Work

RF-Based Direction Finding of UAVs Using DNN

Year: 2017

Type: preprint

Abstract: 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.

Source: arXiv (Cornell University)

DOI: 10.48550/arxiv.1712.01154

Authors: Abeywickrama, Samith, Jayasinghe, Lahiru, Fu, Hua, Nissanka, Subashini, Yuen, Chau

Institutions: Singapore University of Technology and Design, National University of Singapore

Language: English

Topic: Direction-of-Arrival Estimation Techniques

Subfield: Signal Processing

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

OpenAlex ID: W2951463753

Cites: 8

Cited by: 2

Related to: 20