Comments on 'constant false-alarm rate algorithm based on test cell information'

In a recent paper, Cao proposed and analysed a new switching constant false-alarm rate (CFAR) detection algorithm (S-CFAR), which exploits the magnitude of the test cell for the selection of the reference samples. Here we use the order statistic theory to obtain alternative expressions for the detection probability and the false-alarm rate of the S-CFAR in homogeneous background. They are not bulky and thus are easy to calculate for the thresholding parameters.     

复杂非均匀背景下的鲁棒声呐恒虚警检测算法

针对复杂非均匀水下环境中目标检测问题,提出了一种基于背景统计特性的鲁棒声呐恒虚警(Background Statistical Characteristics based Robust Sonar Target Constant False Alarm Ratio,BSCR-CFAR)检测算法。该算法将自动删除平均级检测(Automatic Censored Mean Level Detection,ACMLD)和排序统计恒虚警(Order Statistic CFAR,OS-CFAR)检测算法引入可变指数恒虚警(Variability Index CFAR,VI-CFAR)检测算法中,并通过评估背景特性,自适应选择更匹配的CFAR检测方法。仿真和声呐实测数据分析结果表明,相比较单元平均恒虚警(Cell Average CFAR,CA-CFAR)、单元平均选大恒虚警(Greatest of CFAR,GO-CFAR)、单元平均选小恒虚警(Smallest of CFAR,SO-CFAR)和OS-CFAR、VI-CFAR等检测算法,该算法在混响边缘、混响区、单/多强离散干扰等典型非均匀背景下的恒虚警检测保持了良好的鲁棒性。     

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     

非平稳环境下主动声呐检测器研究

针对在水下存在多途、混响及噪声边缘等非平稳背景条件下主动声呐检测器的检测性能,给出了一种变异指数的恒虚警率(Variability Index Constant False Alarm Rate,VI-CFAR)检测器。VI-CFAR结合了单元平均恒虚警率、最大选择恒虚警率和最小选择恒虚警率的检测算法,它在均匀平稳环境和非平稳环境下都具有较强的自适应性。理论分析和仿真结果表明,该检测器在均匀平稳背景下的检测性能与单元平均恒虚警率检测器相似,在多途、混响等干扰背景条件下具有较强的抗干扰能力,在噪声边缘背景下有较好的虚警概率控制能力且运算量小,是一种稳健的检测器。     

Kernel wavelet-Reed-Xiaoli: an anomaly detection for forward-looking infrared imagery

This paper describes a new kernel wavelet-based anomaly detection technique for long-wave (LW) forward-looking infrared imagery. The proposed approach called kernel wavelet-Reed-Xiaoli (wavelet-RX) algorithm is essentially an extension of the wavelet-RX algorithm (combination of wavelet transform and RX anomaly detector) to a high-dimensional feature space (possibly infinite) via a certain nonlinear mapping function of the input data. The wavelet-RX algorithm in this high-dimensional feature space can easily be implemented in terms of kernels that implicitly compute dot products in the feature space (kernelizing the wavelet-RX algorithm). In the proposed kernel wavelet-RX algorithm, a two-dimensional wavelet transform is first applied to decompose the input image into uniform subbands. A number of significant subbands (high-energy subbands) are concatenated together to form a subband-image cube. The kernel RX algorithm is then applied to this subband-image cube. Experimental results are presented for the proposed kernel wavelet-RX, wavelet-RX, and the classical constant false alarm rate (CFAR) algorithm for detecting anomalies (targets) in a large database of LW imagery. The receiver operating characteristic plots show that the proposed kernel wavelet-RX algorithm outperforms the wavelet-RX as well as the classical CFAR detector.     

