概率推理与影响图

本文讨论了影响图理论在概率推理过程中的应用和要涉及的信息及信息量。提出了虚拟信息滤波过程的概念和方法;讨论了概念网络中的信息溶合和衍殖过程;研究了用虚拟滤波过程表征人们行为中的柔性推理机制;设计了概念网络上的一种柔性推理准则。为决策分析和人工智能领域中的不肯定性信息测度问题提供了一种新的方法。     

A new structural reliability index based on uncertainty theory

The classical probabilistic reliability theory and fuzzy reliability theory cannot directly measure the uncertainty of structural reliability with uncertain variables,i.e.,subjective random and fuzzy variables.In order to simultaneously satisfy the duality of randomness and subadditivity of fuzziness in the reliability problem,a new quantification method for the reliability of structures is presented based on uncertainty theory,and an uncertainty-theory-based perspective of classical Cornell reliability index is explored.In this paper,by introducing the uncertainty theory,we adopt the uncertain measure to quantify the reliability of structures for the subjective probability or fuzzy variables,instead of probabilistic and possibilistic measures.We utilize uncertain variables to uniformly represent the subjective random and fuzzy parameters,based on which we derive solutions to analyze the uncertainty reliability of structures with uncertainty distributions.Moreover,we propose the Cornell uncertainty reliability index based on the uncertain expected value and variance.Experimental results on three numerical applications demonstrate the validity of the proposed method.     

Target classification approach based on the belief function theory

A theoretical framework is presented for target classification based on the belief theory on the continuous space. The proposed approach is applicable when class-conditioned densities of feature/attribute measurements are known only partially, as subjective models of a potential "betting" behaviour. Prior class probabilities may also be unknown. Numerical examples are provided to illustrate how the proposed approach is more cautious in decision making and produces very different results from those obtained using the Bayesian classifier.     

高超声速飞行器研制系统工程风险概率分析

针对高超声速飞行器研制工程的高风险特点,对工程决策方和研制方面临的不同类型风险进行了建模分析。基于系统方案或关键技术的固有风险概率和抽象化的研发与验证过程,分别计算分析了工程决策方误判验收通过不合格产品和研制方过度研发或重复验证较低失败概率产品的风险概率,进而提出了高风险研发项目中研制方过度研发风险的概念,明确了工程决策误判风险与研制方过度研发风险的相互影响规律,并基于概率方法建立了一种可以综合权衡决策方风险和研制方风险、合理确定研制周期的系统工程优化方法。     

Loopy belief propagation based data association for extended target tracking

The data association problem of multiple extended target tracking is very challenging because each target may generate multiple measurements. Recently, the belief propagation based multiple target tracking algorithms with high efficiency have been a research focus. Different from the belief propagation based Extended Target tracking based on Belief Propagation (ET-BP) algorithm proposed in our previous work, a new graphical model formulation of data association for multiple extended target tracking is proposed in this paper. The proposed formulation can be solved by the Loopy Belief Propagation (LBP) algorithm. Furthermore, the simplified measurement set in the ET-BP algorithm is modified to improve tracking accuracy. Finally, experiment results show that the proposed algorithm has better performance than the ET-BP and joint probabilistic data association based on the simplified measurement set algorithms in terms of accuracy and efficiency. Additionally, the convergence of the proposed algorithm is verified in the simulations.     

未知测量噪声分布下的多目标跟踪算法

 粒子概率假设密度滤波(SMC-PHDF)在进行粒子更新时需要知道测量噪声的概率分布以计算似然函数,这使得SMC-PHDF依赖于测量噪声的概率模型。针对这一点不足,提出一种未知测量噪声分布下的多目标跟踪算法——基于风险评估的概率假设密度滤波(RE-PHDF)。该算法在SMC-PHDF进行概率假设密度(PHD)粒子更新时采用风险函数计算每个PHD粒子的风险值,并通过一个风险评估函数评估每个PHD粒子,然后用评估后的结果更新粒子的权值。由于粒子更新时避免了在多维测量空间中计算似然函数,算法不仅不依赖于测量噪声的概率分布,还可以节省大量计算时间。仿真结果表明：和SMC-PHDF相比,RE-PHDF在未知的复杂测量噪声环境下具有更高的鲁棒性和稳定性;同时,在两种算法跟踪精度接近的情况下,所提算法节省了50%的运行时间。     

