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Advanced Techniques for Robust SAR ATR: Mitigating Noise and Phase Errors

机译：稳健的SAR ATR的先进技术：减轻噪声和相位误差

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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.
机译：我们提出了在噪声和信号相位误差存在的情况下，用于鲁棒的合成孔径雷达（SAR）自动目标识别（ATR）的高级深度学习（DL）技术。我们的研究重点是在噪声和对抗攻击下确保SAR ATR算法的鲁棒性能。基于稳健的基于DL的SAR ATR在诸如救灾，搜索和救援以及对自动驾驶汽车进行高度精确的对象分类等操作场景中至关重要。如本文所述，我们的贡献包括算法开发和先进的深度学习技术（称为对抗训练（AT））的实施，以减轻复杂噪声和相位误差的不利影响。我们的研究表明：1）AT可以在扩展的运行条件下提高性能，在某些情况下，与不带AT的型号相比，其性能提高了10％。 2）当存在正弦或宽带相位噪声时，使用AT可以提高性能，在某些情况下，其精度会提高40％，而在存在噪声的情况下，精度会降低。 3）我们发现模型架构对鲁棒性有重大影响，更复杂的网络显示出AT带来的更大改进。 4）多极化数据的可用性始终是有利的。据我们所知，没有人对对抗机器学习（ML）对SAR图像分类的影响进行广泛的分析。因此，本文是一项全面的研究，揭示了对抗性攻击的影响以及如何缓解它。

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