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Sensors 2015, 15(7), 17433-17452;

A Method for Oscillation Errors Restriction of SINS Based on Forecasted Time Series

College of Automation, Harbin Engineering University, Harbin 150001, China
Beijing Institute of Automatic Control Equipment, Beijing 100074, China
Author to whom correspondence should be addressed.
Academic Editor: Vittorio M.N. Passaro
Received: 23 June 2015 / Revised: 13 July 2015 / Accepted: 15 July 2015 / Published: 17 July 2015
(This article belongs to the Special Issue Inertial Sensors and Systems)
Full-Text   |   PDF [1383 KB, uploaded 17 July 2015]   |  


Continuity, real-time, and accuracy are the key technical indexes of evaluating comprehensive performance of a strapdown inertial navigation system (SINS). However, Schuler, Foucault, and Earth periodic oscillation errors significantly cut down the real-time accuracy of SINS. A method for oscillation error restriction of SINS based on forecasted time series is proposed by analyzing the characteristics of periodic oscillation errors. The innovative method gains multiple sets of navigation solutions with different phase delays in virtue of the forecasted time series acquired through the measurement data of the inertial measurement unit (IMU). With the help of curve-fitting based on least square method, the forecasted time series is obtained while distinguishing and removing small angular motion interference in the process of initial alignment. Finally, the periodic oscillation errors are restricted on account of the principle of eliminating the periodic oscillation signal with a half-wave delay by mean value. Simulation and test results show that the method has good performance in restricting the Schuler, Foucault, and Earth oscillation errors of SINS. View Full-Text
Keywords: SINS; oscillation errors; forecasted time series; oscillation errors restriction SINS; oscillation errors; forecasted time series; oscillation errors restriction

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Zhao, L.; Li, J.; Cheng, J.; Jia, C.; Wang, Q. A Method for Oscillation Errors Restriction of SINS Based on Forecasted Time Series. Sensors 2015, 15, 17433-17452.

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