Spectral-spatial target detection based on data field modeling for hyperspectral data

Target detection is always an important application in hyperspectral image processing field. In this paper, a spectral-spatial target detection algorithm for hyperspectral data is proposed. The spatial feature and spectral feature were unified based on the data filed theory and extracted by weighted manifold embedding. The novelties of the proposed method lie in two aspects. One is the way in which the spatial features and spectral features were fused as a new feature based on the data field theory, and the other is that local information was introduced to describe the decision boundary and explore the discriminative features for target detection. The extracted features based on data field modeling and manifold embedding techniques were considered for a target detection task. Three standard hyperspectral datasets were considered in the analysis. The effectiveness of the proposed target detection algorithm based on data field theory was proved by the higher detection rates with lower False Alarm Rates (FARs) with respect to those achieved by conventional hyperspectral target detectors.     

Searching tracks

Search theory is the discipline that studies the problem of how best to search for an object when the amount of searching efforts is limited and only probabilities of the possible position of the object are given. Then, the problem is to find the optimal distribution of this total effort that maximizes the probability of detection. Although the general formalism of search theory will be used subsequently, we consider now a radically different problem. The problem is to detect target tracks. In the "classical" search theory, the target is said detected if a detection occurs during any time of the time frame. Here, on the contrary, the target track will be said to be detected if elementary detections occur at various times. That means that there is a test for acceptance (or detection) of a target track and that the problem is to optimize the allocation of the search effort for track detection. So, specific optimization problems are solved by means of the primal-dual formalism, in an original setup. Other aspects concern Markovian targets and two-sided search for which simple and efficient algorithms are derived.     

Evaluating Radar Detection Probabilities by Steepest Descent Integration

The probability of detection for radars employing noncoherent integration and a fixed threshold or cell-averaging constant false alarm rate (CA-CFAR) processor is computed by numerical contour integration in the complex plane. The technique is applied to both nonfluctuating and chi-squared fluctuating targets. A bound on the truncation error allows for a simple stopping rule for the numerical integration. The method has applicability to many problems in radar detection theory.     

Performance analysis of a dynamic programming track before detectalgorithm

We analyze a dynamic programming (DP)-based track before detect (TBD) algorithm. By using extreme value theory we obtain explicit expressions for various performance measures of the algorithm such as probability of detection and false alarm. Our analysis has two advantages. First the unrealistic Gaussian and independence assumptions used in previous works are not required. Second, the probability of detection and false alarm curves obtained fit computer simulated performance results significantly more accurately than previously proposed analyses of the TBD algorithm     

轴流风扇/压气机管道周向声模态的测量

利用管道内部周向均匀分布的麦克风阵列对某单级风扇在高背景噪声和较大硬壁反射条件下的管道周向声模态进行了实验测量.利用与参考信号互相关(CC)模态分解技术和传统的方均根(RMS)模态分解技术得到了主要的周向声模态振幅.通过与Tyler和Sofrin的理论分析对比,发现在高背景噪声和较大硬壁反射条件下,两种模态分解技术都能获得较为理想的模态分解结果;并且发现CC模态分解技术获得的模态振幅较低,初步分析这是由于和RMS模态分解技术相比,CC模态分解技术可以降低随机噪声对模态振幅分解结果的影响.此外,周向麦克风个数大于所要分解周向声模态阶数的4倍时,使用CC模态分解技术获得的模态振幅误差可以控制在1dB之内.      

Sliding Window Detection Probabilities

A method is proposed for computing sliding window detection probabilities which have applications in track-while-scan acquisition logic and radar detection theory. The sliding window probability is the probability of achieving m successes out of n consecutive events at least once by the Nth opportunity. Expressions are derived for these probabilities as a function of N and p (the per-event success probability) for 1 ? n ? 4 and m ? n. Tabulated results are presented for the mean and standard deviation of the detection delay associated with each m/n logic.     

基于危险模式的无线传感器网络IDS模型

网络安全是无线传感器网络需要解决的重要问题之一,构建基于传感器网络的入侵检测系统是其中重要的技术.简要阐述了基于人工免疫模型及危险模式的入侵检测系统的机理,提出了一种基于危险模式理论的无线传感器网络的入侵检测系统体系结构,并对该模型的特点进行了分析,为进一步深入的研究提出了设想.     

Limiter-Discriminator Detection Performance of Manchester and NRZ Coded FSK

The theory of limiter-discriminator detection of a frequency shift keyed (FSK) carrier is reviewed and this theory is used to predict the bit error probability performance of Manchester coded and nonreturn-to-zero (NRZ) coded data. A major result of the study is that the predetection time bandwidth product BT and the devia tion ratio h needed to give optimum performance for Manchester coding are seen to be larger than optimum NRZ FSK. Specifically, BT ~ 2 and h ~ 1 will result in Manchester performance about 2 dB worse than optimum NRZ.     

