Superimposed HMM transient detection via target tracking ideas

The quickest detection of superimposed hidden Markov model (HMM) transient signals is addressed. It is assumed that a known HMM is always extant but at an unknown time a second known HMM may also be present, and overlapped with the previous. Two approaches are proposed. The first treats the superimposed HMMs as a unit with an expanded state space, thus converting the problem of detecting superimposed HMMs into detection of a change in HMM, this being readily solved using a previously proposed procedure. Such an approach, though excellent in terms of performance, is not suitable for the superposition of multiple HMMs with large state dimensions due to computational complexity. A second detection scheme (based on multiple target tracking ideas) with much lower computational needs but little loss in terms of performance, is therefore developed     

基于HMM-SVM的混合故障诊断模型及应用

针对直升机减速器故障诊断中机器学习方法存在的问题,根据隐马尔可夫模型(HMM)适合于处理连续动态信号与支持向量机(SVM)适合于模式分类的长处,提出了基于HMM-SVM的混合故障诊断模型。先通过小波包分析方法从减速箱振动信号中有效提取非平稳特征,训练HMM-SVM模型,再利用训练好的模型进行监测与诊断,实验结果表明该方法优于单纯的HMM或SVM诊断方法,能利用少量训练样本有效地完成直升机减速器的故障诊断。     

Multiaspect classification of airborne targets via physics-basedHMMs and matching pursuits

Wideband electromagnetic fields scattered from N distinct target-sensor orientations are employed for classification of airborne targets. Each of the scattered waveforms is parsed via physics-based matching pursuits, yielding N feature vectors. The feature vectors are submitted to a hidden Markov model (HMM), each state of which is characterized by a set of target-sensor orientations over which the associated feature vectors are relatively stationary. The N feature vectors extracted from the multiaspect scattering data implicitly sample N states of the target (some states may be sampled more than once), with the state sequence modeled statistically as a Markov process, resulting in an HMM due to the “hidden” or unknown target orientation. In the work presented here, the state-dependent probability of observing a given feature vector is modeled via physics-motivated linear distributions, in lieu of the traditional Gaussian mixtures applied in classical HMMs. Further, we develop a scheme that yields autonomous definitions for the aspect-dependent HMM states. The paradigm is applied to synthetic scattering data for two simple targets     

Identification of ground targets from sequential high-range-resolution radar signatures

An approach to identifying targets from sequential high-range-resolution (HRR) radar signatures is presented. In particular, a hidden Markov model (HMM) is employed to characterize the sequential information contained in multiaspect HRR target signatures. Features from each of the HRR waveforms are extracted via the RELAX algorithm. The statistical models used for the HMM states are formulated for application to RELAX features, and the expectation-maximization (EM) training algorithm is augmented appropriately. Example classification results are presented for the ten-target MSTAR data set.     

改进的基于隐马尔可夫模型的自适应IMM算法

针对机动目标跟踪中交互式多模型算法(IMM)的马尔可夫转移概率矩阵固定不变造成跟踪精度降低的问题,在已有的基于隐马尔科夫模型(HMM)的自适应IMM算法的基础上,对隐马尔可夫链的长度和Baum-Welch算法迭代次数的2个参数对该算法跟踪性能的影响,进行了深入研究分析,进一步明确了这2个参数选择的依据;并针对该算法在目标机动转换时峰值误差增大的问题,给出了2种修正方法,从而提出了改进的基于HMM的自适应IMM算法。最后,通过仿真分析了算法的参数和修正方法对跟踪性能的影响,并与传统IMM算法进行对比,证明了文章提出算法的有效性。     

Multi-aspect radar target recognition method based on scattering centers and HMMs classifiers

A hidden Markov model (HMM)-based method for recognizing aerial targets according to the sequential high-range-resolution (HRR) radar signature is presented. Its recognition features are the location information of scattering centers extracted from the HRR radar echoes by the relax algorithm. The HMM is used to characterize the spatio-temporal information of a target. Several HMMs are cascaded in a chain to model the variation in the target orientation and used as classifiers. Computer simulations with the inverse synthetic aperture radar (ISAR) data are given to demonstrate that for an open-set recognition, average class-recognition rates of 84.50% and 89.88% are achieved, respectively, under two given conditions.     

基于隐马尔科夫模型的故障诊断系统研究

在制造行业中,机械设备的状态检测技术能提供关于设备运行状态的实时信息,为避免生产损失和减少设备的致命故障提供保障。提出了一套基于小波变换和隐马尔科夫模型(Hidden Markov Models,HMMs)的故障检测系统。提出了小波模极大值分布(Wavelet Modulus Maxima Distribution),并将之定义为诊断系统的观察量加以验证。同时该系统采用在线模型参数估计和培训算法,通过选取能最大化对数似然度的HMM模型,确定设备所处状态。     

