Back to Search Start Over

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

Authors :
Phaneendra K. Yalavarthy
Rajesh Reddy Yakkati
Henning Idsoe
Linga Reddy Cenkeramaddi
Prabhat Kumar Rai
Mohammed Zafar Ali Khan
Abhinav Kumar
Source :
IEEE Sensors Journal. 21:16043-16053
Publication Year :
2021
Publisher :
Institute of Electrical and Electronics Engineers (IEEE), 2021.

Abstract

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.

Details

ISSN :
23799153 and 1530437X
Volume :
21
Database :
OpenAIRE
Journal :
IEEE Sensors Journal
Accession number :
edsair.doi...........27c569cb9596a7089f7fefa62771270a
Full Text :
https://doi.org/10.1109/jsen.2021.3075909