MIAKF: Motion Inertia Estimated Adaptive Kalman Filtering for Underground Mine Tunnel Positioning.

Coal mine location-based services are vital fundamental facilities for achieving intelligence. Ultra-wideband (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, nonline of sight (NLOS) as well as trilateral positioning deformation in narrow and long tunnels. An inertial measurement unit (IMU) sensor can effectively solve the interference issue by tracing moving targets without external data, while it also needs precise initial alignment and may cause an 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. First, heading memory is designed to store the target motion historical state and estimate its forthcoming state. Second, 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, the Kalman filtering (KF) error matrix is automatically adjusted by parameterized angles to obtain an accurate position. Extensive experiments in a factory and real scenarios show MIAKF can achieve 19% higher accuracy than benchmark methods.