Wang, Y., Zeng, Y., Tan, P. H., Sun, S., & Ma, Y. (2025). Drone Controller Localization Based on TDoA. 2025 IEEE Wireless Communications and Networking Conference (WCNC), 1–6. https://doi.org/10.1109/wcnc61545.2025.10978550
Abstract:
This paper studies time difference of arrival (TDoA)-based algorithms for drone controller localization and analyzes TDoA estimation in multipath channels. Building on TDoA estimation, we propose two algorithms to enhance localization accuracy in multipath environments: the Maximum Likelihood (ML) algorithm, and the Least Squares Bancroft with GaussNewton (LS-BF-GN) algorithm. We evaluate these proposed algorithms in two typical outdoor channels: Wireless Local Area Network (WLAN) Channel F and the two-ray ground reflection (TRGR) channel. Our simulation results demonstrate that the ML and LS-BF-GN algorithms significantly outperform the LSBF algorithm in multipath channels. To further enhance localization accuracy, we propose averaging multiple tentative location estimations. Additionally, we evaluate the impact of time
synchronization errors among sensors on localization performance through simulation.
License type:
Publisher Copyright
Funding Info:
This research / project is supported by the National Research Foundation, Singapore and Infocomm Media Development Authority - Future Communications Research & Development Programme
Grant Reference no. : FCP-NUS-RG-2022- 018