Target-Aspect Domain Continual SAR-ATR Based on Task Hard Attention Mechanism

In real-world synthetic aperture radar (SAR)-based automatic target recognition (ATR), variations in the target-aspect lead to differences in the distribution of target’s scattering points, which will affect the model’s recognition performance, if the training SAR images are incomplete among target-aspect. Traditional deep learning methods for SAR-based recognition often suffer from catastrophic forgetting when online trained on SAR images from different target-aspect domains. To equip SAR-ATR models with the capability of recognizing SAR images online from subsequent target-aspect domains and retaining previously learned knowledge with minimal forgetting, we propose a target-aspect hard attention continual learning (THAT-CL) method, which applies a hard attention mechanism through embedding the indexes of different target-aspect recognition tasks as vectors in each network layer to memorize information of different tasks. By dynamically scaling network weight gradients, we ensure that weights containing more task-specific information undergo smaller updates, while weights with less relevant information experience larger updates. We evaluate THAT-CL using the moving and stationary target acquisition and recognition (MSTAR) dataset. Comparative analysis against other methods demonstrates that the network with THAT-CL achieves higher average accuracy of 93.58% and lower forgetting rate of 3.01%. The results highlight the excellent recognition capability of THAT-CL in generalizing across different SAR image target recognition tasks with varying target-aspects.