Novel moderate transformation of fuzzy membership function into basic belief assignment

In information fusion, the uncertain information from different sources might be modeled with different theoretical frameworks. When one needs to fuse the uncertain information represented by different uncertainty theories, constructing the transformation between different frameworks is crucial. Various transformations of a Fuzzy Membership Function (FMF) into a Basic Belief Assignment (BBA) have been proposed, where the transformations based on uncertainty maximization and minimization can determine the BBA without preselecting the focal elements. However, these two transformations that based on uncertainty optimization emphasize the extreme cases of uncertainty. To avoid extreme attitudinal bias, a trade-off or moderate BBA with the uncertainty degree between the minimal and maximal ones is more preferred. In this paper, two moderate transformations of an FMF into a trade-off BBA are proposed. One is the weighted average based transformation and the other is the optimization-based transformation with weighting mechanism, where the weighting factor can be user-specified or determined with some prior information. The rationality and effectiveness of our transformations are verified through numerical examples and classification examples.     

基于速度分布的飞机纵向安全间隔概率分析

以概率论为工具,利用飞行速度偏差对飞机纵向位置误差的影响分析,研究飞机速度分布情况,建立纵向安全间隔的概率模型。通过仿真算例和分析讨论,得到了有关时间、初始间隔时间、飞机速度均方差等影响飞机之间发生危险情况概率的定量分析结果,结论可以作为确定合理的航空器间空中交通管制纵向间隔的决策参考。     

De-combination of belief function based on optimization

In the theory of belief functions, the evidence combination is a kind of decision-level information fusion. Given two or more Basic Belief Assignments(BBAs) originated from different information sources, the combination rule is used to combine them to expect a better decision result. When only a combined BBA is given and original BBAs are discarded, if one wants to analyze the difference between the information sources, evidence de-combination is needed to determine the original BBAs. Evidence d...     

Neural network directed Bayes decision rule for moving targetclassification

In this paper, a new neural network directed Bayes decision rule is developed for target classification exploiting the dynamic behavior of the target. The system consists of a feature extractor, a neural network directed conditional probability generator and a novel sequential Bayes classifier. The velocity and curvature sequences extracted from each track are used as the primary features. Similar to hidden Markov model scheme, several hidden states are used to train the neural network, the output of which is the conditional probability of occurring the hidden states given the observations. These conditional probabilities are then used as the inputs to the sequential Bayes classifier to make the classification. The classification results are updated recursively whenever a new scan of data is received. Simulation results on multiscan images containing heavy clutter are presented to demonstrate the effectiveness of the proposed methods     

Addressing Uncertainty in UAV Navigation Decision-Making

We present an analytic framework for modeling and measuring uncertainty for the scenario of unmanned aerial vehicles (UAVs) cooperatively searching for a moving target. Uncertainty exists in a UAVs assessment of teammate locations, target locations, and sensor results. As is frequently done, our framework employs probabilistic maps to represent uncertain information regarding the UAVs environment. We present new methods to update the probabilistic maps when information arrives from onboard sensors or teammate UAVs. When new information is missing or delayed, we propose a novel and straightforward diffusion approach to update probabilistic maps. The UAVs make navigation decisions based on response to potential fields generated by the probabilistic maps. Since map data have uncertainty, this leads to decision-making in uncertainty. We conclude by describing how uncertainty in the environment translates into a unique measure, velocity vector dispersion (D_V), which describes the uncertainty in the UAVs navigation decision. Thresholds related to D_V may be useful to guide real-time decision policies. We present simulation results that show how the use of diffusion affects the time to locate targets. We also describe how D_V varies during UAV flight and comment on its utility.     

