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作 者:姚颖 吴宇[1] 邓嘉宁 YAO Ying;WU Yu;DENG Jianing(School of Aeronautics and Astronautics,Chongqing University,Chongqing 400044,China;Southwest Institute of Electronic Technology,Chengdu 610091,China)
机构地区:[1]重庆大学航空航天学院,重庆400044 [2]西南电子技术研究所,成都610091
出 处:《空军工程大学学报》2025年第1期76-85,共10页Journal of Air Force Engineering University
基 金:国家自然科学基金(52102453)。
摘 要:以无人机一对一近视距空战为研究背景,提出一种融合加权矩阵对策与进化算法,结合粗细粒度优化的空战决策方法。首先,基于实际作战情况建立无人机机动模型和空战态势评估优势函数;其次,考虑到基于机动动作库的离散变量优化与满足机动能力的连续型优化2种方式各自存在精度不足和计算代价高的问题,提出将粗细粒度优化相结合的机动动作决策框架。在粗粒度优化层面上,引入矩阵对策法,同时针对传统矩阵对策法机动权重参考选择保守的问题,基于卡尔曼滤波算法进行权重调整;在细粒度优化层面上,基于差分进化原理,进一步搜索更好的机动动作。仿真算例中,通过4组对比实验,验证了所提决策框架的有效性,基于轨迹预测的加权矩阵对策算法能够针对敌方的动作采取有效的应对策略。Based on the research background of one-to-one near visual range air combat of unmanned aerial vehicles(UAVS),a new air combat decision-making method combining weighted matrix strategy and evolutionary algorithm with coarse and fine granularity optimization is proposed.Firstly,the UAV maneuver model and air combat situation assessment advantage function are established based on the actual combat situation.Secondly,considering that the discrete variable optimization based on maneuvering library and the continuous optimization based on maneuvering ability both have the problems of insufficient accuracy and high calculation cost,a maneuvering decision framework combining coarse and fine granularity optimization is proposed.At the level of coarse-grain optimization,the matrix game method is introduced,and the Kalman filter algorithm is used to adjust the weight of the traditional matrix game method to solve the problem of conservative choice of maneuvering weight reference.At the fine-grained optimization level,based on the differential evolution principle,further search for better maneuvers.The effectiveness of the proposed decision frame is verified by four sets of comparison experiments.The weighted matrix game algorithm based on trajectory prediction can take effective countermeasures against the enemy’s actions.
分 类 号:V219[航空宇航科学与技术—航空宇航推进理论与工程]
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