一种利用 Transformer 的无人集群对抗态势要素识别方法

Since the original situation information in the unmanned swarm confrontation problem is complicated, it is difficult to accurately identify the situation elements such as the swarm formation and swarm movement trend. In order to improve the identification ability for unmanned swarm situation elements, an identification method for unmanned swarm adversarial situation elements using Transformer was designed. On the basis of the Transformer model, a Transformer-Decoder attention layer model that can be applied to unmanned swarm confrontation situation element identification problem was constructed, so as to achieve a good ability to identify swarm situation elements. The inter-layer attention structure was designed to improve the feature expression ability of Transformer-Decoder to further improve the recognition accuracy. First, the situation sequence information of the unmanned swarm was input into LSTM and encoded into time sequence feature information. Then the Transformer-Decoder attention module and the inter-layer attention module were used to extract the comprehensive high-order situation information of the swarm. Finally multi-dimensional classification network and softmax layer were adopted to realize the classification of multiple situation elements. The experimental results showed that the unmanned swarm adversarial situation elements recognition method using Transformer and inter-layer attention exhibited good performance on situation element classification problem, and could accurately classify multiple situation elements synchronously. Compared with the baseline method, the swarm situation recognition method using Transformer and inter-layer attention had higher accuracy in the recognition of swarm formation and movement trend. Especially in the classification of situation elements that reflect the relative trends within the swarm, the proposed method clearly showed better performance.