The initial alignment is one of the difficult problems of the strapdown inertial navigation system based on the microelectromechanical systems (MEMS-SINS) applied to the navigation of boom-type roadheader under a coalmine. To overcome the complex environment of the underground coalmine and the large noise of the MEMS gyroscope, the laser spot perception system (LSPS) was developed to provide the heading information of the roadheader to aid the initial alignment of the MEMS-SINS. During the process of initial alignment, the differential equation of heading error is derived, the heading error is extended as a state variable, and a nonlinear initial alignment model aided by heading error is built up. To cope with the time-varying noise statistics of MEMS-SINS in the working face of the coal mine roadway, a simplified strong tracking Unscented Kalman Filter (SST-UKF) algorithm is proposed by combining covariance matching technology with UKF. In the calculation of the measurement prediction covariance and the cross covariance, the fading factor is introduced, respectively, to avoid the contradiction between the residuals before and after the introduction; according to the characteristics of the observation equation being a linear equation, it proves that the state prediction covariance matrix change does not affect the observation measurement and uses unscented transform (UT) only once in the state estimation and variance prediction; thus, the computational burden of the algorithm is reduced and the real-time performance is improved. The simulation and onboard experiment results show that the proposed scheme can achieve horizontal alignment within 40?s and convergence azimuth misalignment angle to 0.9° within 450?s, which fully meets the requirements of MEMS-SINS initial alignment for underground coalmine roadheader.
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