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Sensors 2015, 15(5), 10547-10568; doi:10.3390/s150510547

A Novel Artificial Fish Swarm Algorithm for Recalibration of Fiber Optic Gyroscope Error Parameters

1
Institute of Inertial Navigation and Measurement & Control Technology, College of Automation, Harbin Engineering University, Harbin 150001, China
2
NavINST-Navigation and Instrumentation Research Group, Department of Electrical and Computer Engineering, Queen's University, Kingston, ON K7L 3N6, Canada
3
No. 16 of China Aerospace Science and Technology Corporation, Xi'an 710001, China
*
Author to whom correspondence should be addressed.
Academic Editor: Gert F. Trommer
Received: 10 March 2015 / Revised: 23 April 2015 / Accepted: 27 April 2015 / Published: 5 May 2015
(This article belongs to the Special Issue Inertial Sensors and Systems)
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Abstract

The artificial fish swarm algorithm (AFSA) is one of the state-of-the-art swarm intelligent techniques, which is widely utilized for optimization purposes. Fiber optic gyroscope (FOG) error parameters such as scale factors, biases and misalignment errors are relatively unstable, especially with the environmental disturbances and the aging of fiber coils. These uncalibrated error parameters are the main reasons that the precision of FOG-based strapdown inertial navigation system (SINS) degraded. This research is mainly on the application of a novel artificial fish swarm algorithm (NAFSA) on FOG error coefficients recalibration/identification. First, the NAFSA avoided the demerits (e.g., lack of using artificial fishes’ pervious experiences, lack of existing balance between exploration and exploitation, and high computational cost) of the standard AFSA during the optimization process. To solve these weak points, functional behaviors and the overall procedures of AFSA have been improved with some parameters eliminated and several supplementary parameters added. Second, a hybrid FOG error coefficients recalibration algorithm has been proposed based on NAFSA and Monte Carlo simulation (MCS) approaches. This combination leads to maximum utilization of the involved approaches for FOG error coefficients recalibration. After that, the NAFSA is verified with simulation and experiments and its priorities are compared with that of the conventional calibration method and optimal AFSA. Results demonstrate high efficiency of the NAFSA on FOG error coefficients recalibration. View Full-Text
Keywords: novel artificial fish swarm algorithm; fiber optic gyroscope; error coefficients recalibration; Monte Carlo simulation; swarm intelligence optimization novel artificial fish swarm algorithm; fiber optic gyroscope; error coefficients recalibration; Monte Carlo simulation; swarm intelligence optimization
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

Gao, Y.; Guan, L.; Wang, T.; Sun, Y. A Novel Artificial Fish Swarm Algorithm for Recalibration of Fiber Optic Gyroscope Error Parameters. Sensors 2015, 15, 10547-10568.

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