The SSA-BP-based potential threat prediction for aerial target considering commander emotion  

作　　者：Xun Wang Jin Liu Tao Hou Chao Pan 

机构地区：[1]College of Information Science and Engineering,Wuhan University of Science and Technology,Wuhan,430081,China [2]Beijing Xin Li Machinery Limited Liability Company,Beijing,100039,China [3]School of Information and Communication Engineering,Hubei University of Economics,Wuhan,430205,China

出　　处：《Defence Technology（防务技术）》2022年第11期2097-2106,共10页Defence Technology

基　　金：the National Natural Science Foundation of China(No.61873196 and No.61501336);the Natural Science Foundation of Hubei Province(2019CFB778);the National Defense Pre-research Foundation of Wuhan University of Science and Technology(GF202007);the Postgraduate Innovation and Entrepreneurship Foundation of Wuhan University of Science and Technology(JCX2020095).

摘　　要：The target's threat prediction is an essential procedure for the situation analysis in an aerial defense system.However,the traditional threat prediction methods mostly ignore the effect of commander's emotion.They only predict a target's present threat from the target's features itself,which leads to their poor ability in a complex situation.To aerial targets,this paper proposes a method for its potential threat prediction considering commander emotion(PTP-CE)that uses the Bi-directional LSTM(BiLSTM)network and the backpropagation neural network(BP)optimized by the sparrow search algorithm(SSA).Furthermore,we use the BiLSTM to predict the target's future state from real-time series data,and then adopt the SSA-BP to combine the target's state with the commander's emotion to establish a threat prediction model.Therefore,the target's potential threat level can be obtained by this threat prediction model from the predicted future state and the recognized emotion.The experimental results show that the PTP-CE is efficient for aerial target's state prediction and threat prediction,regardless of commander's emotional effect.

关 键 词：Aerial targets Emotional factors Potential threat prediction BiLSTM Sparrow search algorithm Neural network 

分 类 号：E926.4[军事—军事装备学]