Aerial threat assessment is a crucial link in modern air combat, whose result counts a great deal for commanders to make decisions. With the consideration that the existing threat assessment methods have difficulties ...Aerial threat assessment is a crucial link in modern air combat, whose result counts a great deal for commanders to make decisions. With the consideration that the existing threat assessment methods have difficulties in dealing with high dimensional time series target data, a threat assessment method based on self-attention mechanism and gated recurrent unit(SAGRU) is proposed. Firstly, a threat feature system including air combat situations and capability features is established. Moreover, a data augmentation process based on fractional Fourier transform(FRFT) is applied to extract more valuable information from time series situation features. Furthermore, aiming to capture key characteristics of battlefield evolution, a bidirectional GRU and SA mechanisms are designed for enhanced features.Subsequently, after the concatenation of the processed air combat situation and capability features, the target threat level will be predicted by fully connected neural layers and the softmax classifier. Finally, in order to validate this model, an air combat dataset generated by a combat simulation system is introduced for model training and testing. The comparison experiments show the proposed model has structural rationality and can perform threat assessment faster and more accurately than the other existing models based on deep learning.展开更多
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...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.展开更多
Threat-judgment is a complicated fuzzy inference problem. Up to now no relevant unified theory and measuring standard have been developed. It is very difficult to establish a threat-judgment model with high reliabilit...Threat-judgment is a complicated fuzzy inference problem. Up to now no relevant unified theory and measuring standard have been developed. It is very difficult to establish a threat-judgment model with high reliability in the air defense system for the naval warships. Air target threat level judgment is an important component in naval warship combat command decision-making systems. According to the threat level judgment of air targets during the air defense of single naval warship, a fuzzy pattern recognition model for judging the threat from air targets is established. Then an algorithm for identifying the parameters in the model is presented. The model has an adaptive feature and can dynamically update its parameters according to the state change of the attacking targets and the environment. The method presented here can be used for the air defense system threat judgment in the naval warships.展开更多
基金supported by the National Natural Science Foundation of China (6202201562088101)+1 种基金Shanghai Municipal Science and Technology Major Project (2021SHZDZX0100)Shanghai Municip al Commission of Science and Technology Project (19511132101)。
文摘Aerial threat assessment is a crucial link in modern air combat, whose result counts a great deal for commanders to make decisions. With the consideration that the existing threat assessment methods have difficulties in dealing with high dimensional time series target data, a threat assessment method based on self-attention mechanism and gated recurrent unit(SAGRU) is proposed. Firstly, a threat feature system including air combat situations and capability features is established. Moreover, a data augmentation process based on fractional Fourier transform(FRFT) is applied to extract more valuable information from time series situation features. Furthermore, aiming to capture key characteristics of battlefield evolution, a bidirectional GRU and SA mechanisms are designed for enhanced features.Subsequently, after the concatenation of the processed air combat situation and capability features, the target threat level will be predicted by fully connected neural layers and the softmax classifier. Finally, in order to validate this model, an air combat dataset generated by a combat simulation system is introduced for model training and testing. The comparison experiments show the proposed model has structural rationality and can perform threat assessment faster and more accurately than the other existing models based on deep learning.
基金the National Natural Science Foundation of China(No.61873196 and No.61501336)the Natural Science Foundation of Hubei Province(2019CFB778)+1 种基金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).
文摘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.
基金This project was supported by the National Defense Foundation of China(40108070103)
文摘Threat-judgment is a complicated fuzzy inference problem. Up to now no relevant unified theory and measuring standard have been developed. It is very difficult to establish a threat-judgment model with high reliability in the air defense system for the naval warships. Air target threat level judgment is an important component in naval warship combat command decision-making systems. According to the threat level judgment of air targets during the air defense of single naval warship, a fuzzy pattern recognition model for judging the threat from air targets is established. Then an algorithm for identifying the parameters in the model is presented. The model has an adaptive feature and can dynamically update its parameters according to the state change of the attacking targets and the environment. The method presented here can be used for the air defense system threat judgment in the naval warships.
