Improving positioning accuracy of vehicular navigation system during GPS outages utilizing ensemble learning algorithm

•We model the INS/GPS position based on the other information.•We use the ensemble algorithm to overcome the complex and dynamic data.•The model accuracy depends on the data condition and diversity. Recently, methods based on Artificial Intelligence (AI) have been widely used to improve positioning...

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Vydáno v:Information fusion Ročník 35; s. 1 - 10
Hlavní autoři: Li, Jing, Song, Ningfang, Yang, Gongliu, Li, Ming, Cai, Qingzhong
Médium: Journal Article
Jazyk:angličtina
Vydáno: Elsevier B.V 01.05.2017
Témata:
Ensemble learning algorithm
Gps outages
Neural network
Sins/gps integration
Ensemble learning algorithm
Gps outages
Neural network
Sins/gps integration
ISSN:1566-2535, 1872-6305
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Popis
Shrnutí:•We model the INS/GPS position based on the other information.•We use the ensemble algorithm to overcome the complex and dynamic data.•The model accuracy depends on the data condition and diversity. Recently, methods based on Artificial Intelligence (AI) have been widely used to improve positioning accuracy for land vehicle navigation by integrating the Global Positioning System (GPS) with the Strapdown Inertial Navigation System (SINS). In this paper, we propose the ensemble learning algorithm instead of traditional single neural network to overcome the limitations of complex and dynamic data cased by vehicle irregular movement. The ensemble learning algorithm (LSBoost or Bagging), similar to the neural network, can build the SINS/GPS position model based on current and some past samples of SINS velocity, attitude and IMU output information. The performance of the proposed algorithm has been experimentally verified using GPS and SINS data of different trajectories collected in some land vehicle navigation tests. The comparison results between the proposed model and traditional algorithms indicate that the proposed algorithm can improve the positioning accuracy for cases of SINS and specific GPS outages.
ISSN:1566-2535
1872-6305
DOI:10.1016/j.inffus.2016.08.001