TDOA based localization mechanism for the UAV positioning in dark and confined environments

With the growing demand for autonomous inspection with Unmanned Aerial Vehicles (UAVs) in dark and confined environments, accurately determining UAV position has become crucial. The Ultra-Wideband (UWB) localization technology offers a promising solution by overcoming challenges posed by signal obstruction, low illumination condition, and confined spaces. However, conventional UWB-based positioning suffers from performance oscillations due to measurement inconsistencies and degradations with time-varying noise models. Furthermore, the widely used Two-Way Time-of-Flight (TW-TOF) method has limitations, such as high energy consumption and a restricted number of tags to be deployed. To address these, a sensor fusion approach combining UWB and Inertial Measurement Unit (IMU) measurements with Time Difference of Arrival (TDOA) localization mechanism is proposed. This method exploits an adaptive Kalman filter, which dynamically adjusts to noise model variations and employs individual weighting factors for each anchor node, enhancing stability and robustness in challenging environments. The comprehensive experiments demonstrate the proposed algorithm achieves a median positioning error of 0.110 m, a 90th percentile error of 0.232 m, and an average standard deviation of 0.075 m with the significantly reduced energy consumption. Additionally, due to TDOA communication principles, this method supports multiple tag nodes, making it ideal for multi-UAV collaborative inspections in future applications.