Reliability Sensitivity Algorithm Based on Stratified Importance Sampling Method for Multiple Failure Modes Systems

 Combining the advantages of the stratified sampling and the importance sampling, a stratified importance sampling method (SISM) is presented to analyze the reliability sensitivity for structure with multiple failure modes. In the presented method, the variable space is divided into several disjoint subspace by n-dimensional coordinate planes at the mean point of the random vector, and the importance sampling functions in the subspaces are constructed by keeping the sampling center at the mean point and augmenting the standard deviation by a factor of l. The sample size generated from the importance sampling function in each subspace is determined by the contribution of the subspace to the reliability sensitivity, which can be estimated by it-erative simulation in the sampling process. The formulae of the reliability sensitivity estimation, the variance and the coeffi-cient of variation are derived for the presented SISM. Comparing with the Monte Carlo method, the stratified sampling method and the importance sampling method, the presented SISM has wider applicability and higher calculation efficiency, which is demonstrated by numerical examples. Finally, the reliability sensitivity analysis of flap structure is illustrated that the SISM can be applied to engineering structure.     

子空间干扰加高斯杂波背景下基于 GLRT的斜对称方向检测器设计

文章研究了背景为子空间干扰加高斯杂波的距离扩展目标方向检测问题。杂波是均值为零协方差矩阵未知但具有斜对称特性的高斯杂波,目标与干扰分别通过具备斜对称特性的目标子空间和干扰子空间描述。针对方向检测问题,利用上述斜对称性,根据广义似然比检验(Generalized Likeli-hood Ratio Test,GLRT)准则的一步与两步设计方法,设计了基于 GLRT的一步法与两步法的距离扩展目标方向检测器。通过理论推导证明了这 2种检测器相对于未知杂波协方差矩阵都具有恒虚警率。对比相同背景下已有检测器,特别是在辅助数据有限的场景下,文章提出的 2个检测器表现出了优越的检测性能。     

Neural Networks Combined with Importance Sampling Techniques for Reliability Evaluation of Explosive Initiating Device

Concerning the issue of high-dimensions and low-failure probabilities including implicit and highly nonlinear limit state function, reliability analysis based on the directional importance sampling in combination with the radial basis function (RBF) neural network is used, and the RBF neural network based on first-order reliability method (FORM) is to approximate the unknown implicit limit state functions and calculate the most probable point (MPP) with iterative algorithm. For good efficiency, based on the ideas that directional sampling reduces dimensionality and importance sampling focuses on the domain contributing to failure probability, the joint probability density function of importance sampling is constructed, and the sampling center is moved to MPP to ensure that more random sample points draw belong to the failure domain and the simulation efficiency is improved. Then the numerical example of initiating explosive devices for rocket booster explosive bolts demonstrates the applicability, versatility and accuracy of the approach compared with other reliability simulation algorithm.     

基于高斯权值-混合建议分布粒子滤波的疲劳裂纹扩展预测

飞行器结构的疲劳裂纹扩展预测对保障结构安全、实现视情维护具有重要意义。结合粒子滤波算法和结构健康监测方法进行在线的疲劳裂纹扩展预测是近年来刚刚开始研究的新方法,该方法通过状态空间模型表征疲劳裂纹扩展过程中的不确定性,同时通过贝叶斯方法将结构健康监测所获取的结构实际裂纹观测值用于修正裂纹扩展模型的预测误差,实现更准确的疲劳裂纹扩展在线预测。由于该方法的研究刚刚开展,已有研究中粒子滤波算法的重要性密度函数往往简单选取为先验转移概率密度,存在严重的粒子退化问题。另一方面出于简单考虑,仅采用表征裂纹稳定扩展区的Paris模型。针对上述问题,本文提出一种基于高斯权值-混合建议分布粒子滤波的疲劳裂纹在线预测方法,基于表征裂纹全扩展区域的NASGRO裂纹扩展模型建立疲劳裂纹扩展状态方程,以主动Lamb波监测方法实现结构裂纹的在线监测,借助在线结构健康监测的优势,在粒子滤波时选取重要性密度函数为观测概率密度和先验转移概率密度的混合分布,同时基于先验估计获取高斯权值进行权值更新。本文进一步进行了仿真研究,结果表明所提出的方法优化了疲劳裂纹扩展预测的准确性。     

