Coupling architecture between INS/GPS for precise navigation on set paths
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 interf...
- Autores:
-
Silva, Jesús
Varela Izquierdo, Noel
Pineda, Omar
Hernández Palma, Hugo
Cueto, Eduardo Nicolas
- Tipo de recurso:
- Article of journal
- Fecha de publicación:
- 2020
- Institución:
- Corporación Universidad de la Costa
- Repositorio:
- REDICUC - Repositorio CUC
- Idioma:
- eng
- OAI Identifier:
- oai:repositorio.cuc.edu.co:11323/7740
- Acceso en línea:
- https://hdl.handle.net/11323/7740
https://doi.org/10.1007/978-981-15-4875-8_35
https://repositorio.cuc.edu.co/
- Palabra clave:
- Global positioning system (GPS)
Inertial measurement unit
Coupling system
Sensors
Kalman filter
Madgwick filter
- Rights
- openAccess
- License
- Attribution-NonCommercial-NoDerivatives 4.0 International
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Coupling architecture between INS/GPS for precise navigation on set paths |
| title |
Coupling architecture between INS/GPS for precise navigation on set paths |
| spellingShingle |
Coupling architecture between INS/GPS for precise navigation on set paths Global positioning system (GPS) Inertial measurement unit Coupling system Sensors Kalman filter Madgwick filter |
| title_short |
Coupling architecture between INS/GPS for precise navigation on set paths |
| title_full |
Coupling architecture between INS/GPS for precise navigation on set paths |
| title_fullStr |
Coupling architecture between INS/GPS for precise navigation on set paths |
| title_full_unstemmed |
Coupling architecture between INS/GPS for precise navigation on set paths |
| title_sort |
Coupling architecture between INS/GPS for precise navigation on set paths |
| dc.creator.fl_str_mv |
Silva, Jesús Varela Izquierdo, Noel Pineda, Omar Hernández Palma, Hugo Cueto, Eduardo Nicolas |
| dc.contributor.author.spa.fl_str_mv |
Silva, Jesús Varela Izquierdo, Noel Pineda, Omar Hernández Palma, Hugo Cueto, Eduardo Nicolas |
| dc.subject.spa.fl_str_mv |
Global positioning system (GPS) Inertial measurement unit Coupling system Sensors Kalman filter Madgwick filter |
| topic |
Global positioning system (GPS) Inertial measurement unit Coupling system Sensors Kalman filter Madgwick filter |
| description |
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. |
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2020 |
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2020 |
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2021-01-21T13:38:50Z |
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2021-01-21T13:38:50Z |
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Corporación Universidad de la Costa |
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eng |
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1. Marston, S., Li, Z., Bandyopadhyay, S., Zhang, J., Ghalsasi, A.: Cloud computing—The business perspective. Decis. Support Syst. 51(1), 176–189 (2011) 2. Bifet, A., De Francisci Morales, G.: Big Data Stream Learning with Samoa (2014). Recuperado de https://www.researchgate.net/publication/282303881_Big_ data_stream_learning_with_SAMOA 3. Lomax, T., Schrank, D., Turner, S., Margiotta, R.: Report for Selecting Travel Reliability Measures. Federal Highway Administration, Washington, D. C. (2003) 4. Pardillo, J., Sánchez, V.: Apuntes de Ingeniería de Tránsito. ETS Ingenieros de Caminos, Canales y Puertos, Madrid, España (2015) 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) 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) 7. Yong-chuan, Z., Xiao-qing, Z., Zhen-ting, C: Traffic congestion detection based on GPS floating-car data. Procedia Eng. 15, 5541–5546 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) 9. Thames, L., Schaefer, D.: Software-defined cloud manufacturing for industry 4.0. Procedia CIRP 52, 12–17 (2016) 10. Viloria, A., Neira-Rodado, D., Pineda Lezama, O.B.: Recovery of Scientific Data Using Intelligent Distributed Data Warehouse. ANT/EDI40 2019, pp 1249–1254 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. 1201–1206 12. Alpaydin, E.: Introduction to Machine Learning. The MIT Press, Massachusetts (2004) 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) 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) 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) 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) 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) 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) 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) |
