient suboptimum implementations of the above schemes that exhibit complexity linear in the packet size are also considered. Simulation studies evaluate the performance of the proposed schemes in the context of packet-data code-division multiple access (CDMA) communications.     

Model-Based Detection of Heart Rate Turbulence Using Mean Shape Information

A generalized likelihood ratio test (GLRT) statistic is proposed for detection of heart rate turbulence (HRT), where a set of Karhunen–LoÈve basis functions models HRT. The detector structure is based on the extended integral pulse frequency modulation model that accounts for the presence of ectopic beats and HRT. This new test statistic takes a priori information regarding HRT shape into account, whereas our previously presented GLRT detector relied solely on the energy contained in the signal subspace. The spectral relationship between heart rate variability (HRV) and HRT is investigated for the purpose of modeling HRV “noise” present during the turbulence period, the results suggesting that the white noise assumption is feasible to pursue. The performance was studied for both simulated and real data, leading to results which show that the new GLRT detector is superior to the original one as well as to the commonly used parameter turbulence slope (TS) on both types of data. Averaging ten ventricular ectopic beats, the estimated detection probability of the new detector, the previous detector, and TS were found to be 0.83, 0.35, and 0.41, respectively, when the false alarm probability was held fixed at 0.1.      

The CFAR adaptive subspace detector is a scale-invariant GLRT

The constant false alarm rate (CFAR) matched subspace detector (CFAR MSD) is the uniformly most-powerful-invariant test and the generalized likelihood ratio test (GLRT) for detecting a target signal in noise whose covariance structure is known but whose level is unknown. Previously, the CFAR adaptive subspace detector (CFAR ASD), or adaptive coherence estimator (ACE), was proposed for detecting a target signal in noise whose covariance structure and level are both unknown and whose covariance structure is estimated with a sample covariance matrix based on training data. We show here that the CFAR ASD is GLRT when the test measurement is not constrained to have the same noise level as the training data, As a consequence, this GLRT is invariant to a more general scaling condition on the test and training data than the well-known GLRT of Kelly (1986)     

典型复杂微动目标的建模和检测

针对战场典型复杂微动目标的检测问题,提出了一种基于能量聚焦的微动目标检测方法。首先对3种复杂微动目标雷达回波的微多普勒特性进行了建模和仿真,分析了回波多普勒的频域和时频域特性,提取了雷达回波多普勒时频分析数据的能量聚焦特性,并提出了一种基于能量聚焦的广义似然比微动目标检测器。数值仿真表明,在不同的信噪比和虚警概率条件下,该检测器均可实现对3种复杂微动目标的有效检测。     

Generalized likelihood ratio detection for fMRI using complex data

The majority of functional magnetic resonance imaging (fMRI) studies obtain functional information using statistical tests based on the magnitude image reconstructions. Recently, a complex correlation (CC) test was proposed based on the complex image data in order to take advantage of phase information in the signal. However, the CC test ignores additional phase information in the baseline component of the data. In this paper, a new detector for fMRI based on a generalized likelihood ratio test (GLRT) is proposed. The GLRT exploits the fact that the fMRI response signal as well as the baseline component of the data share a common phase. Theoretical analysis and Monte Carlo simulation are used to explore the performance of the new detector. At relatively low signal intensities, the GLRT outperforms both the standard magnitude data test and the CC test. At high signal intensities, the GLRT performs as well as the standard magnitude data test and significantly better than the CC test.