航空科学技术2024，Vol.35Issue(10)：86-94,9.DOI:10.19452/j.issn1007-5453.2024.10.010
基于Bi-LSTM和多头自注意力的空战目标意图识别模型
Air Combat Target Intention Recognition Model Based on Bi-LSTM and Multi-Head Self-Attention
摘要
Abstract
In real-time air combat,accurately predicting the enemy's target intention plays a key role in timely adjusting our air combat tactics.However,modern air combat data is usually characterized by temporal sequence,diversity and complexity.In addition,traditional algorithms only rely on the current moment data to make decisions,which leads to a decrease in the accuracy of target intention recognition.To address these issues,an air combat target intention recognition model is proposed based on bi-directional long short-term memory(Bi-LSTM)and multi-head self-attention mechanism(MHSA).First,the target state data is preprocessed to generate temporal sequence features,and then the bidirectional temporal relationships in the feature sequences are captured using the Bi-LSTM neural network,which helps the model to better learn the long term dependencies between the feature sequences;second,the MHSA maps the features extracted by the Bi-LSTM to different subspaces through multiple independent self-attention mechanisms,which guides the model to learn the correlation relationship between different angles of the temporal features.Finally,the enemy target intent is output through the SoftMax layer.The experimental results show that the method effectively improves the accuracy of target intent,which is of scientific and reasonable significance for the timely and flexible adjustment of air combat strategy.关键词
意图识别/空中目标/多头自注意力/Bi-LSTM/时序特征Key words
intention recognition/air targets/multi-head self-attention/Bi-LSTM/temporal features分类
航空航天引用本文复制引用
刘文兵,雷钰,李广飞,高全学..基于Bi-LSTM和多头自注意力的空战目标意图识别模型[J].航空科学技术,2024,35(10):86-94,9.基金项目
航空科学基金(201951081004) Aeronautical Science Foundation of China(201951081004) （201951081004）
