The accurate and robust unmanned aerial vehicle (UAV) localization is significant due to the requirements of safety-critical monitoring and emergency wireless communication 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 equipments, obstacles, and ambient noises. In this case, inaccurate range measurements and intermittent ranging failures inevitably occur, which leads 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.

The state estimation plays an irreplaceable role in many real applications since it lays the foundation for decision-making and control. This paper studies the multi-sensor estimation problem for a contention-based unreliable wireless network. At each time step, no more than one sensor can communicate with the base station due to the potential contention and collision. In addition, data packets may be lost during transmission since wireless channels are unreliable. A novel packet arrival model is proposed which simultaneously takes into account the above two issues. Two scenarios of wireless sensor networks (WSNs) are considered: the sensors transmit the raw measurements directly and the sensors send the local estimation instead. Based on the obtained packet arrival model, necessary and sufficient stability conditions of the estimation at the base station side are provided for both network scenarios. In particular, all offered stability conditions are expressed by simple inequalities in terms of the packet arrival rates and the spectral radius of the system matrix. Their relationships with existing related results are also discussed. Finally, the proposed results are demonstrated by simulation examples and an environment monitoring prototype system.