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

• Published in: IEEE sensors journal 2021-09, Vol.21 (18), p.19993-20001
• Main Authors: Gupta, Siddharth, Rai, Prabhat Kumar, Kumar, Abhinav, Yalavarthy, Phaneendra K., Cenkeramaddi, Linga Reddy
• Format: Article
• Language: English
• Subjects: Automobiles; Autonomous systems; Azimuth; azimuth angle; Chirp; Classification; Continuous radiation; Drone aircraft; Drone vehicles; Drones; elevation angle; enhanced field of view; Field of view; FMCW radar; Frequency ranges; Ground stations; Heating systems; heatmap; Image classification; Machine learning; Millimeter waves; mmWave Radar; Radar; Radar antennas; Radar measurement; System effectiveness; Traffic surveillance; Unmanned aerial vehicles; YOLO v3
• ISSN: 1530-437X; 1558-1748
• DOI: 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.