A fast self alignment method for SINS based on observability analysis

DOI:	10.11809/bqzbgcxb2023.05.039
Keywords:	initial alignment; observability analysis; SINS; static base; reduced order; Kalman filter
Abstract:	The accuracy and speed of initial alignment directly affect the working performance of the inertial navigation system. Aiming at the simplification problem of the initial alignment filter model under static base conditions, this paper proposes an improved fully observable initial alignment model. By analyzing the observable array of the initial alignment model under static base conditions, a fully observable initial alignment model after the reduced order is constructed. The simulation experiments verify the effectiveness of the method under ideal static base conditions, and the results show that the model is greatly simplified by using the reduced order method, which ensures the alignment accuracy while reducing the amount of calculation. By comparing the results of the two models under actual conditions, the reduced order model can accelerate the convergence speed and effectively eliminate the influence of the amount of the unobservable state on the whole system. In general, it is a relatively optimized initial alignment model for static bases.
Issue:	Vol. 44 No. 5 (2023)
Published:	2023-05-28
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