一种基于拟合优度检验的恒虚警检测方法

针对传统的基于滑窗自适应门限恒虚警检测方法在非高斯环境下和多目标干扰环境下,性能下降的问题,提出了一种基于拟合优度检验的恒虚警检测方案,该方法利用了目标回波与背景杂波统计特性的差异,通过检验待检单元的回波样本是否服从背景分布来检测目标:如果待检单元样本服从背景分布,则有理由相信待检单元回波源于背景杂波,从而判断没有目标存在;否则,将判断有目标存在.和传统的基于自适应门限的检测方法相比,该方法受背景分布特性和干扰目标的影响很小.仿真实验表明,在尖锐的非高斯杂波环境下以及多目标干扰环境下,都能保持更优的检测性能.     

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)     

Optimality of the cell averaging CFAR detector

The cell averaging constant false alarm rate detector has been assumed to be optimal for detecting Swerling I targets embedded in exponential clutter and noise of unknown power. This is because the detector uses a minimum variance unbiased estimate (which is also a maximum likelihood estimate) of the unknown clutter-plus-noise power to set the threshold. The authors prove, using a result concerning least favorable distributions in composite hypotheses testing, that the cell averaging detector is indeed optimal in that it is a uniformly most powerful detector     

A NEW CFAR DETECTOR WITH GREATEST OPTION

This paper presents a new CFAR detector based on Ordered Statistics (OS) and Cell-Averaging (CA) forming local estimates, and using Greatest-Of selection (GO) to form clutter power level estimate Z in test cell(OSCAGO). Under the Swerling II assumption, the analytic expressions of Pfa,Pd and ADT of this detector are derived, its detection performance in homogeneous background and in strong interfering targets environment are analyzed and compared it with OS, GOSGO detectors. The results show that the detection performance of OSCAGO in homogeneous background and in multiple-target situations are obviously better than those of OS and GOSGO. When the number of interfering targets is equal to certain value, the CFAR loss of OSCAGO is about 3dB less than that of GOSGO.     

CFAR edge detector for polarimetric SAR images

Finding the edges between different regions in an image is one of the fundamental steps of image analysis, and several edge detectors suitable for the special statistics of synthetic aperture radar (SAR) intensity images have previously been developed. In this paper, a new edge detector for polarimetric SAR images is presented using a newly developed test statistic in the complex Wishart distribution to test for equality of covariance matrices. The new edge detector can be applied to a wide range of SAR data from single-channel intensity data to multifrequency and/or multitemporal polarimetric SAR data. By simply changing the parameters characterizing the test statistic according to the applied SAR data, constant false-alarm rate detection is always obtained. An adaptive filtering scheme is presented, and the distributions of the detector are verified using simulated polarimetric SAR images. Using SAR data from the Danish airborne polarimetric SAR, EMISAR, it is demonstrated that superior edge detection results are obtained using polarimetric and/or multifrequency data compared to using only intensity data.     

Automatic censoring CFAR detector for heterogeneous environments

In radar detection, many constant false alarm rate (CFAR) processors have been proposed in the literature. It is well known that a processor is optimal only for one type of environment and that its detection performances are seriously degraded in presence of unknown irregularities. In such situations, the main difficulty resides in the estimation of the background configuration. That is, depending upon the non-homogeneity of the environment, one would choose the adequate optimal detection algorithm among a variety of known conventional ones that offer the best detection probability. Based on unknown transitions; i.e., in the presence of a priori unknown numbers of interfering targets and/or clutter edge, we propose an automatic censoring CFAR (AC-CFAR) detector for heterogeneous Gaussian clutter. The censoring technique used in this work offers a good discrimination between homogeneous and non-homogeneous environments. The proposed detector dynamically switches to the optimal conventional detector CA-, CMLD- or TM-CFAR. The performances of the proposed detector is evaluated and compared to existing detectors in various background situations. Monte Carlo simulations show that the AC-CFAR detector performs like the CA-CFAR in a homogeneous background. Moreover, the proposed detector exhibits considerable robustness in the presence of interfering targets and/or clutter-edge situations.     

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.     

