A Threshold-Constrained Extended Kalman Filter for TDOA-Based UAV Localization

Abstract

Time Difference of Arrival (TDOA) combined with the Extended Kalman Filter (EKF) is a widely used approach for dynamic target localization. However, in practical UAV localization scenarios, the EKF may suffer from divergence caused by nonlinear measurement characteristics, initialization errors, and measurement disturbances. To improve the robustness of EKF-based TDOA localization, this paper proposes a threshold-constrained EKF method, in which the velocity components of the updated state are limited within a physically reasonable range. The proposed strategy suppresses abnormal state updates and improves tracking stability under noisy conditions. Simulation results under straight, square, and circular trajectories, as well as outdoor experiments with real UAV signal data, show that the proposed method achieves better localization accuracy than the conventional TDOA-EKF. In the real-world tests, the mean localization error is reduced by 38.31% and 74.36% for two flight paths, respectively. The proposed method provides a simple and practical way to improve the robustness of TDOA-based UAV localization.