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Silva, JesúsVarela Izquierdo, NoelPineda, OmarHernández Palma, HugoCueto, Eduardo Nicolas2021-01-21T13:38:50Z2021-01-21T13:38:50Z2020https://hdl.handle.net/11323/7740https://doi.org/10.1007/978-981-15-4875-8_35Corporación Universidad de la CostaREDICUC - Repositorio CUChttps://repositorio.cuc.edu.co/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.Silva, JesúsVarela Izquierdo, Noel-will be generated-orcid-0000-0001-7036-4414-600Pineda, Omar-will be generated-orcid-0000-0002-8239-3906-600Hernández Palma, HugoCueto, Eduardo Nicolasapplication/pdfengCorporación Universidad de la CostaAttribution-NonCommercial-NoDerivatives 4.0 Internationalhttp://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Smart Innovation, Systems and Technologieshttps://link.springer.com/chapter/10.1007/978-981-15-4875-8_35Global positioning system (GPS)Inertial measurement unitCoupling systemSensorsKalman filterMadgwick filterCoupling architecture between INS/GPS for precise navigation on set pathsArtículo de revistahttp://purl.org/coar/resource_type/c_6501http://purl.org/coar/resource_type/c_2df8fbb1Textinfo:eu-repo/semantics/articlehttp://purl.org/redcol/resource_type/ARTinfo:eu-repo/semantics/acceptedVersion1. Marston, S., Li, Z., Bandyopadhyay, S., Zhang, J., Ghalsasi, A.: Cloud computing—The business perspective. Decis. Support Syst. 51(1), 176–189 (2011)2. Bifet, A., De Francisci Morales, G.: Big Data Stream Learning with Samoa (2014). Recuperado de https://www.researchgate.net/publication/282303881_Big_ data_stream_learning_with_SAMOA3. Lomax, T., Schrank, D., Turner, S., Margiotta, R.: Report for Selecting Travel Reliability Measures. Federal Highway Administration, Washington, D. C. (2003)4. Pardillo, J., Sánchez, V.: Apuntes de Ingeniería de Tránsito. ETS Ingenieros de Caminos, Canales y Puertos, Madrid, España (2015)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)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)7. Yong-chuan, Z., Xiao-qing, Z., Zhen-ting, C: Traffic congestion detection based on GPS floating-car data. Procedia Eng. 15, 5541–55468. 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)9. Thames, L., Schaefer, D.: Software-defined cloud manufacturing for industry 4.0. Procedia CIRP 52, 12–17 (2016)10. Viloria, A., Neira-Rodado, D., Pineda Lezama, O.B.: Recovery of Scientific Data Using Intelligent Distributed Data Warehouse. ANT/EDI40 2019, pp 1249–125411. 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. 1201–120612. Alpaydin, E.: Introduction to Machine Learning. The MIT Press, Massachusetts (2004)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)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)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)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)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)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)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)PublicationCC-LICENSElicense_rdflicense_rdfapplication/rdf+xml; charset=utf-8805https://repositorio.cuc.edu.co/bitstreams/f2263e31-d246-4fb6-8416-e9839c13ab84/download4460e5956bc1d1639be9ae6146a50347MD52LICENSElicense.txtlicense.txttext/plain; charset=utf-83196https://repositorio.cuc.edu.co/bitstreams/7d7e0e4f-d707-4457-b2b8-240ea4183a58/downloade30e9215131d99561d40d6b0abbe9badMD53ORIGINALCoupling architecture between INS GPS for precise navigation on set paths.pdfCoupling architecture between INS GPS for precise navigation on set paths.pdfapplication/pdf104677https://repositorio.cuc.edu.co/bitstreams/f81e17f4-a6c1-4756-a27d-19b459ef2738/downloadf868559a6a8c38445e6576a338c056cfMD51THUMBNAILCoupling architecture between INS GPS for precise navigation on set paths.pdf.jpgCoupling architecture between INS GPS for precise navigation on set 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