GNSS/low-cost MEMS-INS integration using variational Bayesian adaptive cubature Kalman smoother and ensemble regularized ELM
type: Item
instance of: scholarly article
title: GNSS/low-cost MEMS-INS integration using variational Bayesian adaptive cubature Kalman smoother and ensemble regularized ELM
zbMATH Open document ID: 1395.94032
DOI: 10.1155/2015/682907
author: Y. Aharonov
published in: Mathematical Problems in Engineering
publication date: 2018-08-27T00:00:00Z
review text: Summary: 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.
Mathematics Subject Classification ID: 94A08
Mathematics Subject Classification ID: 62M20
Mathematics Subject Classification ID: 62P30
zbMATH DE Number: 6926975
Wikidata QID: Q59118915
MaRDI profile type: Publication
full work available at URL: https://doi.org/10.1155/2015/682907
OpenAlex ID: W2105165858
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description: scientific article; zbMATH DE number 6926975