TCM Model for improving track sequence classification in real scenarios with Multi-Feature Fusion and Transformer Block

Abstract

The shipping industry has experienced rapid growth in recent years, prompting a need for advanced target recognition technology based on marine radar. This paper introduces the Track Classification Model (TCM), a novel approach for classifying track sequences in real scenarios. The TCM utilizes a feature extraction network based on multi-feature fusion, taking radar echo images and motion information of the target as input, to improve classification accuracy. Additionally, the paper also presents a dataset production method that addresses the issue of missing labels, a critical problem in track sequence classification. Through ablation experiments, the paper demonstrates the effectiveness of the design strategy, with the multi-feature fusion network successfully extracting features and achieving superior performance over single feature extraction networks. The results show that increasing the number of input track points and raising the upper limit of the input sequence leads to improved classification accuracy. Finally, in real scenarios, the proposed model outperforms other algorithms, showcasing its high engineering application value.