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

Gupta, Siddharth and Rai, Prabhat Kumar and Kumar, Abhinav and et al, . (2021) Target Classification by mmWave FMCW Radars Using Machine Learning on Range-Angle Images. IEEE Sensors Journal, 21 (18). pp. 19993-20001. ISSN 1530-437X

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Abstract

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.

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IITH Creators:

IITH Creators	ORCiD
Kumar, Abhinav	https://orcid.org/0000-0002-5880-4023

Item Type:

Article

Additional Information:

Manuscript received May 4, 2021; revised June 10, 2021; accepted June 11, 2021. Date of publication June 25, 2021; date of current version September 15, 2021. This work was supported in part by the Indo-Norwegian Collaboration in Autonomous Cyber-Physical Systems (INCAPS) Project of the International Partnerships for Excellent Education, Research and Innovation (INTPART) Program under Grant 287918, in part by the Low-Altitude UAV Communication and Tracking (LUCAT) Project of the Program on ICT and Digital Innovation (IKTPLUSS) Program from the Research Council of Norway under Grant 280835, and in part by the Department of Science and Technology (DST), Government of India under Grant INT/NOR/RCN/ICT/P-01/2018. The associate editor coordinating the review of this article and approving it for publication was Prof. Piotr J. Samczynski. (Corresponding author: Linga Reddy Cenkeramaddi.) Siddharth Gupta and Prabhat Kumar Rai are with the Department of Information and Communication Technology, University of Agder, 4630 Kristiansand, Norway, and also with the Indian Institute of Technology, Hyderabad, Telangana 502285, India (e-mail: ee18mtech01003@iith.ac.in; ee18mtech01005@iith.ac.in).

Uncontrolled Keywords:

Autonomous systems; azimuth angle; elevation angle; enhanced field of view; FMCW radar; heatmap; machine learning; mmWave Radar; YOLO v3

Subjects:

Electrical Engineering

Divisions:

Department of Electrical Engineering

Depositing User:

. LibTrainee 2021

Date Deposited:

03 Oct 2022 14:44

Last Modified:

03 Oct 2022 14:44