The shearer positioning method based on inertial navigation has inherent defects such as error accumulation, attitude angle and position drift. Although the introduction of error compensation technology and multi-sensor combination positioning technology can reduce the error to a certain extent, the effect is limited. In order to solve the above problems, a dual inertial navigation shearer positioning method based on adaptive Kalman filtering is proposed. The acceleration and attitude angle of the 2 inertial navigation systems installed on shearer are collected synchronously in real time, the position of the inertial navigation system is used as the state quantity, and the distance and angle between the inertial navigation systems are used as observed quantities. The dual inertial navigation positioning model is established to overcome the shortcomings of accumulated single inertial navigation positioning errors. However, the large difference in the output of the dual inertial navigation system can lead to sudden changes in the state of the dual inertial navigation positioning model and reduce the accuracy of the positioning model. Therefore, the adaptive Kalman filtering algorithm is used to evaluate whether the dual inertial navigation positioning model has sudden changes in the state by calculating the residual-based chi-square test value. And the covariance matrix of the process noise is dynamically adjusted by using a three-segment fuzzy discriminant function to reduce the impact of sudden changes in the state on the positioning accuracy. The simulation and experimental results show that the adaptive Kalman filter has stronger anti-interference ability than the extended Kalman filter, and reduces the estimation error effectively when the state changes suddenly. The positioning error of the dual inertial guidance shearer positioning method based on adaptive Kalman filter is reduced in all directions than that of the single inertial guidance shearer positioning method.
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