Bayesian sampling plans for exponential distributions with interval censored samples

This article studies the problem of designing Bayesian sampling plans (BSP) with interval censored samples. First, an algorithm for deriving the conventional BSP is proposed. The BSP is shown to possess some monotonicity. Based on the BSP and using the property of monotonicity, a new sampling plan modified by the curtailment procedure is proposed. The resulting curtailed Bayesian sampling plan (CBSP) can reduce the duration time of life test experiment, and it is optimal in the sense that its associated Bayes risk is smaller than the Bayes risk of the BSP if the cost of the duration time of life test experiment is considered. A numerical example to compute the Bayes risks of BSP and CBSP and related quantities is given. Also, a Monte Carlo simulation study is performed to illustrate the performance of the CBSP compared with the BSP. The simulation results demonstrate that our proposed CBSP has better performance because it has smaller risk. The CBSP is recommended. © 2015 Wiley Periodicals, Inc. Naval Research Logistics 62: 604–616, 2015     

采用时序多属性评价方法的空战威胁排序

鉴于空战中威胁目标的多属性及动态变化特点,提出采用时序多属性评价方法来解决空战目标动态威胁排序问题。在建立空战威胁指标体系基础上,采用熵权法确定目标属性权重,并通过指数分布法确定各时刻权重,最后综合依据威胁目标多时刻的属性信息,实现了空战动态威胁排序。仿真结果说明该方法的合理性。     

未来中远距协同空战多目标攻击决策研究

根据先进战斗机的技术特点和未来中远距协同空战发展方向,提出一种基于先敌发现、先敌发射、先敌摧毁能力的空战态势分析模型。同时,采用一种将并行遗传算法与分布估计算法相结合的并行分布遗传算法并将其用于多目标攻击决策。最后利用具体算例进行仿真。结果表明空战态势分析模型能够较为准确地描述未来中远距多机协同空战,采用的算法具有较好的可行性和实时性,为中远距多机协同空战的多目标攻击决策提供了新的思路和方法。     

基于时间切片的通信计划合规性评估模型

针对体系作战背景下通信计划规划任务多线性和关系复杂的问题,提出了一种通信计划合规性评估模型。该模型通过构建计划时间切片,描述了多个通信保障任务主线在各阶段的全局关系视图,并定义了通信链路连接矩阵、电磁频谱三级规划和指控系统命名类量化,描述了通信计划的基本内容,同时定义了3类评估规则和3种评估运算法则,形成了完整的合规性评估体系。最后,通过实例分析验证了模型有效性,结果表明,评估模型能够较好地区分不同信息需求下的通信计划合理程度。     

Autonomous maneuver decision-making for a UCAV in short-range aerial combat based on an MS-DDQN algorithm

To solve the problem of realizing autonomous aerial combat decision-making for unmanned combat aerial vehicles (UCAVs) rapidly and accurately in an uncertain environment, this paper proposes a decision-making method based on an improved deep reinforcement learning (DRL) algorithm: the multi-step double deep Q-network (MS-DDQN) algorithm. First, a six-degree-of-freedom UCAV model based on an aircraft control system is established on a simulation platform, and the situation assessment functions of the UCAV and its target are established by considering their angles, altitudes, environments, missile attack performances, and UCAV performance. By controlling the flight path angle, roll angle, and flight velocity, 27 common basic actions are designed. On this basis, aiming to overcome the defects of traditional DRL in terms of training speed and convergence speed, the improved MS-DDQN method is introduced to incorporate the final return value into the previous steps. Finally, the pre-training learning model is used as the starting point for the second learning model to simulate the UCAV aerial combat decision-making process based on the basic training method, which helps to shorten the training time and improve the learning efficiency. The improved DRL algorithm significantly accelerates the training speed and estimates the target value more accurately during training, and it can be applied to aerial combat decision-making.     

Fitting Lanchester equations to the battles of Kursk and Ardennes

Lanchester equations and their extensions are widely used to calculate attrition in models of warfare. This paper examines how Lanchester models fit detailed daily data on the battles of Kursk and Ardennes. The data on Kursk, often called the greatest tank battle in history, was only recently made available. A new approach is used to find the optimal parameter values and gain an understanding of how well various parameter combinations explain the battles. It turns out that a variety of Lanchester models fit the data about as well. This explains why previous studies on Ardennes, using different minimization techniques and data formulations, have found disparate optimal fits. We also find that none of the basic Lanchester laws (i.e., square, linear, and logarithmic) fit the data particularly well or consistently perform better than the others. This means that it does not matter which of these laws you use, for with the right coefficients you will get about the same result. Furthermore, no constant attrition coefficient Lanchester law fits very well. The failure to find a good‐fitting Lanchester model suggests that it may be beneficial to look for new ways to model highly aggregated attrition. © 2003 Wiley Periodicals, Inc. Naval Research Logistics, 2004.     

基于排列法的目标威胁评估模型

目标威胁评估是一个推理、决策的行为,结合现代防空作战的特点和指挥自动化系统的工作过程,分析了影响目标威胁评估的几个重要因素,应用多属性决策中的一种改进排列法建立了数学模型,验证了其可行性。     

模糊合成算子在作战方案综合评估的应用研究

在分布式交互仿真中,根据仿真结果对作战方案进行综合评估,运用模糊综合评判的方法进行评判,并着重对合成算子的选择进行分析,确定乘与有界算子M(·, )为比较适合对作战方案评估的算子.     

舰载作战训练系统的概念设计

本文介绍了舰载作战训练系统的研究方向、功能、组成及各单元的任务和接口关系,并对关键技术进行了简要说明。     

基于模型的舰艇信息融合系统的研究

应用计算机仿真技术建立一个具有战术想定功能的舰艇作战系统仿真环境,来研究舰艇的多传感器信息融合,并给出部分仿真模型。