基于多目标优化的对抗反辐射导弹诱饵部署

针对目前研究部署诱饵保护地基雷达免遭反辐射导弹攻击未考虑多个目标的情况,提出了基于多目标优化的诱饵最优位置模型.首先对影响部署诱饵的各因素进行分析,并用随机仿真模型进行建模,目标是同时最大化反辐射导弹爆炸点与地基雷达以及诱饵之间距离;然后采用概率多目标模拟退火算法获得诱饵位置组合的非支配组合;根据决策者的不完全偏好信息,采用多属性效用函数,获得偏好非支配组合.最后对模型进行仿真,仿真结果表明模型的有效性.     

船载直升机目标搜索最佳方案决策模型

根据现代搜索理论的发展,对船载直升机在目标搜索中的具体运用进行了研究,建立了船载直升机目标搜索时搜索力的最优配置模型,解决了在已知目标分布的条件下船载直升机搜索中选择初始探测点、确定搜索路径、确定搜索次数以及搜索效率评估等问题．     

机动目标的跟踪与反跟踪的建模研究

以机动目标的跟踪与反跟踪为研究对象,分析了目标的状态方程和量测方程,当存在多个目标时,仍需考虑不同目标之间数据关联问题.首先,考虑了雷达1、雷达2和3量测数据的连接和融合问题,建立了机动目标的状态方程和量测方程模型,运用卡尔曼滤波器算法对目标航迹进行了在线追踪,得到目标跟踪航迹.其次,进行一次指数平滑法的时间序列预测和实时灰色预测,得到了其目标的着落轨迹、着落点坐标和时间.最后建立新的动态多目标优化模型,运用计算机仿真对跟踪与反跟踪的目标航迹进行了模拟.     

机动目标的跟踪与反跟踪综述

结合竞赛中存在的问题,得出以下结论:机动量估计关键在于通过被污染的雷达测量信息估计出目标的加速度随时间的变化,其难点是如何准确描述目标的运动模型;跟踪模型选择合适程度直接影响了数据关联和航迹预测的准确程度,这两者决定了了雷达系统的性能;机动目标的跟踪与反跟踪是一种博弈的关系,既相互对立又相互促进,不断推进和提出雷达新的作战需求.     

离散时间马氏决策过程的首达目标准则

本文考虑可数状态离散时间马氏决策过程的首达目标模型的风险概率准则.优化的准则是最小化系统首次到达目标状态集的时间不超过某阈值的风险概率.首先建立最优方程并且证明最优值函数和最优方程的解对应,然后讨论了最优策略的一些性质,并进一步给出了最优平稳策略存在的条件,最后用一个例子说明我们的结果.     

基于多目标影响图效果作战模型

针对目前基于效果作战评估中没有涉及多目标以及决策者的偏好的情况,将多目标影响图引入到基于效果作战模型.首先分析了基于效果作战中各主要要素之间关系,其次提出了基于效果作战模型构造过程,然后将其与多目标决策理论结合建立了多目标基于效果作战模型,用多目标影响图对其进行建模,并给出了求解方法,最后将基于多目标影响图效果作战模型应用于空战任务分配中.仿真结果表明该模型的有效性.     

农田最优复种的多目标分层几何规划模型及其求解

本文对具有多类复种方式的农田种植问题提出了一类多目标分层几何规划数学模型。根据模型的特点,给出了求解的方法,并把它应用于我国某水稻区农田种植方案的最优选择,取得良好的效果。     

基于贝叶斯信息更新的失事飞机发现概率模型

了目标搜索区域的确定方法以及失事飞机在目标搜索区域的初始概率分布,得到发现概率的计算公式。以发现概率为目标,构造了一个求解最优搜寻策略的Max Max化规划模型,模型可以动基于贝叶斯方法,提出了一个失事飞机的发现概率模型,利用飞机失联前后的信息数据,给出态地对坠机点的概率分布进行更新,使下一步搜寻任务得到及时的修正和调整。考虑到洋流对坠机点的影响,本文还提出了一个关于基点先验概率分布的重构策略。此外,对任务搜索区域最优路径的选取问题做了进一步探讨,给出了一个任务搜索区域上搜寻路径的选取方法     

多传感器数据融合与航迹预测

多传感器数据融合技术是未来军事电子领域一个重要趋势.根据6个观测雷达的观测数据进行了数据融合算法的研究.在提取目标航迹对时,对每个雷达的数据依据一定的判定条件(时间变化,角度变化在一定范围内等),分别提取出不同的目标航迹对.在提取同一目标的航迹对时,先将目标航迹的一些异常点弃除,然后把时间重合的两段航迹提取出来,通过样条插值进行时间配准,共提取出多条相关的航迹组有3组.在使用雷达探测目标时,由于技术条件和方法等的限制,使雷达数据存在各种误差.利用卡尔曼滤波自适应算法估计出观测位置的噪声方差,对雷达偏差进行修正后,采用联合卡尔曼滤波算法对多条航迹进行融合,接着利用ARMA模型预测目标在未来10秒内的轨迹,最后,对目标在被锁定后的轨迹做出预测,结合导弹的爆炸范围求得导弹击中飞机的概率约为49.54%.     

印军地基(海基)导弹攻击能力模型分析

印度4月19日成功试射"烈火-5"远程导弹,印度跨入"洲际导弹俱乐部".针对印度高技术地基(海基)导弹武器装备大量装备部队,对印度地基(海基)导弹突防概率、对地毁伤作用、攻击能力—突击某一目标所需导弹数量、攻击强度等方面做了模型分析,以对我军有针对性地开展研究提供一定的参考意义.     

