Non-coherent radar CFAR detection based on goodness-of-fit tests

This paper considers the problem of constant false alarm rate (CFAR) detection of radar targets using multiple observations. In the Gaussian clutter scenario, the structure of the optimum (uniformly most powerful) CFAR detector is rather simple, but when the clutter is heavy-tailed, that is non-Gaussian distributed, the derivation of the optimal detector becomes infeasible. For this latter relevant case, a new CFAR algorithm is porposed based on goodness-of-fit (GoF) tests. The performance of the proposed detector is numerically investigated through Monte Carlo simulations assuming heavy-tailed Weibull and Lognormal distributed clutter. Numerical results shown that, in heavy-tailed clutter and also when several interfering targets exist, the proposed detector outperforms the conventional CFAR detector based on binary integration. Performance is also tested processing real sea clutter data collected by a non-coherent navigation radar     

Performance of the adaptive generalised matched subspace constant false alarm rate detector in non-Gaussian noise: an experimental analysis

The authors deal with the performance analysis of an adaptive version of the generalised matched subspace detector (GMSD) in compound-Gaussian clutter with unknown covariance matrix. The original GMSD was proposed to detect subspace signals in compound-Gaussian noise with known covariance matrix and ensures the constant false alarm rate (CFAR) property. In real situations, this assumption is unrealistic, which means that the covariance matrix must be estimated from training data. The authors use a robust estimate of the covariance matrix called the fixed-point estimate, recently proposed in the literature. The performance of the obtained adaptive detector, in terms of CFAR behaviour and probability of detection, is evaluated in the presence of real sea clutter data, collected by the McMaster IPIX radar.     

合成孔径雷达图像的恒虚警率目标检测

目的　对合成孔径雷达 (SAR)图像实施恒虚警率 (CFAR)目标检测 .方法　利用 K-Gamma和Weibull分布的杂波模型分别对海面和陆地的人造目标实施检测 ;同时 ,对不同杂波背景下 CFAR检测方法进行比较 .结果与结论　通过检测结果的比较 ,证实了 K-Gamma分布和 Weibull分布分别适合于海面和陆地杂波背景下的目标检测 ;在非均匀杂波背景中 ,有序统计量 (OS)较单元平均 (CA)方法具有优越性     

Cumulative detection probabilities and range accuracy of a pulsed Geiger-mode avalanche photodiode laser ranging system

Cumulative pulses detection with appropriate cumulative pulses number and threshold has the ability to improve the detection performance of the pulsed laser ranging system with GM-APD. In this paper, based on Poisson statistics and multi-pulses cumulative process, the cumulative detection probabilities and their influence factors are investigated. With the normalized probability distribution of each time bin, the theoretical model of the range accuracy and precision is established, and the factors limiting the range accuracy and precision are discussed. The results show that the cumulative pulses detection can produce higher target detection probability and lower false alarm probability. However, for a heavy noise level and extremely weak echo intensity, the false alarm suppression performance of the cumulative pulses detection deteriorates quickly. The range accuracy and precision is another important parameter evaluating the detection performance, the echo intensity and pulse width are main influence factors on the range accuracy and precision, and higher range accuracy and precision is acquired with stronger echo intensity and narrower echo pulse width, for 5-ns echo pulse width, when the echo intensity is larger than 10, the range accuracy and precision lower than 7.5 cm can be achieved.     

Application of the radon transform to detect small-targets in sea clutter

The authors present a novel and heuristic approach for the detection of low radar cross-section targets in high-resolution sea clutter. The proposed technique is based on the application of the Radon transform to range?time matrices formed by column-wise storage of consecutive range profiles. The objective of this paper is 2-fold: to analyse the effect of the transform on real high-resolution sea clutter and to describe a detection scheme based on the insight obtained. The proposed technique emulates the behaviour of traditional motion target detection algorithms without the need for reliable Doppler information. It also constitutes a powerful non-coherent integration strategy of the target?s energy along its specific path on the range?time plot. The performance of the detection technique has been tested against real high-resolution sea clutter data, acquired at the south coast of Spain with an in-house developed continuous wave linear frequency modulated millimetre-wave radar system. Monte Carlo simulations show a significant improvement over the conventional cell averaging constant false alarm rate schemes.     

