MIAKF: Motion Inertia Estimated Adaptive Kalman Filtering for Underground Mine Tunnel Positioning
Guan Yuan, Suyang Shi, Gang Shen, Qiang Niu, Zhencai Zhu, Qing‐Guo Wang
Abstract
Coal mine location-based services are vital fundamental facilities for achieving intelligence. Ultra-wide band (UWB) is widely used for underground mine positioning because of its powerful anti-interference capability. However, UWB suffers from range errors caused by first-arrival time (FAT) delay, non-line of sight (NLOS) as well as trilateral positioning deformation in narrow and long tunnels. Inertial Measurement Unit (IMU) sensor can effective solve the interference issue by tracing moving targets without external data, while it also needs precise initial alignment and may cause accumulative error. To efficiently increase positioning accuracy in underground tunnels, Motion Inertia Estimated Adaptive Kalman Filtering (MIAKF) is proposed combining the advantages of IMU and UWB. Firstly, heading memory is designed to store target motion historical state and estimate its forth coming state. Secondly, an dynamically updated inertial confidence parameter pair is introduced as the error weights of two sensors to measure the inertia motion deviation. Then, by converting the vector differences into angles and parameterizing these angles, the bias of each sensor can be measured. Finally, Kalman filtering error matrix is automatically adjusted by parameterized angles to obtain accurate position. Extensive experiments in factory and real scenarios show MIAKF can achieve 19% higher accuracy than benchmark methods.