Analysis of some modified ordered statistic CFAR: OSGO and OSSOCFAR

It is necessary for automatic detection radars to be adaptive to variations in background clutter in order to maintain a constant false alarm rate (CFAR). A CFAR based on an ordered statistic technique (OS CFAR) has some advantages over the cell-averaging technique (CA CFAR), especially in clutter edges or multiple target environments; unfortunately the large processing time required by this technique limits its use. The authors present two new OS CFARs that require only ahlf the processing time. One is an ordered statistic greatest of CFAR (OSGO), while the other is an ordered statistic smallest of CFAR (OSSO). The OSGO CFAR has the advantages of the OS CFAR with only a negligible increment to the CFAR loss     

Detection Performance of the Cell Averaging LOG/CFAR Receiver

The cell averaging LOG/CFAR receiver is a special implementation of a constant-false-alarm-rate (CFAR) receiver in which the noise level estimate is derived from a set of contiguous time samples of the output of a logarithmic (LOG) detector as obtained from a tapped delay line. This CFAR receiver is capable of operating over a larger dynamic range of noise levels than a conventional cell averaging CFAR receiver, but with somewhat poorer detectability. The performance in stationary Gaussian noise of the cell averaging LOG/CFAR receiver with no post-detection integration is determined in this paper. For a small number of reference noise samples, results were obtained by a Monte Carlo simulation using the technique of importance sampling. For a large number of reference noise samples, a second moment analysis gave the desired results. Both these results can be summarized in the following simple formula, NLOG = 1.65NLIN - 0.65, which relates the number of reference samples required by each of the two receivers for equivalent performance. Thus, for the cell averaging LOG/CFAR receiver to give the same detection performance as the conventional cell averaging CFAR receiver, the number of reference noise samples has to be increased by up to 65 percent.     

Constant-False-Alarm-Rate Signal Processors for Several Types of Interference

Distribution-free methods and maximum-likelihood estimation technique have been previously suggested for constant-false-alarm-rate (CFAR) processors. The first technique assumes no a priori environmental knowledge and the second assumes almost complete environmental knowledge. Several intermediate environmental assumptions are considered. The performance of single-pulse transmission signal processors that produce CFAR for the different environments is analyzed. Probability of target detection is evaluated for Rayleigh interference and Swerling I target. It is shown that adaptive threshold techniques implemented by logarithmic amplifiers, instead of linear amplifiers, can attain better false-alarm-rate control with only small loss in target detectability.     

常用雷达视频信号处理算法在FPGA上的实现

采用FPGA技术对雷达原始的视频信号进行求模、视频积累与恒虚警检测处理,克服了DSP处理速度有限、实时性差和ASIC器件灵活性差的问题。详细介绍了求模、视频积累以及恒虚警算法在FPGA芯片上实现的原理和过程,并结合仿真结果说明了利用FPGA进行雷达视频信号处理的优势。     

Radar CFAR Thresholding in Clutter and Multiple Target Situations

Radar detection procedures involve the comparison of the received signal amplitude to a threshold. In order to obtain a constant false-alarm rate (CFAR), an adaptive threshold must be applied reflecting the local clutter situation. The cell averaging approach, for example, is an adaptive procedure. A CFAR method is discussed using as the CFAR threshold one single value selected from the so-called ordered statistic (this method is fundamentally different from a rank statistic). This procedure has some advantages over cell averaging CFAR, especially in cases where more than one target is present within the reference window on which estimation of the local clutter situation is based, or where this reference window is crossing clutter edges.     

A new and robust CFAR detection algorithm

A constant false alarm rate (CFAR) detection method which is based on a combination of median and morphological filters (MEMO) is proposed. The MEMO algorithm has robust performance with small CFAR loss, very good behavior at clutter edges and high detection performance in the case of closely spaced narrowband signals (targets). The proposed MEMO method is favourably compared with cell averaging (CA) and ordered statistics (OS) CFAR detectors. The Monte Carlo method is employed to analyze the MEMO-CFAR detector     