Infrared target detection with probability density functions of wavelet transform subbands

We report the development of a wavelet multiresolution texture-based algorithm that uses the probability density functions (PDFs) of the subband of the wavelet decomposition of an image. The moments of these pdfs are used in a clustering algorithm to segment the targets from their background clutter. Using the tools of experimental methodology, we evaluate the performance of this algorithm on real infrared imagery under varying algorithm parameter sets as well as scene, image, and false-alarm conditions. We estimate a set of multidimensional predictive analytic performance models that relate the detection probabilities as functions of false alarm, algorithm internal parameter, target pixel number, target-to-background interference ratio, target-interference ratio, and Fechner-Weber and local entropy metrics in the scene. These models can be used to predict performance in regions were no data are available and to optimize performance by selection of the optimum parameter and constant false-alarm values in regions with known scene and metric conditions.     

Target detection with randomized thresholds for lidar applications

Light detection and ranging (lidar) systems use binary hypothesis tests to detect the presence of a target in a range interval. For systems that count photon detections, hypothesis test thresholds are normally set so that a target detection is declared if the number of detections exceeds a particular number. When this method is employed, the false alarm probability can not be selected arbitrarily. In this paper, a hypothesis test that uses randomized thresholds is described. This randomized method of thresholding allows lidar operation at any false alarm probability. When there is a maximum allowable false alarm probability, the hypothesis test that uses randomized thresholds generally produces higher target detection probabilities than the conventional (nonrandom) hypothesis test.     

Impact of atmospheric clutter on Doppler-limited gas sensors in the submillimeter/terahertz

It is well known that clutter (spectral interference) from atmospheric constituents can be a severe limit for spectroscopic point sensors, especially where high sensitivity and specificity are required. In this paper, we will show for submillimeter/terahertz (SMM/THz) sensors that use cw electronic techniques the clutter limit for the detection of common target gases with absolute specificity (probability of false alarm ? 10?1?) is in the ppt (1 part in 1012) range or lower. This is because the most abundant atmospheric gases are either transparent to SMM/THz radiation (e.g., CO?) or have spectra that are very sparse relative to the 10? Doppler-limited resolution elements available (e.g., H?O). Moreover, the low clutter limit demonstrated for cw electronic systems in the SMM/THz is independent of system size and complexity.     

基于背景杂波自适应预测的微小目标检测

本文主要研究基于图像的强空域杂波背景下微小目标检测.本文提出了一种新的基于邻域梯度差平方累积函数最小原则的背景杂波自适应预测算法.该算法能显著改善微小目标的信杂比(SCNR).试验证明,本算法相对于已有的多种算法,有着更好的性能.本文还引入了一种基于统计分析的快速检测算法,该算法能在较低的虚警概率情况下,获得更高的检测概率.理论分析及仿真表明,本文提出的检测系统在微小目标检测中,具有很高的实用性.     

Sea clutter transient spatial coherence and scan-to-scan constant false alarm rate

High-resolution radar sea clutter exhibits long-term temporal and spatial correlation features consistent with the sea surface wave structure. Data are presented showing that there is a transient coherence in the long-term fluctuations of the clutter, which locally appears to show the single periodic fluctuation of a dominant wave, but when averaged over a large area is seen to contain a wide band of fluctuation frequencies. This apparently non-stationary behaviour makes it difficult to predict the local sea clutter level for constant false alarm rate (CFAR) thresholding using conventional techniques. A new method of scan-to-scan CFAR processing is therefore proposed, which takes account of this behaviour. The scheme is analysed and its performance characterised.     

