Ahmed, Mostafa Mohamed and Shawky, Mahmoud A and Zahran, Shady and Moussa, Adel and EL-Shimy, Naser and Elmahallawy, Adham A and Ansari, Shuja and Shah, Syed Tariq and Abdellatif, Ahmed Gamal (2024) An experimental analysis of outdoor UAV localisation through diverse estimators and crowd-sensed data fusion. Physical Communication, 66. p. 102475. DOI https://doi.org/10.1016/j.phycom.2024.102475
Ahmed, Mostafa Mohamed and Shawky, Mahmoud A and Zahran, Shady and Moussa, Adel and EL-Shimy, Naser and Elmahallawy, Adham A and Ansari, Shuja and Shah, Syed Tariq and Abdellatif, Ahmed Gamal (2024) An experimental analysis of outdoor UAV localisation through diverse estimators and crowd-sensed data fusion. Physical Communication, 66. p. 102475. DOI https://doi.org/10.1016/j.phycom.2024.102475
Ahmed, Mostafa Mohamed and Shawky, Mahmoud A and Zahran, Shady and Moussa, Adel and EL-Shimy, Naser and Elmahallawy, Adham A and Ansari, Shuja and Shah, Syed Tariq and Abdellatif, Ahmed Gamal (2024) An experimental analysis of outdoor UAV localisation through diverse estimators and crowd-sensed data fusion. Physical Communication, 66. p. 102475. DOI https://doi.org/10.1016/j.phycom.2024.102475
Abstract
Motivated by the challenge of achieving precise 3D outdoor localisation for unmanned aerial vehicles (UAVs) in global navigation satellite system (GNSS)-denied environments, this paper introduces an innovative technique. Integrating crowd-sensed data fusion to counter inertial navigation system (INS) drift during GNSS signal outages, the proposed method exploits diverse estimators to enhance its efficacy. A micro lightweight frequency modulated continuous wave (FMCW) radar mounted on the UAV captures ground scatterer reflections, processed via fast Fourier transform (FFT) to generate a range-Doppler map. This map facilitates forward velocity estimation during GNSS signal loss. This approach employs adaptive thresholding, image binarisation, and connected components-based techniques for target detection from a computer vision standpoint. The derived radar-based velocity fuses with magnetometer, barometer, and inertial measurement unit (IMU) data using diverse estimators like extended Kalman filter (EKF) and particle filter (PF). Real-time flight data evaluation and simulated outage periods using EKF and PF validate the outdoor localisation system. Experimental analyses demonstrate substantial improvements, enhancing 3D positioning accuracy by 99.89% and 99.83% for the initial and subsequent flights, respectively, leveraging PF to fortify the standalone INS mode during GNSS signal loss. This approach significantly enhances UAV localisation precision, particularly in challenging GNSS-denied scenarios, showcasing the potential for real-world applications.
Item Type: | Article |
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Uncontrolled Keywords: | Extended Kalman filter; Global navigation satellite system; Inertial navigation system; Modulated continuous wave; Particle filter; Unmanned aerial vehicles |
Divisions: | Faculty of Science and Health > Computer Science and Electronic Engineering, School of |
SWORD Depositor: | Unnamed user with email elements@essex.ac.uk |
Depositing User: | Unnamed user with email elements@essex.ac.uk |
Date Deposited: | 12 Sep 2024 16:15 |
Last Modified: | 12 Sep 2024 16:15 |
URI: | http://repository.essex.ac.uk/id/eprint/39016 |
Available files
Filename: Accepted_Manuscript.pdf
Licence: Creative Commons: Attribution 4.0
Embargo Date: 21 August 2025