Noncoherent summation of multiple reduced-rank test statistics forfrequency-hopped STAP

A new noncoherent CFAR detection algorithm for STAP radar is developed, which noncoherently integrates a number of coherent, reduced-rank, CFAR tests developed by the authors in a previous paper. This new noncoherent detection criterion provides a significant improvement in detection performance with no need to increase the transmitter power     

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.     

一种改进的小波去噪方法在OTDR中的应用

光时域反射仪(OTDR)测试中,系统链路噪声严重影响测试准确度,尤其是对于0.05~0.2 dB熔接损耗的准确检测,需要提高测试曲线的信噪比。根据OTDR波形特点,在小波阈值去噪的基础上提出一种改进的小波去噪方法,改进阈值利用对数函数的非线性并引入噪声方差,更利于滤除噪声;改进阈值判断函数留下原始数据细节并滤除噪声,且不改变反射事件和非反射事件的波形属性。实际测试表明:测试曲线相对信噪比提升了10 dB以上,去噪后配以曲线平滑滤波和恒虚警(CFAR)检测,准确检测出熔接事件。     

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)     

基于有序数据可变索引的SAR图像目标检测方法

该文提出了一种基于有序数据可变索引(Ordered Data Variability Index, ODVI)的SAR图像目标恒虚警检测算法,该算法首先对待测像素的参考窗进行基于ODVI的自适应筛选处理(Automatic Censoring, AC),以去除窗内的强杂波和干扰像素,并以窗内保留的均匀像素对背景的统计特性进行建模,估计其概率密度函数的参量,同时构建双参数恒虚警检测(CFAR)的检验统计量,计算检测的自适应阈值,实现检测的判决。论文给出了该算法的检测性能曲线,并利用实测的X波段SAR图像进行实验验证,与其它检测方法进行比较,结果显示该文算法具有较好的检测性能和较低的虚警概率。     

训练样本不足时的子空间信号检测方法

为了解决训练样本不足时的子空间信号检测问题,提出了两种有效的降秩检测器.基于主分量分析(PCA)的思想,先把常规自适应子空间检测器中采样协方差矩阵(SCM)的求逆运算用噪声特征子空间矩阵与其共轭转置的乘积代替,构造降秩子空间检测器;为进一步提高算法稳健性,把降秩子空间检测器的求逆运算用Moore-Penrose逆代替.仿真结果表明,所提方法在训练样本充足及不足时,均比现有方法具有更好的检测性能.     

A fast CFAR detection space-time adaptive processing algorithm

All of the conventional CFAR detection algorithms that use space-time processing involve a time-consuming matrix-inversion operation. Based on today's technology, this computational complexity sometimes makes the full-rank solution difficult to realize. In this correspondence, a CFAR detection algorithm, which does not need a matrix inversion, is developed by an adaptation and extension of Hotelling's principal-component method studied recently by Kirsteins and Tufts (1994). Finally, the performance of the new CFAR test statistic is analyzed, and the effect of the rank reduction on performance is evaluated for an example scenario     

DS-CDMA系统中基于信号子空间的盲降秩多用户检测

 本文提出了一种基于可变阈值的降秩子空间选择算法及改进维数估计的盲降秩多用户检测技术.采用可变阈值的降秩子空间选择算法,能较快地得到合适的降秩子空间,且计算结果具有可重用性.在子空间追踪中用一种改进的AIC准则进行维数估计,在不提高误差概率的基础上,降低了维数估计的计算量.在维数过高估计时,分析了采用降秩算法的检测性能.仿真结果表明,该算法能用较低的计算复杂度满足系统要求的检测性能.     

一种非均匀Weibull杂波背景中的CFAR检测器

针对传统恒虚警(CFAR)算法在非均匀环境下,待检测单元(CUT)与参考窗的分辨单元不具有独立同分布(IID)特性,检测器性能出现剧烈下降的问题,提出一种新的CFAR检测器。该检测器首先引入一种M-N杂波边缘二元积累实现非均匀杂波边缘提取;然后,对数据平面内相邻杂波边缘内的数据,利用一种地形特征分类算法实现对地形的分类编号;最后,根据地形编号选择与CUT相同地形的分辨单元作为参考单元实现CFAR检测,则所选择的参考单元与CUT具有IID特性。利用实测数据验证M-N杂波边缘二元积累检测算法和地形特征分类算法的有效性。计算机仿真证明:文中提出的CFAR检测器的性能,比传统CFAR检测器的性能有明显提升。     

