Exemplar-free Incremental Learning For Micro-Doppler Signature Classification

The utilization of machine learning techniques has greatly improved the accuracy of micro-Doppler(m-D) signatures-based radar signal recognition. However, the "catastrophic forgetting" problem commonly exists in data-driven algorithms severely limits the adaptability of recognition algorithms in real-world applications, as models cannot incrementally train and learn new categories. In this paper, we propose an incremental learning method, "Boundary Transfer and Uncertainty Augmentation (BTUA)" for continuous learning of m-D signatures. BTUA utilizes boundary transfer(BT) to generate pseudo-decision boundaries for old categories and avoid the forgetting problem. It also employs an uncertainty augmentation(UA) algorithm to enhance the model’s generalization and improve the correctness of feature extraction. Finally, the validation demonstrates the advantages of our algorithm in terms of both accuracy and practicality.