SINS Stationary Initial Alignment Based on SimplifiedAutocovariance Least-Squares Method

Objective Initial alignment is one of the key technologies of the strapdown inertial navigation system.The applications of the strapdown inertial navigation system however,are directly affected by the ac-curacy of initial alignment.Kalman filtering is an effective algorithm for SINS initial alignment,butthe optimal estimates are based on the filtering model and the noise covariance matrices which are al-ready known.This paper focuses on the simplified autocovariance least-squares noise estimation meth-od in the strapdown inertial navigation system’s stationary initial alignment.The proposed method es-tablishes a relationship between unknown measurement noise and the autocovariance.The noise co-variance can be estimated by solving it as a linear least squares problem.The proposed method esti-mates measurement noise and corrects INS attitude by iterative calculation.Simulation results showthat the proposed method performs very well in noise covariance estimation and strapdown inertialnavigation system initial alignment.