两部雷达部署的研究

本文研究了两部雷达联合发现目标概率 ,采用特征尺度的分析方法解决了最佳部署问题 ,得到了极其简单 ,计算量很小的关于确定最佳距离的有效公式 ,并用实际算例进行了验证     

超竞争环境下企业困境并购定价与时机选择的期权博弈分析

基于期权博弈理论,以超竞争环境为研究背景,在多个主并企业合作的情形下,建立目标企业存在财务困境的并购模型,得出主并方的最优并购时机;并进行数值模拟分析,重点分析了代表超竞争特点的三个变量(竞争强度、竞争不确定性、企业价值损失因子)、主并企业联盟中参与者的重要程度及联盟中发生机会主义的概率、企业困境因子这六个变量对并购阈值的影响。     

Tabu Search When Noise is Present: An Illustration in the Context of Cause and Effect Analysis

In the field of combinatorial optimization, it may be possible to more accurately represent reality through stochastic models rather than deterministic ones. When randomness is present in a problem, algorithm designers face new difficulties which complicate their task significantly. Finding a proper mathematical formulation and a fast evaluation of the objective function are two major issues. In this paper we propose a new tabu search algorithm based on sampling and statistical tests. The algorithm is shown to perform well in a stochastic environment where the quality of feasible solutions cannot be computed easily. This new search principle is illustrated in the field of cause and effect analysis where the true cause of an undesirable effect needs to be eliminated. A set of n potential causes is identified and each of them is assumed to be the true cause with a given probability. The time to investigate a cause is a random variable with a known probability distribution. Associated with each cause is the reward obtained if the cause is really the true cause. The decision problem is to sequence the n potential causes so as to maximize the expected reward realized before a specified time horizon.     

正态分布和瑞利分布混合情形下的参数估计及分类问题

学者往往用单一的分布模拟和拟合杂波,如正态分布、瑞利分布和威布尔分布等。然而在实际中,雷达杂波由多种类型的杂波组成,单一分布通常不能精确刻画雷达杂波规律,因此,应用混合分布模型对雷达杂波数据建模更准确。本文考虑用正态分布和瑞利分布的混合分布拟合杂波,并应用矩估计方法和基于EM算法的极大似然估计方法估计模型参数,最后,应用最大后验概率分类准则验证2种估计方法的分类准确率。通过数据模拟,得出极大似然估计的效果和分类准确率都要优于矩估计的估计效果和分类准确率。     

Bayesian variable selection in generalized linear models using a combination of stochastic optimization methods

In this paper the usage of a stochastic optimization algorithm as a model search tool is proposed for the Bayesian variable selection problem in generalized linear models. Combining aspects of three well known stochastic optimization algorithms, namely, simulated annealing, genetic algorithm and tabu search, a powerful model search algorithm is produced. After choosing suitable priors, the posterior model probability is used as a criterion function for the algorithm; in cases when it is not analytically tractable Laplace approximation is used. The proposed algorithm is illustrated on normal linear and logistic regression models, for simulated and real-life examples, and it is shown that, with a very low computational cost, it achieves improved performance when compared with popular MCMC algorithms, such as the MCMC model composition, as well as with “vanilla” versions of simulated annealing, genetic algorithm and tabu search.     

弹体分离识别的模糊聚类算法

天地基联合预警中的引导交接问题是导弹预警成功的关键,通过分析弹体分离多目标飞行特性,构建弹头及伴飞物空间位置和速度的模糊等价关系,并基于空间点共线及对称性的模糊判定方法,提出基于模糊聚类识别弹头的一种新算法,解决在弹头伴飞物中准确识别出弹头的问题。仿真计算结果表明,方法具有较高的准确率和很好的实用价值。     

Design and analysis of fractal detector for high resolution radars

Recently, fractal geometry has been used as a tool for improving the detection of targets in radar systems. The fractal dimension is utilized as a feature to distinguish between target and clutter in fractal detectors. In this paper, a general model is proposed for the target and clutter signals in high resolution radar (HRR). The fractal dimensions of the clutter and the target plus clutter are evaluated. Performing statistical tests on the distribution of the fractal dimension, it is proved that a gaussian distribution can approximately model the distribution of the fractal dimension for HRR signals. The fractal detector is designed based on the gaussian distribution of the fractal dimension and its performance is compared with a semi-optimum detector. It is demonstrated that the fractal detector has great capabilities in the rejection of colored clutter. Moreover, we show that the fractal detector is CFAR, i.e., the probability of false alarm remains approximately constant in different interference powers.     

More Higher-Order Efficiency: Concentration Probability

Based on concentration probability of estimators about a true parameter, third-order asymptotic efficiency of the first-order bias-adjusted MLE within the class of first-order bias-adjusted estimators has been well established in a variety of probability models. In this paper we consider the class of second-order bias-adjusted Fisher consistent estimators of a structural parameter vector on the basis of an i.i.d. sample drawn from a curved exponential-type distribution, and study the asymptotic concentration probability, about a true parameter vector, of these estimators up to the fifth-order. In particular, (i) we show that third-order efficient estimators are always fourth-order efficient; (ii) a necessary and sufficient condition for fifth-order efficiency is provided; and finally (iii) the MLE is shown to be fifth-order efficient.