Abstract
Initial alignment of the strapdown inertial navigation system (SINS) is intended to determine the initial attitude matrix in a short time with certain accuracy. The alignment accuracy of the quaternion filter algorithm is remarkable, but the convergence rate is slow. To solve this problem, this paper proposes an improved quaternion filter algorithm for faster initial alignment based on the error model of the quaternion filter algorithm. The improved quaternion filter algorithm constructs the K matrix based on the principle of optimal quaternion algorithm, and rebuilds the measurement model by containing acceleration and velocity errors to make the convergence rate faster. A doppler velocity log (DVL) provides the reference velocity for the improved quaternion filter alignment algorithm. In order to demonstrate the performance of the improved quaternion filter algorithm in the field, a turntable experiment and a vehicle test are carried out. The results of the experiments show that the convergence rate of the proposed improved quaternion filter is faster than that of the tradition quaternion filter algorithm. In addition, the improved quaternion filter algorithm also demonstrates advantages in terms of correctness, effectiveness, and practicability.
Highlights
The strapdown inertial navigation system (SINS) plays an important role in both military and civil navigation fields, and has become a core navigation system because of advantages in autonomy, continuity, and comprehensiveness [1]
Typical coarse alignment algorithms in the inertial frame on a swing base include three categories: a solidified analytical algorithm based on double-vector attitude determination, an optimal quaternion algorithm solving the Wahba [6] problem, and a filter algorithm for parameter estimation
In order to compare the optimal quaternion algorithm to the improved quaternion filter algorithm described in this paper, it is more practical to assume that the velocity error model consists of constant error than random error, as in the four cases shown in the Table 3
Summary
The strapdown inertial navigation system (SINS) plays an important role in both military and civil navigation fields, and has become a core navigation system because of advantages in autonomy, continuity, and comprehensiveness [1]. Since the carrier has no linear velocity using a swing base, other external auxiliary information is not required to complete the process of coarse alignment. Typical coarse alignment algorithms in the inertial frame on a swing base include three categories: a solidified analytical algorithm based on double-vector attitude determination, an optimal quaternion algorithm solving the Wahba [6] problem, and a filter algorithm for parameter estimation. Li proposed a new alignment algorithm for shipborne SINS based on an in-movement filter quaternion estimation [11], which had higher alignment accuracy and faster convergence rate. An improved quaternion filter algorithm, based on Kalman filter, is proposed; it improves the accuracy and convergence rate of coarse alignment using a swing base, and completes coarse alignment on a moving base. The results of the experiments based on a turntable and a vehicle confirm the effectiveness and stability of the proposed algorithm
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