More on the mis‐specification of the shape parameter with Weibull‐to‐exponential transformation

When lifetimes follow Weibull distribution with known shape parameter, a simple power transformation could be used to transform the data to the case of exponential distribution, which is much easier to analyze. Usually, the shape parameter cannot be known exactly and it is important to investigate the effect of mis‐specification of this parameter. In a recent article, it was suggested that the Weibull‐to‐exponential transformation approach should not be used as the confidence interval for the scale parameter has very poor statistical property. However, it would be of interest to study the use of Weibull‐to‐exponential transformation when the mean time to failure or reliability is to be estimated, which is a more common question. In this paper, the effect of mis‐specification of Weibull shape parameters on these quantities is investigated. For reliability‐related quantities such as mean time to failure, percentile lifetime and mission reliability, the Weibull‐to‐exponential transformation approach is generally acceptable. For the cases when the data are highly censored or when small tail probability is concerned, further studies are needed, but these are known to be difficult statistical problems for which there are no standard solutions. Copyright © 2000 John Wiley & Sons, Ltd.     

The Constant Shape Parameter Assumption in Weibull Regression

ABSTRACT

The usual assumption in Weibull regression is that the scale parameter is a function of the predictor variables, and the shape parameter is constant. We consider the problem of estimating parameters in the presence of a nonconstant shape parameter and the effect of assuming a constant shape parameter when it really is not constant. We consider both classical and Bayesian methods of estimation. The misspecification of a constant shape parameter can lead to a loss of power for tests regarding the slope parameters. We find that prediction intervals can be inaccurate when the shape parameter is incorrectly assumed to be constant.     

Analysis of an automatic sinusoidal detector with FFT and cell‐averaging processor

Abstract

A sinusoidal detector with adaptive thresholding CFAR (constant‐false‐alarm‐rate) processor operating in noise of changing statistics is analyzed for some very general situations. The detector contains FFT and a cell‐averaging processor, which can be easily implemented for real time applications, such as in pulse doppler radar. For signals in stationary noise in particular, the second‐order statistics of the FFT‐based periodogram of the windowed data are derived. In the AWGN case, this detector possesses the desired CFAR characteristics. For Gaussian colored noise, the detector performance is derived analytically with certain reasonable approximations. It is shown from some numerical examples that in this case the variation of the false alarm probability with respect to the unknown sinusoidal frequency and noise spectrum is not significant, which reveals the property of robustness. Moreover, the detection probability is satisfactory for most cases.     

Adaptive detectors with diagonal loading for airborne multi-input multi-output radar

The problem of adaptive target detection for airborne multi-input multi-output (MIMO) radars with space-time receivers in the presence of Gaussian interference (including clutter and noise) is studied. Previous work has assumed the interference covariance matrix to be known. The case with unknown covariance matrix is investigated here. By exploiting the low rank property of clutter subspace, generalised likelihood ratio test detector and adaptive matched filter detector with diagonal loading are suggested to improve the detection performance of MIMO radars in limited secondary data case. The closed-form detection probabilities and false alarm probabilities of the two proposed detectors are derived and numerically evaluated. Theoretical analysis and numerical results show the advantages of the proposed detectors.     

A study of the effects of mis‐specification of the Weibull shape parameter on confidence bounds based on the Weibull‐to‐exponential transformation

Many authors of reliability texts and papers recommend or employ the Weibull‐to‐exponential transformation for ease of use in testing hypotheses and in constructing confidence intervals and bounds on the Weibull characteristic value. In making this transformation, it is assumed that the Weibull shape parameter is fixed and known. Our research shows that if this parameter is mis‐specified by an amount as small as 0.10, very poor confidence intervals and bounds will result. Hence, since the shape parameter is seldom known with perfect accuracy, based on this and other research, the use of this transformation is not recommended. Copyright © 2000 John Wiley & Sons, Ltd.     

