BiLSTM-TCN: An Aerial Target Intent Recognition Model

This study presents a novel deep learning method called BiLSTM-TCN, which aims to enhance the proficiency of recognizing air target operational intent. The traditional air target operational intent method makes judgments based on single moment data, and thus cannot effectively capture feature information on temporal data. To tackle this issue, we select suitable features for air target intent feature set construction and then encode them into time-series features. We mine the potential feature information in the data by TCN. Meanwhile, we captured the prolonged correlations within the dataset by using BiLSTM. Through experimental validation, we have shown that BiLSTM-TCN surpasses current superior techniques regarding the accuracy of identifying airborne targets' intentions.