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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...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2019
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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. |
format | Online Article Text |
id | pubmed-6480632 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
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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