基于搜索论的远程反舰导弹搜捕概率建模方法

针对传统捕捉概率模型难以满足远程反舰导弹机动搜捕概率建模计算的问题,提出利用搜索论进行导弹机动搜捕概率计算的建模方法。该方法根据目标机动规律建立其分布概率密度函数,并依据末制导雷达发现目标概率的"倒四次方律"及弹目相对运动轨迹,构建其探测函数,通过求取两者之积的积分实现搜捕概率计算。计算结果显示:若远程反舰导弹不采取机动搜捕策略,目标指示误差增加1km时搜捕概率降低0.47,目标速度增加10节时搜捕概率降低0.3,末制导雷达搜索半径减小50%时搜捕概率降低0.3;若采取平行搜捕策略,上述因素对其影响大幅下降。可见,该方法综合考虑了目标机动规律、传感器探测规律、导弹搜捕策略,可实现远程反舰导弹机动搜捕概率的解算。     

反舰导弹对岛礁区目标的搜捕概率研究

针对打击岛礁区目标时反舰导弹搜捕概率计算问题,通过分析末制导雷达实际搜索过程和捕获条件,构建了目标选择规则,建立了岛礁影响下的反舰导弹搜捕概率计算模型,给出了综合距离、方位、分辨率和目标选择的搜捕判据,并依据概率分布函数,研究了反舰导弹自控终点散布、水面舰艇机动模型及其对搜捕概率的影响。最后仿真分析了目指坐标位于岛礁四周不同位置、搜索扇面变化对搜捕概率的影响。结果显示,水面舰艇靠近岛礁、或位于岛礁后方长条形区域内时,搜捕概率越低;位于岛礁前方即使距离较近时搜捕概率也较高;距岛礁较近时,较小的搜索扇面更优。     

海上机动目标均匀概略航向下的散布规律研究

研究了海上机动目标散布规律对反舰导弹目标搜捕具有的重要意义。在分析正态圆分布下目标以确定速度和均匀概略航向机动散布的内涵特征基础上,基于条件概率密度方法建立了CND-CS-ACUD散布概率密度模型,证明了该散布概率密度的对称特性。理论推导与数值分析表明:CND-CS-ACUD散布概率密度关于过航向分布范围角平分线的垂面对称;最大概率密度取值点必有一个位于航向分布范围角平分线上,与原点之间的距离小于、但随航向分布范围的压缩而趋近于目标机动距离;航向分布范围越小,高概率密度区域越集中,目标散布区域越小。CND-CS-ACUD散布对优化反舰导弹搜索参数、计算搜捕概率具有较强的应用价值。     

基于搜索论的反舰导弹捕捉概率研究

反舰导弹已成为现代海战中的主要进攻武器,正确选择反舰导弹捕捉概率计算模型,对火控系统解算、指挥决策至关重要。针对传统捕捉概率计算方法存在的不足,根据搜索论的基本原理、反舰导弹自控终点分布函数、目标位置域函数、目标分布函数、导弹雷达扫描特征等,提出了基于搜索论的捕捉概率计算方法,确定了常见目标分布条件下的现在点射击捕捉概率计算模型,通过仿真验证了模型的正确性,并由仿真结果确定了敌舰高速规避条件下目标瞄准点选取模型。     

潜艇使用机动搜索鱼雷攻击水面舰艇方法

发挥鱼雷航程远,能够根据线导指令按指定搜索策略对位置散布目标实施机动搜索的特点,潜艇利用特殊海洋环境远距离发现目标。在目标方位不连续、不准确的情况下,建立对目标运动要素的概略解算,根据概略目标运动要素,进行鱼雷机动搜索策略规划,对目标距离进行粗判,定下攻击决心,发射并控制导引鱼雷在目标散布区域实施机动搜索。在目标运动要素更新后,可重新规划鱼雷搜索策略,通过导引线发送至鱼雷,鱼雷实施新的机动搜索,提高鱼雷发现目标概率。     

远程反舰导弹搜索区及其对目标的覆盖概率

根据远程智能型反舰导弹的战术使用特点,结合海面目标在战术背景下的机动特点,分两种情况对远程反舰导弹的搜索区进行了划分,并探讨了各子搜索区及其对目标覆盖概率.     

