战场环境下巡航导弹低空突防航迹判断研究

抗击巡航导弹(CM)是地面防空兵部队面临的一项重要任务,而准确判断巡航导弹的进袭航线是地面防空兵部队合理调整战斗部署、组织侦察射击的重要依据。通过对巡航导弹战场环境建模,建立巡航导弹战场环境的Voronoi图,并进行了基于不同威胁体的改进,在计算分段航线上巡航导弹的突防概率基础上,用模糊最大树法判断敌巡航导弹最/次可能的来袭航路,从而为后续的地面防空兵兵力部署提供了依据。     

Technical note: An improved range chart for normal and long‐tailed symmetrical distributions

The distribution of the range of a sample, even in the case of a normal distribution, is not symmetric. Shewhart's control chart for range and other approximations for range from skewed distributions and long‐tailed (leptokurtic) symmetrical distributions assume the distribution of range as symmetric and provide 3 sigma control limits. We provide accurate approximations for the R‐chart control limits for the leptokurtic symmetrical distributions, using a range quantile approximation (RQA) method and illustrate the use of the RQA method with a numerical example. As special cases, we provide constants for the R‐chart for the normal, logistic, and Laplace distributions. © 2007 Wiley Periodicals, Inc. Naval Research Logistics, 2008     

DEA models for two‐stage processes: Game approach and efficiency decomposition

Data envelopment analysis (DEA) is a method for measuring the efficiency of peer decision making units (DMUs). This tool has been utilized by a number of authors to examine two‐stage processes, where all the outputs from the first stage are the only inputs to the second stage. The current article examines and extends these models using game theory concepts. The resulting models are linear, and imply an efficiency decomposition where the overall efficiency of the two‐stage process is a product of the efficiencies of the two individual stages. When there is only one intermediate measure connecting the two stages, both the noncooperative and centralized models yield the same results as applying the standard DEA model to the two stages separately. As a result, the efficiency decomposition is unique. While the noncooperative approach yields a unique efficiency decomposition under multiple intermediate measures, the centralized approach is likely to yield multiple decompositions. Models are developed to test whether the efficiency decomposition arising from the centralized approach is unique. The relations among the noncooperative, centralized, and standard DEA approaches are investigated. Two real world data sets and a randomly generated data set are used to demonstrate the models and verify our findings. © 2008 Wiley Periodicals, Inc. Naval Research Logistics, 2008     

基于证据熵对不确定性度量的决策表约简

知识约简是粗糙集理论的核心内容之一,产生的粗糙决策规则往往具有一定的不确定性.在变精度粗糙集的基础上,本文构造了符合证据理论框架的一组焦元,利用基本概率分配函数计算了证据的总体信息熵,度量了决策表的不确定性;以该度量作为启发信息,给出了决策表的启发式知识约简算法.计算实例表明了本文方法的有效性.     

基于球坐标变换的双星编队伪距相对定位

针对NASA的ST -3中自主编队飞行 (AFF)的双星星座 ,讨论利用星载的类GPS高精度的伪距观测数据确定星座之间相对位置和时间参数的问题。在常用的直角坐标系中建立系统的数学模型 ,并进行解算 ;特别针对双星编队定位解算几何结构弱、解算结果的协方差矩阵不稳定而无法进行传统协方差分析的问题 ,提出了基于球坐标变化的伪距定位方法     

基于AHP-Fuzzy的装甲机械化部队综合信息系统评估

考虑到综合信息系统存在大量的模糊信息,很难用精确数学模型描述其内部关系,采用模糊综合评估法对其进行了评估.首先通过对装甲机械化部队综合信息系统的功能需求进行分析,建立了评估指标体系.然后介绍了确定指标权重的AHP法,并建立了对装甲机械化部队综合信息系统进行评估的模糊综合评判模型.最后通过实例对设想中的装甲机械化部队综合信息系统进行了评估,并说明了该模型的可行性.     

多属性模糊决策对舰炮射击目标优选

舰炮在现代高科技条件下海战中承担着重要的作战使命和任务,它能否完成被赋予的作战使命和任务,射击目标的优选起着决定性作用.为解决舰炮射击目标优选、决策问题,研究分析了多属性模糊决策方法,以及影响舰炮射击目标优选的因素,提出了多属性模糊决策方法的数学模型和求解最优射击目标排序的新方法.该方法在舰炮射击目标优选中的应用实例表明:通过定量和定性相结合,专家判断和信息处理相结合的多属性模糊决策方法对舰炮射击目标优选、决策具有一定的借鉴意义.     

基于评价指标模糊集的复杂系统效能评估方法研究

提出了一种基于评价指标模糊集的复杂系统效能评估方法。该方法利用系统评价指标的模糊性构造模糊集,以模糊集贴近度来定义评价指标的模糊隶属度函数,结合模糊层次分析法,提出了一种适用于复杂系统功能与多层评估指标体系的效能评估新方法。仿真应用的结果表明:该方法行之有效,具有较好的实用前景。     

基于模糊层次分析法的坦克优先攻击目标选择

针对现代战场上坦克往往面临多个目标威胁的特点,运用模糊层次分析法帮助指挥员定下攻击目标的优先顺序。引入了模糊一致矩阵和一致性检验方法,运用新的权重计算方法。根据模糊层次分析法可以迅速确定我方坦克优先攻击的目标。该算法有效克服层次分析法存在的不足,较好地实现了定量与定性的有机结合,得到的结论符合战场实际,准确可信。     

基于模糊综合评判的战场目标价值研究

现代战场目标繁多,从价值分析角度出发,建立了基于模糊综合评判的战场目标价值评估模型,并结合实例说明了该方法的具体应用,为指挥员和决策机关制定正确的策略提供了科学的依据.