Drone localization based on 3D-AoA signal measurements

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openAccess
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Journal Title

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Volume Title

A4 Artikkeli konferenssijulkaisussa

Date

2022

Major/Subject

Mcode

Degree programme

Language

en

Pages

5

Series

2022 IEEE 95th Vehicular Technology Conference - Spring, VTC 2022-Spring - Proceedings, IEEE Vehicular Technology Conference

Abstract

This paper presents a method for three dimension (3D) drone location estimation based on measured signals transmitted from a flying drone. During the experiment, we considered a single antenna mounted on the drone for signal transmission and a 4-by-4 rectangular array positioned at a known stationary location for receiving the incoming signal. Once the signal strength from the source is measured, the 3D position of the drone is estimated using the MUltiple SIgnal Classification (MUSIC) algorithm. The estimated values are then fed into an Extended Kalman Filter (EKF) as a measurement model, and the movement of the drone is formulated in a 3D trajectory plane. In order to evaluate the performance of our approach, we considered a histogram distribution and probability density function (pdf) of the drone position estimation error during its trajectory. The estimated 3D position of the drone is compared with the GPS values, and we ensure that the drone is localized based on the received signal from the experimental setup by first estimating the direction of the signal using MUSIC, and then tracking it using EKF in the predefined drone trajectory area.

Description

Publisher Copyright: © 2022 IEEE.

Keywords

drone, EKF, GPS, measurement, MUSIC

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Citation

Meles, M, Mela, L, Rajasekaran, A, Ruttik, K & Jantti, R 2022, Drone localization based on 3D-AoA signal measurements . in 2022 IEEE 95th Vehicular Technology Conference - Spring, VTC 2022-Spring - Proceedings . IEEE Vehicular Technology Conference, IEEE, IEEE Vehicular Technology Conference, Helsinki, Finland, 19/06/2022 . https://doi.org/10.1109/VTC2022-Spring54318.2022.9860965