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Target Classification by mmWave FMCW Radars Using Machine Learning on Range-Angle Images

Gupta, S and Rai, PK and Kumar, A and Yalavarthy, PK and Cenkeramaddi, LR (2021) Target Classification by mmWave FMCW Radars Using Machine Learning on Range-Angle Images. In: IEEE Sensors Journal, 21 (18). pp. 19993-20001.

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Official URL: https://doi.org/10.1109/JSEN.2021.3092583


In this paper, we present a novel multiclass-target classification method for mmWave frequency modulated continuous wave (FMCW) radar operating in the frequency range of 77 - 81 GHz, based on custom range-angle heatmaps and machine learning tools. The elevation field of view (FoV) is increased by orienting the Radar antennas in elevation. In this orientation, the radar focuses the beam in elevation to improve the elevation FoV. The azimuth FoV is improved by mechanically rotating the Radar horizontally, which has antenna elements oriented in the elevation direction. The data from the Radar measurements obtained by mechanical rotation of the Radar in Azimuth are used to generate a range-angle heatmap. The measurements are taken in a variety of real-world scenarios with various objects such as humans, a car, and an unmanned aerial vehicle (UAV), also known as a drone. The proposed technique achieves accuracy of 97.6 and 99.6 for classifying the UAV and humans, respectively, and accuracy of 98.1 for classifying the car from the range-angle FoV heatmap. Such a Radar classification technique will be extremely useful for a wide range of applications in cost-effective and dependable autonomous systems, including ground station traffic monitoring and surveillance, as well as control systems for both on-ground and aerial vehicles. © 2001-2012 IEEE.

Item Type: Journal Article
Publication: IEEE Sensors Journal
Publisher: Institute of Electrical and Electronics Engineers Inc.
Additional Information: The copyright for this article belongs to Institute of Electrical and Electronics Engineers Inc.
Keywords: Cost effectiveness; Frequency modulation; Image classification; Machine learning; Millimeter waves; Radar; Radar antennas; Radar imaging; Unmanned aerial vehicles (UAV), Autonomous systems; Classification technique; Frequency ranges; Mechanical rotation; Mm-wave frequencies; Real-world scenario; Target Classification; Traffic monitoring, Radar measurement
Department/Centre: Division of Interdisciplinary Sciences > Computational and Data Sciences
Date Deposited: 22 Feb 2023 04:09
Last Modified: 22 Feb 2023 04:09
URI: https://eprints.iisc.ac.in/id/eprint/80467

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