基于自适应二阶终端滑模的航天器有限时间姿态机动算法

针对航天器有限时间姿态机动问题,提出一种自适应二阶终端滑模控制算法。设计一种终端滑模面,保证系统状态能够在有限时间内沿滑模面收敛到系统原点;为克服系统抖振,设计了二阶终端滑模控制器,并采用参数自适应估计项补偿系统中的外部干扰力矩。基于Lyapunov函数法证明了二阶自适应终端滑模控制器能够保证闭环系统实际有限时间稳定。仿真结果表明,提出的姿态机动算法响应速度快、精度高,能够有效实现对系统抖振和外部干扰的抑制,具有重要的科学意义和工程应用价值。     

群决策理论在多机协同空战机动决策中的应用

针对近距空战中多架战机对空中的多个敌对目标进行协同攻击的机动决策问题进行了研究。将群决策理论引入多机协同空战机动决策,首先确定了决策成员与候选方案,然后基于战场态势评估提出了采用风险决策准则的偏好排序确定方法,给出了集结偏好的序数型群决策方法。在不同空战想定条件下进行仿真,结果表明,该方法合理、可行,具有良好的应用前景。     

The Role of Storeroom Collections in the Identification of Battle Debris

Abstract

In connection with the work to find the Fulford battlesite, it is recognised that the techniques required to find the site of a conflict are different from those employed to confirm and research a battlesite. Access to museum storeroom collections allowed the surface fragments from the investigation to be interpreted and also revealed that iron was in all probability gathered together and reprocessed immediately after the battle. Thus, hearth debris is a potential pointer to locate other sites because the metal-working sites at Fulford coincided with the area of action of the battle suggested by other, independent avenues of research.     

Autonomous maneuver decision-making for a UCAV in short-range aerial combat based on an MS-DDQN algorithm

To solve the problem of realizing autonomous aerial combat decision-making for unmanned combat aerial vehicles (UCAVs) rapidly and accurately in an uncertain environment, this paper proposes a decision-making method based on an improved deep reinforcement learning (DRL) algorithm: the multi-step double deep Q-network (MS-DDQN) algorithm. First, a six-degree-of-freedom UCAV model based on an aircraft control system is established on a simulation platform, and the situation assessment functions of the UCAV and its target are established by considering their angles, altitudes, environments, missile attack performances, and UCAV performance. By controlling the flight path angle, roll angle, and flight velocity, 27 common basic actions are designed. On this basis, aiming to overcome the defects of traditional DRL in terms of training speed and convergence speed, the improved MS-DDQN method is introduced to incorporate the final return value into the previous steps. Finally, the pre-training learning model is used as the starting point for the second learning model to simulate the UCAV aerial combat decision-making process based on the basic training method, which helps to shorten the training time and improve the learning efficiency. The improved DRL algorithm significantly accelerates the training speed and estimates the target value more accurately during training, and it can be applied to aerial combat decision-making.     

一种抗云雾散射干扰的激光引信信号检测方法

激光引信在云雾环境中工作时,云雾的后向散射信号对其正常的目标信号检测产生干扰,严重时有可能引起虚警。研究了云雾产生的后向散射信号与目标回波信号的不同特点,并由此提出了双门限检测方法,即同时对信号幅度与信号幅度的增速进行检测,从而有效识别云雾干扰信号以避免产生虚警。通过分析实际的试验数据,对回波信号的特点进行了验证,实际应用双门限检测方法,验证了其可行性。     

海上编队雷达网优化部署系统的设计与实现

建立海上编队雷达网优化部署系统模型,包括实体模型和认知模型。仿真海上编队雷达网在无威胁条件下的优化部署和面临干扰和超低空突防威胁时的对抗过程,经过海上编队雷达网优化部署认知模型的解算,指挥舰队编队成员进行机动部署,编队成员通过重新机动,使编队雷达网获得较好的探测性能。仿真结果表明所建立模型的可行性和正确性。对于在未来海战场复杂电磁环境下,更好地发挥编队雷达网探测、预警能力具有重要意义。     

海上机动目标打击效果评估决策支持系统设计

针对海上机动目标打击效果评估的特点,运用遥感技术、人工智能技术和模糊评判的方法探索打击效果评估问题。提出一种基于高分辨率遥感图像的打击效果评估决策支持系统,融合人定性分析和机器定量分析的优点,核心是问题处理系统综合运用处理后的遥感信息和支持系统的模型、知识以及数据来评估打击效果,评估模型为二级综合评判模型。对尚处于理论研究阶段的海上机动目标打击效果评估问题有积极的参考意义。     

防空兵侦察力量机动部署方案优选与评估

针对防空兵侦察力量机动部署问题,分析并生成了机动部署方案,运用模糊灰色关联分析法选出其中的最优方案。通过实例计算得出了信息化条件下,防空兵侦察力量进行机动部署可有效提高其作战效能的科学结论,并验证了算法的有效性。