Generating Counterfactual Explanations for Misclassification of Automotive Radar Targets

Neeraj Pandey;Devansh Mathur;Debojyoti Sarkar;Shobha Sundar Ram
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Abstract

Prior studies have demonstrated that the inverse synthetic aperture radar (ISAR) images of automotive targets at millimeter-wave (mmW) frequencies provide useful information regarding the target’s shape, size, and trajectory and serve as excellent classification features for deep neural networks. However, the classification performance is limited by environmental conditions, such as multipath, clutter, and occlusion, even when the radar receivers have a high signal-to-noise ratio (SNR). Therefore, for the widespread adoption of deep learning-based ISAR classification in real-world advanced driver assistance systems (ADASs), it is essential to provide a framework for explaining the physics-based phenomena responsible for misclassification and building trust among end users. In this work, we use the deep learning-based generative framework that introduces minimal perturbations on ISAR images belonging to one class to synthesize counterfactual realistic ISAR images that are misclassified as belonging to a second class of automotive vehicles. The networks are specifically trained to emulate occlusions of parts of the target vehicles from the radar. Due to the requirement of controlled experiments for occluding specific parts of the vehicle, simulation radar data are adopted to generate ISAR images. Our results show that the analyses of the counterfactual images generated through this process provide valuable insights into the physics-based causes of misclassification.
汽车雷达目标误分类的反事实解释
先前的研究表明,毫米波(mmW)频率下的汽车目标的逆合成孔径雷达(ISAR)图像提供了有关目标形状、大小和轨迹的有用信息,并可作为深度神经网络的优秀分类特征。然而,即使雷达接收机具有高信噪比(SNR),分类性能也会受到多径、杂波和遮挡等环境条件的限制。因此,为了在现实世界的高级驾驶辅助系统(ADASs)中广泛采用基于深度学习的ISAR分类,有必要提供一个框架来解释导致错误分类的基于物理的现象,并在最终用户之间建立信任。在这项工作中,我们使用基于深度学习的生成框架,该框架在属于一类的ISAR图像上引入最小扰动,以合成被错误分类为属于第二类汽车的反事实逼真ISAR图像。这些网络经过专门训练,可以模拟雷达上目标车辆部分的遮挡。由于对车辆特定部位遮挡的控制实验要求,采用模拟雷达数据生成ISAR图像。我们的研究结果表明,通过这一过程产生的反事实图像的分析为错误分类的物理原因提供了有价值的见解。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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