1. The SSA-BP-based potential threat prediction for aerial target considering commander emotion
- Author
-
Chao Pan, Jin Liu, Xun Wang, and Tao Hou
- Subjects
0209 industrial biotechnology ,Artificial neural network ,Computer science ,business.industry ,Mechanical Engineering ,Metals and Alloys ,Computational Mechanics ,02 engineering and technology ,Machine learning ,computer.software_genre ,01 natural sciences ,Backpropagation ,010305 fluids & plasmas ,020901 industrial engineering & automation ,Search algorithm ,Prediction methods ,0103 physical sciences ,Ceramics and Composites ,State prediction ,State (computer science) ,Artificial intelligence ,business ,computer ,Threat level ,Situation analysis - Abstract
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.
- Published
- 2022