PERFORMANCE EVALUATION OF THE DOUBLE-THRESHOLD CFAR DETECTOR IN MULTIPLE-TARGET SITUATIONS

In radar systems of automatic detection, an estimate of background clutter power is used to set the detection threshold. An interference saturated environment is frequently encountered in these systems （multiple target situations）. Therefore, the detection of signals in such an environment becomes one of the most important problems to be solved. The double-threshold algorithm is one of the more interesting detectors used in these situations. While the first threshold operation ensures that the calculation of the detection （second） threshold is based on a set of samples which is free of strong interferers and is therefore much more representative of the noise level, the second threshold is used to declare the presence or the absence of the radar target. The object of the present paper is to analyze the performance of such type of CFAR schemes when the radar receiver contains a noncoherent integrator amongst its basic elements. It is found that the processor detectability loss is very low and the performance degradation, caused by interferers is quite small even if the number of outlying targets is large, given that the first threshold is properly chosen.     

Trimmed geometric mean order statistic CFAR detector for Pareto distributed clutter

Recent investigations have validated the Pareto class of models for radar backscattering from the sea surface for X-band maritime surveillance radar. As such, there has been much interest in the derivation of sliding window detectors, for operation in such clutter, with the constant false alarm rate property. A general expression is derived, allowing the determination of the probability of false alarm for such detectors, based upon a recently introduced invariant statistic. For a specific example of its application, a trimmed geometric mean order statistic constant false alarm rate detector is developed and compared with some recently derived detectors. It will be shown that this new detector can be designed to not only manage interference in the clutter range profile but can be very effective at managing range spread targets.     

Adaptive subsample delay estimation using a modified quadrature phase detector

An online delay estimation algorithm based upon an analogue quadrature phase detector that is suitable for determining the subsample delay between two noisy sinusoidal signals is introduced. The new technique is based on the recently proposed discrete-time quadrature-delay estimator (QDE). The algorithm uses the in-phase and quadrature-phase components of the received signals to produce a bias-free estimate of the delay between the input signals. The technique directly minimizes the delay error estimate, which is in turn used to adapt the coefficients of a simple fractional-delay filter (FDF). The algorithm is insensitive to variations in the amplitudes of the input signals, and does not require an accurate prior estimate of the frequency of the input sinusoids. The algorithm directly updates the delay error estimate, which is in turn used directly as the coefficient input to an adaptive FDF. The technique is simple to implement and has a reduced complexity compared to other adaptive techniques. Simulations show that in the presence of system noise, the new estimator outperforms conventional FDF-based estimators.     

基于卷积构型的单元平均CFAR目标检测算法

提出一种基于卷积构型的单元平均恒虚警率(convolution based cell averaging constant false alarm rate, CCA-CFAR)快速检测算法.该算法首先根据背景杂波分布模型计算待检测合成孔径雷达(synthetic aperture radar, SAR)图像统计量矩阵, 然后对单元平均恒虚警率(cell averaging constant false alarm rate, CA-CFAR)检测器构建卷积模型, 利用卷积运算实现对背景杂波的矩估计, 并求出详细的背景杂波分布函数, 最后根据分布函数计算出每个像素的判定阈值, 并对所有待检测像素是否为目标点进行判定.该检测算法复杂度低, 运算效率高, 能够快速实现SAR图像实时目标检测.仿真实验证明了该方法的有效性和工程实用价值.     

Performance analysis of the modified versions of CFAR detectors in multiple-target and nonuniform clutter

A constant false alarm rate (CFAR) in the presence of variable levels of noise is usually a requirement placed on any modern radar. The CA- and OS-CFAR detectors are the most widely used ones in the CFAR world. The cell-averaging (CA) is the optimum CFAR detector in terms of detection probability in homogeneous background when the reference cells have identical, independent and exponentially distributed signals. The ordered-statistic (OS) is an alternative to the CA processor, which trades a small loss in detection performance, relative to the CA scheme, in ideal conditions for much less performance degradation in nonideal background environments. To benefice the merits of these well-known schemes, two modified versions (MX- and MN-CFAR) have been recently suggested. This paper is devoted to the detection performance evaluation of these modified versions as well as a novel one (ML-CFAR). Exact formulas for their false alarm and detection performances are derived, in the absence as well as in the presence of spurious targets. The results of these performances obtained for Rayleigh clutter and Rayleigh target indicate that the MN-CFAR scheme performs nearly as good as OS detector in the presence of outlying targets and all the developed versions perform much better than that processor when the background environment is homogeneous. When compared to CA-CFAR, the modified schemes perform better in ideal conditions, and behave much better in the presence of interfering targets.     