涡轮泵实时故障检测的改进自适应相关阈值算法

1引言火箭发动机在飞行使用之前,需要分析试车振动数据以评估发动机的性能与状态[1]。涡轮泵是液体火箭发动机中最复杂故障概率最高的部件[2],而振动是涡轮泵故障的重要起因[3],因此,涡轮泵振动数据的分析显得尤为重要。目前,它的主要分析方法有时域统计[4]、频谱分析[5]、神经     

Critique of some neural network architectures and claims forcontrol and estimation

While there is great potential for successful use of neural network (NN) algorithms in automatic target recognition (ATR) and other pattern identification/classification applications, significant barriers have been encountered that, to date, defy rigorous use of NNs within feedback control designs. The status of several problems and contradictions involving NNs relating to control and estimation theory applications (and to practical failure detection within INS/GPS navigation systems) are summarized here. To give a positive spin and for a balanced perspective, we also mention many novel laudable NN results obtained by invoking the techniques and results of control and estimation theory     

Detection of Changes in the Characteristics of a Gauss-Markov Process

By an extension to the theory of sequential detection with dependent measurements, it is possible to develop a sequential probability ratio test (SPRT) to detect changes in regime in a Gauss-Markov process rather than detecting which of the two regimes exists. It is shown how a posterior form of this extended SPRT may be simplified to reduce computational complexity. The simplified SPRT's are in fact modifications of the original SPRT detecting the regime and not the change. The tests are applied to the problem of fault detection in a gyro navigational system; the results of a detailed computer simulation are given.     

Application of frequency correlation function to radar target detection

Analysis of high-resolution 35 GHz synthetic aperture radar (SAR) imagery of terrain reveals that when point targets, such as vehicles, are viewed at angles close to grazing incidence, they are often difficult to distinguish from tree trunks because the radar cross section (RCS) intensities of the vehicles are comparable to the upper end of the RCS exhibited by tree trunks. To resolve the point target/tree trunk ambiguity problem, a detailed study was conducted to evaluate the use of new detection features based on the complex frequency correlation function (FCF). This paper presents an analytical examination of FCF and its physical meaning, the results of a numerical simulation study conducted to evaluate the performance of a detection algorithm that uses FCF, and the corroboration of theory with experimental observations conducted at 35 and 95 GHz. The FCF-based detection algorithm was found to correctly identify tree trunks as such in over 90% of the cases, while exhibiting a false alarm rate of only 3%.     

Decentralized CFAR signal detection

The authors develop the theory of CA-CFAR (cell-averaging constant false-alarm rate) detection using multiple sensors and data fusion, where detection decisions are transmitted from each CA-CFAR detector to the data fusion center. The overall decision is obtained at the data fusion center based on some k out of n fusion rule. For a Swerling target model I embedded in white Gaussian noise of unknown level, the authors obtain the optimum threshold multipliers of the individual detectors. At the data fusion center, they derive an expression for the overall probability of detection while the overall probability of false alarm is maintained at the desired value for the given fusion rules. An example is presented showing numerical results     

碰摩故障静电监测方法及模拟实验

针对航空发动机叶片碰摩故障难以实现在线监测的问题,展开基于静电感应原理的碰摩故障监测新方法研究。分析了碰摩颗粒产生与荷电机理,以静电传感器理论和点电荷感应信号特征为依据,建立了周期碰摩静电感应理论模型。研究结果表明:周期碰摩在静电传感器上的输出是一系列以碰摩频率为间隔的随频率增大幅值迅速衰减的周期离散谱线。为了验证模型的正确性,利用自行研制的静电传感器,采用模拟碰摩的方式进行了两组实验。第1组实验结果表明传感器可以监测到模拟碰摩故障产生的带电颗粒,碰摩阶段的信号活动率水平明显增大,但事件率没有出现明显的变化;第2组实验在信号频域出现了非常明显的特征频率及其二倍频,且二倍幅值频随频率增大迅速衰减,证明了模型的有效性。     

从探测概率的角度评价飞机的隐身性能

采用雷达散射截面(RCS)均值来衡量飞机的隐身性能并不能给出足够充分的信息,从信号检测概率的角度来衡量飞机的可探测性可以提供更完整的信息。本文从探测概率的角度详细分析了4种典型隐身飞机RCS起伏数据与虚警概率、探测概率、探测距离、信噪比(SNR)等参数间的关系,并给出了完整的推导过程,采用此方法可以对任意飞机目标在给定雷达参数下的可探测性进行准确计算。一般在雷达性能评估中需要用到RCS起伏模型,本文对飞机目标回波信号进行检测分析时采用数值计算方法,信号的概率密度函数(PDF)直接来源于原始回波起伏信号,避免了模型拟合带来的回波特性失真,且不会产生大的计算误差。通过对比分析计算结果发现:单脉冲检测下回波信号均值中值比越大,回波取值范围越小,则信号的检测概率越小,其中均值中值比一般相差3倍以上时可以得到明显不同的检测概率;在快起伏假设下,非相参积累检测的积累脉冲数Nin即便较小,也能得到较大的信噪比增益,此增益可能大于Nin。     