同时考虑基本变量和失效域模糊性的广义失效概率数字计算方法

针对基本变量和失效域均具有模糊性的广义可靠性分析问题,提出了一种基于模拟退火优化的高效数字计算方法。在数字模拟的过程中,由模拟退火优化的Metropolis准则逐渐优化提取样本的重要抽样密度函数。由于基本变量和失效状态均含有模糊不确定性,因此所提算法在构造重要抽样函数时考虑了两个因素的影响,其一是基本变量的等价联合概率密度函数,其二是状态变量对模糊失效域的隶属函数,从而使得对广义失效概率贡献大的样本出现的概率较大,提高了抽样效率和计算精度。所提算法在模拟退火逐渐寻优的过程中,充分利用了过程中的信息,进一步提高了计算的效率,算例的结果也表明本文所提方法是合理可行的。     

小失效概率情况下的全局可靠性灵敏度分析的高效方法

针对目前很多算法都无法准确、高效地计算小失效概率（10^-4,甚至更小）情况下的全局可靠性灵敏度问题,本文提出了一种高效求解小失效概率情况下的全局可靠性灵敏度新算法。所提算法通过扩大标准差构造重要抽样密度函数来进行空间分割（SP）,再与无迹变换（UT）结合,利用函数在分割后的子空间内非线性程度的降低和无迹变换方法可以高效计算低非线性程度函数的前二阶矩,来高效准确地计算小失效概率情况下的全局可靠性灵敏度。所提算法的优点有：重要抽样密度函数的选择可以使得空间分割时向重要区域偏移,并且在分割区域内功能函数的复杂性被降低,从而可以利用无迹变换方法高效计算失效概率,进而高效求得全局可靠性灵敏度。与已有的算法相比,算例说明了本文所提方法的优势。     

Maneuvering Target Tracking in Dense Clutter Based on Particle Filtering

An improved particle filtering(IPF) is presented to perform maneuvering target tracking in dense clutter.The proposed filter uses several efficient variance reduction methods to combat particle degeneracy,low mode prior probabilities and measure-ment-origin uncertainty.Within the framework of a hybrid state estimation,each particle samples a discrete mode from its poste-rior distribution and the continuous state variables are approximated by a multivariate Gaussian mixture that is updated by an unscented Ka...     

Three-dimensional time-varying sliding mode guidance law against maneuvering targets with terminal angle constraint

This paper deals with the problem of intercepting maneuvering targets with terminal angle constraints for missiles subjected to three-dimensional non-decoupling engagement geometry.To achieve the finite-time interception and satisfactory overload characteristics, a time varying sliding mode control methodology is developed based on a time base generator function. The main feature of the proposed guidance law guarantees the Line-of-Sight(LOS) angles to converge to small neighborhoods of the desir...     

Matched subspace CFAR detection of hovering helicopters

A constant false alarm rate (CFAR) strategy for detecting a Gaussian distributed random signal against correlated non-Gaussian clutter is developed. The proposed algorithm is based on Scharf's matched subspace detector (MSD) and has the CFAR property with respect to the clutter amplitude probability density function (apdf), provided that the clutter distribution belongs to the compound-Gaussian family and the clutter covariance matrix is known to within a scale factor. Analytical expressions of false alarm and detection probabilities are derived. An application to the problem of detecting hovering helicopters against vegetated ground clutter is reported     

Joint parameter and state estimation for stochastic uncertain system with multivariate skew t noises

Due to the pulse interference, measurement outliers and artificial modeling errors, the multivariate skew t noise widely exists in the real environment. However, to date, little attention has been paid to the state estimation for systems in which the process noise and the measurement noise are both modeled as the heavy-tailed and skew non-Gaussian noise. In this paper, the multivariate skew t distribution is utilized to model the heavy-tailed and skew non-Gaussian noise. Then a probabilistic gra...     

