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GPS Data Correction Based on Fuzzy Logic for Tracking Land Vehicles

Author

Listed:
  • 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
    Cátedras-CONACyT, Av. Insurgentes Sur 1582, Col. Crédito Constructor, Ciudad de Mexico 03940, 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
    Cátedras-CONACyT, Av. Insurgentes Sur 1582, Col. Crédito Constructor, Ciudad de Mexico 03940, Mexico)

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.

Suggested Citation

  • 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, 2021. "GPS Data Correction Based on Fuzzy Logic for Tracking Land Vehicles," Mathematics, MDPI, vol. 9(21), pages 1-18, November.
    • Handle: RePEc:gam:jmathe:v:9:y:2021:i:21:p:2818-:d:673370
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    References listed on IDEAS

    as
    1. Yuzhan Wu & Susheng Ding & Yuanhao Ding & Meng Li, 2021. "UWB Base Station Cluster Localization for Unmanned Ground Vehicle Guidance," Mathematical Problems in Engineering, Hindawi, vol. 2021, pages 1-23, April.
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    Cited by:

    1. Yongjun Lee & Byungwoon Park, 2022. "Nonlinear Regression-Based GNSS Multipath Modelling in Deep Urban Area," Mathematics, MDPI, vol. 10(3), pages 1-15, January.
    2. Daniel Doz & Darjo Felda & Mara Cotič, 2023. "Demographic Factors Affecting Fuzzy Grading: A Hierarchical Linear Regression Analysis," Mathematics, MDPI, vol. 11(6), pages 1-19, March.

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