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Sensors 2017, 17(7), 1551;

A Damping Grid Strapdown Inertial Navigation System Based on a Kalman Filter for Ships in Polar Regions

College of Automation, Harbin Engineering University, Harbin 150001, China
Author to whom correspondence should be addressed.
Received: 26 May 2017 / Revised: 27 June 2017 / Accepted: 29 June 2017 / Published: 3 July 2017
(This article belongs to the Special Issue Inertial Sensors for Positioning and Navigation)
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The grid strapdown inertial navigation system (SINS) used in polar navigation also includes three kinds of periodic oscillation errors as common SINS are based on a geographic coordinate system. Aiming ships which have the external information to conduct a system reset regularly, suppressing the Schuler periodic oscillation is an effective way to enhance navigation accuracy. The Kalman filter based on the grid SINS error model which applies to the ship is established in this paper. The errors of grid-level attitude angles can be accurately estimated when the external velocity contains constant error, and then correcting the errors of the grid-level attitude angles through feedback correction can effectively dampen the Schuler periodic oscillation. The simulation results show that with the aid of external reference velocity, the proposed external level damping algorithm based on the Kalman filter can suppress the Schuler periodic oscillation effectively. Compared with the traditional external level damping algorithm based on the damping network, the algorithm proposed in this paper can reduce the overshoot errors when the state of grid SINS is switched from the non-damping state to the damping state, and this effectively improves the navigation accuracy of the system. View Full-Text
Keywords: grid SINS; Schuler periodic oscillation; Kalman filter; level damping grid SINS; Schuler periodic oscillation; Kalman filter; level damping

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Huang, W.; Fang, T.; Luo, L.; Zhao, L.; Che, F. A Damping Grid Strapdown Inertial Navigation System Based on a Kalman Filter for Ships in Polar Regions. Sensors 2017, 17, 1551.

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