基于启发式蚁群算法的协同多目标攻击空战决策研究

协同多目标攻击空战决策是现代战机在超视距条件下进行协同空战的关键技术之一。它是寻求一个优化分配方案,将目标分配给各友机,力求使攻击效果最优。本文在对协同多目标攻击战术进行深入分析的基础上,提出了一种用于空战决策的启发式蚁群算法,该算法通过求解友机导弹对目标的最优分配来确定空战决策方案。仿真实验表明所提出的启发式蚁群算法对最优解的搜索效率明显优于基本蚁群算法,是一种求解协同多目标攻击空战决策问题的有效算法。     

Learning Spatial Localization: From Rat Studies to Computational Models of the Hippocampus

In his landmark article, Richard Morris (1981) introduced a set of rat experiments intended “to demonstrate that rats can rapidly learn to locate an object that they can never see, hear, or smell provided it remains in a fixed spatial location relative to distal room cues” (p. 239). These experimental studies have greatly impacted our understanding of rat spatial cognition. In this article, we address a spatial cognition model primarily based on hippocampus place cell computation where we extend the prior Barrera–Weitzenfeld model (2008) intended to allow navigation in mazes containing corridors. The current work extends beyond the limitations of corridors to enable navigation in open arenas where a rat may move in any direction at any time. The extended work reproduces Morris's rat experiments through virtual rats that search for a hidden platform using visual cues in a circular open maze analogous to the Morris water maze experiments. We show results with virtual rats comparing them to Morris's original studies with rats.     

连续时变信道对协作通信性能影响的研究

协作通信能够抵抗无线信道衰落,是提高无线通信系统性能的有效手段。但是,其性能优势的发挥受信道时变的影响。文章研究了连续时变信道下协作通信系统的性能,为了准确反映信道连续时变情况,采用内插滤波高斯噪声法构建连续时变信道模型,选取误包率作为性能指标。仿真结果表明,连续时变信道下,随数据传输逐渐加重程度的过时信道状态信息将导致协作通信误包率性能大幅恶化。     

基于信息预处理的无人机集群协同电子侦察

为了提供长期有效的电子侦察能力,高机动性无人机电子侦察平台需要组成 1个多无人机电子侦察网络。多无人机电子侦察网络在面对复杂电子侦察任务时,需要具有对不同平台电子侦察结果判别和整合的能力。针对传统基于证据理论(DempsterShafer,DS)的协同电子侦察方法中,因为电磁环境的复杂性和基于 DS合成公式对不可靠信息缺少必要的筛除而导致的证据悖论问题,文章提出了基于电子侦察信息预处理的无人机集群协同电子侦察融合判决新方法。该方法对每架无人机的电子侦察信息进行预判断,得到每架无人机电子侦察信息的可信度权值,提出信息融合算法,对预处理后的电子侦察信息进行融合判决。仿真结果表明,该方法可以有效提升对多无人机电子侦察信息的甄别能力,提高无人机集群协同电子侦察性能。     

分布式协同微纳遥感星群的智能控制系统关键技术

近年来，国内外微纳遥感星座发展不断加速，通过高频重访大幅提升了对地观测的时间分辨率，但是由于单颗微纳卫星观测能力受限，难以满足多源同步与融合、高品质、大幅宽等数据应用需求。为此，将星座与星簇相结合，以星座化分布满足高时间分辨率需求，以星座节点上的星簇协同观测获取多源、高品质、宽幅数据，构建基于微纳卫星的分布式协同遥感系统，是兼顾上述遥感应用需求的有效途径。智能分布式协同控制是该系统的核心关键，为此，必须研究解决星座+星簇大规模动态微纳遥感星群控制系统的分布协同自主导航、智能运动规划与控制、智能健康预测与管理、智能组网等关键技术难题。在梳理分析国内外分布式微纳遥感系统的基础上，给出分布式协同微纳遥感星群的概念内涵，分析其特有的四方面难题，梳理总结相关技术发展现状与趋势，以期为后续该方向的研究起到一定的借鉴作用。     

STABC-IR: An air target intention recognition method based on bidirectional gated recurrent unit and conditional random field with space-time attention mechanism

The battlefield environment is changing rapidly, and fast and accurate identification of the tactical intention of enemy targets is an important condition for gaining a decision-making advantage. The current Intention Recognition (IR) method for air targets has shortcomings in temporality, interpretability and back-and-forth dependency of intentions. To address these problems, this paper designs a novel air target intention recognition method named STABC-IR, which is based on Bidirectional Gated Recurrent Unit (BiGRU) and Conditional Random Field (CRF) with Space-Time Attention mechanism (STA). First, the problem of intention recognition of air targets is described and analyzed in detail. Then, a temporal network based on BiGRU is constructed to achieve the temporal requirement. Subsequently, STA is proposed to focus on the key parts of the features and timing information to meet certain interpretability requirements while strengthening the timing requirements. Finally, an intention transformation network based on CRF is proposed to solve the back-and-forth dependency and transformation problem by jointly modeling the tactical intention of the target at each moment. The experimental results show that the recognition accuracy of the jointly trained STABC-IR model can reach 95.7%, which is higher than other latest intention recognition methods. STABC-IR solves the problem of intention transformation for the first time and considers both temporality and interpretability, which is important for improving the tactical intention recognition capability and has reference value for the construction of command and control auxiliary decision-making system.