MIMO阵列雷达检测器的性能分析

 本文提出了一种适用于MIMO阵列雷达系统的空域降维检测器,该检测器先将接收到的观测矢量按所对应的不同分集波形分别在接收阵列上进行空时自适应Capon滤波,然后基于各个分集波形的滤波输出设计CFAR检验统计量,从而避免了在高维虚拟阵列上进行完全自适应处理而带来的高计算复杂度和对训练数据量的过分需求.理论推导表明,在大杂噪比条件下,该检测器可等价地表述为各个分集波形自适应Capon滤波输出结果的相参积累与非相参积累之比.同时,大杂噪比条件下的检测性能分析表明,随着波形分集数的增长:当各发射阵元辐射功率一定时,该检测器的检测性能逐渐接近完全自适应CFAR检测器,而计算复杂度以及对训练数据的需求几乎不变;而当总发射功率一定时,其检测性能先是快速增长,然后呈阶梯式缓慢增长,且存在性能上限.这些结论对于MIMO阵列雷达系统设计来说具有一定的指导意义.     

Adaptive Detection and Interference Rejection of Multiple Point-Like Radar Targets

We present an adaptive algorithm aimed at detecting multiple point-like radar targets embedded in correlated Gaussian noise. The proposed detector modifies and improves the adaptive beamformer orthogonal rejection test (ABORT) idea to address detection of multiple targets. More precisely, it relies on the so-called two-step generalized likelihood ratio test (GLRT) design procedure implemented without assignment of a distinct set of secondary data. The newly proposed detector can guarantee the constant false alarm rate (CFAR) property and the performance assessment, conducted resorting to simulated data, has shown that it exhibits better rejection capabilities of mismatched signals than previously proposed detectors, at the price of an acceptable performance loss for matched signals     

Robust radar detection of CA, GO and SO CFAR in Pearson measurements based on a non linear compression procedure for clutter reduction

This paper deals with the constant false alarm rate (CFAR) radar detection of targets embedded in Pearson distributed clutter. We develop new CFAR detection algorithms-notably cell averaging (CA), greatest of selection (GO) and smallest of selection SO-CFAR operating in Pearson measurements based on a non-linear compression method for spiky clutter reduction. The technique is similar to that used in non uniform quantization where a different law is used. It consists of compressing the output square law detector noisy signal with respect to a non-linear law in order to reduce the effect of impulsive noise level. Thus, it can be used as a pre-processing step to improve the performance of automatic target detection especially in lower generalised signal-to-noise ratio (GSNR). The performance characteristics of the proposed CFAR detectors are presented for different values of the compression parameter. We demonstrate, via simulation results, that the pre-processed compression procedure is computationally efficient and can significantly enhance detection performance.     

Detection performance of the reduced-rank linear predictor ROCKET

This paper assesses the frequency detection capabilities of a new signal-dependent reduced-rank linear predictor applied to autoregressive spectrum estimation. The new technique is called reduced-order correlation kernel estimation technique (ROCKET). Its detection performance is examined by comparison to a full-rank autoregressive (FR-AR) estimator and two reduced-rank principal component autoregressive (PC-AR) estimators based on both the standard signal-independent version and a modified signal-dependent method. The performance of the new autoregressive estimator is also compared as a function of rank to the popular pseudo-spectrum estimator MUSIC. The performance metrics examined are the probability of detection (P/sub D/) and the false alarm rate (FAR) of detecting the spatial frequencies of plane waves impinging on a uniform line array in additive white Gaussian noise. These metrics are studied as a function of subspace rank, sample support, and signal-to-noise ratio (SNR). Simulations show that the signal-dependent reduced-rank estimators significantly outperform both the signal-independent version of PC-AR and the FR-AR estimator for low sample support and low SNR environments. One notable characteristic of ROCKET that highlights its distinct subspace selection is its performance as a function of subspace rank. It is observed that for equal powered signals, its peak performance is nearly invariant to signal rank and that at almost any subspace rank ROCKET meets or exceeds FR-AR performance. This provides an extra degree of robustness when the signal rank is unknown.     

