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Machine-Learning Methods for Material Identification Using mmWave Radar Sensor
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AI
DOI：10.1109/JSEN.2022.3227207.png)
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Abstract
En 中文
In recent years, radar sensors are gaining a paramount role in noninvasive inspection of different objects and materials. In this article, we present a framework for using machine learning in material identification based on their reflected radar signature. We employ multiple receiving (RX) channels of the radar module to capture the signatures of the reflected signal from different target materials. Within the proposed framework, we present three approaches suitable for material classification, namely: 1) convolutional neural networks (CNNs); 2) k-nearest neighbor (k-NN); and 3) dynamic time warping (DTW). The proposed framework is tested using extensive experimentation and found to provide near-ideal classification accuracy in classifying six distinct material types. Furthermore, we explore the possibility of utilizing the framework to detect the volume of the identified material, where the obtained classification accuracy is above 98% in distinguishing three different volume levels.
Keywords:
Radar
Sensors
Feature extraction
Radar antennas
Receiving antennas
Transmitting antennas
Reflector antennas
Convolutional neural networks (CNNs)
dynamic time warping (DTW)
frequency-modulated continuous-wave (FMCW)
k-nearest neighbor (k-NN)
machine learning
material identification
millimeter wave (mmWave) sensors
Journal
IF:
4.5
Papers:
2.2W
Citations:
7.3W
Organization
Cited Papers
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