GPS Data Correction Based on Fuzzy Logic for Tracking Land Vehicles

GPS sensors are widely used to know a vehicle’s location and to track its route. Although GPS sensor technology is advancing, they present systematic failures depending on the environmental conditions to which they are subjected. To tackle this problem, we propose an intelligent system based on fuzz...

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Autores principales:	Pedro J. Correa-Caicedo, Horacio Rostro-González, Martin A. Rodriguez-Licea, Óscar Octavio Gutiérrez-Frías, Carlos Alonso Herrera-Ramírez, Iris I. Méndez-Gurrola, Miroslava Cano-Lara, Alejandro I. Barranco-Gutiérrez
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	localization fuzzy systems unscented Kalman filter adaptive neuro-fuzzy inference system (ANFIS) GPS autonomous navigation Mathematics QA1-939
Acceso en línea:	https://doaj.org/article/37325985c2f944bab319375afec95c99
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spelling	oai:doaj.org-article:37325985c2f944bab319375afec95c992021-11-11T18:20:55ZGPS Data Correction Based on Fuzzy Logic for Tracking Land Vehicles10.3390/math92128182227-7390https://doaj.org/article/37325985c2f944bab319375afec95c992021-11-01T00:00:00Zhttps://www.mdpi.com/2227-7390/9/21/2818https://doaj.org/toc/2227-7390GPS sensors are widely used to know a vehicle’s location and to track its route. Although GPS sensor technology is advancing, they present systematic failures depending on the environmental conditions to which they are subjected. To tackle this problem, we propose an intelligent system based on fuzzy logic, which takes the information from the sensors and correct the vehicle’s absolute position according to its latitude and longitude. This correction is performed by two fuzzy systems, one to correct the latitude and the other to correct the longitude, which are trained using the MATLAB ANFIS tool. The positioning correction system is trained and tested with two different datasets. One of them collected with a Pmod GPS sensor and the other a public dataset, which was taken from routes in Brazil. To compare our proposal, an unscented Kalman filter (UKF) was implemented. The main finding is that the proposed fuzzy systems achieve a performance of 69.2% higher than the UKF. Furthermore, fuzzy systems are suitable to implement in an embedded system such as the Raspberry Pi 4. Another finding is that the logical operations facilitate the creation of non-linear functions because of the ‘if else’ structure. Finally, the existence justification of each fuzzy system section is easy to understand.Pedro J. Correa-CaicedoHoracio Rostro-GonzálezMartin A. Rodriguez-LiceaÓscar Octavio Gutiérrez-FríasCarlos Alonso Herrera-RamírezIris I. Méndez-GurrolaMiroslava Cano-LaraAlejandro I. Barranco-GutiérrezMDPI AGarticlelocalizationfuzzy systemsunscented Kalman filteradaptive neuro-fuzzy inference system (ANFIS)GPSautonomous navigationMathematicsQA1-939ENMathematics, Vol 9, Iss 2818, p 2818 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	localization fuzzy systems unscented Kalman filter adaptive neuro-fuzzy inference system (ANFIS) GPS autonomous navigation Mathematics QA1-939
spellingShingle	localization fuzzy systems unscented Kalman filter adaptive neuro-fuzzy inference system (ANFIS) GPS autonomous navigation Mathematics QA1-939 Pedro J. Correa-Caicedo Horacio Rostro-González Martin A. Rodriguez-Licea Óscar Octavio Gutiérrez-Frías Carlos Alonso Herrera-Ramírez Iris I. Méndez-Gurrola Miroslava Cano-Lara Alejandro I. Barranco-Gutiérrez GPS Data Correction Based on Fuzzy Logic for Tracking Land Vehicles
description	GPS sensors are widely used to know a vehicle’s location and to track its route. Although GPS sensor technology is advancing, they present systematic failures depending on the environmental conditions to which they are subjected. To tackle this problem, we propose an intelligent system based on fuzzy logic, which takes the information from the sensors and correct the vehicle’s absolute position according to its latitude and longitude. This correction is performed by two fuzzy systems, one to correct the latitude and the other to correct the longitude, which are trained using the MATLAB ANFIS tool. The positioning correction system is trained and tested with two different datasets. One of them collected with a Pmod GPS sensor and the other a public dataset, which was taken from routes in Brazil. To compare our proposal, an unscented Kalman filter (UKF) was implemented. The main finding is that the proposed fuzzy systems achieve a performance of 69.2% higher than the UKF. Furthermore, fuzzy systems are suitable to implement in an embedded system such as the Raspberry Pi 4. Another finding is that the logical operations facilitate the creation of non-linear functions because of the ‘if else’ structure. Finally, the existence justification of each fuzzy system section is easy to understand.
format	article
author	Pedro J. Correa-Caicedo Horacio Rostro-González Martin A. Rodriguez-Licea Óscar Octavio Gutiérrez-Frías Carlos Alonso Herrera-Ramírez Iris I. Méndez-Gurrola Miroslava Cano-Lara Alejandro I. Barranco-Gutiérrez
author_facet	Pedro J. Correa-Caicedo Horacio Rostro-González Martin A. Rodriguez-Licea Óscar Octavio Gutiérrez-Frías Carlos Alonso Herrera-Ramírez Iris I. Méndez-Gurrola Miroslava Cano-Lara Alejandro I. Barranco-Gutiérrez
author_sort	Pedro J. Correa-Caicedo
title	GPS Data Correction Based on Fuzzy Logic for Tracking Land Vehicles
title_short	GPS Data Correction Based on Fuzzy Logic for Tracking Land Vehicles
title_full	GPS Data Correction Based on Fuzzy Logic for Tracking Land Vehicles
title_fullStr	GPS Data Correction Based on Fuzzy Logic for Tracking Land Vehicles
title_full_unstemmed	GPS Data Correction Based on Fuzzy Logic for Tracking Land Vehicles
title_sort	gps data correction based on fuzzy logic for tracking land vehicles
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/37325985c2f944bab319375afec95c99
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_version_	1718431876457168896

GPS Data Correction Based on Fuzzy Logic for Tracking Land Vehicles

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