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。     

基于非线性超声技术的热损伤混凝土微裂纹检测实验研究

混凝土结构在受热或者火灾作用后其内部极易形成微裂纹,这将严重影响混凝土的耐久性与强度等特性.基于非线性声场调制理论,提出边带峰计数法及损伤指标概念,以评估热损伤混凝土微裂纹的发展.通过高温制备热损伤混凝土,对热损伤混凝土微裂纹的检测结果表明,损伤指标会随微裂纹的发展而增大,与理论研究吻合.证明了非线性声场调制法的可行性...     

Imperfect repair models: Sequential goodness-of-fit testing based on predictive performances

Imperfect repair (IR) modelling has attracted a lot of attention during the last decades. To assess and predict repairable systems' reliability, analysts have to choose among a plethora of models: renewal processes, non-homogeneous Poisson processes (NHPPs), Brown–Proschan, Kijima, ARA, ARI, Quasi-Renewal (QR), Trend-Renewal and so forth. Choosing an appropriate model for a given failures, dataset is an important practical issue. The fit of an IR model can be assessed using goodness-of-fit (GoF) tests but very few have been proposed and a good fit to past data does not guarantee good reliability predictions. This work proposes general GoF tests for IR models based on sequential (or on-line) assessment of times-to-failures forecasts. The suggested predictive-sequential (or prequential) GoF tests have a low computational complexity and a common test statistic for several IR models. These tests have been proved to be asymptotically distribution-free (ADF) for renewal and power-law processes (PLPs). Our numerical simulations and preliminary theoretical results highly suggest that this ADF property still holds for several other IR models. The prequential tests are much easier to use than the already known bootstrap tests, and a simulation study shows that they are also slightly more powerful. The simulations also show that the prequential tests are powerful to identify classes of similar appropriate models but are much less powerful to distinguish models belonging to the same class. A comparison of MTTF estimates show that models from the same class give close reliability predictions and that the tests are able to reject models which would yield to dramatic errors in reliability predictions.     

Contributions of empirical and quantile processes to the asymptotic theory of goodness-of-fit tests

This paper analyzes the evolution of the asymptotic theory of goodness-of-fit tests. We emphasize the parallel development of this theory and the theory of empirical and quantile processes. Our study includes the analysis of the main tests of fit based on the empirical distribution function, that is, tests of the Cramér-von Mises or Kolmogorov-Smirnov type. We pay special attention to the problem of testing fit to a location scale family. We provide a new approach, based on the Wasserstein distance, to correlation and regression tests, outlining some of their properties and explaining their limitations. Dedicated to Miguel Martín Díaz whose scientific criticism has greatly inspirated our research by years. Research partially supported by DGICYT, grants PB98-0369-C02-01 and 02. E. del barrio and C. Matrán have also been supproted by PAPIJCL grant VA08/97.     

雷达非合作目标恒虚警检测和分类一体化方案

提出一种雷达识别非合作目标的检测和分类一体化方案。用广义序贯拒绝检验的方法把目标检测和分类作为一个问题来处理。不同种类的目标用隶属于由投影矩阵确定的不同子空间上的随机信号建模。观测数据“并行”通过具有恒虚警（CFAR）能力的各“局部”检测器,各检测器输出的逻辑位通过逻辑电路最后选择一个目标输出。详细评价了新方案的工作性能。仿真结果表明,本方案在以给定的虚警率检测到目标的同时,也能正确地对目标进行分类。     

Robustification of noise radar detection

A study of robust detection scheme for continuous wave noise radars is presented. The probability density function (PDF) of the noise at the input of the radar is not usually Gaussian and has heavy tails generated by impulse interferences. Although the PDF of interferences at the output of the noise radar correlator is Gaussian, impulse interferences increase the processing floor, and thus decrease the overall radar sensitivity. The proposed robustification applied to the correlator?s input signal increases the radar sensitivity in the presence of impulse interferences, and does not introduce any significant losses if the input noise is purely Gaussian.     

