一种限定虚警概率的PN码捕获自适应门限估计算法

提出了一种限定虚警概率的PN码捕获的自适应门限估计算法,首先在对判决变量的统计特性分析的基础上,计算出了判决门限的有偏估计量;然后分析了估计偏差对捕获系统检测概率和虚警概率的影响;最后,计算机仿真表明,在限定虚警概率的前提下,捕获系统在高斯白噪声信道和瑞利衰落信道下具有较高的检测概率,自适应门限的估计方法易于实现,且适合工程应用。     

末制导雷达虚警概率检验方法

针对只有10^-8～10^-9量级的末制导雷达虚警概率难以检验的问题,研究虚警概率与发现概率、虚警时间的关系,论证了将虚警概率转化为虚警时间的检验方法,利用定时截尾统计检验理论,研究了不同鉴别比、不同试验时间、不同接收故障数时研制方与使用方的风险,提出了新型末制导雷达虚警概率的检验方法,解决了该项指标检验的难题。     

基于时间应力分析的机内测试系统综合降虚警技术

 虚警率高是困扰机内测试（BIT）系统得到广泛应用的主要原因。针对该问题,从机电系统所承受时间应力的角度构建了机内测试系统综合降低虚警技术的总体模型。首先采用双支持向量机（SVM）的方法将实时应力信息与机内测试诊断结果相互关联。在此基础上,提出了基于核主元模糊聚类的虚警识别方法将机电系统多源信息进行综合分析,并通过优化决策实现多级降低机内测试系统虚警的目的。最后,针对某型直升机航空地平仪的机内测试系统进行了试验验证与分析。     

False Alarm Rate for Rank Quantizer When Input Range Samples are not Independent

Under the assumption that the non-independent range samples fed into the rank quantizer form a first-order Markov sequence, the first threshold false alarm probability Pn is written in the form of a multiple integral, the multiplicity being equal to the number of samples or quantizer taps. The Rice model for narrow-band noise (clutter) is used to check the effect of correlation on Pn using the derived formula. For the limited case examined, correlation be tween samples decreases Pn as compared with the independent samples case.     

机内测试虚警分析和控制

现代飞行器广泛采用机内测试(Built-intest,BIT)技术,以便对其内部故障进行自动检测、诊断和隔离,但是常规BIT面临诊断能力不足和诊断模糊性等问题,导致BIT虚警率高,难以有效发挥其应有的作用.本文论述了BIT虚警的基本理论、虚警的危害及现状,并从BIT虚警产生的原因分析入手,提出了解决虚警问题的一些方法和措施.     

A Method for Setting a Test Signal Level Relative to Noise

A method is described for adjusting the leval of an RF test signal generator relative to the noise level at the receiver output. The method compares a detected output to a threshold and counts the number of times noise and signal plus noise cross the threshold in a given number of tries. By setting the threshold at a given false alarm probability for noise alone and then adding the test signal and adjusting its level to give a specified detection probability, the signal-to-noise ratio can be calibrated to an accuracy that depends on the number of samples used to measure the probabilities. The false alarm and detection probabilities are given for best accuracy as well as the rms error in signal-to-noise ratio as a function of the number of samples used.     

False alarm effects on estimation in multitarget trackers

An analysis of false alarm effects on tracking filter performance in multitarget track-while-scan radars, using variable correlation gates, is presented. The false alarms considered originate from noise, clutter, and crossing targets. The dimensions of the correlation gates are determined by filter prediction and measurement error variances. Track association is implanted either by means of a distance weighted average of the observations or by the nearest neighbor rule. State estimation is performed by means of a second-order discrete Kalman filter, taking into consideration random target maneuvers. Measurements are made in polar coordinates, while target dynamics are estimated in Cartesian coordinates, resulting in coupled linear filter equations. the effect of false alarms on the observation noise covariance matrix, and hence on state estimation errors, is analyzed. A computer simulation example, implementing radar target tracking with a variable correlation gate in the presence of false alarms, is discussed     

False alarm analysis of the envelope detection GO-CFAR processor

The greatest of constant false alarm rate processor (GO CFAR) is a useful architecture for adaptively setting a radar detection threshold in the presence of clutter edges. The GO CFAR input is often the envelope detected in-phase (I) and quadrature (Q) channels of the baseband signal (x_e=√(I²+Q²)). This envelope detection can also be approximated using x=a max{|I|,|Q|}+b min{|I|,|Q|} which requires less complex hardware (a and b are simple multiplying coefficients). The envelope GO CFAR processor and several envelope approximation GO CFAR processors are compared in terms of the probability of false alarm (PFA) performance. Closed-form expressions which describe the PFA performance are given and their accuracy evaluated. It is shown that for all cases, the PFA is proportional to the number of reference cells n for small threshold multiplier T and inversely proportional to n for large T. A region of intersection occurs where the PFA is the same for two different values of n. For example, at T'=1.68 in the |I|+|Q| GO CFAR (a=1, b=1) the PFA for n=1 is equal to the optimal n=∞ fixed-threshold PFA (PFA=0.112)     

Calculating Detection Probabilities for Adaptive Thresholds

In calculating detection probabiities for radar and sonar systems it is usually assumed that the threshold required to yield a certain probability of false alarm is known. This is often not the case for real systems and therefore the threshold must be estimated using some measure related to the test statistic. This paper presents a calculation technique that handles estimated (adaptive) thresholds in a general framework that can be applied easily to many detection problems. False alarm and detection probabilities are calculated from the characteristic function of the noise or signal plus noise variate and the characteristic function of the threshold estimate. To illustrate the method the detection performance of overlapped discrete Fourier transforms (DFTs) is calculated for a narrowband Gaussian target signal.     

