论文标题
基于雷达的材料分类使用深波散射变换：厘米与毫米波单元的比较
Radar-based Materials Classification Using Deep Wavelet Scattering Transform: A Comparison of Centimeter vs. Millimeter Wave Units
论文作者
论文摘要
近年来，基于雷达的材料的检测受到了对掌握和制造质量保证和控制的对象识别的潜在包含在消费者和工业应用中的潜在关注。在受控设置具有特定材料的特性和形状的受控设置下开发了一些雷达出版物。最近的文献提出了有关基于雷达的材料分类的早期发现，声称由于各种现实世界问题，早期的解决方案不容易扩展到工业应用。关于这些因素对基于雷达的雷达传统特征鲁棒性的影响的已发表实验已经表明，深度神经网络的应用可以在某种程度上减轻产生可行解决方案的影响。然而，先前的研究缺乏对较低频率雷达单元（特别是<10GHz）对较高范围内和高于60GHz的较高范围单位的有用性的研究。这项研究考虑了两个具有不同频率范围的雷达单元：Walabot-3d（6.3-8 GHz）CM-WAVE和ImageVK-74（62-69 GHz）MM-WAVE成像单元通过Vayyar Imaging。对每个单元在材料分类的适用性上进行了比较。这项工作扩展了以前的努力，通过将深小波散射变换应用于基于反射信号的不同材料的识别。在小波散射特征提取器中，数据通过一系列小波变换，非线性和平均以产生反射雷达信号的低变化表示。与材料分类中的雷达单元和算法相比，这项工作是独一无二的，其中包括表现出两个单元表现出强劲性能的实时演示，并提高了CM-WAVE雷达单元提供的鲁棒性。
Radar-based materials detection received significant attention in recent years for its potential inclusion in consumer and industrial applications like object recognition for grasping and manufacturing quality assurance and control. Several radar publications were developed for material classification under controlled settings with specific materials' properties and shapes. Recent literature has challenged the earlier findings on radars-based materials classification claiming that earlier solutions are not easily scaled to industrial applications due to a variety of real-world issues. Published experiments on the impact of these factors on the robustness of the extracted radar-based traditional features have already demonstrated that the application of deep neural networks can mitigate, to some extent, the impact to produce a viable solution. However, previous studies lacked an investigation of the usefulness of lower frequency radar units, specifically <10GHz, against the higher range units around and above 60GHz. This research considers two radar units with different frequency ranges: Walabot-3D (6.3-8 GHz) cm-wave and IMAGEVK-74 (62-69 GHz) mm-wave imaging units by Vayyar Imaging. A comparison is presented on the applicability of each unit for material classification. This work extends upon previous efforts, by applying deep wavelet scattering transform for the identification of different materials based on the reflected signals. In the wavelet scattering feature extractor, data is propagated through a series of wavelet transforms, nonlinearities, and averaging to produce low-variance representations of the reflected radar signals. This work is unique in comparison of the radar units and algorithms in material classification and includes real-time demonstrations that show strong performance by both units, with increased robustness offered by the cm-wave radar unit.