A goodness-of-fit testing approach for normality based on the posterior predictive distribution

In this paper, we propose several new goodness-of-fit tests for normality based on the distance between the observed sample and the predictive sample drawn from the posterior predictive distribution. Note that the predictive sample is stochastic for a set of given sample observations, the distance being consequently random. To circumvent the randomness, we choose the conditional expectation and qth quantile as the test statistics. Two statistics are related to the well-known Shapiro–Francia test, and their asymptotic distributions are derived. The simulation study shows that the new tests are able to better discriminate between the normal distribution and heavy-tailed distributions or mixed normal distributions. Against those alternatives, the new tests are more powerful than existing tests including the Anderson–Darling test and the Shapiro–Wilk test, which are two of the best tests of normality in the literature.     

Analysis of order-statistic CFAR threshold estimators for improved ultrasonic flaw detection

In the pulse-echo method using broadband transducers, flaw detection can be improved by using optimal bandpass filtering to resolve flaw echoes surrounded by grain scatterers. Optimal bandpass filtering is achieved by examining spectral information of the flaw and grain echoes where frequency differences have been experimentally shown to be predictable in the Rayleigh scattering region. Using optimal frequency band information, flaw echoes can then be discriminated by applying adaptive thresholding techniques based on surrounding range cells. The authors present order-statistic (OS) processors, ranked and trimmed mean (TM), to robustly estimate the threshold while censoring outliers. The design of these OS processors is accomplished analytically based on constant false-alarm rate (CFAR) detection. It is shown that OS-CFAR and TM-CFAR processors can detect flaw echoes robustly with the CFAR of 10 (-4) where the range cell used for the threshold estimate contains outliers.     

Buried container detection using ground-probing radar

This paper contains the results of a continuing study to assess the capabilities and limitations of a ground-penetrating radar (GPR) system to detect and locate various subsurface anomalies — in this case, various size containers, both metal and non-metalic.This containers are located in various configurations at different burial depths. The site consisted of a relatively uniform sandy soil of low water content and represented nearly ideal conditions for the tests. The results show that steel drums are the most easily detected and located. Plastic drums, if empty, cannot be located; however, if liquid-filled they can be detected. Closely spaced drums cannot be resolved to determine the exact number of drums. Accurate depth predictions were made to all drums located using the GPR system.     

三脉冲激光雷达的目标检测

讨论了如何利用数字信号处理技术提高激光雷达的目标探测能力。采用三脉冲累加技术提高信噪比1．732倍。根据三脉冲激光雷达回波信号的特点,对信号进行差分滤波和匹配滤波处理,信噪比提高约1．5倍;根据目标的相关性采用多帧相关检测对目标进行检测,通过多次的匹配,检测概率增加,虚警概率降低。外场实验结果表明,三脉冲数字激光雷达的成力大大提高,最小可检测信噪比为1．5,迎头截获距离可提高约2-3倍。     

Tracking laser radar for 3-D shape measurements of large industrialobjects based on time-of-flight laser rangefinding andposition-sensitive detection techniques

A tracking laser radar device is proposed for the 3-D measurement of the shapes of large industrial objects. The measuring system includes a servo-controlled measuring head and small reflectors attached to the object. The sensor consists of a tracking sensor, a pulsed time-of-flight rangefinder and angle encoders. The implementation of the tracking sensor and its test results are explained in detail. Tracking is accomplished by illuminating the reflectors of the target object and focusing the reflected light on the surface of a four-quadrant (4Q) position-sensitive detector, the signals of which are used to drive the servo motors of the measuring head. The test results show a tracking sensor resolution of about 0.003 mm (σ value) and bias of about ±0.1 mm when the distance and angle of the target reflector vary in the ranges 2-5 m and ±45°, respectively. The pointing accuracy of the tracking system was better than ±0.3 mm     

