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GA-SVR and pseudo-position-aided GPS/INS integration during GPS outage

Tan, Xinglong; Wang, Jian; Jin, Shuanggen; Meng, Xiaolin

Authors

Xinglong Tan

Jian Wang

Shuanggen Jin

Xiaolin Meng



Abstract

The performance of Global Positioning System and Inertial Navigation System (GPS/INS) integrated navigation is reduced when GPS is blocked. This paper proposes an algorithm to overcome the condition where GPS is unavailable. Together with a parameter-optimised Genetic Algorithm (GA), a Support Vector Regression (SVR) algorithm is used to construct the mapping function between the specific force, angular rate increments of INS measurements and the increments of the GPS position. During GPS outages, the real-time pseudo-GPS position is predicted with the mapping function, and the corresponding covariance matrix is estimated by an improved adaptive filtering algorithm. A GPS/INS integration scheme is demonstrated where the vehicle travels along a straight line and around a curve, with respect to both low-speed-stable and high-speed-unstable navigation platforms. The results show that the proposed algorithm provides a better performance when GPS is unavailable.

Citation

Tan, X., Wang, J., Jin, S., & Meng, X. (2015). GA-SVR and pseudo-position-aided GPS/INS integration during GPS outage. Journal of Navigation, 68(4), https://doi.org/10.1017/S037346331500003X

Journal Article Type Article
Acceptance Date Jan 10, 2015
Online Publication Date Feb 13, 2015
Publication Date Jul 1, 2015
Deposit Date Jul 25, 2016
Publicly Available Date Jul 25, 2016
Journal Journal of Navigation
Print ISSN 0373-4633
Electronic ISSN 1469-7785
Publisher Cambridge University Press
Peer Reviewed Peer Reviewed
Volume 68
Issue 4
DOI https://doi.org/10.1017/S037346331500003X
Keywords GPS/INS Integrated Navigation; Pseudo-GPS Position; Support Vector Regression; Improved Adaptive Filtering
Public URL https://nottingham-repository.worktribe.com/output/983170
Publisher URL http://journals.cambridge.org/action/displayAbstract?fromPage=online&aid=9719975&fileId=S037346331500003X

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