Advanced techniques for robust SAR ATR: Mitigating noise and phase errors
We present advanced Deep Learning (DL) techniques for robust Synthetic Aperture Radar (SAR) automatic target recognition (ATR) in the presence of noise and signal phase errors. Our research focuses on ensuring robust performance of SAR ATR algorithms under noise and adversarial attacks. Robust DL-based SAR ATR is paramount in operational scenarios such as disaster relief, search and rescue, and highly accurate object classification for autonomous vehicles. Our contributions, as described in this paper, include algorithm development and implementation of an advanced deep learning technique known as adversarial training (AT) to mitigate the detrimental effects of sophisticated noise and phase errors. Our research demonstrated that 1) AT improves performance under extended operating conditions, in some cases improving up to 10% over models without AT. 2) The use of AT improves performance when sinusoidal or wideband phase noise is present, in some cases gaining 40% in accuracy that would be lost in the presence of noise. 3) We find the model architecture has significant impact on robustness, with more complex networks showing a greater improvement from AT. 4) The availability of multi-polarization data is always advantageous. To our knowledge no one has provided an extensive analysis of the impact of adversarial machine learning (ML) on SAR image classification. Thus, this paper serves as a comprehensive research revealing the impact of adversarial attack and how to mitigate it.