非相干Rice杂波中的恒虚警检测

 地杂波的统计特性常常可以用Rice模型来描述,其物理基础是认为地杂波由一些大的固定散射体引起的稳定分量和大量小的随机分布的运动散射体引起的瑞利起伏分量所合成。文献[2]研究了稳定分量不相干时Rice杂波中离散时间最佳检测的估值器——相关器结构,但无显式解,实现有困难。文献[3]导出了Rice杂波中SwerlingⅡ目标的离散时间检测的似然比检测器结构。在此基础上,本文给出了一种修正平方律结构的似然比检测器,并和通常的平方律检测器作了性能比较。     

Low-Loss Almost Constant False-Alarm Rate Processors

Conventional normalizing constant false-alarm rate (CFAR) circuits use the same configurations when detecting targets in interference regions and in clear regions. The CFAR penalty incurred in the clear region can be reduced by using CFAR processors that recognize the region is clear so that normalization is not necessary. An analysis of the target-detection performance for a particular modified CFAR processor, for an active-radar sensor, and for a passive infrared (IR), or sonar, sensor is given. It is shown that the decreased CFAR penalty in the clear is coupled with an increase of false-alarm rate in the clutter regions.     

Radar modelling to improve CFAR performance in the littoral

Littoral operation of radars poses severe signal processing difficulties due to the highly stressing, inhomogeneous clutter. This report describes an initial investigation into the feasibility of utilising site specific radar modelling to provide a localized estimate of the clutter statistics which can then be used to predict the required threshold to maintain a given false alarm rate. The technique has been applied to littoral clutter recordings obtained from the experimental S-band phase array radar, MESAR2. Results are presented for the technique in comparison with a conventional, non-adaptive, cell averaging CFAR. The paper concludes that significant performance enhancements are possible through the use of this new technique.     

Improved CFAR performance in the littoral

Littoral operation of radars poses severe signal processing difficulties due to the highly stressing, inhomogeneous clutter. This report describes an initial investigation into the feasibility of utilising site-specific radar modelling to provide a localised estimate of the clutter statistics which can then be used to predict the required threshold to maintain a given false alarm rate. The technique has been applied to littoral clutter recordings obtained from the experimental S-band phased array radar, MESAR2. Results are presented for the technique in comparison with a conventional, non-adaptive, cell averaging CFAR. This paper concludes that significant performance enhancements are possible through the use of this new technique.     

Analysis of CFAR processors in homogeneous background

Five different constant false alarm rate (CFAR) radar processing schemes are considered and their performances analyzed in homogeneous and nonhomogeneous backgrounds, the latter specifically being the multiple target environment and regions of clutter transitions. The average detection threshold for each of the CFAR schemes was computed to measure and compare the detection performance in homogeneous noise background. The exponential noise model was used for clear and clutter backgrounds to get closed-form expressions. The processor types compared are: the cell-averaging CFAR, the `greatest of' CFAR, the `smallest of' CFAR, the ordered-statistics CFAR, and a modified ordered-statistics processor called the trimmed-mean CFAR     

Clutter Map CFAR Analysis

An analysis of the probability of target detection for a clutter map CFAR using digital exponential filtering has been performed. General performance equations are derived. The probability of detection versus signal-to-noise ratio is plotted for a false alarm probability of 1.E-06 for several weight values. The CFAR loss is plotted for a detection probability of 0.9 and false alarm probabilities of 1.E-06 and 1.E-08.     

基于杂波对消-自聚焦的多通道SAR-GMTI

对超高频(UHF)波段多通道合成孔径雷达(SAR)动目标检测技术进行研究,解决了长相干积累时间导致动目标在方位向散焦严重的问题。采用分块自聚焦技术对多通道SAR地面移动目标指示(GMTI)系统自适应杂波抑制后的SAR图像进行处理,改善杂波抑制后的SAR图像中动目标的聚焦情况,增强动目标与周围剩余杂波的对比度,进而提高恒虚警率(CFAR)检测的性能。与传统杂波抑制后直接进行CFAR检测方法相比较,该方法降低了检测虚警概率。实测数据处理结果显示动目标的信杂比明显提高,动目标方位向聚焦成功,证明了该方法的有效性。     

UWB radar detection of targets in foliage using alpha-stableclutter models

We address the problem of detection of targets obscured by a forest canopy using an ultrawideband (UWB) radar. The forest clutter observed in the radar imagery is a highly impulsive random process that is more accurately modeled with the recently proposed class of alpha-stable processes as compared with Gaussian, Weibull, and K-distribution models. With this more accurate model, segmentation is performed on the imagery into forest and clear regions. Further, a region-adaptive symmetric alpha stable (SαS) constant false-alarm rate (CFAR) detector is introduced and its performance is compared with the Weibull and Gaussian CFAR detectors. The results on real data show that the SαS CFAR performs better than the Weibull and Gaussian CFAR detectors in detecting obscured targets     

