一种非高斯噪声中未知参数的信号的检测方法

研究了非高斯噪声中具有未知参数的信号的渐进最优检测，应用非高斯噪声中线性模型信号以及随机信号的Rao检验，导出了Rao检测的解析式，并与广义似然比检验的性能做比较。仿真结果表明，该检测器性能大大优于传统的能量检测器和高斯噪声假设下的广义似然比检测器。     

高斯混合噪声中弱信号的Rao检测方法

期望最大化(Expectation Maximization,EM)算法是求解参数最大似然估计(MLE)的最优迭代算法,但若参数初始化不恰当,会使估计值落入"初值陷阱",导致错误的参数估计值.为此,本文提出了估计高斯混合噪声参数的矩 - EM算法,即先求参数的矩估计,并用矩估计值初始化参数,再通过EM迭代算法估计参数.在此基础上,经高斯化滤波,导出了高斯混合噪声背景下未知幅度弱信号的Rao检验统计量.仿真结果表明,矩 - EM算法可以更准确地估计噪声参数;基于矩 - EM算法的Rao检测性能优于基于EM算法的Rao检测性能.     

非高斯噪声中信号的神经网络检测

论述了用多层感知器神经网络检测非高斯噪声中确知信号与随机信号的工作原理，网络结构和训练算法，讨论了几种非高斯噪声中信号的神经网络检测器性能。计算机仿真证明，在非高斯噪声条件下神经网络检测性能优于线性最佳匹配滤波器检测器和局部最佳检测器。     

基于SαS分布的高斯化处理研究

针对非高斯背景干扰下的弱信号检测需求,在综述高斯化处理与扩展匹配滤波概念及思路的基础上,该文提出了两种基于对称α稳定分布建模的高斯化处理方法,构建了相应的扩展匹配滤波检测器,并对比早先建立的混合高斯分布下的高斯化处理,对其高斯化效果、对应检测性能、运行速度等进行了仿真研究。研究表明,高斯化处理可以降低背景干扰的非高斯性,从而提高后续匹配滤波的检测性能;基于对称α稳定分布的高斯化处理,在保持性能的同时,具有更高的运行效率。     

复合高斯杂波中知识辅助的自适应检测算法

研究了在复合高斯杂波中利用先验知识自适应检测目标的问题,证明了基于双参数逆高斯分布纹理的复合高斯模型比传统的K分布、复t分布和单参数逆高斯分布纹理的复合高斯分布模型能够更好地拟合实际杂波。文中选择双参数逆高斯分布作为纹理分量的先验分布、基于广义似然比准则和贝叶斯方法设计得到了一种复合高斯杂波中的自适应检测器。理论分析和数值仿真表明,与自适应匹配滤波器和正则化自适应匹配滤波器相比,该检测器具有更好的检测性能     

α稳定噪声中基于双参数CGM模型的Rao检测

针对α稳定分布概率密度函数无闭式表达的问题,给出了一种解析的近似模型,该模型采用双参数的柯西和高斯混合形式.由分数低阶矩,给出了混合比率的解析表达式.同传统的柯西-高斯混合模型和高斯混合模型相比,该模型具有完全的解析形式.基于该模型,导出了a稳定噪声条件下正弦信号的Rao检测统计量.通过仿真给出了不同特征指数α时Rao...     

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。      

复合高斯杂波中自适应目标检测算法

本文研究了复合高斯杂波中已知给定距离-多普勒单元多普勒频率、未知目标幅度的自适应检测目标的问题。杂波散斑分量用未知自回归系数的自回归过程建模表示,通过求解自回归系数和目标幅度的最大似然估计,推导得到复合高斯杂波中的广义似然比检测器。在目标信号向量维数很大的情况下,该检测器对激励信号方差与纹理分量的乘积具有恒虚警率特性。数值仿真表明该检测器性能优于正则化自适应匹配滤波器。最后分析了目标信号向量维数、自回归模型阶数在不同信杂比下对检测器性能的影响。      

基于Bayes框架的复合高斯杂波下稳健检测

复合高斯杂波中的纹理分量决定了杂波的非高斯性,而纹理分量的不确定性会影响常规检测器的性能。基于 Bayes 框架,该文采用先验分布描述杂波纹理分量的不确定性,分析先验模型选择对检测器检测性能与稳健性的影响。先验信息模型包括无信息先验分布和有信息先验分布。无信息先验分布包括Jeffery先验模型和广义无信息先验模型两种,所得到的检测器结构就是归一化匹配滤波器(NMF)。有信息先验模型采用共轭先验分布,得到的是一种知识辅助的归一化匹配滤波器(KA-NMF),该检测器结构与判决门限都是先验分布参数的函数,该文分析了 KA-NMF 检测性能对先验分布参数的敏感性。进一步采用无信息先验模型描述先验分布参数,可以获得分层Bayes 归一化匹配滤波器(HB-NMF)。计算机仿真与实测海杂波数据分析结果表明,HB-NMF 的性能与分布参数无关,稳健性优于KA-NMF,而检测性能优于NMF。     

