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Initial alignment is one of the key technologies in Strapdown inertial navigation system (SINS). It is divided into coarse alignment and fine alignment. The conventional method of fine alignment is to adopt Kalman filtering and uses single filter to estimate system states. In Kalman filtering, it is known that system model should be matched with actual system and the statistical characteristics of...
The error equation of the azimuth misalignment angle for SINS on the static base is nonlinear, and it's an urgent problem to align it when the angle is large. This paper proposes a method for the initial alignment of large azimuth misalignment based on Krein space linear estimation. The sum quadratic constraint (SQC) for nonlinear system is obtained by using Lipschitz condition of nonlinearity. Thus...
Conventional attitude algorithms for strapdown inertial navigation system (SINS) use angular increment as algorithm input. However, these algorithms do not address the issue of angular rate which is the output of modern-day gyro. The integration of angular rate for the purpose of getting angular increment degrades the algorithm performance inevitably. To solve this problem, a generalized method for...
The optimal two-position alignment using Kalman filter is newly derived for strapdown inertial navigation system (SINS). During the alignment process, SINS is rotated with respect to the heading axis. The Kalman filter estimates the misalignments and gyro drifts at these two positions. In terms of the relationship between yaw misalignments and gyro drifts, the horizontal gyro drifts can be computed...
The paper derived the initial alignment error equation of the SINS (strapdown inertial navigation system) when the azimuth errors are considerably large. Because of including the nonlinear part in the equation, two methods of extended Kalman filter and scaled unscented Kalman filter are used to simulate the nonlinear system. The simulation results show that the accuracy of scaled unscented Kalman...
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