非线性非高斯模型的高斯和PHD滤波算法(英文)

A new multi-target filtering algorithm, termed as the Gaussian sum probability hypothesis density （GSPHD） filter, is proposed for nonlinear non-Gaussian tracking models. Provided that the initial prior intensity of the states is Gaussian or can be identified as a Gaussian sum, the analytical results of the algorithm show that the posterior intensity at any subsequent time step remains a Gaussian sum under the assumption that the state noise, the measurement noise, target spawn intensity, new target birth intensity, target survival probability, and detection probability are all Gaussian sums. The analysis also shows that the existing Gaussian mixture probability hypothesis density （GMPHD） filter, which is unsuitable for handling the non-Gaussian noise cases, is no more than a special case of the proposed algorithm, which fills the shortage of incapability of treating non-Gaussian noise. The multi-target tracking simulation results verify the effectiveness of the proposed GSPHD.     

New SAR Processor Based on Matched Subspace Detectors

This paper deals with a new synthetic aperture radar (SAR) Processor based on a subspace detector designed for man-made target (MMT) detection. As MMTs are more accurately decribed by a set of canonical elements than with isotropic points, we develop a new algorithm which aims at using new models, instead of the isotropic point model commonly used in SAR processors. A subspace detector matched to canonical elements is included in the SAR processing. The implementation and the optimization of subspace detector SAR (SDSAR) algorithm is described. Simple examples of MMT detection in simulations and real data with a target hidden in a forest show the power of our approach. The SDSAR algorithm is shown to be the first robust and tractable algorithm relying on realistic scattering assumptions about the target.     

基于序列图像的航天器自主导航降维滤波方法

非合作目标的运动感知与状态估计,是太空领域技术发展的重要组成部分。非合作目标相对状态的精确估计是相对导航的难点问题。传统的非合作目标扩展卡尔曼滤波算法需要结合非合作目标的质心位置,增加了状态变量的维数,提高了系统不确定性,从而会影响状态扩展卡尔曼滤波的收敛速度。提出了一种基于序列图像的非合作目标相对导航方法,该方法在不对质心进行估计的情况下首先对非合作目标姿态进行估计,在完成非合作目标姿态估计后再对其质心进行估计。本文推导了光学相机测量值与目标真实姿态的关系,构建了基于序列图像的测量模型,分别建立了不含有非合作目标质心位置的状态方程和基于非合作目标位置、速度矢量的状态方程,设计了适用于非合作目标状态估计的扩展卡尔曼滤波算法。仿真实验表明该方法可在10 Hz采样频率下经过50次采样（即5 s）内快速收敛,从而有利于空间飞行器的在轨服务与维护。     

一种改进的航空发动机结构概率风险评估方法

针对航空发动机适航条款FAR33.75中关于发动机限寿件(ELLP)结构失效概率要求,提出了一种基于Kriging和蒙特卡罗半径外重要抽样(MCROIS)混合的结构概率风险评估方法。该方法针对ELLP高维、小失效概率事件以及极限状态函数为隐式、高度非线性的特点,利用Kriging元模型模拟隐式极限状态函数,然后通过主动学习迭代算法,计算最优点(MPP,最接近设计验算点的样本点),更新实验设计(DOE)并提高Kriging元模型的模拟精度。在此基础上,利用Kriging元模型确定最优抽样半径,构造半径外重要抽样密度函数,在最优抽样半径确定区域进行抽样,通过构造主动学习函数,使样本点更多落在抽样半径确定的球区域附近,加速失效概率计算的收敛,并构建了ELLP风险概率模型,解决了高维、小失效概率事件以及隐式、非线性极限状态函数的发动机结构概率风险评估难题,以某型发动机低压压气机轮盘为应用示例,与传统的蒙特卡罗仿真(MCS)方法进行了对比,验证了该方法的高效率、鲁棒性和仿真精度。     