Prony算法在步进频率雷达多 目标检测中的建模研究

步进频率信号是一种以增大信号带宽为代价来提高雷达距离分辨力的宽带信号。在步进频率雷达中,将接收的基带信号看作是包含目标信息的指数函数的线性组合;而Prony算法是用指数函数的一组线性组合来描述等间距采样数据的数学模型。将扩充的Prony算法应用到步进频率体制的雷达中,可以得到更为精确的目标信息,并且可以克服直接采用IDFT合成高分辨距离像的一些缺点。相应的建模分析和实验仿真,说明了该算法的优越性。     

一种基于ADS-B的雷达跟踪精度计算方法

分析目前雷达精度测量方法的弊端,介绍广播式自动相关监视技术(ADS-B)的基本原理和数据格式转换方法,并提出一种基于ADS-B数据的雷达跟踪精度计算方法。在利用ADS-B系统进行多目标跟踪的过程中,运用改进的数据关联算法(MPDA)和基于"当前"统计模型的自适应卡尔曼滤波算法,计算出雷达的跟踪精度。仿真结果表明:运用该方法 ADS-B系统能够高精度地跟踪目标,可以满足测量雷达跟踪精度的要求。     

基于有源超材料的可调超薄雷达吸波体研究

在超材料结构中引入电阻和有源变容二极管,通过合理设计微结构型式以及微结构之间的连线方式,实现吸波频带的动态可调,研究电阻、电容和入射波极化方向对吸波特性的影响。结果表明:通过改变外加电压调整超材料的吸收频段,在3.7倍频带范围内实现吸波频段的主动自调节;吸波体的总厚度仅为波长的1/181,相比于传统吸波材料,在同等吸波性能条件下,表现出了优异的超薄特性;TE和TM极化电磁波表现出相同的吸波效果,即吸波特性对入射波的极化方向不敏感。     

Radial basis function neural network for pulse radar detection

A new approach using a radial basis function network (RBFN) for pulse compression is proposed. In the study, networks using 13-element Barker code, 35-element Barker code and 21-bit optimal sequences have been implemented. In training these networks, the RBFN-based learning algorithm was used. Simulation results show that RBFN approach has significant improvement in error convergence speed (very low training error), superior signal-to-sidelobe ratios, good noise rejection performance, improved misalignment performance, good range resolution ability and improved Doppler shift performance compared to other neural network approaches such as back-propagation, extended Kalman filter and autocorrelation function based learning algorithms. The proposed neural network approach provides a robust mean for pulse radar tracking     

结合CFAR与自适应窗口的舰船目标检测

针对大多数CFAR算法在检测舰船目标时受滑动窗口的限制,检测耗时长、效率低和易造成漏检等问题,提出一种将自适应窗口与CFAR算法结合的舰船目标检测方法。首先利用目标的全局显著性找到其大致位置,根据目标区是否满足自适应窗口设置条件分别进行处理：采用剔除掉目标干扰的背景参数检测内部目标;采用检出目标与整体背景参数双重筛选边缘目标;最后将两部分合并进行后续处理。实验结果表明,本文方法能够准确快速地定位目标区域,且能精确检测并刻画目标形状,取得了较为满意的检测结果。     

Distribution-free high-dimensional two-sample tests based on discriminating hyperplanes

In this article, we propose a general procedure for multivariate generalizations of univariate distribution-free tests involving two independent samples as well as matched pair data. This proposed procedure is based on ranks of real-valued linear functions of multivariate observations. The linear function used to rank the observations is obtained by solving a classification problem between the two multivariate distributions from which the observations are generated. Our proposed tests retain the distribution-free property of their univariate analogs, and they perform well for high-dimensional data even when the dimension exceeds the sample size. Asymptotic results on their power properties are derived when the dimension grows to infinity and the sample size may or may not grow with the dimension. We analyze several high-dimensional simulated and real data sets to compare the empirical performance of our proposed tests with several other tests available in the literature.