Paper Info
Title
Object Classification Technique for mmWave FMCW Radars using Range-FFT Features
Abstract
In this article, we present a novel target classification technique by mmWave frequency modulated continuous wave (FMCW) Radars using the Machine Learning on raw data features obtained from range fast Fourier transform (FFT) plot. FFT plots are extracted from the measured raw data obtained with a Radar operating in the frequency range of 77- 81 GHz. The features such as peak, width, area, standard deviation, and range on range FFT plot peaks are extracted and fed to a machine learning model. Two light weight classification models such as Logistic Regression, Naive Bayes are explored to assess the performance. Based on the results, we demonstrate and achieve an accuracy of 86.9% using Logistic Regression. The proposed technique will be highly useful for several applications in cost-effective and reliable ground station traffic management systems for autonomous systems. The end-to-end framework presented here, expands the capabilities of mmWave Radar beyond range detection to classification. The implications of this added functionalities will facilitate utilization of mmWave Radars in computer vision, object recognition, and towards fully autonomous traffic control and management systems.
Year
DOI
Venue
2021
10.1109/COMSNETS51098.2021.9352894
2021 International Conference on COMmunication Systems & NETworkS (COMSNETS)
Keywords
DocType
ISSN
Autonomous systems,FMCW Radar,Machine learning,mmWave Radar,range FFT,Target classification
Conference
2155-2487
ISBN
Citations 
PageRank 
978-1-7281-9128-7
1
0.34
References 
Authors
0
11
Name
Order
Citations
PageRank
Jyoti Bhatia110.34
Aveen Dayal280.93
Ajit Jha310.68
Santosh K. Vishvakarma410.34
Soumya J.510.68
M. B. Srinivas610.34
Phaneendra K. Yalavarthy710.34
Kumar Abhinav86120.99
V. Lalitha910.34
Sagar Koorapati1010.34
Linga Reddy Cenkeramaddi11107.49