GNSSLow-Cost MEMS-INS Integration Using Variational Bayesian Adaptive Cubature Kalman Smoother and Ensemble Regularized ELM
Joint Authors
Xiao, Zhu
Georges, Hassana Maigary
Wang, Dong
Source
Mathematical Problems in Engineering
Issue
Vol. 2015, Issue 2015 (31 Dec. 2015), pp.1-13, 13 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2015-10-25
Country of Publication
Egypt
No. of Pages
13
Main Subjects
Abstract EN
Among the inertial navigation system (INS) devices used in land vehicle navigation (LVN), low-cost microelectromechanical systems (MEMS) inertial sensors have received more interest for bridging global navigation satellites systems (GNSS) signal failures because of their price and portability.
Kalman filter (KF) based GNSS/INS integration has been widely used to provide a robust solution to the navigation.
However, its prediction model cannot give satisfactory results in the presence of colored and variational noise.
In order to achieve reliable and accurate positional solution for LVN in urban areas surrounded by skyscrapers or under dense foliage and tunnels, a novel model combining variational Bayesian adaptive Kalman smoother (VB-ACKS) as an alternative of KF and ensemble regularized extreme learning machine (ERELM) for bridging global positioning systems outages is proposed.
The ERELM is applied to reduce the fluctuating performance of GNSS during an outage.
We show that a well-organized collection of predictors using ensemble learning yields a more accurate positional result when compared with conventional artificial neural network (ANN) predictors.
Experimental results show that the performance of VB-ACKS is more robust compared with KF solution, and the prediction of ERELM contains the smallest error compared with other ANN solutions.
American Psychological Association (APA)
Georges, Hassana Maigary& Wang, Dong& Xiao, Zhu. 2015. GNSSLow-Cost MEMS-INS Integration Using Variational Bayesian Adaptive Cubature Kalman Smoother and Ensemble Regularized ELM. Mathematical Problems in Engineering،Vol. 2015, no. 2015, pp.1-13.
https://search.emarefa.net/detail/BIM-1074455
Modern Language Association (MLA)
Georges, Hassana Maigary…[et al.]. GNSSLow-Cost MEMS-INS Integration Using Variational Bayesian Adaptive Cubature Kalman Smoother and Ensemble Regularized ELM. Mathematical Problems in Engineering No. 2015 (2015), pp.1-13.
https://search.emarefa.net/detail/BIM-1074455
American Medical Association (AMA)
Georges, Hassana Maigary& Wang, Dong& Xiao, Zhu. GNSSLow-Cost MEMS-INS Integration Using Variational Bayesian Adaptive Cubature Kalman Smoother and Ensemble Regularized ELM. Mathematical Problems in Engineering. 2015. Vol. 2015, no. 2015, pp.1-13.
https://search.emarefa.net/detail/BIM-1074455
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1074455