方位角
仰角(弹道)
雷达
遥感
计算机科学
连续波雷达
仰角
雷达成像
雷达工程细节
方向(向量空间)
三维雷达
雷达跟踪器
航程(航空)
人工智能
计算机视觉
地质学
工程类
光学
电信
物理
航空航天工程
数学
结构工程
几何学
作者
Sujata Gupta,Prabhat Kumar,Abhinav Kumar,Phaneendra K. Yalavarthy,Linga Reddy Cenkeramaddi
出处
期刊:IEEE Sensors Journal
[Institute of Electrical and Electronics Engineers]
日期:2021-09-15
卷期号:21 (18): 19993-20001
被引量:28
标识
DOI:10.1109/jsen.2021.3092583
摘要
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.
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