直接网络中的自适应路由算法分析

互连网络是大规模并行计算机的重要组成部分，路由算法是其中决定网络性能的重要因素，本文在直接网络结构基础上对路由算法进行讨论，给出了一种分类方法，并着重对采用虫孔路由开关技术的自适应路由算法进行分析，为进一步的评价和设计新的算法提供了参考。     

支持QoS的无线Mesh网络机会路由优化算法

针对空中骨干Mesh网络资源有限、计算能力相对不足的特点以及传统简单机会路由(Simple Opportunistic Adaptive Routing,SOAR)路由算法未充分考虑负载均衡与不同业务服务质量(Quality of Service,Qo S)保障需求差异性的问题,提出一种支持业务区分的改进型SOAR路由算法。该算法在考虑链路拥塞控制和负载均衡的基础上,定义综合预期传输次数来描述链路的综合状态,有效降低网络拥塞概率;同时根据传输业务类型的不同,设计一种基于层次分析法的路由选择策略,实现路径选择与业务类型的动态匹配。仿真结果表明,在重负载条件下,改进型SOAR路由算法相比传统SOAR路由算法其时延、吞吐量和吞吐率性能明显提升。当网络中存在不同类型业务时,改进型SOAR路由算法能够根据业务Qo S保障需求的差异性自适应选择最佳传输路径。     

基于节点综合效能的DTN路由算法

针对容忍延迟网络(DTN)高延迟、数据传输成功率低等问题,提出了一种基于节点综合效能的DTN路由算法SERA。该算法综合考虑移动节点的活跃度和剩余能量,使消息副本向综合效能高的节点扩散。SERA节点活跃度描述了节点的社会和动态特性,SERA尽量将消息副本传递给活跃度高的节点,以提高消息传输的成功率;在选择中继节点时,充分考虑节点的能量状态,以避免能量不足的节点承担更多的信息传输任务,从而提高网络节点的存活率。仿真结果表明,与典型的DTN路由算法相比,SERA能够更好地平衡节点的能耗,获得更高的消息递交成功率和更长的网络生存期。     

基于参数优化的SVM联合作战战场建设方案优选

联合作战战场建设方案优选可最大可能降低建设工程的损耗、提高整体建设效益。针对联合作战战场建设方案优选问题进行研究,首先梳理联合作战战场建设方案优选流程,然后构建包括作战保障工程、后勤保障工程、装备保障工程和战场基础信息数据库的指标体系,并提出基于网格搜索和交叉验证优化的改进SVM方法实施方案优选,仿真结果表明优化后参数的性能明显优于默认参数,更加适用于联合作战战场建设方案优选问题。     

战术移动自组织网络路由方法

战术移动自组织网络(Mobile Ad Hoc Network,MANET)作为网络中心战的重要组成,有望解决未来战场上传统作战力量与新型无人作战平台之间高效组网通信问题。战术MANET作为MANET在战场环境下的应用,可在基础设施缺乏或遭破坏的地域快速展开,具有节点自由出入、移动性高、抗毁伤性强等特点。综述了战术MANET路由协议和仿真方法,对研究方法和面临的挑战作了简要评述。     

北斗卫星导航系统商业化发展策略

为应对海军在全球公域投送力量时面临的严重"反介入/区域拒止"(anti-access/areal denial,A2/AD)挑战,美国海军利用电磁作战方面的技术优势,率先提出并重点发展了"电磁机动战"。虽然该理论还处于进一步完善当中,但随着研究和实施的深入,将有助于提高美国海军在电磁领域的作战优势。     

物元理论在优选对地打击目标中的应用

优选对地打击目标是现代信息化战争条件下联合作战对地火力协调中心在计划火力时的首要任务。旨在从目标的重要性、可靠性、射击紧迫性及对其射击时自身被发现的机会四个因素入手,举例证明如何构造物元模型,做到定性与定量相结合,对于编制炮兵C4I系统计划火力软件具有一定的参考价值。     

A worst‐case formulation for constrained ranking and selection with input uncertainty

In this research, we consider robust simulation optimization with stochastic constraints. In particular, we focus on the ranking and selection problem in which the computing time is sufficient to evaluate all the designs (solutions) under consideration. Given a fixed simulation budget, we aim at maximizing the probability of correct selection (PCS) for the best feasible design, where the objective and constraint measures are assessed by their worst‐case performances. To simplify the complexity of PCS, we develop an approximated probability measure and derive the asymptotic optimality condition (optimality condition as the simulation budget goes to infinity) of the resulting problem. A sequential selection procedure is then designed within the optimal computing budget allocation framework. The high efficiency of the proposed procedure is tested via a number of numerical examples. In addition, we provide some useful insights into the efficiency of a budget allocation procedure.     

利用预报误差分布拟合实现卫星历史轨道机动检测的方法

轨道机动会引起在轨卫星的轨道异常,是空间态势感知的重点关注对象之一。为此,提出一种从卫星的两行轨道要素编目数据中检测历史机动的方法。通过分析预报误差检测轨道参数的异常编目值,进而获得相应的历史机动信息。对卫星的两行轨道要素编目数据两两预报求差得到预报误差的样本数据后,利用期望最大化算法可以拟合得到以高斯混合模型表达的预报误差的概率分布模型;以此为基础,可以确定利用预报误差检测轨道参数异常编目值的门限;利用轨道机动与异常编目值之间的对应关系,最终实现机动检测。对典型活动卫星的机动检测结果表明,所提方法能够在准确检出历史机动事件的同时保持较低的误检率。     

Information theory for ranking and selection

We study the classical ranking and selection problem, where the ultimate goal is to find the unknown best alternative in terms of the probability of correct selection or expected opportunity cost. However, this paper adopts an alternative sampling approach to achieve this goal, where sampling decisions are made with the objective of maximizing information about the unknown best alternative, or equivalently, minimizing its Shannon entropy. This adaptive learning is formulated via a Bayesian stochastic dynamic programming problem, by which several properties of the learning problem are presented, including the monotonicity of the optimal value function in an information-seeking setting. Since the state space of the stochastic dynamic program is unbounded in the Gaussian setting, a one-step look-ahead approach is used to develop a policy. The proposed policy seeks to maximize the one-step information gain about the unknown best alternative, and therefore, it is called information gradient (IG). It is also proved that the IG policy is consistent, that is, as the sampling budget grows to infinity, the IG policy finds the true best alternative almost surely. Later, a computationally efficient estimate of the proposed policy, called approximated information gradient (AIG), is introduced and in the numerical experiments its performance is tested against recent benchmarks alongside several sensitivity analyses. Results show that AIG performs competitively against other algorithms from the literature.