舰艇规避敌反舰导弹袭击最佳机动航向的仿真分析

利用舰艇机动和反舰导弹搜索目标的原理,建立并仿真了规避反舰导弹机动航向的数学模型;得到了不同条件下舰艇规避敌反舰导弹的最佳机动航向和有价值的结论.     

基于航路规划的导弹"概略前置点"射击方式研究

针对中远程反舰导弹现有射击方式存在的主要问题,提出了"概略前置点"射击方式的基本设想.通过对不同目标数据精度条件下,导弹采用"概略前置点"射击方式仿真计算,得出导弹从不同方位进入时,对海上目标的捕捉概率.     

基于先验目标概略航向的双机搜潜方法

针对在反潜直升机执行应召搜潜任务时,已知目标潜艇概略航向,当出动两架反潜直升机执行应召搜潜任务时,反潜直升机采取的双机搜潜方法,声纳投放位置及使用时机问题和难点,提出了反潜直升机的一种新搜潜方法"前堵后追"法,即一架反潜直升机在潜艇目标丢失点位置沿潜艇逃离的概略方向执行扇形搜索,另一架反潜直升机在所确定概略方向的远界投放声纳浮标,充分利用吊放声纳与声纳浮标执行对潜搜索任务,发挥反潜直升机高机动性能的优势,提高发现概率。最后,进行了仿真实验,仿真实验结果表明反潜双机"前堵后追"法能较好地解决双机搜潜情况下,对已知概略航向目标潜艇的搜索问题,反潜直升机发现潜艇的概率相对于其他双机搜潜方法有较明显的改善。     

导弹蛇形机动与摆式机动突防反导舰炮仿真

为探讨反舰导弹的近距离机动突防能力,用仿真方法计算了反舰导弹末端作水平蛇形机动和摆式机动时对近程反导舰炮武器系统的突防概率,以图示方式给出了2种机动周期、2种舰炮射速、多种机动幅值条件下的计算结果。结果表明,短周期大幅度的频繁机动可显著提高反舰导弹的突防能力,且2种机动方式的突防效果基本相同,而提高舰炮射速则是削弱反舰导弹突防能力的有效途径。     

一种基于部分搜索的GNSS模糊度解算方法

为解决LAMBDA算法搜索策略效率不高的问题,提出了一种基于部分搜索的模糊度解算新方法。该方法的解算成功率接近LAMBDA算法,且只需对最后3维模糊度元素采取搜索策略。同时,通过合理设定搜索椭球的大小和搜索策略;大大提高了搜索效率。通过实验对该新算法与Bootraping算法、LAMBDA算法进行了比较分析,进一步验证了该新算法的有效性。     

A search game on a cyclic graph

There is a finite cyclic graph. The hider chooses one of all nodes except the specified one, and he hides an (immobile) object there. At the beginning the seeker is at the specified node. After the seeker chooses an ordering of the nodes except the specified one, he examines each nodes in that order until he finds the object, traveling along edges. It costs an amount when he moves from a node to an adjacent one and also when he checks a node. While the hider wishes to maximize the sum of the traveling costs and the examination costs which are required to find the object, the seeker wishes to minimize it. The problem is modeled as a two‐person zero‐sum game. We solve the game when unit costs (traveling cost + examination cost) have geometrical relations depending on nodes. Then we give properties of optimal strategies of both players. © 2004 Wiley Periodicals, Inc. Naval Research Logistics, 2004.     

智能搜救机器人在障碍地形的自主构型规划

为了解决带有辅助摆臂的智能搜救机器人自动规划构型以实现自主越障的难题,提出一种能够适应复杂地面形状的搜救机器人越障构型规划新方法,其核心是一种高适应性、高效率的机器人姿态预测算法。通过将地形表示为离散的点集,建立了搜救机器人的单侧姿态预测数学模型;进一步提出了快速求解该问题的算法,每秒可预测1 000～1 500个姿态。基于此,设计了机器人越障过程中状态、动作的评价指标,运用动态规划算法与滚动优化思想构建了具有优化能力的、能够实时运行的构型规划器。仿真与实物实验的结果表明,该方法能够使机器人自主调整构型穿越复杂地形,且相较强化学习算法和人工操作具有更平稳的越障效果。     

