基于时序注意力的不平衡辐射源个体识别方法OA

To address the problems of prominent long-tailed data distribution and high-dimensional sampling features in specific emitter identification(SEI),an improved SEI algorithm based on temporal attention was proposed.By construct-ing a multi-scale feature fusion module to integrate feature information under different temporal granularities,and de-signing an identity-aware temporal attention module that generates and embeds identity-adaptive attention weights,the model was encouraged to focus on individuals with a small number of tail samples and identity-related temporal features.Experiments on the self-collected AIS dataset and public ADS-B dataset demonstrate that the proposed algorithm achieves recognition accuracies of 94.89%and 93.23%respectively,and the accuracy decreases by less than 2%under a low signal-to-noise ratio(SNR)of 3 dB.