Uniform versus nonuniform sampling when tracking in clutter

Many target tracking subsystems have the ability to schedule their own data rates; essentially they can "order" new information whenever they need it, and the cost is in terms of the sensor resource. But among the unmanaged schemes, uniform sampling, in which a new measurement is requested periodically and regularly, is the most commonly-used sampling scheme; deliberately nonuniform schemes are seldom given serious consideration. In this paper, however, we show that such schemes may have been discarded prematurely: a nonuniform sampling can have its benefits. Specifically, the nonuniform and uniform sampling schemes are compared for two kind of trackers: the probabilistic data association filter (PDAF), which updates its track based on a single scan of information at a time; and N-D assignment (an optimization-based implementation of the multi-hypothesis tracker (MHT)), in which the sliding window involves many scans of observations. We find that given the ground rule of maintenance of the same overall scan rate (i.e., the same sensor effort) uniform sampling is always optimal for the single-scan tracker in the sense of track life. However, nonuniform sampling can outperform uniform sampling if a more sophisticated multi-scan tracker is used, particularly when 1) the target has a high process noise, and/or 2) the false alarm density is high, and/or 3) the probability of detection is high.     

Fluctuating Target Detection in Clutter Using Sidelobe Blanking Logic

In an earlier paper, Maisel [6] considered two-channel detection systems using a sidelobe blanking logic when a nonfluctuating target was present. This paper is an extension of the earlier work to include fluctuating targets. The Swerling I, II, III, and IV models are considered when single-pulse detection is of interest. An adaptive threshold procedure is also briefly discussed whereby the probability of false alarm at any given resolution cell is maintained constant, even though the input clutter level may vary from cell to cell or from beam position to beam position. Useful data are presented for detection probabilities in the range 0.5 to 0.9, for false alarm probabilities in the range 104 to 10-8, and for a false detection probability of 0.1 for a sidelobe target yielding an apparent signal to total noise power density ratio of 13.0 dB in the main beam receiver.     

Adaptive False Alarmrm Regulation in Double Threshold Radar Detection

In practical situations the false alarm probability in double threshold radar detection, sometimes known as binary integration with sliding window detection, is dependent on the nonstationarity and azimuthal correlation of the clutter which is present. Control of the false alarm probability can be achieved, to a certain extent, by the adjustment of the second threshold in the detection process. In this study two adaptive control techniques which are based on the statistical characteristics of the data are compared. Comparing the results for a technique based on first-order statistics with one based on second-order statistics, it is shown that the second-order, or correlation sensitive, technique can give a reduction of 30 to 45 percent in the false alarm probability with no corresponding loss in the detection probability. An interesting aspect of the results is the fact that the effects of the size of the sample area and the bias in the correlation estimator are clearly evident.     

Optimal radar threshold determination in Weibull clutter andGaussian noise

A technique is presented for determining the ideal detection threshold when Gaussian noise and Weibull distributed clutter returns are present on a radar receiver and neither is dominant. Quantitative data is presented for several clutter types and false alarm probabilities     

Noise, Radar Moving-Window Detectors, and False Returns

Moving-window false report probability is shown to overestimate effects of noise. "False-report-start" probability is calculated and compared to the usual false report probability. The ratio of the two exceeds a simple function of window length, threshold, and noise probability, which is tabulated for some representative values.     

An algorithm for detection of moving optical targets

An algorithm is described for detecting moving optical targets against spatially nonstationary Poisson background and noise. The algorithm has applications in optical detection of objects such as meteors, asteroids, and satellites against a stellar background. A maximum-likelihood approach is used which results in reducing interference from stars. It is shown that by choosing a detection threshold to provide a constant false alarm rate, the resulting algorithm is independent of the signal strength of the target. An analysis of this algorithm is presented, showing the probability of detection for several false-alarm rates     

Importance Sampling Applied to Simulation of False Alarm Statistics

The application of importance sampling to the efficient simulation of low-probability events is discussed. Several examples are given to illustrate how the technique works for processes involving single and multiple statistical inputs. A procedure is also given for handling the case of multiple inputs where each is from a different statistical process.     

Energy Detection of a Random Process in Colored Gaussian Noise

A likelihood receiver for a Gaussian random signal process in colored Gaussian noise is realized with a quadratic form of a finite-duration sample of the input process. Such a receiver may be called a "filtered energy detector." The output statistic is compared with a threshold and if the threshold is exceeded, a signal is said to be present. False alarm and detection probabilities may be estimated if tabulated distributions can be fitted to the actual distributions of the test statistic which are unknown. Gamma distributions were fitted to the conditional probability densities of the output statistic by equating means and variances, formulas for which are derived assuming a large observation interval. A numerical example is given for the case in which the noise and signal processes have spectral densities of the same shape or are flat. The optimum filter turns out to be a band-limited noise whitener. The factors governing false alarm and detection probabilities are the filter bandwidth, the sample duration, and the signal level compared to the noise. Two sets of receiver operating characteristic curves are presented to complete the example.     

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     

基于卡尔曼滤波器的主燃油计量装置故障诊断

为了建立主燃油计量电液执行机构(EHA)的故障诊断算法,分析了主燃油计量液压机械控制装置的工作原理,提出了1种建立主燃油计量线性变参数(LPV)模型的方法,设计并得到基于离散卡尔曼滤波器的主燃油计量电液执行机构位移输出的残差生成器。仿真结果表明:该残差生成器具有对故障敏感而对噪声和建模误差不敏感、对残差的动态响应快,幅值变化较大的特点,有效降低故障误报警率和漏报警率。