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Adaptively robust filtering algorithm for maritime celestial navigation

Published online by Cambridge University Press:  29 October 2021

Chong-hui Li*
Affiliation:
State Key Laboratory of Geo-Information Engineering, Xi'an, China Institute of Geospatial Information, Information Engineering University, Zhengzhou, China
Zhang-lei Chen
Affiliation:
Institute of Geospatial Information, Information Engineering University, Zhengzhou, China
Xin-jiang Liu
Affiliation:
Institute of Geospatial Information, Information Engineering University, Zhengzhou, China
Bin Chen
Affiliation:
Institute of Geospatial Information, Information Engineering University, Zhengzhou, China
Yong Zheng
Affiliation:
Institute of Geospatial Information, Information Engineering University, Zhengzhou, China
Shuai Tong
Affiliation:
Institute of Geospatial Information, Information Engineering University, Zhengzhou, China
Ruo-pu Wang
Affiliation:
Institute of Geospatial Information, Information Engineering University, Zhengzhou, China
*
*Corresponding author. E-mail: lichonghui6501@126.com

Abstract

Celestial navigation is an important means of maritime navigation; it can automatically achieve inertially referenced positioning and orientation after a long period of development. However, the impact of different accuracy of observations and the influence of nonstationary states, such as ship speed change and steering, are not taken into account in existing algorithms. To solve this problem, this paper proposes an adaptively robust maritime celestial navigation algorithm, in which each observation value is given an equivalent weight according to the robust estimation theory, and the dynamic balance between astronomical observation and prediction values of vessel motion is adjusted by applying the adaptive factor. With this system, compared with the frequently used least square method and extended Kalman filter algorithm, not only are the real-time and high-precision navigation parameters, such as position, course, and speed for the vessel, calculated simultaneously, but also the influence of abnormal observation and vessel motion status change could be well suppressed.

Type
Research Article
Copyright
Copyright © The Author(s), 2021. Published by Cambridge University Press on behalf of The Royal Institute of Navigation

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