RF-based Direction Finding of UAVs Using DNN

This paper presents a sparse denoising autoencoder (SDAE)-based deep neural network (DNN) for the direction finding (DF) of small unmanned aerial vehicles (UAVs). It is motivated by the practical challenges associated with classical DF algorithms such as MUSIC and ESPRIT. The proposed DF scheme is p...

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Bibliographic Details
Published in:2018 IEEE International Conference on Communication Systems (ICCS) pp. 157 - 161
Main Authors: Abeywickrama, Samith, Jayasinghe, Lahiru, Fu, Hua, Nissanka, Subashini, Yuen, Chau
Format: Conference Proceeding
Language:English
Published: IEEE 01.12.2018
Subjects:
Antenna arrays
Antenna measurements
Antenna radiation patterns
Autonomous aerial vehicles
Calibration
direction finding
Directional antennas
Drone surveillance
Drones
Power measurement
Surveillance
Synchronization
UAV tracking
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Summary:This paper presents a sparse denoising autoencoder (SDAE)-based deep neural network (DNN) for the direction finding (DF) of small unmanned aerial vehicles (UAVs). It is motivated by the practical challenges associated with classical DF algorithms such as MUSIC and ESPRIT. The proposed DF scheme is practical and low-complex in the sense that a phase synchronization mechanism, an antenna calibration mechanism, and the analytical model of the antenna radiation pattern are not essential. Also, the proposed DF method can be implemented using a single-channel RF receiver. The paper validates the proposed method experimentally as well.
DOI:10.1109/ICCS.2018.8689177