Scaled UKF with reduced sigma points for initial alignment of SINS

The error model of the initial alignment of strapdown inertial navigation system (SINS) is nonlinear when the azimuth angle error is large. As the dimension of the system increases, the computation of the sigma points needed by UKF becomes burdensome, and the sigma points are no longer local sampling points. These non-local sigma points can't represent the system state correctly, and are prone to causing estimate error. In this paper, an improved filtering method combing the reduced sigma points UKF and the scaled UKF is proposed and implemented for initial alignment of SINS. The experiment results show that it yields better performance than EKF.

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