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Abstract:

Aiming at the problem of difficult vehicle localization in underground coal mines, a real-time NLOS(Non-Line-of-Sight) error suppression positioning algorithm with a tight combination of UWB(Ultra-Wideband) and IMU( Inertial Measurement Unit) is proposed. First, a recursive model of UWB ranging is constructed to dynamically identify the NLOS error by combining the historical filtering information, and a moving average model is adopted to correct the ultra-wideband measurement. Second, a UWB / IMU tight combination model based on the ESKF(Error State Kalman Filter) is designed to adaptively regulate the process noise covariance matrix through the introduction of a forgetting factor enhancing the correction effect of the measurement information on the state estimation. Simulation experiments show that the proposed scheme improves the maximum 3D localization accuracy by 30. 68% and the average accuracy by 28. 61% compared to the scheme in the least squares support vector machine-based correction method. Compared to the scheme in relative to the residual cliscrimination-based method, the 3D localization accuracy is improved by 35. 86% and the average accuracy is improved by 27. 88% . This research provides a high-precision and low-latency solution for vehicle positioning in the underground complex environment, which is of great engineering significance for promoting the construction of intelligent transportation system in coal mines.

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