基于航迹消除与策略迭代的无人机集群区域目标搜索方法

无人机集群区域搜索在军事领域以及民用领域的搜救、巡逻、监测、环境勘测等方面有着广泛的应用,但如何保证不同场景下无人机集群搜索方法的效率问题依然是个难题。为了更好地解决搜索目标先验信息已知的无障碍区域内多无人机集群搜索航迹规划问题,提高无人机集群搜索效率,本文根据目标区域热度以及传感器探测概率等先验信息,提出了一种基于无人机航迹消除策略的概率计算方法,并在此基础上结合策略迭代算法动态规划无人机航迹,找到单个无人机航迹覆盖率最优策略;进而通过适当组合顺序实现无人机集群区域目标搜索整体覆盖率最优;最后,通过仿真计算验证了算法的有效性。     

基于飞蛾信息素寻偶机制的无人机集群协同搜索

为了提高无人机集群协同搜索移动目标的效率,提出一种基于飞蛾信息素寻偶机制的无人机集群协同搜索方法。根据飞蛾基于信息素选择飞行方向的寻偶行为,建立信息素图风向模型和飞蛾信息素寻偶模型。考虑无人机机间避撞约束,提出从飞蛾信息素寻偶机制到无人机集群分布式协同搜索的映射,并给出具体实现流程。仿真实验结果表明了所提方法在解决单个移动目标的协同搜索问题时的有效性和稳定性;外场飞行试验表明了所提方法在实际应用中的可行性。     

搜索路径给定时的搜索对策问题及其应用

搜索路径给定时的最优搜索方案问题,也可以理解为是关于搜索者和目标的二人对策问题,主要讨论了当搜索路径给定时的单个搜索者和单个目标的搜索对策问题。首先根据问题的特点,利用动态规划和迭代的方法,确定关于目标逃逸路径混合策略的最优分区,证明该分区是多面体凸集;针对目标不同逃逸路径的分区,求出搜索者的最大期望收益,再将问题转化为二人有限零和对策,计算出搜索者的支付矩阵,确定最优搜索策略。最后结合海军护航行动,对我舰载直升机搜索小型海盗船进行分析和计算,说明搜索路径给定时的最优搜索对策对于双方的资源分配和提高搜索效率具有一定的应用价值。     

集群搜索对策理论在搜索-规避对抗中的应用

使用对策论的观点和方法 ,结合搜索论的知识 ,建立了一类搜索 -规避对抗对策模型 .对模型的结论做了系统分析 ,考虑了对策双方的最优策略及使用 .     

应召搜潜中直升机六边形搜索方法

分析了应召搜潜中直升机方形搜索模型,在此基础上提出了六边形搜索模型.在固定参数情况下,对两种方法进行了仿真实验,结果表明采用六边形搜索方法对潜搜索效果比方形搜索方法要好.     

舰艇编队对潜搜索效能分析

对潜搜索是水面舰艇编队反潜作战的重要组成部分,是海军兵力实施对潜攻击的前提和保证.分析了水面舰艇对潜搜索的基本特点和水面舰艇编队对潜搜索有效宽度,并分析了潜艇逃脱初始位置处于搜索有效区时编队的搜索概率,提出了水面舰艇编队对潜搜索效能的基本模型,使水面舰艇编队对潜搜索的实施和提高搜索效能具有理论指导意义.     

潜艇离散搜索分析

以潜艇离散搜索战术为背景,对离散搜索的定义及优缺点进行了阐述,建立了离散搜索发现概率模型,模型中对单位周期的发现目标期望数和发现概率进行了计算,并对隐蔽与非隐蔽两种离散搜索样式的搜索效果进行了模拟仿真,但计算中没有考虑水声环境的影响和敌方潜艇实施反搜索的情况,最后,结合仿真结果,针对潜艇实际运用确定了高低速航行时间,提出了实施此战术时的几点建议,以期为部队合理使用离散搜索战术提供决策依据。     

舰载直升机编队反潜封锁搜索

舰载直升机相对于常规潜艇而言,具有较大的速度优势,据此,提出了一种近似逆螺旋曲线的搜索样式,并把它定义为封锁搜索.以单机封锁搜索为例,介绍了封锁搜索的过程、特点,推导出搜索条件、搜索路线、搜索转移时机的公式,把封锁搜索与随机搜索进行了比较,最后讨论了直升机编队实施封锁搜索的可行性.封锁搜索的模式丰富了应召搜索的理论.     

