Drone Controller Localization Based on TDoA

2025-10-02

AI Summary

To address degraded Time Difference of Arrival (TDoA) localization accuracy in multipath channels, this paper proposes two novel UAV controller localization algorithms: ML-BF-GN—integrating Maximum Likelihood estimation, Bancroft-based initial positioning, and Gauss–Newton optimization—and LS-BF-GN—combining Least Squares estimation, Bancroft initialization, and Gauss–Newton refinement. Additionally, a multi-estimate averaging strategy is introduced to mitigate the impact of clock synchronization errors. Simulation results under WLAN Channel F and a two-ray ground-reflection multipath channel demonstrate that both algorithms significantly outperform the conventional LS-BF method. In representative multipath scenarios, the proposed approaches reduce localization error by 35%–52%. This improvement enhances both robustness and accuracy of controller localization in complex wireless environments, particularly under severe multipath interference.

Technology Category

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Abstract

We study time difference of arrival (TDoA)-based algorithms for drone controller localization and analyze 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 Gauss-Newton (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 LS-BF 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.

Problem

• Enhancing drone controller localization accuracy in multipath channels
• Proposing ML and LS-BF-GN algorithms to improve TDoA estimation
• Evaluating sensor time synchronization errors on localization performance

Innovation

• Maximum Likelihood algorithm enhances multipath localization
• Least Squares Bancroft with Gauss-Newton improves accuracy
• Averaging multiple estimations further reduces localization errors