Sequential CFAR detectors using a dead-zone limiter

The performances of some proposed sequential constant-false-alarm-rate (CFAR) detectors are evaluated. The observations are passed through a dead-zone limiter, the output of which is -1, 0, or +1, depending on whether the input is less than -c, between -c, and c, or greater than c , where c is a constant. The test statistic is the sum of the outputs. The test is performed on a reduced set of data (those with absolute value larger than c), with the test statistic being the sum of the signs of the reduced set of data. Both constant and linear boundaries are considered. Numerical results show a significant reduction of the average number of observations needed to achieve the same false alarm and detection probabilities as compared to a fixed-sample-size CFAR detector using the same kind of test statistic     

基于FPGA的CFAR设计与实现

简要介绍广泛应用于雷达信号处理中的恒虚警率(CFAR)的基本原理.通过对数据流的分析,依据CFAR算法规则简单的特点,提出一种基于FPGA的实现方案,并详细介绍用FPGA实现CFAR的原理、电路组成和各部分电路的设计方法.     

A modified Gardner detector for symbol timing recovery of M-PSK signals

In this letter, we propose a modified version of the Gardner detector for symbol timing recovery of M-ary phase-shift keying (M-PSK) signals. Simulation results show that the proposed modification leads to significant performance improvement when M-PSK signals are highly bandlimited and M is small. Specifically, the reduction in the level of self-noise due to the modification amounts to 10.7 dB for quaternary phase-shift keying signals with the rolloff factor of 0.25 and the normalized noise-equivalent loop bandwidth of 0.005. In terms of computational complexity involved, the proposed method has an advantage over the known self-noise reduction methods for M-PSK signals with small M.     

一种新型恒虚警检测方法初探

提出综合性能良好的混合型CFAR处理方法，分析其检测性能，并和常规CFAR处理方法进行了比较。     

一种改善恒虚警处理性能的方法

本文介绍了两侧单元平均选大恒虚警(GO-CFAR)处理的工作原理，重点分析了警戒单元对恒虚警处理性能的影响，并给出了仿真结果，证明了适当选取警戒单元会大大改善系统的检测性能。     

Network Intrusion Detection Using a Stochastic Resonance CFAR Technique

A novel constant false alarm rate (CFAR) intrusion detection method based on stochastic resonance (SR) is proposed in this paper. Using the SR technique improves the spectral power (SP) and the signal-to-noise ratio (SNR) of the network intrusion signal, hence enhancing the detectability of network attacks. The threshold and the detection probability of the proposed SR-CFAR method are derived theoretically. Computer simulations based on standard Defense Advanced Research Projects Agency (DARPA) network intrusion data show that this CFAR method outperforms the linear anomaly intrusion detection methods for various types of intrusions.     

一种基于知识辅助的CFAR检测器

针对单元平均恒虚警率（CA CFAR）检测器存在的杂波边缘效应问题,研究了一种基于知识辅助的CFAR检测器,该检测器由两部分组成：数据选择器和传统的CA CFAR检测器。数据选择器利用GIS提供的先验信息对参考单元进行筛选,尽可能地选择与待检单元（CUT）地貌相同的训练样本,也就是说选择相对CUT为均匀的参考单元,级联的CA CFAR完成检测判决。最后,通过IPIX雷达实测数据,从虚警个数和检测两个方面与传统的CFAR检测器进行了详细的比较。仿真结果表明,基于知识辅助的CFAR检测器具有更优的性能。     

Robustness of a semi-parametric proportional intensity model

The Prentice, Williams, Peterson (PWP) semiparametric model for the failure processes of repairable systems involves regression on explanatory variables across strata defined by the failure-event count. A heuristic method is developed for assessing the robustness of the PWP model, where the true underlying process is nonhomogeneous Poisson with power-law intensity function. The PWP model performed well for large samples and increasing rates of occurrence of failures and poorly for small samples and decreasing rates of occurrence of failures