基于目标中心战的舰载UAV协同对海突击作战构想

针对舰载无人机编队协同对海突击作战的特点,在充分吸收目标中心战理论的基础上,提出了基于目标中心战的舰载无人机编队协同对海突击作战构想,论证了目标中心战应用于舰载无人机编队协同对海突击作战的可行性,并对其概念和特征进行了界定和分析,着重对目标中心战理论中的“侦-控-打-评”作战过程进行适应性改造,提出了基于目标中心战的舰载无人机编队协同对海突击作战的主要环节,包括态势评估,目标选择与分配,航路规划,协同打击和效能评估等。     

Panjwai: a tale of two COINs in Afghanistan

The US Army has two approaches to counterinsurgency operations in Afghanistan. One is hard, or combat-focused, and the other is soft, or development-focused. This study examines two US Army task forces deployed to Panjwai District, Afghanistan from 2012 to 2013. CTF 4-9 and 1-38 offer a meaningful comparison because they pursued these contrasting approaches among the same population and against the same enemy at the same time and place. The study compares each unit’s approach and finds that neither approach was successful absent the other. The article concludes by recommending further research into combining the approaches at the operational level.     

When Programming Trumps Policy and Plans: The Case of the US Department of the Navy11

Traditionally, policy and planning have been institutionally weak in the Naval Staff (Office of the Chief of Naval Operations – OPNAV). In their place, the N8 (Programming) has dominated resource decision-making, and, by default, decisions relating to policy and planning. Recent uncertainty over defense authorization and appropriations has resulted in calls for a greater role to be played by the N3/5, Policy and Plans Directorate. The article argues that reform of the Department of the Navy’s planning process is urgently needed. OPNAV’s weak planning and overly dominant programming practices are compared with those of the Departments of the Army and Air Force and are shown to be out of conformance with them. The article concludes with specific and detailed recommendations for reform of both the current planning and programming processes.     

The Evolution of US Extended Deterrence and South Korea’s Nuclear Ambitions

ABSTRACT

Extended deterrence has been a main pillar of the security alliance between the United States and South Korea (Republic of Korea [ROK]) since the end of the Korean War. The changing dynamics of US extended deterrence in Korea, however, affected Seoul’s strategic choices within its bilateral alliance relationship with Washington. Examining the evolution of US extended deterrence in the Korean Peninsula until the Nixon administration, this article explains why South Korea began its nuclear weapons programme in a historical context of the US–ROK alliance relationship. This article argues that President Park Chung-hee’s increasing uncertainty about the US security commitment to South Korea in the 1960s led to his decision to develop nuclear weapons in the early 1970s despite the fact that US tactical nuclear weapons were still stationed in South Korea.     

基于主观综合评判的作战仿真可信度评估方法研究

可信度是作战仿真的生命线,对作战仿真系统建设全生命周期进行校核、验证与确认(verification,validation and accreditation,VVA)是确保其可信度的重要途径。合适的评估方法是进行可信度评估的前提条件。在全面分析与比较已有可信度评估方法基础上,针对作战仿真可信度评估中突出主题专家作用的特点,提出了一种基于主观综合评判的可信度评估方法,重点设计了基于专长权的主题专家权重定量计算方法。这些方法都被应用于"××作战仿真系统"的可信度评估工作中。实践表明,本文的研究对于作战仿真系统等复杂仿真系统的可信度评估非常有效。     

面向信息化战争的广义兰切斯特作战模型

由于经典兰切斯特方程不能描述信息对于信息化战争的影响,因此在分析兰切斯特线性律和平方律的基础上,通过引入战场感知系数和信息优势系数,并利用信息优势系数对线性律和平方律中毁伤系数进行修正,提出了能够描述信息对战争影响的广义兰切斯特作战模型,仿真实验结果说明其能够有效地描述信息优势对于改变战争进程所发挥的重要作用。该模型为研究信息化战争的交战过程提供理论参考。     

基于AHP和灰色评估法的水面舰艇编队作战效能评估

水面舰艇编队的作战效能是衡量水面舰艇编队完成各种战斗任务能力的基本依据。为定量评估水面舰艇编队的作战效能,建立了评估水面舰艇编队作战效能的指标体系,并对其中较为重要的指标进行了分析。首先采用层次分析法确定各因素的权重,然后运用灰色评估法对水面舰艇编队作战效能进行综合评价,主要用于对水面舰艇编队作战效能进行排序以及对水面舰艇编队进行编成优化,为水面舰艇编队指挥人员提供一定的量化分析和决策依据。     

电子战训练仿真系统开发方法研究

为了提高电子战训练仿真系统开发的效率和质量,参照建模与仿真领域和软件工程领域中的模型开发与运行过程模型,提出了一个基本开发过程,将基于高层体系结构HLA电子战训练仿真系统的开发划分为相互关联的9个阶段,规定了每个阶段的开发活动。展示了联邦成员软件自动生成工具KD-FedWizard在基于HLA电子战训练仿真系统分析、设计和实现中的应用。     

MGIS技术在海军信息战中的应用

针对军事地理信息系统(Military Geographic Information System,MGIS)在世界海军信息化作战中的重要作用及其研究现状,给出了MGIS的概念、信息流程和系统主要组成,详细阐述了MGIS在海军作战中的主要功能及其实现技术,系统论述了MGIS在海军信息化作战中的应用。最后总结了MGIS技术研究的重要意义。     

基于学习矢量量化(LVQ)神经网络的雷达体制识别

由于现代战争中雷达体制的多样化、复杂化及其综合应用使得雷达体制识别中要处理大量复杂的高维数据,学习矢量量化(LVQ)神经网络不仅能处理有监督分类,而且相对于其他神经网络能以较小的计算量处理大量输入数据,所以采用LVQ对雷达体制进行识别,同时针对LVQ学习速率的变化可能引起学习算法不稳定,采用修正的学习速率算法.在简要介绍雷达体制和LVQ的基础上构造了LVQ神经网络对雷达体制进行分类.通过与径向基神经网络(RBFN)识别算法的仿真对比,证实了方法的有效性.