Show simple item record

dc.creatorSilva, Jesús
dc.creatorVarela Izquierdo, Noel
dc.creatorPineda, Omar
dc.creatorHernández Palma, Hugo
dc.creatorCueto, Eduardo Nicolas
dc.description.abstractGPS 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
dc.publisherCorporación Universidad de la Costaspa
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International*
dc.sourceSmart Innovation, Systems and Technologiesspa
dc.subjectGlobal positioning system (GPS)spa
dc.subjectInertial measurement unitspa
dc.subjectCoupling systemspa
dc.subjectKalman filterspa
dc.subjectMadgwick filterspa
dc.titleCoupling architecture between INS/GPS for precise navigation on set pathsspa
dcterms.references1. Marston, S., Li, Z., Bandyopadhyay, S., Zhang, J., Ghalsasi, A.: Cloud computing—The business perspective. Decis. Support Syst. 51(1), 176–189 (2011)spa
dcterms.references2. Bifet, A., De Francisci Morales, G.: Big Data Stream Learning with Samoa (2014). Recuperado de data_stream_learning_with_SAMOAspa
dcterms.references3. Lomax, T., Schrank, D., Turner, S., Margiotta, R.: Report for Selecting Travel Reliability Measures. Federal Highway Administration, Washington, D. C. (2003)spa
dcterms.references4. Pardillo, J., Sánchez, V.: Apuntes de Ingeniería de Tránsito. ETS Ingenieros de Caminos, Canales y Puertos, Madrid, España (2015)spa
dcterms.references5. Skabardonis, A., Varaiya, P., Petty, K.: Measuring recurrent and non-recurrent traffic congestion. Transp. Res. Rec. J. Transp. Res. Board 1856, 60–68 (2003)spa
dcterms.references6. 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)spa
dcterms.references7. Yong-chuan, Z., Xiao-qing, Z., Zhen-ting, C: Traffic congestion detection based on GPS floating-car data. Procedia Eng. 15, 5541–5546spa
dcterms.references8. 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)spa
dcterms.references9. Thames, L., Schaefer, D.: Software-defined cloud manufacturing for industry 4.0. Procedia CIRP 52, 12–17 (2016)spa
dcterms.references10. Viloria, A., Neira-Rodado, D., Pineda Lezama, O.B.: Recovery of Scientific Data Using Intelligent Distributed Data Warehouse. ANT/EDI40 2019, pp 1249–1254spa
dcterms.references11. 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. 1201–1206spa
dcterms.references12. Alpaydin, E.: Introduction to Machine Learning. The MIT Press, Massachusetts (2004)spa
dcterms.references13. 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)spa
dcterms.references14. Bizama, J.: Modelación y simulación mediante un microsimulador de la zona de influencia del Puente Llacolén. Universidad del Bio Bio, Memoria de Título (2012)spa
dcterms.references15. 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)spa
dcterms.references16. 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)spa
dcterms.references17. 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)spa
dcterms.references18. 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)spa
dcterms.references19. Viloria, A., Robayo, P.V.: Inventory reduction in the supply chain of finished products for multinational companies. Indian J. Sci. Technol. 8(1) (2016)spa

Files in this item


This item appears in the following Collection(s)

Show simple item record

Attribution-NonCommercial-NoDerivatives 4.0 International
Except where otherwise noted, this item's license is described as Attribution-NonCommercial-NoDerivatives 4.0 International