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Triaxial Accelerometer Error Coefficients Identification with a Novel Artificial Fish Swarm Algorithm
Joint Authors
Gao, Yanbin
Guan, Lianwu
Wang, Tingjun
Source
Issue
Vol. 2015, Issue 2015 (31 Dec. 2015), pp.1-17, 17 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2015-06-02
Country of Publication
Egypt
No. of Pages
17
Main Subjects
Abstract EN
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.
American Psychological Association (APA)
Gao, Yanbin& Guan, Lianwu& Wang, Tingjun. 2015. Triaxial Accelerometer Error Coefficients Identification with a Novel Artificial Fish Swarm Algorithm. Journal of Sensors،Vol. 2015, no. 2015, pp.1-17.
https://search.emarefa.net/detail/BIM-1070139
Modern Language Association (MLA)
Gao, Yanbin…[et al.]. Triaxial Accelerometer Error Coefficients Identification with a Novel Artificial Fish Swarm Algorithm. Journal of Sensors No. 2015 (2015), pp.1-17.
https://search.emarefa.net/detail/BIM-1070139
American Medical Association (AMA)
Gao, Yanbin& Guan, Lianwu& Wang, Tingjun. Triaxial Accelerometer Error Coefficients Identification with a Novel Artificial Fish Swarm Algorithm. Journal of Sensors. 2015. Vol. 2015, no. 2015, pp.1-17.
https://search.emarefa.net/detail/BIM-1070139
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1070139