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何胜杰郭强王兴虎程家林陈韵竹毛延静 《无人系统技术》2022,(2):106-116
针对将ADC法应用于察打无人机对地攻击任务的效能评估问题,首先,对经典ADC方法进行模型创新,重新构建能力矩阵;然后,将改进ADC方法用于3款典型察打无人机(MQ-9、翼龙-2、TB-2)对地攻击效能评估,根据作战过程分析和评价指标梳理,设计效能评估指标体系和能力表达公式;最后,采用层次分析法对不同能力指标赋予权重系数,综合得出效能评估结果。效能评估结果表明,MQ-9、翼龙-2、TB-2这3型无人机在对地攻击任务中的作战效能分别为0.6293、0.5962、0.4822,可知MQ-9综合作战能力及作战效能较高,而其余两型无人机可在远距作战能力及协同侦察能力等指标中进行改进提升。研究结果可用于评估和比较不同察打无人机的作战效能,分析不同指标对作战效能的影响程度,提炼出关键能力指标和改进方向,对无人机装备的立项论证和作战使用提供理论参考。 相似文献
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针对无人机使用传统合同网算法进行任务分配存在的投标个数多、网络吞吐量不均衡、工作负载高等问题,提出一种改进合同网算法任务分配模型.首先对无人机任务分配的空间环境进行建模,在传统合同网算法的投标阶段,结合一种基于无人机能力评估方法的投标策略,该策略建立了基于代价函数和收益函数的任务效能函数.通过多次仿真,对历史任务效能、... 相似文献
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舰载无人机是现代海军的重要装备,对舰载无人机作战效能的研究是近年来作战效能评估领域研究的热点。针对舰载无人机对海突击作战效能评估问题,提出了一种将层次分析法(AHP)、模糊综合评判法(FCE)和ADC法相结合的方法。基于舰载无人机对海突击作战使命任务,运用OODA环理论抽象出舰载无人机对海突击作战任务剖面,开展了关于舰载无人机对海突击作战能力的研究,引入对抗因子Z,通过建立效能评估指标体系,构建舰载无人机对海突击作战效能评估模型,同时引入算例对模型进行验证。通过算例计算出舰载无人机作战效能为0.8602,并对其作战过程进行仿真推演,表明了模型的可行性,为舰载无人机的设计论证和作战运用提供技术支撑。 相似文献
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本文探讨了通用小型无人机系统作战效能的系统分析方法,根据各种作战任务的任务设备配置分析,采用专家团体决策和模糊数学方法进行了评估,并在其基础上建立了小型无人机系统的作战效能评估指数模型。 相似文献
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《防务技术》2020,16(1):150-157
A formation model of manned/unmanned aerial vehicle (MAV/UAV) collaborative combat can qualitatively and quantitatively analyze the synergistic effects. However, there is currently no effective and appropriate model construction method or theory, and research in the field of collaborative capability evaluation is basically nonexistent. According to the actual conditions of cooperative operations, a new MAV/UAV collaborative combat network model construction method based on a complex network is presented. By analyzing the characteristic parameters of the abstract network, the index system and complex network are combined. Then, a method for evaluating the synergistic effect of the cooperative combat network is developed. This method provides assistance for the verification and evaluation of MAV/UAV collaborative combat. 相似文献
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Tracking maneuvering target in real time autonomously and accurately in an uncertain environment is one of the challenging missions for unmanned aerial vehicles(UAVs).In this paper,aiming to address the control problem of maneuvering target tracking and obstacle avoidance,an online path planning approach for UAV is developed based on deep reinforcement learning.Through end-to-end learning powered by neural networks,the proposed approach can achieve the perception of the environment and continuous motion output control.This proposed approach includes:(1)A deep deterministic policy gradient(DDPG)-based control framework to provide learning and autonomous decision-making capa-bility for UAVs;(2)An improved method named MN-DDPG for introducing a type of mixed noises to assist UAV with exploring stochastic strategies for online optimal planning;and(3)An algorithm of task-decomposition and pre-training for efficient transfer learning to improve the generalization capability of UAV's control model built based on MN-DDPG.The experimental simulation results have verified that the proposed approach can achieve good self-adaptive adjustment of UAV's flight attitude in the tasks of maneuvering target tracking with a significant improvement in generalization capability and training efficiency of UAV tracking controller in uncertain environments. 相似文献
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