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A Novel KGP Algorithm for Improving INS/GPS Integrated Navigation Positioning Accuracy

The fusion of multi-source sensor data is an effective method for improving the accuracy of vehicle navigation. The generalization abilities of neural-network-based inertial devices and GPS integrated navigation systems weaken as the nonlinearity in the system increases, resulting in decreased posit...

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Autores principales: Zhang, Huibing, Li, Tong, Yin, Lihua, Liu, Dingke, Zhou, Ya, Zhang, Jingwei, Pan, Fang
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6480632/
https://www.ncbi.nlm.nih.gov/pubmed/30987372
http://dx.doi.org/10.3390/s19071623
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author Zhang, Huibing
Li, Tong
Yin, Lihua
Liu, Dingke
Zhou, Ya
Zhang, Jingwei
Pan, Fang
author_facet Zhang, Huibing
Li, Tong
Yin, Lihua
Liu, Dingke
Zhou, Ya
Zhang, Jingwei
Pan, Fang
author_sort Zhang, Huibing
collection PubMed
description The fusion of multi-source sensor data is an effective method for improving the accuracy of vehicle navigation. The generalization abilities of neural-network-based inertial devices and GPS integrated navigation systems weaken as the nonlinearity in the system increases, resulting in decreased positioning accuracy. Therefore, a KF-GDBT-PSO (Kalman Filter-Gradient Boosting Decision Tree-Particle Swarm Optimization, KGP) data fusion method was proposed in this work. This method establishes an Inertial Navigation System (INS) error compensation model by integrating Kalman Filter (KF) and Gradient Boosting Decision Tree (GBDT). To improve the prediction accuracy of the GBDT, we optimized the learning algorithm and the fitness parameter using Particle Swarm Optimization (PSO). When the GPS signal was stable, the KGP method was used to solve the nonlinearity issue between the vehicle feature and positioning data. When the GPS signal was unstable, the training model was used to correct the positioning error for the INS, thereby improving the positioning accuracy and continuity. The experimental results show that our method increased the positioning accuracy by 28.20–59.89% compared with the multi-layer perceptual neural network and random forest regression.
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spelling pubmed-64806322019-04-29 A Novel KGP Algorithm for Improving INS/GPS Integrated Navigation Positioning Accuracy Zhang, Huibing Li, Tong Yin, Lihua Liu, Dingke Zhou, Ya Zhang, Jingwei Pan, Fang Sensors (Basel) Article The fusion of multi-source sensor data is an effective method for improving the accuracy of vehicle navigation. The generalization abilities of neural-network-based inertial devices and GPS integrated navigation systems weaken as the nonlinearity in the system increases, resulting in decreased positioning accuracy. Therefore, a KF-GDBT-PSO (Kalman Filter-Gradient Boosting Decision Tree-Particle Swarm Optimization, KGP) data fusion method was proposed in this work. This method establishes an Inertial Navigation System (INS) error compensation model by integrating Kalman Filter (KF) and Gradient Boosting Decision Tree (GBDT). To improve the prediction accuracy of the GBDT, we optimized the learning algorithm and the fitness parameter using Particle Swarm Optimization (PSO). When the GPS signal was stable, the KGP method was used to solve the nonlinearity issue between the vehicle feature and positioning data. When the GPS signal was unstable, the training model was used to correct the positioning error for the INS, thereby improving the positioning accuracy and continuity. The experimental results show that our method increased the positioning accuracy by 28.20–59.89% compared with the multi-layer perceptual neural network and random forest regression. MDPI 2019-04-04 /pmc/articles/PMC6480632/ /pubmed/30987372 http://dx.doi.org/10.3390/s19071623 Text en © 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Zhang, Huibing
Li, Tong
Yin, Lihua
Liu, Dingke
Zhou, Ya
Zhang, Jingwei
Pan, Fang
A Novel KGP Algorithm for Improving INS/GPS Integrated Navigation Positioning Accuracy
title A Novel KGP Algorithm for Improving INS/GPS Integrated Navigation Positioning Accuracy
title_full A Novel KGP Algorithm for Improving INS/GPS Integrated Navigation Positioning Accuracy
title_fullStr A Novel KGP Algorithm for Improving INS/GPS Integrated Navigation Positioning Accuracy
title_full_unstemmed A Novel KGP Algorithm for Improving INS/GPS Integrated Navigation Positioning Accuracy
title_short A Novel KGP Algorithm for Improving INS/GPS Integrated Navigation Positioning Accuracy
title_sort novel kgp algorithm for improving ins/gps integrated navigation positioning accuracy
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6480632/
https://www.ncbi.nlm.nih.gov/pubmed/30987372
http://dx.doi.org/10.3390/s19071623
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