Detection in incompletely characterized colored non-Gaussian noisevia parametric modeling

The problem of detecting a weak signal known except for amplitude in incompletely characterized colored non-Gaussian noise is addressed. The problem is formulated as a test of composite hypotheses, using parameteric models for the statistical behavior of the noise. A generalized likelihood ratio test (GLRT) is employed. It is shown that for a symmetric noise probability density function the detection performance is asymptotically equivalent to that obtained for a similar detector designed with a priori knowledge of the noise parameters. Non-Gaussian distributions are found to be more favorable for the purpose of detection than the Gaussian distribution. The computational burden of the GLRT may be partially reduced by employing a Rao efficient score test which shares all the nice asymptotic properties of the GLRT for small signal amplitudes. Computer simulations of the performance of the Rao detector support the theoretical results     

Performance analysis of a two-stage Rao detector

This paper deals with the problem of adaptive signal detection in the presence of Gaussian disturbance with unknown covariance matrix. A new two-stage Rao test detector is proposed, which is obtained by cascading a GLRT-based subspace detector (SD) and the Rao test. The statistical characterization for the proposed two-stage test statistic is provided, under both noise-only and signal-plus-noise hypotheses. The associated probability of false alarm (P_fa) and probability of detection (P_d) are derived in closed form. Performance of the proposed detector is demonstrated by simulation studies, wherein recently proposed detectors are involved for performance comparison. The results show that our detector can achieve better robustness with respect to (w.r.t.) the existing two-stage Rao test detector (AMF-RAO), and has better selectivity than the improved adaptive sidelobe blankers (WAS-ASB and KWAS-ASB) for small values of system parameters.     

An adaptive detector for deterministic signals in noise of unknownspectra using the Rao test

An adaptive detector for a known deterministic signal of unknown amplitude in Gaussian noise of unknown spectra is described. The detector is based on the Rao test, which is asymptotically equivalent to the generalized likelihood ratio. The detector achieves constant false alarm probability in the presence of large changes in input noise bandwidth and variance while providing optimum detection performance. The results are supported by simulation     

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。      

A CFAR adaptive subspace detector for second-order Gaussian signals

We study the problem of detecting subspace signals described by the Second-Order Gaussian (SOG) model in the presence of noise whose covariance structure and level are both unknown. Such a detection problem is often called Gauss-Gauss problem in that both the signal and the noise are assumed to have Gaussian distributions. We propose adaptive detectors for the SOG model signals based on a single observation and multiple observations. With a single observation, the detector can be derived in a manner similar to that of the generalized likelihood ratio test (GLRT), but the unknown covariance structure is replaced by sample covariance matrix based on training data. The proposed detectors are constant false alarm rate (CFAR) detectors. As a comparison, we also derive adaptive detectors for the First-Order Gaussian (FOG) model based on multiple observations under the same noise condition as for the SOG model. With a single observation, the seemingly ad hoc CFAR detector for the SOG model is a true GLRT in that it has the same form as the GLRT CFAR detector for the FOG model. We give an approximate closed form of the probability of detection and false alarm in this case. Furthermore, we study the proposed CFAR detectors and compute the performance curves.     

A New Nonstationarity Detector

A new test to determine the stationarity length of a locally wide sense stationary Gaussian random process is proposed. Based on the modeling of the process as a time-varying autoregressive process, the time-varying model parameters are tested using a Rao test. The use of a Rao test avoids the necessity of obtaining the maximum likelihood estimator of the model parameters under the alternative hypothesis, which is intractable. Computer simulation results are given to demonstrate its effectiveness and to verify the asymptotic theoretical performance of the test. Applications are to spectral analysis, noise estimation, and time series modeling.     

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.     

Detection of weak signals in non-Gaussian noise

A locally optimum detector structure is derived for the detection of weak signals in non-Gaussian environments. Optimum performance is obtained by employing a zero-memory nonlinearity prior to the matched filter that would be optimum for detecting the signal were the noise Gaussian. The asymptotic detection performance of the locally optimum detector under non-Gaussian conditions is derived and compared with that for the corresponding detector optimized for operations in Gaussian noise. Numerical results for the asymptotic detection performance are shown for signal detection in noise environments of practical interest.