Probabilistic inference of fatigue damage propagation with limited and partial information

A general method of probabilistic fatigue damage prognostics using limited and partial information is developed.Limited and partial information refers to measurable data that are not enough or cannot directly be used to statistically identify model parameter using traditional regression analysis.In the proposed method, the prior probability distribution of model parameters is derived based on the principle of maximum entropy(Max Ent) using the limited and partial information as constraints.The posterior distribution is formulated using the principle of maximum relative entropy(MRE) to perform probability updating when new information is available and reduces uncertainty in prognosis results.It is shown that the posterior distribution is equivalent to a Bayesian posterior when the new information used for updating is point measurements.A numerical quadrature interpolating method is used to calculate the asymptotic approximation for the prior distribution.Once the prior is obtained, subsequent measurement data are used to perform updating using Markov chain Monte Carlo(MCMC) simulations.Fatigue crack prognosis problems with experimental data are presented for demonstration and validation.     

一种新的评判任意电磁环境中被检测件电磁发射电平的方法

提出了一种新的评判任意电磁环境中被检测件的电磁发射电平不高于极限值电平的方法。并阐述了由给出的概率公式和概率极限值得出的若干重要结论。     

基于概率数据关联的地磁匹配算法

针对轮廓匹配算法存在的虚定位问题,在轮廓匹配算法的基础上,提出了一个新的分批地磁匹配方法——基于概率数据关联滤波的地磁匹配算法。算法把满足一定条件的相关值作为滤波器的有效量测,把巡航导弹的位置作为状态变量,建立了基于概率数据关联的地磁匹配模型,利用概率数据关联滤波算法计算巡航导弹的位置坐标。仿真结果表明,该算法有效地降低了虚定位发生的概率,正确匹配率、导航效果均优于轮廓匹配算法。     

基于概率方法的航天器姿态控制系统参数设计

针对传统航天器控制系统参数设计方法中存在的设计保守、主观经验性较强等缺点,使用概率分布对参数不确定性进行量化描述,采用概率指标代替确定性指标,对参数随机分布的系统进行概率分析,并使用可行域图对设计空间进行表征。在此基础上,应用概率优化方法对控制器参数进行优化设计。最后,通过实例对提出的方法进行仿真验证。仿真结果表明,概率分析方法能够放宽参数的设计空间,通过概率优化方法可以得到满足概率指标要求的设计结果,提高了设计效率。     

Passive ranging of a low observable ballistic missile in agravitational field

In this paper we present an estimation algorithm for tracking the motion of a low-observable target in a gravitational field, for example, an incoming ballistic missile (BM), using angle-only measurements. The measurements, which are obtained from a single stationary sensor, are available only for a short time. Also, the low target detection probability and high false alarm density present a difficult low-observable environment. The algorithm uses the probabilistic data association (PDA) algorithm in conjunction with maximum likelihood (ML) estimation to handle the false alarms and the less-than-unity target detection probability. The Cramer-Rao lower bound (CRLB) in clutter, which quantifies the best achievable estimator accuracy for this problem in the presence of false alarms and nonunity detection probability, is also presented. The proposed estimator is shown to be efficient, that is, it meets the CRLB, even for low-observable fluctuating targets with 6 dB average signal-to-noise ratio (SNR). For a BM in free flight with 0.6 single-scan detection probability, one can achieve a track detection probability of 0.99 with a negligible probability of false track acceptance     

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。     

Optimal m-ary data fusion with distributed sensors

An optimal data fusion rule is derived for an m-ary detection problem. Each detector determines a local decision using a local decision rule and transmits the local decision to the fusion center. Considering the reliability of local detectors, local decisions are combined to produce the final decision. In this study, based upon the maximum posterior probability concept, optimal decision rules for m-ary detection problems are proposed for the local detector and the data fusion center     

Distributed sensor data fusion with binary decision trees

A distributed sensor object recognition scheme that uses object features collected by several sensors is presented. Recognition is performed by a binary decision tree generated from a training set. The scheme does not assume the availability of any probability density functions, thus it is practical for nonparametric object recognition. Simulations have been performed for Gaussian feature objects, and some of the results are presented