Coupling Architecture Between INS/GPS for Precise Navigation on Set Paths

Conference paper
Part of the Smart Innovation, Systems and Technologies book series (SIST, volume 181)


GPS offers the advantage of providing high long-term position accuracy with residual errors that affect the final positioning solution to a few meters with a sampling frequency of 1 Hz (Marston et al. in Decis Support Syst 51:176–189, 2011 [1]). The signals are also subject to obstruction and interference, so GPS receivers cannot be relied upon for a continuous navigation solution. On the contrary, the inertial navigation system has a sampling frequency of at least 50 Hz and exhibits low noise in the short term. In this research, a prototype based on development cards is implemented for the coupling of the inertial navigation system with GPS to improve the precision of navigation on a trajectory.


Global positioning system (GPS) Inertial measurement unit Coupling system Sensors Kalman filter Madgwick filter 


  1. 1.
    Marston, S., Li, Z., Bandyopadhyay, S., Zhang, J., Ghalsasi, A.: Cloud computing—The business perspective. Decis. Support Syst. 51(1), 176–189 (2011)CrossRefGoogle Scholar
  2. 2.
    Bifet, A., De Francisci Morales, G.: Big Data Stream Learning with Samoa (2014). Recuperado de data_stream_learning_with_SAMOA
  3. 3.
    Lomax, T., Schrank, D., Turner, S., Margiotta, R.: Report for Selecting Travel Reliability Measures. Federal Highway Administration, Washington, D. C. (2003)Google Scholar
  4. 4.
    Pardillo, J., Sánchez, V.: Apuntes de Ingeniería de Tránsito. ETS Ingenieros de Caminos, Canales y Puertos, Madrid, España (2015)Google Scholar
  5. 5.
    Skabardonis, A., Varaiya, P., Petty, K.: Measuring recurrent and non-recurrent traffic congestion. Transp. Res. Rec. J. Transp. Res. Board 1856, 60–68 (2003)CrossRefGoogle Scholar
  6. 6.
    U.S. Department of Transportation: Archived Data Management Systems—A Cross-Cutting Study. Publication FHWA- JPO-05-044. FHWA, U.S. Department of Transportation (2004)Google Scholar
  7. 7.
    Yong-chuan, Z., Xiao-qing, Z., Zhen-ting, C: Traffic congestion detection based on GPS floating-car data. Procedia Eng. 15, 5541–5546Google Scholar
  8. 8.
    Viloria, A., Lis-Gutiérrez, J.P., Gaitán-Angulo, M., Godoy, A.R.M., Moreno, G.C., Kamatkar, S.J.: Methodology for the design of a student pattern recognition tool to facilitate the teaching—learning process through knowledge data discovery (Big Data). In: Tan, Y., Shi, Y., Tang, Q. (eds.) Data Mining and Big Data. DMBD 2018. Lecture Notes in Computer Science, vol 10943. Springer, Cham (2018)Google Scholar
  9. 9.
    Thames, L., Schaefer, D.: Software-defined cloud manufacturing for industry 4.0. Procedia CIRP 52, 12–17 (2016)CrossRefGoogle Scholar
  10. 10.
    Viloria, A., Neira-Rodado, D., Pineda Lezama, O.B.: Recovery of Scientific Data Using Intelligent Distributed Data Warehouse. ANT/EDI40 2019, pp 1249–1254Google Scholar
  11. 11.
    Viloria, A., Pineda Lezama, O.B.: Improvements for Determining the Number of Clusters in k-Means for Innovation Databases in SMEs. ANT/EDI40 2019, pp. 1201–1206Google Scholar
  12. 12.
    Alpaydin, E.: Introduction to Machine Learning. The MIT Press, Massachusetts (2004)zbMATHGoogle Scholar
  13. 13.
    Álvarez, P., Hadi, M., Zhan, C.: Using Intelligent transportation systems data archives for traffic simulation applications. Transp. Res. Rec. J. Transp. Res. Board 2161, 29–39 (2010)CrossRefGoogle Scholar
  14. 14.
    Bizama, J.: Modelación y simulación mediante un microsimulador de la zona de influencia del Puente Llacolén. Universidad del Bio Bio, Memoria de Título (2012)Google Scholar
  15. 15.
    Cortés, C.E., Gibson, J., Gschwender, A., Munizaga, M., Zúñiga, M.: Commercial bus speed diagnosis based on GPS- monitored data. Transp. Res. Part C 19(4), 695–707 (2011)CrossRefGoogle Scholar
  16. 16.
    Courage, K.G., Lee, S.: Development of a Central Data Warehouse for Statewide ITS and Transportation Data in Florida: Phase II Proof of Concept. Florida Department of Transportation (2008)Google Scholar
  17. 17.
    Diker, A.C.: Estimation of traffic congestion level via FN-DBSCAN algorithm by using GPA data. In: Problems of Cybernetics and Informatics (PCI), 2012 IV International Conference, Baku, Azerbaijan (2012)Google Scholar
  18. 18.
    Amelec, V.: Increased efficiency in a company of development of technological solutions in the areas commercial and of consultancy. Adv. Sci. Lett. 21(5), 1406–1408 (2015)CrossRefGoogle Scholar
  19. 19.
    Viloria, A., Robayo, P.V.: Inventory reduction in the supply chain of finished products for multinational companies. Indian J. Sci. Technol. 8(1) (2016)Google Scholar

Copyright information

© The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  1. 1.Universidad Peruana de Ciencias AplicadasLimaPerú
  2. 2.Departamento de Ingeniería Industrial, Agroindustrial y OperacionesUniversidad de la CostaBarranquillaColombia
  3. 3.Universidad Tecnológica Centroamericana (UNITEC)San Pedro SulaHonduras
  4. 4.Corporación Universitaria LatinoamericanaBarranquillaColombia
  5. 5.Corporación Universitaria Minuto de Dios—UniminutoBogotáColombia

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