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Journal of Sensors
Volume 2015, Article ID 509143, 17 pages
Research Article

Triaxial Accelerometer Error Coefficients Identification with a Novel Artificial Fish Swarm Algorithm

1Institute of Inertial Navigation and Measurement & Control Technology, College of Automation, Harbin Engineering University, Harbin, Heilongjiang 150001, China
2Navigation and Instrumentation Research Group (NavINST), Department of Electrical and Computer Engineering, Queen’s University, Kingston, ON, Canada K7L 3N6
3China Aerospace Science and Technology Corporation, No. 16, Xi’an, Shanxi 710001, China

Received 4 March 2015; Accepted 13 May 2015

Academic Editor: Jong-Jae Lee

Copyright © 2015 Yanbin Gao et al. 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.


Artificial fish swarm algorithm (AFSA) is one of the state-of-the-art swarm intelligence techniques, which is widely utilized for optimization purposes. Triaxial accelerometer error coefficients are relatively unstable with the environmental disturbances and aging of the instrument. Therefore, identifying triaxial accelerometer error coefficients accurately and being with lower costs are of great importance to improve the overall performance of triaxial accelerometer-based strapdown inertial navigation system (SINS). In this study, a novel artificial fish swarm algorithm (NAFSA) that eliminated the demerits (lack of using artificial fishes’ previous experiences, lack of existing balance between exploration and exploitation, and high computational cost) of AFSA is introduced at first. In NAFSA, functional behaviors and overall procedure of AFSA have been improved with some parameters variations. Second, a hybrid accelerometer error coefficients identification algorithm has been proposed based on NAFSA and Monte Carlo simulation (MCS) approaches. This combination leads to maximum utilization of the involved approaches for triaxial accelerometer error coefficients identification. Furthermore, the NAFSA-identified coefficients are testified with 24-position verification experiment and triaxial accelerometer-based SINS navigation experiment. The priorities of MCS-NAFSA are compared with that of conventional calibration method and optimal AFSA. Finally, both experiments results demonstrate high efficiency of MCS-NAFSA on triaxial accelerometer error coefficients identification.