基于改进HMM和LS-SVM的机载设备故障预测研究

针对传统故障预测方法不能直接预测设备状态的不足,提出了将改进隐马尔科夫模型（HMM）和最小二乘支持向量机（LS—SVM）相结合的机载设备故障预测方法。首先,采用多智能体遗传算法对HMM参数进行训练优化,克服了B-W算法易陷入局部最优解的缺陷;其次,分别研究设计了设备是否具有使用阶段状态退化过程数据2种情况下的故障预测算法流程;最后,以飞机发动机温控放大器为应用对象进行仿真计算。结果表明,该算法不仅预测精度高,而且预测结果直接与设备状态相关,易于理解分析。     

Bearings-only tracking for maneuvering sources

Classical bearings-only target-motion analysis (TMA) is restricted to sources with constant motion parameters (usually position and velocity). However, most interesting sources have maneuvering abilities, thus degrading the performance of classical TMA. In the passive sonar context a long-time source-observer encounter is realistic, so the source maneuver possibilities may be important in regard to the source and array baseline. This advocates for the consideration and modeling of the whole source trajectory including source maneuver uncertainty. With that aim, a convenient framework is the hidden Markov model (HMM). A basic idea consists of a two-levels discretization of the state-space. The probabilities of position transition are deduced from the probabilities of velocity transitions which, themselves, are directly related to the source maneuvering capability. The source state sequence estimation is achieved by means of classical dynamic programming (DP). This approach does not require any prior information relative to the source maneuvers. However, the probabilistic nature of the source trajectory confers a major role to the optimization of the observer maneuvers. This problem is then solved by using the general framework of the Markov decision process (MDP)     

Analysis of an all-digital data-transition tracking loop

An all-digital implementation of the data-transition tracking loop (DTTL) is proposed and analyzed. The input waveform is assumed to be nonreturn-to-zero (NRZ)-coded binary signals. The mathematical analysis of the loop is reduced to the study of a Markov chain which allows the derivation of expressions for the mean square phase error, steady-state probabilities, transient behavior (time to acquire and time to bit slippage), and bit error rate (BER). Theoretical results have been validated using a computer simulation of the loop. The all-digital implementation can take full advantage of advanced technology in memory and processing speeds     

Rate-aided multisignal time delay detection and tracking

Time delay detection and tracking problems have been investigated in previous papers. The approach used is applicable to many problems. In those papers, a first-order Markov process was assumed for the signal parameter (time delay) dynamic model and a constant signal parameter value was assumed within a “block”, or processing unit of time. A performance degradation is incurred when the signal parameter value is “nearly constant-rate”. Also, “crossing” signal parameter values from different signals cannot be tracked. The detector-trackers will “switch” signals. To combat these difficulties, the signal parameter dynamic model is assumed to be a second-order Markov process, and signal parameter rate of change information is computed within a block. A rate-aided multisignal detection and tracking system is developed with these assumptions     

Spatio-temporal pattern recognition using hidden Markov models

A spatio-temporal method for identifying objects contained in an image sequence is presented. The Hidden Markov Model (HMM) technique is used as the classification algorithm, making classification decisions based on a spatio-temporal sequence of observed object features. A five class problem is considered. Classification accuracies of 100% and 99.7%, are obtained for sequences of images generated over two separate regions of viewing positions. HMMs trained on image sequences of the objects moving in opposite directions showed a 98.1% successful classification rate by class and direction of movement. The HMM technique proved robust to image corruption with additive correlated noise and had a higher accuracy than a single-look nearest neighbor method. A real image sequence of one of the objects used was successfully recognized with the HMMs trained on synthetic data. This study shows the temporal changes that observed feature vectors undergo due to object motion hold information that can yield superior classification accuracy when compared with single-frame techniques     

Minimal-Order Models for False-Alarm Calculations on Sliding Windows

A procedure is developed to obtain practical numerical results in connection with the m-out-of-n sliding-window detection problem. This effort was motivated by difficulties with previous approaches involving approximation, Markov models, and Monte Carlo simulation. Generating-function methods were found to be unsatisfactory for window lengths greater than 6 due to their complexity. Instead, a Markov model is described that is then constructively reduced to the minimum number of state variables. The results are derived for binary strings with intersymbol correlation. Computational aids are discussed for obtaining design information, such as quantiles, from the minimal-order Markov models. Numerical results are given comparing the methods of the paper with a "jumping" window approximation for an 8/10 problem.     

飞机铆接件隐藏缺陷的远场涡流检测探头优化与试验

远场涡流检测技术因检测深度深和检测结果可靠性高等诸多优点适合飞机多层金属铆接构件的快速检测。针对飞机铆接件铆钉孔沿边隐藏裂纹的原位检测，建立了多层金属铆接构件隐藏缺陷平面远场涡流检测有限元模型，对激励线圈内径、磁路结构以及屏蔽阻尼进行了仿真优化，研制了激励线圈和检测线圈均带组合屏蔽结构的传感器，采用激励-检测线圈环绕铆钉旋转扫描的方式，研究多层金属铆接构件铆钉孔沿边隐藏裂纹信号特征。仿真与试验结果表明：罐形磁芯聚磁效果是柱形磁芯的1.85倍，采用铝+铜组合屏蔽罩能够将远场区提前10 mm，检测线圈位于缺陷正上方时，检测信号的幅值和相位存在极大值，且极大值随着缺陷埋深的增加逐渐下降，研究成果可望用于指导飞机多层金属铆接构件的工程检测实践。     

