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Sensors 2017, 17(4), 709; doi:10.3390/s17040709

A Coarse Alignment Method Based on Digital Filters and Reconstructed Observation Vectors

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1,2,* , 1,2
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1,2
and
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1
Key Laboratory of Micro-Inertial Instrument and Advanced Navigation Technology, Ministry of Education, Nanjing 210096, China
2
School of Instrument Science and Engineering, Southeast University, Nanjing 210096, China
3
School of Information and Control, Nanjing University of Information Science and Technology, Nanjing 210044, China
*
Author to whom correspondence should be addressed.
Received: 27 November 2016 / Revised: 26 March 2017 / Accepted: 27 March 2017 / Published: 29 March 2017
(This article belongs to the Special Issue Inertial Sensors and Systems 2016)

Abstract

In this paper, a coarse alignment method based on apparent gravitational motion is proposed. Due to the interference of the complex situations, the true observation vectors, which are calculated by the apparent gravity, are contaminated. The sources of the interference are analyzed in detail, and then a low-pass digital filter is designed in this paper for eliminating the high-frequency noise of the measurement observation vectors. To extract the effective observation vectors from the inertial sensors’ outputs, a parameter recognition and vector reconstruction method are designed, where an adaptive Kalman filter is employed to estimate the unknown parameters. Furthermore, a robust filter, which is based on Huber’s M-estimation theory, is developed for addressing the outliers of the measurement observation vectors due to the maneuver of the vehicle. A comprehensive experiment, which contains a simulation test and physical test, is designed to verify the performance of the proposed method, and the results show that the proposed method is equivalent to the popular apparent velocity method in swaying mode, but it is superior to the current methods while in moving mode when the strapdown inertial navigation system (SINS) is under entirely self-contained conditions. View Full-Text
Keywords: SINS; coarse alignment; digital filter; vector reconstruction; robust Kalman filter SINS; coarse alignment; digital filter; vector reconstruction; robust Kalman filter
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Xu, X.; Xu, X.; Zhang, T.; Li, Y.; Wang, Z. A Coarse Alignment Method Based on Digital Filters and Reconstructed Observation Vectors. Sensors 2017, 17, 709.

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