GPS Data Correction Based on Fuzzy Logic for Tracking Land Vehicles

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 and Alejandro I. Barranco-Gutiérrez
Additional contact information
Pedro J. Correa-Caicedo: Autotrónica, Tecnológico Nacional de Mexico en Celaya, Celaya 38010, Mexico
Horacio Rostro-González: Departamento de Electrónica, DICIS, Universidad de Guanajuato, Salamanca 36885, Mexico
Martin A. Rodriguez-Licea: Autotrónica, Tecnológico Nacional de Mexico en Celaya, Celaya 38010, Mexico
Óscar Octavio Gutiérrez-Frías: SEPI, UPIITA, Instituto Politécnico Nacional, Ciudad de Mexico 07340, Mexico
Carlos Alonso Herrera-Ramírez: Departamento de Ingeniería Robótica, Universidad Politécnica de Guanajuato, Guanajuato 38496, Mexico
Iris I. Méndez-Gurrola: Departamento de Diseño, Universidad Autónoma de Ciudad Juárez, Cd. Juárez, Chihuahua 32310, Mexico
Miroslava Cano-Lara: Departamento de Ingeniería Mecatrónica, Tecnológico Nacional de Mexico, ITS de Irapuato, Guanajuato 36821, Mexico
Alejandro I. Barranco-Gutiérrez: Autotrónica, Tecnológico Nacional de Mexico en Celaya, Celaya 38010, Mexico

Mathematics, 2021, vol. 9, issue 21, 1-18

Abstract: 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.

Keywords: localization; fuzzy systems; unscented Kalman filter; adaptive neuro-fuzzy inference system (ANFIS); GPS; autonomous navigation (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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