Target-Aspect Domain Continual Learning for SAR Target Recognition

In recent years, impressive progress has been achieved in synthetic aperture radar (SAR)-based automatic target recognition (ATR) with the development of deep learning. In practice, a complete training SAR image dataset in all target-aspect domains is limited available for one measurement. When SAR images in the unseen aspect domains are newly acquired, direct retraining of the trained SAR-ATR models with them may lead to a significant performance decline for the seen aspect domains. In this article, we propose an aspect continual recognition model (ACRM) to address the learned feature forgetting problem when SAR images with different target aspects come sequentially in the real-world SAR-ATR. Considering the abundant variations of SAR images with target aspects, we introduce the Bayesian probabilistic frame to improve the model's generalization of characterizing the varied target features across different aspects. To acquire a better solution for the posterior probability of the model parameters, we integrate an online Monte Carlo variational inference into the deep neural network in the ACRM. Furthermore, to mitigate the accumulation of estimation errors caused by the repetitive approximations in inference, we leverage the coreset method by retaining a small subset of important samples from previous tasks as a coreset. We conduct extensive experiments on the MSTAR and FUSARship datasets. Compared with a variety of baseline algorithms in continual learning, our methods exhibit excellent SAR-ATR performance and robustness, when the SAR images from different target aspects are acquired sequentially.