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。      

有序统计恒虚警率技术综述

在噪声或杂波环境中进行自适应雷达目标检测是每部雷达接收机中非常重要的设计。在几乎所有的检测程序中,都将接收回波信号幅度与某一门限作简单比较。目标检测的主要目的是在极低的恒虚警率(CFAR)约束条件下使目标检测概率最大化。噪声和杂波背景可以用一个统计模型来加以描述,如独立相同瑞利模型,或用已知平均噪声功率的指数分布随机变量进行描述。但是在实际应用中,平均噪声或杂波功率绝对是未知的,并且还会随着距离、时间和方位角发生变化。因此,对用于几种不同背景信号情况的某些距离CFAR技术进行描述。在这些背景信号情况下,平均噪声功率和另外一些统计参数都被假设是未知的。因而所有的距离CFAR技术都通过将幅度门限应用于检测单元内的回波信号幅度,把估算流程(用以获取噪声功率的精确值或估算值)与判定步骤结合起来。许多研究工作都分析了这种通用的检测方案,对这一课题投入了大量精力。对这些重要的距离CFAR检测方案中的几种作一简短描述,然后进行技术比较。     

A generalized switching CFAR processor based on test cell statistics

In this paper, a new constant false alarm rate (CFAR) thresholding algorithm which is a generalisation of the switching CFAR (S-CFAR) that takes into account the statistics of the sample in the test cell for reference sample selection is proposed. It employs a composite approach based on the switching CFAR and the order statistic CFAR (OS-CFAR). A mathematical analysis in a homogeneous environment is provided for this detector. The results obtained show that the detection performance of the generalised S-CFAR (GS-CFAR) is improved both in homogenous background and non-homogenous environment caused by interfering targets and clutter edge.     

Large system transient analysis of adaptive least squares filtering

The performance of adaptive least squares (LS) filtering is analyzed for the suppression of multiple-access interference. Both full-rank LS filters and reduced-rank LS filters, which reside in a lower dimensional Krylov space, are considered with training, and without training but with known signature for the desired user. We compute the large system limit of output signal-to-interference-plus-noise ratio (SINR) as a function of normalized observations, load, and noise level. Specifically, the number of users K, the degrees of freedom N, and the number of training symbols or observations i all tend to infinity with fixed ratios K/N and i/N. Our results account for an arbitrary power distribution over the users, data windowing (e.g., recursive LS (RLS) with exponential windowing), and initial diagonal loading of the covariance matrix to prevent ill-conditioning. Numerical results show that the large system analysis accurately predicts the simulated convergence performance of the algorithms considered with moderate degrees of freedom (typically N=32). Given a fixed, short training length, the relative performance of full- and reduced-rank filters depends on the selected rank and diagonal loading. With an optimized diagonal loading factor, the performance of full- and reduced-rank filters are similar. However, full-rank performance is generally much more sensitive to the choice of diagonal loading factor than reduced-rank performance.     

基于GIS信息的CFAR检测器

在雷达信号检测过程中,为了实现恒虚警处理,必须采用动态门限。恒虚警检测器的门限设置通常是利用待检测单元附近的距离单元杂波数据进行计算得到的。然而,杂波环境的非均匀性导致了杂波功率随着距离变化剧烈,常规的恒虚警检测器性能会显著下降。文中给出了基于地理信息系统的恒虚警检测算法,利用对杂波环境的了解程度,可以显著提高CFAR检测器的性能。利用IPIX雷达实测数据,验证了该算法性能优于常规的其他CFAR处理器。     

CFAR detection of multidimensional signals: an invariant approach

The paper deals with constant false alarm rate (CFAR) detection of multidimensional signals embedded in Gaussian noise with unknown covariance. We attack the problem by resorting to the principle of invariance,which proves a valuable statistical tool for ensuring a priori, namely at the design stage, the CFAR property. In this context, we determine a maximal invariant statistic with respect to a proper group of transformations that leave unaltered the hypothesis-testing problem under study, devise the optimum invariant detector, and show that no uniformly most powerful invariant (UMPI) test exists. Thus, we establish the conditions an invariant detector must fulfill in order to ensure the CFAR property. Finally, we discuss several suboptimal (implementable) invariant receivers and, remarkably, show that the generalized likelihood ratio test (GLRT) detector is a member of this class. The performance analysis, which has been carried out in the presence of a Gaussian signal array, shows that the proposed detectors exhibit a quite acceptable loss with respect to the optimum Neyman-Pearson detector.