Machine-Learning Methods for Material Identification Using mmWave Radar Sensor

• Published in: IEEE sensors journal Vol. 23; no. 2; pp. 1471 - 1478
• Main Authors: Skaria, Sruthy, Hendy, Nermine, Al-Hourani, Akram
• Format: Journal Article
• Language: English
• Published: New York IEEE 15.01.2023; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Artificial neural networks; Classification; Convolutional neural networks (CNNs); dynamic time warping (DTW); Experimentation; Feature extraction; frequency-modulated continuous-wave (FMCW); Inspection; k-nearest neighbor (k-NN); Machine learning; material identification; millimeter wave (mmWave) sensors; Millimeter waves; Neural networks; Radar; Radar antennas; Radar signatures; Receiving antennas; Reflector antennas; Sensors; Transmitting antennas
• ISSN: 1530-437X; 1558-1748
• DOI: 10.1109/JSEN.2022.3227207

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) <inline-formula> <tex-math notation="LaTeX">{k} </tex-math></inline-formula>-nearest neighbor (<inline-formula> <tex-math notation="LaTeX">{k} </tex-math></inline-formula>-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.