## 2 State of the Art  
Due to the harsh, complex, and closed underground working environment of the shearer in the coal mines, the conventional ground positioning methods such as radio navigation, satellite positioning, and astronomical navigation cannot function underground, while traditional shearer position and attitude monitoring methods such as the active infrared intrusion detector method can only measure the intermittent position instead of the dynamic position of the shearer, with complicated installation and relatively evident signal loss [13]. The gear counting method can only calculate the walking distance of the shearer, with a cumulative error existing [14]. The ultrasonic reflection method is difficult for signal acquisition, with relatively poor system scalability [15]. The wireless sensor network method is subject to a significant disturbance in the harsh environment of the coal mines, with such problems as unstable positioning data [16]. At the same time, the above methods cannot measure the shearer attitude and cannot meet the accuracy requirements of intelligent mining [17].  
At the beginning of the 21 st century, CSIRO started research on the automation and intelligence of the fully mechanized mining face, and designed and developed the LASC (Longwall Automation Steering Committee) system which was successfully applied to the Beltana coal mine in Australia by using military grade high precision fiber optic gyroscopes and navigation algorithms to realize the positioning of shearer and the automatic straightening of the scraper conveyor. In 2008, the LASC system was upgraded in technology. Combining the detailed geological data and the 3D model of the mining space, the position monitoring of the working face production equipment, the memory cutting of the shearer, and the 3D visualization of the mining space were realized [18]. JOY has developed Faceboss and advanced shearer automation (ASA) for intelligent shearers, which can realize automatic traction, horizontal control, Golp offline graphics editing, automatic triangular coal mining and automatic integration of working faces, and install traction encoder, inclination sensor, D gear sensor, fuselage pitch/tilt sensor, etc. on the shearer (the pitch/tilt sensor is used as a backup supplement of INS) through these sensors monitoring the attitude, position, and mining height [19]. Eickhoff sbaarer automation technology has mainly gone through four stages: memory cutting, EiControl, EiControlSB, EiControlPlus. The shearer supplied with the EiControlPlus system is equipped with vibration sensors,position sensors, travel sensors, infrared sensors, inclination sensors and radar sensors, etc. A variety of sensors can realize real-time monitoring of the position and attitude of the shearer on the basis of redundancy [20]. At present, the SINS is still lacking in adaptability, error modeling and compensation under temperature and vibration disturbances [21].  
For the purpose of achieving accurate shearer attitude sensing, this paper, on the basis of the research on intelligent mining technology, deeply studies SINS error compensation using the shearer attitude sensing method based on SINS to improve the shearer attitude measurement and sensing accuracy in an underground coal mine. The results of this paper will provide research foundation and technical reference for the prediction of production state and the intelligent control of shearer at fully mechanized mining face.