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UWB/IMU Fusion Localization in Coal Mine Faces Using Improved Particle Filter
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Abstract
Positioning systems in underground coal mines are challenged by severe multi-path propagation and electromagnetic interference, resulting in non-line-of-sight (NLOS) ranging errors and inertial drift. This study proposes a breakthrough cooperative Ultra-Wideband (UWB)-Inertial Measurement Unit (IMU) positioning framework integrating an enhanced Particle Filter (PF) architecture, aimed at delivering a high-precision and robust solution for personnel positioning in high-risk underground coal mining areas. First, a hybrid error compensation model integrating Taylor Series Expansion (TSE) and Sage-Husa Unscented Kalman Filter (SH-UKF) is constructed. The TSE method linearizes NLOS errors in UWB ranging values through polynomial approximation, while SH-UKF dynamically estimates residual error coefficients to achieve nonlinear correction of compensation quantities. Subsequently, a Hunter-Prey Optimization-based Adaptive Weighted Particle Filter (HPO-AWPF) is proposed. This algorithm enhances the global search capability of HPO by introducing adaptive weight factors and designing a Mahalanobis Distance-based adaptive weight allocation strategy to optimize the probability density matching of UWB/IMU observation data. The experimental results demonstrate that the proposed algorithm can alleviate the influence of nonlinear observation on the distribution of particles, effectively solve the problem of particle degradation, and has better robustness and positioning accuracy compared with existing algorithms.
Keywords
Ultra-Wideband; Inertial Navigation; Hunter Prey Optimization; Adaptive Particle Filter; Multi-Sensor Fusion Localization
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How to cite this paper
UWB/IMU Fusion Localization in Coal Mine Faces Using Improved Particle Filter
How to cite this paper: Junyan Qi, Yuxue Sun, Lei Wang, Zhuli Ren. (2026) UWB/IMU Fusion Localization in Coal Mine Faces Using Improved Particle Filter. Advances in Computer and Communication, 7(1), 22-37.
DOI: http://dx.doi.org/10.26855/acc.2026.03.004
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