Field programmable gate array-based design and realisation of automatic censored cell averaging constant false alarm rate detector based on ordered data variability

The design and field programmable gate array (FPGA)-based realisation of automatic censored cell averaging (ACCA) constant false alarm rate (CFAR) detector based on ordered data variability (ODV) is discussed here. The ACCA-ODV CFAR algorithm has been recently proposed in the literature for detecting radar target in non-homogeneous background environments. The ACCA-ODV detector estimates the unknown background level by dynamically selecting a suitable set of ranked cells and doing successive hypothesis tests. The proposed detector does not require any prior information about the background environment. It uses the variability index statistic as a shape parameter to accept or reject the ordered cells under investigation. Recent advances in FPGA technology and availability of sophisticated design tools have made it possible to realise the computation intensive ACCA-ODV detector in hardware, in a cost-effective way. The architecture is modular and has been implemented and tested on an Altera Stratix II FPGA using Quartus II software. The post place and route result show that the proposed design can operate at 100 MHz, the maximum clock frequency of the prototyping board and for this frequency the total processing time required to perform a single run is 0.21 μs. This amounts to a speedup for the FPGA-based hardware implementation by a factor of ~110 as compared to software-based implementation, which takes 23 μs to perform the same operation.     

雷达信号的恒虚警率检测

雷达信号的恒虚警率检测是雷达信号处理的重要组成部分。本文首先论述了雷达信号恒虚警率检测的设计原理，分析了评价其性能的准则，在此基础上介绍了恒虚警率技术在雷达信号处理中的发展和应用。最后概述了恒虚警率技术的研究动态。     

水下无人航行器自主检测方法研究

自主检测技术是实现水下无人航行器(Underwater Unmanned Vehicle,UUV)智能化的关键技术,是无人航行器能够自主执行水下预警、目标跟踪等任务的前提。针对当前基于均值类和有序统计类恒虚惊(Constant False Alarm Rate,CFAR)技术的自主检测方法在背景起伏严重、多目标情况下,背景噪声统计特性估计不准确、自主检测性能下降的问题,文章提出了一种基于方位-时间二维参考窗联合有序截断平均算法的自主检测方法。首先,该方法设计了一种方位-时间二维参考窗,解决了一维参考窗检测参考样本过少、噪声统计量估计不准的问题;其次,采用有序截断平均算法估计背景噪声统计量,对起伏背景进行均衡;最后,利用背景噪声均值和方差构造恒虚警检测器,采用检测前跟踪技术,实现起伏背景下、多目标自动检测与跟踪。湖上试验结果表明,在水下无人航行器的自噪声干扰下,该方法对多目标依然具有较好的自主检测效果。     

Network Intrusion Detection Using CFAR Abrupt-Change Detectors

In this paper, the constant false alarm rate (CFAR) detectors are proposed for network intrusion detection. By using an autoregressive system to model the network traffic, predictor error is shown to closely follow a Gaussian distribution. CFAR detector approaches are then developed on the prediction error distribution. In the present study, we consider the optimal CFAR, the cell-averaging CFAR, and the order statistics CFAR. The use of these CFAR techniques can significantly improve the detection performance. In addition, we propose the use of fusion of these CFAR detectors by using Dempster-Shafer and Bayesian techniques. Computer simulations based on the DARPA traffic data show that the proposed approach achieves higher detection probabilities than the conventional detection method. Even under different types of attacks, the intrusion detection performances based on the proposed CFAR detectors shows consistent improvement.     

Ultrasonic nondestructive evaluation of highly scattering materialsusing adaptive filtering and detection

Adaptive filtering and detection has been applied to the problem of detecting ultrasonic echo signals from test targets where the wanted signals are masked by coherent scattering from grain boundaries present in highly scattering materials. The filter is based on the normalized least mean square (LMS) error algorithm, and can be operated with either an independent reference signal or by using the delayed input signal as the reference. Tests made on a collection of 64 ultrasonic A-scans using the same processing parameters show that an up to 10 dB improvement in signal-to-noise ratio can typically be obtained. A cell-averaging constant false alarm rate (CFAR) detector is used to detect the signals automatically. The performance of the method is compared to that of split spectrum processing, both with and without polarity thresholding     

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.     

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.     

基于多背景杂波分布模型的自适应CFAR检测

针对现有的CFAR(Constant False Alarm Rate,恒虚警)算法采用全局建模,在待检测的所有区城采用同种背景杂波分布模型,导致使用的模型在不适应区域失配,使CFAR检测性能下降的现象,本文提出一种基于多背景杂波分布模型的自适应CFAR检测算法.该方法根据背景区域的不同统计特性即统计方差和均值比来判断...     

