基于粒子群优化的稀疏序列Bayes反卷积方法研究

在地下目标低频声波探测中,由于探测信号的混叠,难以判读反射目标的空间位置.应用信号处理方法求解时,目标信号是稀疏序列,求解方程是病态的.运用Bayes反卷积方法修正其病态性,并采用优化的粒子群算法求解,提高了系统的探测分辨率,同时降低了计算量.实际应用表明,该方法是有效的.     

基于贝叶斯网络的态势估计时间推理方法

基于贝叶斯网络的态势估计方法是目前态势估计领域中的主要方法之一,然而,传统的贝叶斯网络不具备时间语义,因此无法解决态势估计中的时间推理问题.基于此,对贝叶斯网络进行改造,研究了时间贝叶斯网络的构建方法.通过一个战场想定,说明了时间贝叶斯网络构建、推理的过程与方法,证明了提出方法的有效性.     

A sequential partial information bomber-defender shooting problem

A bomber carrying homogenous weapons sequentially engages ground targets capable of retaliation. Upon reaching a target, the bomber may fire a weapon at it. If the target survives the direct fire, it can either return fire or choose to hold fire (play dead). If the former occurs, the bomber is immediately made aware that the target is alive. If no return fire is seen, the true status of the target is unknown to the bomber. After the current engagement, the bomber, if still alive, can either re-engage the same target or move on to the next target in the sequence. The bomber seeks to maximize the expected cumulative damage it can inflict on the targets. We solve the perfect and partial information problems, where a target always fires back and sometimes fires back respectively using stochastic dynamic programming. The perfect information scenario yields an appealing threshold based bombing policy. Indeed, the marginal future reward is the threshold at which the control policy switches and furthermore, the threshold is monotonic decreasing with the number of weapons left with the bomber and monotonic nondecreasing with the number of targets left in the mission. For the partial information scenario, we show via a counterexample that the marginal future reward is not the threshold at which the control switches. In light of the negative result, we provide an appealing threshold based heuristic instead. Finally, we address the partial information game, where the target can choose to fire back and establish the Nash equilibrium strategies for a representative two target scenario.     

对策理论的防空作战决策级融合模型

随着防空C3I系统的发展,防空作战决策的过程较以往更为自动化,用于生成各种作战辅助决策方案的计算机模型也具备随着作战环境改变所表现出一定的自适应调整的特征.影响防空作战决策结果正确性的因素众多,表现这些因素的信息有些是冲突的,为得到更为准确、有效的决策方案,各类冲突信息的处理成为防空作战的核心问题.构造了一种在冲突信息环境下基于对策理论的融合模型,以对策理论的相关算法实现了融合中心各类信息的交互决策.实践表明,这一融合模型能有效地解决防空作战决策中各类信息的综合处理问题,具有一定的实用价值.     

Designing rewards-based crowdfunding campaigns for strategic (but distracted) contributors

In rewards-based crowdfunding, entrepreneurs solicit donations from a large number of individual contributors. If total donations exceed a prespecified funding target, the entrepreneur distributes nonmonetary rewards to contributors; otherwise, their donations are refunded. We study how to design such campaigns when contributors choose not just whether to contribute, but also when to contribute. We show that strategic contribution behavior—when contributors intentionally delay until campaign success is likely—can arise from the combination of nonrefundable (potentially very small) hassle costs and the risk of campaign failure, and can explain pledging patterns commonly observed in crowdfunding. Furthermore, such delays do not hurt the entrepreneur if contributors are perfectly rational, but they do if contributors are distracted, that is, if they might fail to return to the campaign after an intentional delay. To mitigate this, we find that an entrepreneur can use a simple menu of rewards with a fixed number of units sold at a low price, and an unlimited number sold at a higher price; this segments contributors over time based on the information they observe upon arrival. We show that, despite its simplicity, such a menu performs well compared to a theoretically optimal menu consisting of an infinite number of different rewards and price levels under many conditions.     

The timing of contests

We develop a simple model to analyze the timing of contests. When the odds of winning a contest are exogenously given – we show that if either the players discount the future or if the total cost of contest is smaller in the future – there exist subgame perfect equilibria where both players settle, anticipating a contest in the future. With endogenous efforts, the aggregate efforts expanded in a contest are smaller if the contest occurs in the future when the relative effort productivities remain constant or diverge over time, thus creating scope for delay in contests. When the effort productivities converge over time, the total efforts may be greater under a future contest. As a consequence, players either settle over the two periods, or else they initiate a contest immediately.     

中国空间安全形势

随着中国在太空领域的不断崛起,太空地位不断提升的同时,也面临着各种各样的挑战,这些挑战既有源自传统政治领域的大国阻挠,也有来自外太空的人类共同威胁。为了应对这些威胁和挑战,保护中国的太空利益和国家利益,我们需要从多方面着手,多管齐下,以维护日益重要的中国国家太空利益。     

基于合作博弈的智能集群自主聚集策略

以无人车集群系统协同监视再入体着靶过程为任务背景,开展智能集群自组织策略相关技术研究。设计无人车集群执行再入体着靶协同监视的集群行为模式;针对协同监视过程中的集群聚集行为,提出基于合作博弈的智能集群自主聚集策略。各智能体以实现群体聚集为"合作目标",以降低自身能量消耗为"竞争目标",开展博弈;基于微粒群算法规划局部路径,最终使群体系统涌现出聚集行为。仿真实验验证了设计的自主聚集策略的有效性。     

军事云环境下基于动态博弈的资源调度方法

针对军事云环境下联合指挥资源调度优化问题,综合考虑了多部队任务需求,多目标优化,多部队资源竞争等约束条件,运用动态博弈理论及方法,建立并提出了一种基于完全信息扩展博弈的资源调度模型及方法,提高了资源调度效率,兼顾了多部队利益,增加了联合作战效益。最后通过实验验证了其有效性及准确性。     

舰载反潜直升机吊放声纳区域反潜策略建模

为提高舰载反潜直升机吊放声纳区域反潜搜索作战效能,对舰载反潜直升机吊放声纳区域反潜搜索策略问题进行了建模。根据潜艇目标位置信息的不确定性,研究了潜艇目标位置信息的概率分布函数,采用Markov状态转移概率矩阵描述了潜艇目标位置信息变化的方法。其次,给出了基于贝叶斯理论的潜艇目标信息概率分布函数更新公式。再次,推导了舰载反潜直升机吊放声纳区域反潜最优策略,给出了舰载直升机吊放声纳区域反潜搜索算法。最后给出了典型案例,验证了反潜搜索策略的有效性。研究成果可为舰载反潜直升机吊放声纳区域反潜提供决策依据。