A cooperative game in search theory

Search theory originates from the military research efforts of WWII. Most researchers of that period modeled their search games in noncooperative games, where players are enemies or compete against each other. In this article, we deal with a cooperative search game, where multiple searchers behave cooperatively. First we describe several search problems and discuss the possibility of a coalition or cooperation among searchers. For the cooperative search game, we define a function named quasi‐characteristic function, which gives us a criterion similar to the so‐called characteristic function in the general coalition game with transferable utility. The search operation includes a kind of randomness with respect to whether the searchers can detect a target and get the value of the target. We also propose a methodology to divide the obtained target value among members of the coalition taking account of the randomness. As a concrete problem of the cooperative search game, we take the so‐called search allocation game, where searchers distribute their searching resources to detect a target in a cooperative way and the target moves in a search space to evade the searchers. Lastly, we discuss the core of the cooperative search allocation game. © 2009 Wiley Periodicals, Inc. Naval Research Logistics, 2009     

Rendezvous search on labeled networks

Two players are independently placed on a commonly labelled network X. They cannot see each other but wish to meet in least expected time. We consider continuous and discrete versions, in which they may move at unit speed or between adjacent distinct nodes, respectively. There are two versions of the problem (asymmetric or symmetric), depending on whether or not we allow the players to use different strategies. After obtaining some optimality conditions for general networks, we specialize to the interval and circle networks. In the first setting, we extend the work of J. V. Howard; in the second we prove a conjecture concerning the optimal symmetric strategy. © 2002 Wiley Periodicals, Inc. Naval Research Logistics 49: 256–274, 2002; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/nav.10011     

一类对固定目标的集群搜寻对策问题及求解

利用集群搜索对策的理论与方法 ,建立了集群对固定目标的一类搜索对策模型 ,给出了集群的ε -最优搜寻策略 ,并考虑了其在搜索过程中的应用     

Rendezvous search when marks are left at the starting points

Leaving marks at the starting points in a rendezvous search problem may provide the players with important information. Many of the standard rendezvous search problems are investigated under this new framework which we call markstart rendezvous search. Somewhat surprisingly, the relative difficulties of analysing problems in the two scenarios differ from problem to problem. Symmetric rendezvous on the line seems to be more tractable in the new setting whereas asymmetric rendezvous on the line when the initial distance is chosen by means of a convex distribution appears easier to analyse in the original setting. Results are also obtained for markstart rendezvous on complete graphs and on the line when the players' initial distance is given by an unknown probability distribution. © 2001 John Wiley & Sons, Inc. Naval Research Logistics 48: 722–731, 2001     

Discrete search allocation game with false contacts

This paper deals with a two‐person zero‐sum game called a search allocation game, where a searcher and a target participate, taking account of false contacts. The searcher distributes his search effort in a search space in order to detect the target. On the other hand, the target moves to avoid the searcher. As a payoff of the game, we take the cumulative amount of search effort weighted by the target distribution, which can be derived as an approximation of the detection probability of the target. The searcher's strategy is a plan of distributing search effort and the target's is a movement represented by a path or transition probability across the search space. In the search, there are false contacts caused by environmental noises, signal processing noises, or real objects resembling true targets. If they happen, the searcher must take some time for their investigation, which interrupts the search for a while. There have been few researches dealing with search games with false contacts. In this paper, we formulate the game into a mathematical programming problem to obtain its equilibrium point. © 2006 Wiley Periodicals, Inc. Naval Research Logistics, 2007     

Adaptive random search for continuous simulation optimization

We present, analyze, and compare three random search methods for solving stochastic optimization problems with uncountable feasible regions. Our adaptive search with resampling (ASR) approach is a framework for designing provably convergent algorithms that are adaptive and may consequently involve local search. The deterministic and stochastic shrinking ball (DSB and SSB) approaches are also convergent, but they are based on pure random search with the only difference being the estimator of the optimal solution [the DSB method was originally proposed and analyzed by Baumert and Smith]. The three methods use different techniques to reduce the effects of noise in the estimated objective function values. Our ASR method achieves this goal through resampling of already sampled points, whereas the DSB and SSB approaches address it by averaging observations in balls that shrink with time. We present conditions under which the three methods are convergent, both in probability and almost surely, and provide a limited computational study aimed at comparing the methods. Although further investigation is needed, our numerical results suggest that the ASR approach is promising, especially for difficult problems where the probability of identifying good solutions using pure random search is small. © 2010 Wiley Periodicals, Inc. Naval Research Logistics, 2010