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     

三维高分辨工业X-CT系统

介绍了采用高分辨CCD面阵列探测器的微焦点三维CT系统的基本结构，应用基于Feldkamp的近似三维重建算法获得了令人满意的图像质量，与以往用线阵列探测器的CT系统比较，该系统具有采集数据快，成像速度快，检测周期短的特点，可实现对被检测陶瓷样品100％的检测，给出了使用该系统检测陶瓷轴承球的DR（数字照相）及CT图像。     

Automated vehicle detection in forward-looking infrared imagery

We describe an algorithm for the detection and clutter rejection of military vehicles in forward-looking infrared (FLIR) imagery. The detection algorithm is designed to be a prescreener that selects regions for further analysis and uses a spatial anomaly approach that looks for target-sized regions of the image that differ in texture, brightness, edge strength, or other spatial characteristics. The features are linearly combined to form a confidence image that is thresholded to find likely target locations. The clutter rejection portion uses target-specific information extracted from training samples to reduce the false alarms of the detector. The outputs of the clutter rejecter and detector are combined by a higher-level evidence integrator to improve performance over simple concatenation of the detector and clutter rejecter. The algorithm has been applied to a large number of FLIR imagery sets, and some of these results are presented here.     

Determining sample differences in a sample series of complex liquid chromatography-diode array detector data using chemometrics

In analytical laboratories, one often has to deal with sample series that have minor differences between them. For example, different batches of the same material may cause manufacturing problems, and quality control is an important issue. Another example is decomposition studies, where changes may or may not occur in a sample. To extract the differences between such highly related samples from LC-DAD (liquid chromatography-diode array detector) data is a time-consuming and subjective task. This paper describes an algorithm that efficiently extracts such differences.     

Adaptive pseudo-noise code acquisition scheme using automatic censoring for DS/SS communication in frequency-selective Rayleigh fading channel

An adaptive pseudo-noise code acquisition scheme based on automatic multipath signal cancellation for mobile communication systems in frequency-selective Rayleigh fading channel is proposed. The proposed system combines a serial strategy and an automatic censored cell-averaging constant false alarm rate (CFAR) detector based on ordered data variability. This system does not require any a priori information about the number of interferences caused by the presence of multipath signals in the reference channel. The mean acquisition time and the detection performances of the proposed processor are evaluated and compared with those of the conventional adaptive acquisition scheme based on fixed-censoring point detector. It is shown that the considered scheme outperforms significantly the conventional one. The effects of various channel parameters on the acquisition performance, namely the number of resolvable paths, the partial correlation length and the signal-to-noise ratio are also investigated.     

基于分数阶功率谱熵的未知水声脉冲信号检测方法

水声侦察的核心问题是在无先验知识条件下捕获其他平台发射的脉冲信号,单频(Continuous Wave,CW)信号和调频(Frequency Modulation,FM)信号是常用的水声探测脉冲.功率谱熵算法能有效检测低信噪比的CW信号,但对FM信号性能不佳,分数阶傅里叶变换(Fractional Fourier Tr...     

A data analysis algorithm for programmed field-flow fractionation

An algorithm that employs numerical integration for analysis of field-flow fractionation (FFF) data is presented. The algorithm utilizes detector response, field strength, and channel flow rate data, monitored at discrete time intervals during sample elution to generate a distribution of sample components according to particle size or molecular weight. The field strength and channel flow rate may either be held constant or programmed as functions of time, and it is not necessary for these programs to follow specific mathematical functions. If experimental conditions are monitored during a run, the algorithm can account for any deviation from nominal set conditions. The algorithm also allows calculation of fractionating power for the actual conditions as monitored during the run. The method provides greatly increased flexibility in the application of the FFF family of techniques. It removes the limitations on experimental conditions incurred by adherence to analytically available solutions to FFF theory, allowing ad hoc variation of field strength and other experimental parameters as necessary to increase sensitivity and specificity of the method. An implementation of the algorithm is described that is independent of the FFF technique (i.e., independent of field type) and mode of operation. To reduce computation time, it uses mathematical techniques to reduce the required number of numerical integrations. This is of particular importance when the perturbations to ideal FFF theory, such as those due to the effects of hydrodynamic lift forces, particle-wall or particle-particle interactions, and secondary relaxation, necessitate relatively lengthy numerical calculations.