最优搜索力的确定及增量搜索计划

当静止目标位置服从圆正态分布时,提出了在一定的期望搜索效果前提下确定需参加搜索的最优搜索兵力的求算方法,导出了在首轮搜索未果时追加搜索力进行后续搜索的最优增量搜索计划,并且证明了最优总量搜索(即一次性搜索)与最优增量搜索效率相等的重要结论。     

舰载无人机光电载荷对海搜索方式与搜索宽度

搜索方式与搜索宽度是舰载无人机对海搜索效率与搜索力配置研究的基础。基于舰载无人机及其光电载荷的性能特点,提出了舰载无人机对海搜索的七种基本搜索方式,并从不同的角度对舰载无人机对海搜索方式进行了分类,给出了搜索方式的选择方法;建立了舰载无人机光电载荷搜索宽度计算模型,为舰载无人机系统采用不同的搜索方式进行搜索时计算其搜索效率和搜索力的配置提供了决策依据。     

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     

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     

Constrained minimax optimization of continuous search efforts for the detection of a stationary target

Analytical resolution of search theory problems, as formalized by B.O. Koopman, may be applied with some model extension to various resource management issues. However, a fundamental prerequisite is the knowledge of the prior target density. Though this assumption has the definite advantage of simplicity, its drawback is clearly that target reactivity is not taken into account. As a preliminary step towards reactive target study stands the problem of resource planning under a min–max game context. This paper is related to Nakai's work about the game planning of resources for the detection of a stationary target. However, this initial problem is extended by adding new and more general constraints, allowing a more realistic modeling of the target and searcher behaviors. © 2007 Wiley Periodicals, Inc. Naval Research Logistics, 2007     

Continuous accumulation games on discrete locations

In an accumulation game, a HIDER attempts to accumulate a certain number of objects or a certain quantity of material before a certain time, and a SEEKER attempts to prevent this. In a continuous accumulation game the HIDER can pile material either at locations $1, 2, …, n, or over a region in space. The HIDER will win (payoff 1) it if accumulates N units of material before a given time, and the goal of the SEEKER will win (payoff 0) otherwise. We assume the HIDER can place continuous material such as fuel at discrete locations i = 1, 2, …, n, and the game is played in discrete time. At each time k > 0 the HIDER acquires h units of material and can distribute it among all of the locations. At the same time, k, the SEEKER can search a certain number s < n of the locations, and will confiscate (or destroy) all material found. After explicitly describing what we mean by a continuous accumulation game on discrete locations, we prove a theorem that gives a condition under which the HIDER can always win by using a uniform distribution at each stage of the game. When this condition does not hold, special cases and examples show that the resulting game becomes complicated even when played only for a single stage. We reduce the single stage game to an optimization problem, and also obtain some partial results on its solution. We also consider accumulation games where the locations are arranged in either a circle or in a line segment and the SEEKER must search a series of adjacent locations. © 2002 John Wiley & Sons, Inc. Naval Research Logistics, 49: 60–77, 2002; DOI 10.1002/nav.1048     

A search game taking account of attributes of searching resources

This article deals with a two‐person zero‐sum game called a search allocation game (SAG), in which a searcher and a target participate as players. The searcher distributes his searching resources in a search space to detect the target. The effect of resources lasts a certain period of time and extends to some areas at a distance from the resources' dropped points. On the other hand, the target moves around in the search space to evade the searcher. In the history of search games, there has been little research covering the durability and reachability of searching resources. This article proposes two linear programming formulations to solve the SAG with durable and reachable resources, and at the same time provide an optimal strategy of distributing searching resources for the searcher and an optimal moving strategy for the target. Using examples, we will analyze the influences of two attributes of resources on optimal strategies. © 2007 Wiley Periodicals, Inc. Naval Research Logistics 2008     

离散搜索力的最优配置模型及增量搜索计划

目前,离散搜索正越来越普遍地应用于各种搜索实践。为了优化搜索过程,提高离散搜索的效率,应用最优搜索理论,导出了待搜目标服从均匀分布、正态分布时离散搜索力的最优配置模型、目标的踪迹预测方法和最优增量搜索计划的求法,并通过实例作了演示,上述结论和方法为离散搜索力如何实施最优搜索提供了一定的理论依据。     

The solution to an open problem for a caching game

In a caching game introduced by Alpern et al. (Alpern et al., Lecture notes in computer science (2010) 220–233) a Hider who can dig to a total fixed depth normalized to 1 buries a fixed number of objects among n discrete locations. A Searcher who can dig to a total depth of h searches the locations with the aim of finding all of the hidden objects. If he does so, he wins, otherwise the Hider wins. This zero‐sum game is complicated to analyze even for small values of its parameters, and for the case of 2 hidden objects has been completely solved only when the game is played in up to 3 locations. For some values of h the solution of the game with 2 objects hidden in 4 locations is known, but the solution in the remaining cases was an open question recently highlighted by Fokkink et al. (Fokkink et al., Search theory: A game theoretic perspective (2014) 85–104). Here we solve the remaining cases of the game with 2 objects hidden in 4 locations. We also give some more general results for the game, in particular using a geometrical argument to show that when there are 2 objects hidden in n locations and n→∞, the value of the game is asymptotically equal to h/n for h≥n/2. © 2016 Wiley Periodicals, Inc. Naval Research Logistics 63: 23–31, 2016     

基于约束条件的海上机动目标位置长期预测模型

海上目标运动模型已经有很多,但是对海上机动目标位置的长期预测问题还没有得到很好的解决。灰色系统理论是解决时间序列问题的有效工具,将其与海上机动目标的运动特点相结合,可以较准确地预测海上机动目标在未来较长时期内的位置分布,为引导卫星搜索海上机动目标提供理论依据。