Precise Shearer Positioning Technology Based on Interacting Multiple Model With Adaptivity and Robustness

Shearer positioning technology can effectively improve the automation level of a fully mechanized mining face. The strapdown inertial navigation system (SINS) and odometer (OD) integrated navigation system assisted by nonholonomic constraint (NHC) and closed path optimal estimation model (CPOEM) has been widely studied in shearer positioning. Because of the difficulty in accurately measuring and controlling the distance and direction of the hydraulic support, abnormal errors often occur between the predicted position calculated by CPOEM and the actual position of the shearer, and measurement noise cannot be accurately determined, thereby seriously reducing the positioning accuracy of the shearer. To address this issue, an adaptive and robust shearer navigation system based on the interacting multiple model (IMM) algorithm is proposed in this article. First, the maximum correntropy criterion Kalman filter (MCCKF) is used to replace Kalman filter (KF) to reject outliers in predicted positions, and adaptive kernel bandwidth is introduced to MCCKF to further enhance robustness; Second, Sage-Husa adaptive KF (SHAKF) is used to estimate the measurement noise in the predicted position in real time; Finally, MCCKF and SHAKF are used as two subfilters of IMM working in parallel, and the state estimation of each subfilter is fused based on the quality of innovation. Through simulation and experiment, it has been proven that the navigation scheme proposed in this article can integrate the advantages of MCCKF and SHAKF, avoiding the interference of outliers and estimating measurement noise in real time, effectively improving the positioning accuracy of the shearer.