Drone localization based on 3D-AoA signal measurements

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.

Experimental Evaluation of an SDR-Based UAV Localization System

“…In [14], the authors present a 3D drone location estimation method using a 4-by-4 rectangular array and MUSIC algorithm. After the estimation of the AoA, an extended Kalman filter (EKF) is applied to improve the accuracy and to track the drone.…”

Impact of carrier frequency offset and phase noise on the steering vector for 3D drone localization based on angle of arrival (AOA)

“…In our previous work [10], the localization algorithm was tested for linearly moving drone. In this paper, we test the algorithm to cope with rapid changes in drone direction by maneuvering in a spiral trajectory, as shown in Fig.…”

“…2 illustrates the geometry of the utilized 4 × 4 URA positioned on the y − z plane, where the spacing between the y and z elements is represented by d y and d z , respectively. We selected to use this array in our simulations as it coincides with the array we used in measurements in [10], [11].…”

3D Radio Map-Based GPS Spoofing Detection and Mitigation for Cellular-Connected UAVs

“…[14]- [19]) and mobile cellular network positioning (e.g. [20]- [23]). The GPS navigation signal analysis methods use GPS signals features for spoofing detection, such as GPS satellites' signals fingerprints, the Direction of Arrival (DOA), or the Time of Arrival (TOA).…”

“…For that reason, Dang et al in [21] used deep ensemble learning on edge servers to detect GPS spoofing with only a single base station. Simultaneously, Meles et al did measurements in [22] and [23] that proved the 3D Angle of Arrival (AOA) of cellular signals can assist UAV self-localization and help UAV to detect and mitigate GPS spoofing attacks. Although the above mobile cellular network positioning methods demonstrate effectiveness in detecting GPS spoofing, those methods cannot be implemented on cellular-connected UAVs in the urban canyon because of the dense and irregular buildings with complex electromagnetic propagation environments.…”