UAV Localization with Unreliable Observations in Hostile Underground Environments

The accurate and robust unmanned aerial vehicle (UAV) localization is significant due to safety-critical monitoring and emergency wireless network communication requirements in hostile underground environments. Existing range-based localization approaches fundamentally rely on the assumption that the environment is relatively ideal, which enables a precise range for localization. However, radio propagation in the underground environments may be dramatically influenced by various equipment, obstacles, and ambient noises. In this case, inaccurate range measurements and intermittent ranging failures inevitably occur, which lead to severe localization performance degradation. To address the challenges, a novel UAV localization scheme is proposed in this paper, which can effectively handle unreliable observations in hostile underground environments. We first propose an adaptive extended Kalman filter (EKF) based on the fusion of ultra-wideband (UWB) and inertial measurement unit (IMU) to detect and adjust the inaccurate range measurements. Aiming to deal with intermittent ranging failures, we further design the constraint condition by limiting the system state. Specifically, the auto-regressive model is proposed to implement the localization in the ranging blind areas by reconstructing the lost measurements. Finally, extensive simulations have been conducted to verify the effectiveness. We carry out field experiments in an underground garage and a coal mine based on P440 UWB sensors. Results show that the localization accuracy is improved by 16.9% compared with the recent methods in the hostile underground environments.