基于改进型CFAR的SAR图像目标检测算法

针对传统的双参数恒虚警率(Constant False Alarm Rate,CFAR)算法存在的虚警率高、实现过程繁琐、算法执行效率低等问题,提出了一种改进型的CFAR检测算法。该算法根据SAR图像的统计直方图,对可疑的目标像素进行预筛选,再用2个滑动窗口对像素进行判别。改进型的CFAR检测算法简化了原检测算法的结构,降低了检测结果的虚警率,提高了算法的计算效率,并在国际公开的雷达数据集上进行软件与DSP硬件的应用验证,测试表明该算法的有效性。     

Matched subspace CFAR detection of hovering helicopters

A constant false alarm rate (CFAR) strategy for detecting a Gaussian distributed random signal against correlated non-Gaussian clutter is developed. The proposed algorithm is based on Scharf's matched subspace detector (MSD) and has the CFAR property with respect to the clutter amplitude probability density function (apdf), provided that the clutter distribution belongs to the compound-Gaussian family and the clutter covariance matrix is known to within a scale factor. Analytical expressions of false alarm and detection probabilities are derived. An application to the problem of detecting hovering helicopters against vegetated ground clutter is reported     

Two CFAR algorithms for interfering targets and nonhomogeneousclutter

The greatest-of-order statistics estimator (GOOSE) constant false alarm rate (CFAR) and the censored greatest-of (CGO) CFAR are described. Both are designed to accommodate interfering targets in the reference window as well as control false alarms in the presence of clutter boundaries. Both succeed in accomplishing these tasks although the CGO-CFAR is preferred as it has a designed graceful degradation as the number of interfering targets exceeds the number of samples censored. The advantage of the GOOSE-CFAR is a theoretical one in that one can derive analytical results for clutter boundary analysis and thus not have to resort to simulations     

CFAR data fusion center with inhomogeneous receivers

Detection systems with distributed sensors and data fusion are increasingly used by surveillance systems. A system formed by N inhomogeneous constant false alarm rate (CFAR) detectors (cell-averaging (CA) and ordered statistic (OS) CFAR detectors) is studied. A recursive formulation of an algorithm that permits a fixed level of false alarms in the data fusion center is presented, to set the optimum individual threshold levels in the CFAR receivers and the optimum `K out of N' decision rule in order to maximize the total probability of detection. The algorithm also considers receivers of different quality or with different communication channel qualities connecting them with the fusion center. This procedure has been applied to several hypothetical networks with distributed CA-CFAR and OS-CFAR receivers and for Rayleigh targets and interference, and it was seen that in general the fusion decision OR rule is not always the best     

空域CFAR处理方法综述

综述了空域恒虚警（Constanl False Alarm Rate,CFAR）处理研究的发展、现状和最新进展。首先,讨论了空域CFAR处理的概念及其在整个CFAR处理中所处的位置;然后,将空域CFAR处理算法分为ML类、OS类和自适应算法,介绍了每类CFAR算法的研究现状;最后,展望了空域CFAR处理算法的发展,指出...     

Biparametric linear estimation for CFAR against Weibull clutter

The authors deal with constant false alarm rate (CFAR) procedures against nonstationary clutter, modeled as a Weibull distributed process whose scale parameter α and shape parameter β are both variable. It is shown that conventional CFAR procedures, which compensate only for α, degrade intolerably as β deviates from β=2, namely, as the Rayleigh distributional assumption is violated. A biparametric CFAR procedure is shown to be suited to such situations. The authors introduce a logarithmic transformation to reduce the Weibull probability density function (pdf) to a Gumbel pdf, i.e., to the location-scale type, and then exploit the best linear unbiased estimation (BLUE) of location-scale parameters to adjust the detection threshold. True CFAR is thus achieved when the clutter is locally homogeneous. Resilience against local inhomogeneities can also be conferred since BLUE lends itself to censoring. Through a performance analysis, the influence of various system and distributional parameters is elicited