Abstract
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.
Highlights
Initial Alignment ModelInitial alignment can be divided into two stages: coarse alignment and fine alignment [19]. e purpose of coarse alignment is to calculate the attitude transformation matrix Cbn between the navigation coordinate system n and the body coordinate system b
Yang Shen, Shichen Fu, Pengjiang Wang, Rui Li, Yu Lan, Xiaodong Ji, Chao Liu, and Miao Wu
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; the computational burden of the algorithm is reduced and the real-time performance is improved. e 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
Summary
Initial alignment can be divided into two stages: coarse alignment and fine alignment [19]. e purpose of coarse alignment is to calculate the attitude transformation matrix Cbn between the navigation coordinate system n and the body coordinate system b. To simplify the process of the strong tracking UKF algorithm, reduce the calculation amount, and improve the real-time performance of the algorithm, according to the characteristics of the system observation equation being a linear equation, this paper proves that the measurement prediction is only related to the state transition matrix and the state prediction and has nothing to do with the state prediction covariance; only UT is used once in the state estimation and variance prediction. E normal strong tracking algorithm needs to perform the UT again to calculate zk+(1/k) after the state prediction covariance matrix Pk+(1/k) after the introduction of the fading factor ck+1 was obtained, and the change of the state prediction covariance matrix Pk+(1/k) does not affect the estimation of the observed measurement zk+(1/k) when the observation equation is linear. Where λ0 tr(Nk+1)/tr(Mk+1) and tr(·) is the tracing operator
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