一种基于一致性理论的卫星协同自主探测方法

为了提高天基空间碎片探测系统对空间碎片探测能力,研究了一种基于一致性理论的卫星协同自主探测方法,对系统中的卫星进行协同控制,从而实现对空间碎片进行最长时间的持续跟踪探测。探测星座利用招投标机制选取探测效果最好的卫星,空间碎片周围的卫星载荷再利用一致性理论,协同自主地指向空间碎片,以实现探测星座对空间碎片连续跟踪。仿真结果说明了该方法的有效性,1d内探测星座对典型空间碎片的最大连续观测时间由协同前的 6 min提高到了协同后的 39 min,提高了约 5倍,总观测时间由协同前的 1.5 h提高到 13 h,提高了约 8倍。该方法提高了观测效能,可为天基空间碎片探测系统提供一种对空间碎片协同探测的技术途径。     

多传感器协同探测证据理论分类融合方法

随着智能化、网络化集群作战等理念和技术的兴起,精确制导武器越来越向智能化、协同化方向发展。多传感器协同探测能够针对不同的探测任务背景和作战需求,提升目标探测性能,还可以跨域整合多种探测平台。但是由于信息的不确定性等特点,使得多传感器数据直接融合可能造成决策困难。因此,在证据理论体系下对信息融合的有效性进行合理分析与度量是很有必要的。提出了一种基于Deng熵的证据理论分类融合算法,以熵减为主要思想,将证据进行分类融合。在决策过程中,将含有证据数最多的类别融合结果作为总体融合结果,避免高冲突证据的影响,提升融合结果的信息有效性。采用算例说明了所提方法不仅能够得到合理正确的结果,并且融合可靠性较高,便于决策与后续的信息处理。     

一种非规则采样航空时序数据异常检测方法

航天器遥测数据的实时异常检测对于航天任务具有重要意义。以往方法大都考虑规则采样且缺失率较低的时序数据,然而航空时序数据具有维度大、噪声多、缺失率高、采样间隔不规则等特点,因此异常检测任务较为困难。针对非规则采样且具有缺失值的多维航空时序数据提出非规则采样多维时序数据异常检测（IMAD）算法。首先,采用带有可训练迟滞项的门控循环单元（GRU-D）对缺失值和非规则采样的时序数据进行建模;然后,采用变分自编码器建立随机性模型,学习正常时序数据的分布,从而对噪声数据具有鲁棒性;最后,利用基于极值理论的自适应阈值确定法确定合适阈值进行异常检测。结果显示,在两个真实航空时序数据集上,IMAD具有超出当前最新异常检测算法的性能;多个实验表明,IMAD在缺失率、参数以及数据集变化时,能够维持较好的异常检测效果,具有较强的鲁棒性。     

Temporal evolution of the RCS of aircraft wake vortices

Knowledge of the radar scattering characteristics is one of the key issues for the development of wake vortex detection technology. This paper studies the temporal evolution of the RCS (radar cross-section) of wake vortices. The RCS–time plot is observed to increase as a whole and step at a certain time. These properties could provide help to the design of wake vortex detection radar and the optimization of radar station layout. The special spiral structures within the wake, together with the Bragg scattering theory, are used to well explain these phenomena, and some representative radar experiments are also included to verify them.     

基于机器视觉与UMAC的自动铺丝成型构件缺陷检测闭环控制系统

为保证自动铺丝成型构件的性能,实现对成型构件表面质量的高效检测,本文基于机器视觉检测技术,自行研发自动铺丝成型构件表面缺陷检测闭环控制系统,使自动铺丝过程中预浸纱之间的间隙或重叠在允许范围内,从而保证铺丝精度。研究主要分为三个部分:铺丝成型构件表面图像提取与缺陷检测,检测数据传输,运动轴反馈控制,实现在自动铺丝过程中对成型构件表面缺陷检测实时闭环控制。针对电荷耦合相机采集到的预浸纱图像存在运动模糊和信噪比低等问题,将图像预处理划分为图像复原和降噪两大功能,根据不同类型的噪声采用对应滤波法进行降噪处理,提高了图像的信噪比,为后续检测提供高质量图像;预浸纱缺陷主要由其边缘直线这一几何特征进行表征,因此通过对预浸纱边缘检测实现缺陷识别;利用工控机向图像采集控制器发送命令实现图像采集,同时通过工控机与运动控制器之间信息流和数据流的交换实现检测数据的传输。探讨模糊控制理论用于闭环控制的研究,在反模糊化环节后面,加入自适应参数调节,以适应加工过程的突变,并成功地应用到自动铺丝缺陷检测闭环控制系统中,提高了伺服跟踪精度和跟踪实时性,从而提高铺丝精度。