Micro-Doppler Signature-Based Detection, Classification, and Localization of Small UAV With Long Short-Term Memory Neural Network

Along with the popularization of small unmanned aerial vehicles (UAVs), societal concerns related to security, privacy, and public safety have gained more attention, thus opening a new avenue for small UAV surveillance. However, the conventional radar technologies pose challenges for the surveillanc...

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Bibliographic Details
Published in:IEEE transactions on geoscience and remote sensing 2021-08, Vol.59 (8), p.6285-6300
Main Authors: Sun, Yingxiang, Abeywickrama, Samith, Jayasinghe, Lahiru, Yuen, Chau, Chen, Jiajia, Zhang, Meng
Format: Article
Language:English
Subjects:
Classification
Detection
Doppler sonar
Feature extraction
Flight
Learning algorithms
Localization
Long short-term memory
long short-term memory (LSTM)
Machine learning
micro-Doppler signature (MDS)
Miniature aircraft
Neural networks
Propellers
Public safety
Radar
Radar cross sections
Receivers
Security
Short term
Surveillance
Surveillance radar
unmanned aerial vehicle (UAV)
Unmanned aerial vehicles
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Summary:Along with the popularization of small unmanned aerial vehicles (UAVs), societal concerns related to security, privacy, and public safety have gained more attention, thus opening a new avenue for small UAV surveillance. However, the conventional radar technologies pose challenges for the surveillance of small UAVs due to the high cost, small radar cross section, low flying altitude, and slow flying speed. In this article, we propose a novel micro-Doppler signature-based surveillance method using machine learning techniques, for detection, classification, and localization of small UAVs. Via extensive experiments, we demonstrate the performance gain of our proposed method by applying long short-term memory neural network.
ISSN:0196-2892
1558-0644
DOI:10.1109/TGRS.2020.3028654