Localization and Activity Classification of Unmanned Aerial Vehicle using mmWave FMCW Radars

Rai, Prabhat Kumar and Idsoe, Henning and Yakkati, Rajesh Reddy and Kumar, Abhinav and Khan, Mohammed Zafar Ali and Yalavarthy, Phaneendra K. and Cenkeramaddi, Linga Reddy (2021) Localization and Activity Classification of Unmanned Aerial Vehicle using mmWave FMCW Radars. IEEE Sensors Journal. p. 1. ISSN 1530-437X

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In this article, we present a novel localization and activity classification method for aerial vehicle using mmWave frequency modulated continuous wave (FMCW) Radar. The localization and activity classification for aerial vehicle enables the utilization of mmWave Radars in security surveillance and privacy monitoring applications. In the proposed method, Radar's antennas are oriented vertically to measure the elevation angle of arrival of the aerial vehicle from ground station. The height of the aerial vehicle and horizontal distance of the aerial vehicle from Radar station on ground are estimated using the measured radial range and the elevation angle of arrival. The aerial vehicle's activity is classified using machine learning methods on micro-Doppler signatures extracted from Radar measurements taken in an outdoor environment. To evaluate performance, various light weight classification models such as logistic regression, support vector machine (SVM), Light gradient boosting machine (GBM), and a custom lightweight convolutional neural network (CNN) are investigated. Based on the results, the logistic regression, SVM, and Light GBM achieve an accuracy of 93%. Furthermore, the custom lightweight CNN can achieve activity classification accuracy of 95%. The performance of the proposed lightweight CNN is also compared with the pre-trained models (VGG16, VGG19, ResNet50, ResNet101, and InceptionResNet). The proposed lightweight CNN suits best for embedded and/or edge computing devices.

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IITH Creators:
IITH CreatorsORCiD
Rai, Prabhat KumarUNSPECIFIED
Yakkati, Rajesh ReddyUNSPECIFIED
Khan, Mohammed Zafar AliUNSPECIFIED
Item Type: Article
Uncontrolled Keywords: Aerial vehicles angle; aerial vehicles height; angle estimation; angle of arrival (AoA); convolutional neural network (CNN) classifier; frequency modulated continuous wave (FMCW) radar; ground station radar; height estimation; mmWave radar; range estimation Indexed keywords
Subjects: Electrical Engineering
Divisions: Department of Electrical Engineering
Depositing User: . LibTrainee 2021
Date Deposited: 07 Aug 2021 09:04
Last Modified: 07 Aug 2021 09:04
URI: http://raiith.iith.ac.in/id/eprint/8724
Publisher URL: http://doi.org/10.1109/JSEN.2021.3075909
OA policy: https://v2.sherpa.ac.uk/id/publication/3570
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