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Classification of targets using statistical features from range fft of mmwave fmcw radars

Bhatia, J and Dayal, A and Jha, A and Vishvakarma, SK and Joshi, S and Srinivas, MB and Yalavarthy, PK and Kumar, A and Vadlamani, L and Koorapati, S and Cenkeramaddi, LR (2021) Classification of targets using statistical features from range fft of mmwave fmcw radars. In: Electronics (Switzerland), 10 (16).

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Official URL: https://doi.org/10.3390/electronics10161965


Radars with mmWave frequency modulated continuous wave (FMCW) technology accurately estimate the range and velocity of targets in their field of view (FoV). The targeted angle of arrival (AoA) estimation can be improved by increasing receiving antennas or by using multiple-input multiple-output (MIMO). However, obtaining target features such as target type remains challenging. In this paper, we present a novel target classification method based on machine learning and features extracted from a range fast Fourier transform (FFT) profile by using mmWave FMCW Radars operating in the frequency range of 77�81 GHz. The measurements are carried out in a variety of realistic situations, including pedestrian, automotive, and unmanned aerial vehicle (UAV) (also known as drone). Peak, width, area, variance, and range are collected from range FFT profile peaks and fed into a machine learning model. In order to evaluate the performance, various light weight classification machine learning models such as logistic regression, Naive Bayes, support vector machine (SVM), and lightweight gradient boosting machine (GBM) are used. We demonstrate our findings by using outdoor measurements and achieve a classification accuracy of 95.6 by using LightGBM. The proposed method will be extremely useful in a wide range of applications, including cost-effective and dependable ground station traffic management and control systems for autonomous operations, and advanced driver-assistance systems (ADAS). The presented classification technique extends the potential of mmWave FMCW Radar beyond the detection of range, velocity, and AoA to classification. mmWave FMCW Radars will be more robust in computer vision, visual perception, and fully autonomous ground control and traffic management cyber-physical systems as a result of the added new feature. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.

Item Type: Journal Article
Publication: Electronics (Switzerland)
Publisher: MDPI AG
Additional Information: The copyright for this article belongs to Authors
Department/Centre: Division of Interdisciplinary Sciences > Computational and Data Sciences
Date Deposited: 18 Oct 2021 10:01
Last Modified: 18 Oct 2021 10:01
URI: http://eprints.iisc.ac.in/id/eprint/69817

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