基于PSO-ELM的卫星导航欺骗式干扰检测CSCD

近年来,卫星导航系统在军事监测、精细农业、交通监控、资源勘探、灾害评估等领域得到了广泛应用,但由于卫星导航信号结构公开且到达地面时强度微弱,卫星导航系统极易受到各种各样的干扰,其中欺骗式干扰因具有较强的隐蔽性,对卫星导航系统构成巨大的安全威胁。传统的欺骗式干扰检测方法大多采用单一参数进行检测,具有一定局限性。考虑到欺骗干扰源在欺骗过程中会引起一系列参数变化,构造了一个多参数输入的卫星导航欺骗式干扰检测模型,即将多个特征参数作为极限学习机(ELM)的输入,并通过训练和学习将真实信号与欺骗信号区分开,从而实现欺骗式干扰检测。同时,利用粒子群优化(PSO)算法优化ELM中的输入权值矩阵和隐层偏置,改善由于网络参数随机生成导致分类精度低的问题。仿真实验证明了该方法在卫星导航欺骗干扰检测方面的可行性和有效性。     

Supervised learning with probability interpretation in airfoil transition judgment

Transition prediction has always been a frontier issue in the field of aerodynamics. A supervised learning model with probability interpretation for transition judgment based on experimental data was developed in this paper. It solved the shortcomings of the point detection method in the experiment, that which was often only one transition point could be obtained, and comparison of multi-point data was necessary. First, the Variable-Interval Time Average (VITA) method was used to transform the fluctuating pressure signal measured on the airfoil surface into a sequence of states which was described by Markov chain model. Second, a feature vector consisting of one-step transition matrix and its stationary distribution was extracted. Then, the Hidden Markov Model (HMM) was used to pre-classify the feature vectors marked using the traditional Root Mean Square (RMS) criteria. Finally, a classification model with probability interpretation was established, and the cross-validation method was used for model validation. The research results show that the developed model is effective and reliable, and it has strong Reynolds number generalization ability. The developed model was theoretically analyzed in depth, and the effect of parameters on the model was studied in detail. Compared with the traditional RMS criterion, a reasonable transition zone can be obtained using the developed classification model. In addition, the developed model does not require comparison of multi-point data. The developed supervised learning model provides new ideas for the transition detection in flight experiments and other experiments.     

Detection of long-duration narrowband processes

Detecting long, weak signals that are narrowband but of unknown frequency structure is an important signal processing challenge, with many applications in remote sensing and process monitoring. An ad hoc scheme is developed. Its stages include the discrete Fourier transform (DFT), a multiresolution decomposition in the frequency domain, and a generalized likelihood ratio test (GLRT). The computational load is light, and the performance is remarkably good. This is so not just in the original narrowband situation, but also, due to an inherent adaptivity to the data, in the detection of signals that are relatively broadband in nature. Generalizations are given to constant false alarm rate (CFAR) operation in both prewhitened and unwhitened cases, and to the detection of multiband signals. As regards the last, it is discovered that there is little loss from overestimating the number of bands     

校正训练算法在半连续HMM中的应用

隐马尔柯夫模型用最大似然准则训练的结果，能保证训练过程的最佳，但却不能保证识别过程的最佳，从识别时的最小误识率出发导出的各种准则之下的训练方法，能有效地提高系统的性能。本文将校正训练（CT）算法应用于半连续隐马尔柯夫模型（SCHMM）的训练过程，给出了算法的具体实现步骤，同时对于所需的易混集的建立方法，采用一种适于中小词表系统的动态构造方法来实现。     

Research on Auto-detection for Remainder Particles of Aerospace Relay Based on Wavelet Analysis

Aerospace relay is one kind of electronic components which is used widely in national defense system and aerospace system. The existence of remainder particles induces the reliability declining, which has become a severe problem in the development of aerospace relay. Traditional particle impact noise detection (PIND) method for remainder detection is ineffective for small particles, due to its low precision and involvement of subjective factors. An auto-detection method for PIND output signals is proposed in this paper, which is based on direct wavelet de-noising (DWD), cross-correlation analysis (CCA) and homo-filtering (HF), the method enhances the affectiv-ity of PIND test about the small particles. In the end, some practical PIND output signals are analysed, and the validity of this new method is proved.     

EM Transient Protection Requirements for Avionics LRUs

Avionics and electronic equipment installed in aircraft and air breathing missiles are required to operate without upset or damage when subjected to EM environments caused by lightning, NEMP, and Intrasystems transients. To simplify the design effort required to protect electronic equipment and minimize overlapping transient requirements and qualification tests, unified test requirements and procedures are needed. The goal of these unified test procedures would be to insure a known susceptibility level for each newly designed electronic Line Replaceable Unit (LRU). Ten avionics equipments in current Air Force inventory were tested for the purpose of developing unified procedure test methods. Operating LRUs were subjected to transients using common mode cable current (CMCC), groung potentials and chatteriing relay cable injection methods. Based on observed test results and review of current test requirements, test methods and test levels for unified test procedures for power-on transient tests of avionics equipment have been formulated.