高超声速飞行器前体外形设计与气动特性分析

利用数值计算开展了高超声速飞行器前体气动特性的考察,重点研究了三维效应对于前体流场及预压缩性能的影响,提出了抑制三维侧缘溢流影响、改善三维前体气动性能的前体侧缘设计思想,考察了侧缘外形的容积效率,提出了有效容积效率的概念和评估方法,对于不同侧缘形式的前体构型进行了气动特性的考察,给出了不同前体构型气动特征的变化规律。     

战斗机末端机动的非线性模型预测控制规避策略

利用非线性模型预测控制的思想建立了战斗机末端规避导弹的机动策略求解方法。根据导弹与战机的空战态势,建立了导弹与战机的相对运动微分方程;将导弹的导引律引入到导弹运动模型中,与飞机模型一起构建了系统预测模型,并对飞机和导弹的运动约束进行了分析。通过对导弹结构限制和战术特性的分析,给出了飞机机动规避导弹的性能指标,进而建立了机动规避导弹的最优控制模型。利用高斯伪谱法对模型进行求解,采用滚动优化策略实现了对机动规避策略的闭环求解。针对导弹气动参数和导航比未知以及相对测量量具有噪声的问题,利用极大似然法对导弹的气动参数和导航比进行估计,实现了对系统预测模型的反馈校正。仿真结果表明,此方法能够实现对导弹的机动规避。     

基于VFGPIR联合特征的决策级加权融合检测方法

车载前视地表成像雷达(VFGPIR)探测浅埋目标时面临虚警率过高问题,可利用序列图像特征的联合检测解决这一问题.首先利用Fisher鉴别比(FDR)定量评估从单帧和序列图像中提取的单个特征的鉴别能力;然后针对单个最优序列特征无法满足探测指标要求提出一种基于决策级加权融合的多特征联合检测方法;最后利用接收机工作特性(ROC)曲线来验证所提方法的有效性.试验结果表明:序列特征比单帧图像特征具有更好的鉴别能力;所提方法性能优于单个最优序列特征、特征向量和多数票融合准则对应的检测结果,有望满足实际探雷应用需求.     

多源信息的验前分布融合方法研究

验前分布的获取和表示是B ayes理论应用中的一个关键问题。针对目前多源验前信息融合中存在的过分依赖专家信息的问题,提出了一种基于第2类极大似然估计原理(M L-II)确定验前分布权重的方法,该方法将现场试验数据看作是由其边缘分布产生的样本,然后根据不同验前分布下现场样本似然性的大小来确定其在验前分布融合中的作用,并以此进一步确定不同验前分布的权重因子。最后通过算例证明了该方法比基于专家信息的融合方法更为合理和有效。     

基于GA-ML-PMHT的多基站无源协同定位方法

针对无源协同定位系统中低可观测目标的航迹初始及维持问题,提出一种基于遗传算法的极大似然概率多假设的多基站无源协同定位方法。首先,建立多基站无源协同定位系统数学模型。其次,提出基于极大似然概率多假设的无源协同定位航迹初始算法,并首次利用遗传算法解决极大似然概率多假设中的优化求解问题,以提高目标检测跟踪性能。最后,通过滑窗法实现航迹维持。仿真结果表明,所提方法能够有效解决多基站无源协同定位系统中低可观测目标的航迹初始及维持问题。     

球形译码算法中不均匀半径分配方案及性能分析

在球形译码算法中,初始半径的选择与分配是影响算法性能与复杂度的重要因素.针对球形译码算法在低信噪比时复杂度高的问题,提出了一种新的基于V-BLAST信号模型的初始半径选择与(准)线性半径分配方案,并分析了应用该方案的两种典型球形译码算法流程的性能与复杂度,其分析方法适用于任意不均匀半径分配方案.分析与仿真表明,与传统的球形译码算法相比,在信噪比较低时,采用该方案的球形译码算法的复杂度显著降低;在较宽信噪比范围内,其误码率性能接近最大似然检测性能.     

A stochastic approximation algorithm to compute bid prices for joint capacity allocation and overbooking over an airline network

In this article, we develop a stochastic approximation algorithm to find good bid price policies for the joint capacity allocation and overbooking problem over an airline network. Our approach is based on visualizing the total expected profit as a function of the bid prices and searching for a good set of bid prices by using the stochastic gradients of the total expected profit function. We show that the total expected profit function that we use is differentiable with respect to the bid prices and derive a simple expression that can be used to compute its stochastic gradients. We show that the iterates of our stochastic approximation algorithm converge to a stationary point of the total expected profit function with probability 1. Our computational experiments indicate that the bid prices computed by our approach perform significantly better than those computed by standard benchmark strategies and the performance of our approach is relatively insensitive to the frequency with which we recompute the bid prices over the planning horizon. © 2011 Wiley Periodicals, Inc. Naval Research Logistics, 2011     

Signature based analysis of m‐Consecutive‐k‐out‐of‐n: F systems with exchangeable components

In this article, we study reliability properties of m‐consecutive‐k‐out‐of‐n: F systems with exchangeable components. We deduce exact formulae and recurrence relations for the signature of the system. Closed form expressions for the survival function and the lifetime distribution as a mixture of the distribution of order statistics are established as well. These representations facilitate the computation of several reliability characteristics of the system for a given exchangeable joint distribution or survival function. Finally, we provide signature‐based stochastic ordering results for the system's lifetime and investigate the IFR preservation property under the formulation of m‐consecutive‐k‐out‐of‐n: F systems. © 2011 Wiley Periodicals, Inc. Naval Research Logistics, 2011     

Improving series and parallel systems through mixtures of duplicated dependent components

We discuss suitable conditions such that the lifetime of a series or of a parallel system formed by two components having nonindependent lifetimes may be stochastically improved by replacing the lifetimes of each of the components by an independent mixture of the individual components' lifetimes. We also characterize the classes of bivariate distributions where this phenomenon arises through a new weak dependence notion. © 2011 Wiley Periodicals, Inc. Naval Research Logistics, 2011     

Annealing adaptive search,cross‐entropy,and stochastic approximation in global optimization

The Annealing Adaptive Search (AAS) algorithm for global optimization searches the solution space by sampling from a sequence of Boltzmann distributions. For a class of optimization problems, it has been shown that the complexity of AAS increases at most linearly in the problem dimension. However, despite its desirable property, sampling from a Boltzmann distribution at each iteration of the algorithm remains a practical challenge. Prior work to address this issue has focused on embedding Markov chain‐based sampling techniques within the AAS framework. In this article, based on ideas from the recent Cross‐Entropy method and Model Reference Adaptive Search, we propose an algorithm, called Model‐based Annealing Random Search (MARS), that complements prior work by sampling solutions from a sequence of surrogate distributions that iteratively approximate the target Boltzmann distributions. We establish a novel connection between MARS and the well‐known Stochastic Approximation method. By exploiting this connection, we prove the global convergence of MARS and characterize its asymptotic convergence rate behavior. Our empirical results indicate promising performance of the algorithm in comparison with some of the existing methods. © 2011 Wiley Periodicals, Inc. Naval Research Logistics, 2011