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Sensors 2017, 17(3), 432;

A RLS-SVM Aided Fusion Methodology for INS during GPS Outages

Key Laboratory of Micro-Inertial Instrument and Advanced Navigation Technology, Ministry of Education, School of Instrument Science and Engineering, Southeast University, Nanjing 210096, China
Author to whom correspondence should be addressed.
Academic Editor: Gert F. Trommer
Received: 23 November 2016 / Revised: 15 January 2017 / Accepted: 16 February 2017 / Published: 24 February 2017
(This article belongs to the Section Physical Sensors)
Full-Text   |   PDF [1911 KB, uploaded 24 February 2017]   |  


In order to maintain a relatively high accuracy of navigation performance during global positioning system (GPS) outages, a novel robust least squares support vector machine (LS-SVM)-aided fusion methodology is explored to provide the pseudo-GPS position information for the inertial navigation system (INS). The relationship between the yaw, specific force, velocity, and the position increment is modeled. Rather than share the same weight in the traditional LS-SVM, the proposed algorithm allocates various weights for different data, which makes the system immune to the outliers. Field test data was collected to evaluate the proposed algorithm. The comparison results indicate that the proposed algorithm can effectively provide position corrections for standalone INS during the 300 s GPS outage, which outperforms the traditional LS-SVM method. Historical information is also involved to better represent the vehicle dynamics. View Full-Text
Keywords: INS/GPS integrated navigation system; GPS outage; robust LS-SVM; outlier INS/GPS integrated navigation system; GPS outage; robust LS-SVM; outlier

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Yao, Y.; Xu, X. A RLS-SVM Aided Fusion Methodology for INS during GPS Outages. Sensors 2017, 17, 432.

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