一种基于关联处理的声呐图像去噪技术

为了改善声呐图像中存在大量杂波而导致目标跟踪困难,设备虚警率高的缺点,提出一种图像关联降噪算法。首先利用目标特性对静止目标进行剔除,之后利用目标运动的持续性和目标回波在连续多帧图像中的空间位置关联性,对随机噪声进行进一步滤除。仿真和试验数据验证,所提出的算法对于背景比较稳定的图像,降噪效率不低于95%。     

A Method for Discriminating Between Failure Density Functions Used In Reliability Predictions

Unless sufficient evidence to the contrary exists, the exponential distribution is often assumed as a model for the failure density function in reliability predictions.

The generalized gamma distribution, with known location parameter, is a three parameter distribution which encompasses the exponential, Weibull, gamma and many others. In this paper, (i) maximum likelihood estimation for the three parameters is indicated, (ii) it is noted that these estimators are asymptotically multivariate normally distributed, and (iii) using the distribution of the estimators, probability regions for the estimators of the parameters of the generalized gamma distribution are established for large sample situations.

In situations where the generalized gamma can be assumed as the correct density function, the exponential and the Weibull are special cases. A method is presented using experimental or life data for rejecting (with a known probability of false rejection) the Weibull and (or) the exponential functions when they do not appear to describe the failure density function of a unit.     

Bias correction for the least squares estimator of Weibull shape parameter with complete and censored data

Estimation of the Weibull shape parameter is important in reliability engineering. However, commonly used methods such as the maximum likelihood estimation (MLE) and the least squares estimation (LSE) are known to be biased. Bias correction methods for MLE have been studied in the literature. This paper investigates the methods for bias correction when model parameters are estimated with LSE based on probability plot. Weibull probability plot is very simple and commonly used by practitioners and hence such a study is useful. The bias of the LS shape parameter estimator for multiple censored data is also examined. It is found that the bias can be modeled as the function of the sample size and the censoring level, and is mainly dependent on the latter. A simple bias function is introduced and bias correcting formulas are proposed for both complete and censored data. Simulation results are also presented. The bias correction methods proposed are very easy to use and they can typically reduce the bias of the LSE of the shape parameter to less than half percent.     

DDoS Attack Detection via Multi-Scale Convolutional Neural Network

Distributed Denial-of-Service (DDoS) has caused great damage to the network in the big data environment. Existing methods are characterized by low computational efficiency, high false alarm rate and high false alarm rate. In this paper, we propose a DDoS attack detection method based on network flow grayscale matrix feature via multiscale convolutional neural network (CNN). According to the different characteristics of the attack flow and the normal flow in the IP protocol, the seven-tuple is defined to describe the network flow characteristics and converted into a grayscale feature by binary. Based on the network flow grayscale matrix feature (GMF), the convolution kernel of different spatial scales is used to improve the accuracy of feature segmentation, global features and local features of the network flow are extracted. A DDoS attack classifier based on multi-scale convolution neural network is constructed. Experiments show that compared with correlation methods, this method can improve the robustness of the classifier, reduce the false alarm rate and the missing alarm rate.     

Dynamic probability control limits for CUSUM charts for monitoring proportions with time-varying sample sizes

We consider the problem of monitoring a proportion with time-varying sample sizes. Control charts are generally designed by assuming a fixed sample size or a priori knowledge of a sample size probability distribution. Sometimes, it is not possible to know, or accurately estimate, a sample size distribution or the distribution may change over time. An improper assumption for the sample size distribution could lead to undesirable performance of the control chart. To handle this problem, we propose the use of dynamic probability control limits (DPCLs) which are determined successively as the sample sizes become known. The method is based on keeping the conditional probability of a false alarm at a predetermined level given that there has not been any earlier false alarm. The control limits dynamically change, and the in-control performance of the chart can be controlled at the desired level for any sequence of sample sizes. The simulation results support this result showing that there is no need for any assumption of a sample size distribution with the use of this proposed approach.