摘要
现代空中战场中,对敌方空中作战群组意图判定的结果将直接影响我方对局势的掌握和决策的下达,因此空中群组态势的评估识别是现代战场的重要任务。空中作战群组通常会根据飞行任务执行相应意图,监测相关过程并从获取的数据中挖掘相应的特征,再通过智能化的方法进行学习预测。基于此,本文提出一种基于GRU-Attention神经网络的识别方法,将获取的行为事件库预处理后输入门控循环(GRU)神经网络挖掘事件中深层特征;注意力机制(Attention)为深层特征自动计算相应的权重分配;最后利用softmax层对输入的信息进行态势意图分类。实验结果表明GRU-Attention态势识别方法的准确率达到96.10%,验证了该方法的准确性、高效性和稳定性。该方法的提出对丰富神经网络识别方法体系和提高空中群组态势的评估识别准确率具有重要的理论意义和实践意义。
In the modern air battlefield, the results of the intentional determination of the enemy air operations group directly affect our mastery of the situation and the decision-making. Therefore, the assessment of the air group situation is an important task of the modern battlefield. The air combat groups usually perform the corresponding intent according to the mission, monitor the relevant process and mine the corresponding features from the acquired data, and then learn and predict through the intelligent method. This paper proposes a recognition method based on GRU-Attention neural network, which inputs the pre-processed behavior event library into the GRU neural network to mine deep features. The corresponding weight assignment is automatically calculated by Attention mechanism. Finally, the input information is classified by the softmax layer. The experimental results show that the accuracy of the GRU-Attention situation identification method reaches 96.10%, which verifies the accuracy, efficiency and stability of the proposed method. The proposed method has important theoretical and practical significance for enriching the neural network identification method system and improving the assessment accuracy of the air group configuration potential.
作者
苟先太
吴南方
GOU Xian-tai;WU Nan-fang(School of Electrical Engineering,Southwest Jiaotong University,Chengdu 610031,China)
出处
《计算机与现代化》
2019年第10期11-16,33,共7页
Computer and Modernization
基金
四川省新一代人工智能重大科技专项项目(18ZDZX0132)
四川省科技计划项目重点研发项目(2017GZ0159)
关键词
群组态势识别
门控循环神经网络
注意力机制
group situation recognition
Gated Recurrent Unit neural network
attention mechanism
