Non-Line-of-Sight Error Compensation Method for Ultra-Wideband Positioning System

Existing Ultra-Wideband (UWB) positioning methods are poorly suited for underground mobile devices and have limited positioning effectiveness in complex scenarios such as narrow tunnels, high dust levels, metallic structures, moving personnel, and machinery. To address this, we propose a UWB positioning method for non-line-of-sight (NLOS) error compensation, significantly improving the positioning accuracy of mobile equipment in coal mine tunnels. First, the characteristics of the impulse response waveform channel of the dataset are extracted, and the AdaBoost-based ensemble learning method is used to identify the mixture propagation channel. Then, combined with the UWB range noise model, the extended Kalman filter (EKF) algorithm is used to compensate for UWB NLOS errors. Finally, a mobile tag is used in conjunction with four positioning base stations to obtain positioning data, and the positioning effect in coal mine tunnels is simulated using a ranging noise model. The experimental results show that the EKF error compensation algorithm has good positioning accuracy and algorithm stability in different motion states in a noisy environment.