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.

翻译：本文研究了基于到达时间差（TDoA）的无人机控制器定位算法，并分析了多径信道中的TDoA估计问题。在TDoA估计的基础上，我们提出了两种算法以提升多径环境下的定位精度：最大似然（ML）算法和带高斯-牛顿迭代的最小二乘班克罗夫特（LS-BF-GN）算法。我们在两种典型的室外信道中评估了所提出的算法：无线局域网（WLAN）F信道和双射线地面反射（TRGR）信道。仿真结果表明，在多径信道中，ML算法和LS-BF-GN算法的性能显著优于LS-BF算法。为了进一步提升定位精度，我们提出了对多个初步位置估计进行平均的方法。此外，我们通过仿真评估了传感器间时间同步误差对定位性能的影响。