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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2510.02622 |
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| _version_ | 1866915531715510272 |
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| author | Wang, Yuhong Zeng, Yonghong Tan, Peng Hui Sun, Sumei Ma, Yugang |
| author_facet | Wang, Yuhong Zeng, Yonghong Tan, Peng Hui Sun, Sumei Ma, Yugang |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_02622 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Drone Controller Localization Based on TDoA Wang, Yuhong Zeng, Yonghong Tan, Peng Hui Sun, Sumei Ma, Yugang Information Theory 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. |
| title | Drone Controller Localization Based on TDoA |
| topic | Information Theory |
| url | https://arxiv.org/abs/2510.02622 |