Vector subspace detection in compound-Gaussian clutter. Part I: survey and new results

Deals with the problem of detecting subspace random signals against correlated non-Gaussian clutter exploiting different degrees of knowledge on target and clutter statistical characteristics. The clutter process is modeled by the compound-Gaussian distribution. In the first part of the paper, the optimum Neyman-Pearson (NP) detector, the generalized likelihood ratio test (GLRT), and a constant false-alarm rate (CFAR) detector are sequentially derived both for the Gaussian and the compound-Gaussian scenarios. Different interpretations of the various detectors are provided to highlight the relationships and the differences among them. In particular, we show how the GLRT detector may be recast into an estimator-correlator form and into another form, namely a generalized whitening-matched filter (GWMF), which is the GLRT detector against Gaussian disturbance, compared with a data-dependent threshold. In the second part of this paper, the proposed detectors are tested against both simulated data and measured high resolution sea clutter data to investigate the dependence of their performance on the various clutter and signal parameters.     

Radar detection and preclassification based on multiple hypothesis

This work presents a single-scan-processing approach to the problem of detecting and preclassifying a radar target that may belong to different target classes. The proposed method is based on a hybrid of the maximum a posteriori (MAP) and Neyman-Pearson (NP) criteria and guarantees the desired constant false alarm rate (CFAR) behavior. The targets are modeled as subspace random signals having zero mean and given covariance matrix. Different target classes are discriminated based on their different signal subspaces, which are specified by their corresponding projection matrices. Performance is investigated by means of numerical analysis and Monte Carlo simulation in terms of probability of false alarm, detection and classification; the extra signal-to-noise power ratio (SNR) necessary to classify once target detection has occurred is also derived.     

Multitarget tracking using the joint multitarget probability density

This work addresses the problem of tracking multiple moving targets by recursively estimating the joint multitarget probability density (JMPD). Estimation of the JMPD is done in a Bayesian framework and provides a method for tracking multiple targets which allows nonlinear target motion and measurement to state coupling as well as nonGaussian target state densities. The JMPD technique simultaneously estimates both the target states and the number of targets in the surveillance region based on the set of measurements made. We give an implementation of the JMPD method based on particle filtering techniques and provide an adaptive sampling scheme which explicitly models the multitarget nature of the problem. We show that this implementation of the JMPD technique provides a natural way to track a collection of targets, is computationally tractable, and performs well under difficult conditions such as target crossing, convoy movement, and low measurement signal-to-noise ratio (SNR).     

基于优化Kriging模型和重要抽样法的结构可靠度混合算法

 结构可靠度分析计算通常采用多项式响应面拟合隐式极限状态函数,但对于复杂航空航天机械结构产品极限状态方程往往表现出高度非线性,此时多项式响应面的模拟精度不够就会造成计算不收敛。为了提高结构可靠度计算的精度、效率和收敛性,提出了基于优化Kriging模型和重要抽样法的结构可靠度计算方法。首先,利用人工蜂群算法对Kriging模型的参数进行优化;再用优化后的模型模拟隐式极限状态函数,结合重要抽样法不断修正抽样重心,逐步提高模拟精度以达到给定要求;最后,结合一阶矩法(FORM)/二阶矩法(SORM)经典算法求解结构可靠度。该方法提高了高度非线性隐式极限状态方程可靠度计算的精度和收敛性,并且具有较高的计算效率。     

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.     

某型飞机平尾转轴可靠性灵敏度分析

采用了基于改进一次二阶矩法、基于Monte-Carlo直接模拟法、基于重要抽样法的可靠性灵敏度分析方法以及一种基于重要抽样的可靠性灵敏度分析方法,对某型飞机平尾转轴进行可靠性灵敏度的求解,并分析了某型飞机平尾转轴可靠性灵敏度随变异系数变化的趋势,为其可靠性设计提供了参考信息。通过4种方法结果相比较,可以说明各种方法的特点并验证了它们在工程问题上的可行性。