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SVR and ARIMA models as machine learning solutions for solving the latency problem in real-time clock corrections

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SVR and ARIMA models as machine learning solutions for solving the latency problem in real-time clock corrections

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dc.contributor.author Qafisheh, Mutaz es_ES
dc.contributor.author Martín Furones, Ángel Esteban es_ES
dc.contributor.author Capilla, Raquel M. es_ES
dc.contributor.author Anquela Julián, Ana Belén es_ES
dc.date.accessioned 2023-10-05T18:02:10Z
dc.date.available 2023-10-05T18:02:10Z
dc.date.issued 2022-07 es_ES
dc.identifier.issn 1080-5370 es_ES
dc.identifier.uri http://hdl.handle.net/10251/197781
dc.description.abstract [EN] Real-time precise point positioning (PPP) has become a prevalent technique in global navigation satellite systems (GNSS). However, GNSS real-time users must receive space state representation (SSR) products to correct for satellite clock, orbit, and phase biases. The International GNSS Service (IGS) provides GNSS users with real-time services (RTSs) through different real-time correction SSR products. These products arrive at the GNSS users with some latency, which affects the quality of real-time PPP positioning. The autoregressive integrated moving average (ARIMA) and support vector regression (SVR) models are used in this research to predict those corrections to eliminate the latency effect. ARIMA model reduces the standard deviation by 28% and 13% for GPS and GLONASS constellations, respectively, compared to the real-time solution, which includes the latency effect, the research simulated the latency effect and named it a forced-latency solution, and the SVR model reduces the standard deviation by 28% and 23% for GPS and GLONASS constellations, respectively. The results for the permanent GNSS stations used in this study across different years 2013, 2014, 2015, 2019, and 2021 show a mean reduction in the 3D positioning standard deviation by 13% compared with the forced-latency solution for the ARIMA solution and 9% for the SVR solution. The potential of both models to overcome the latency effect is apparent based on the findings. es_ES
dc.description.sponsorship Open Access funding provided thanks to the CRUE-CSIC agreement with Springer Nature. We greatly appreciate the eforts of the IGS, Analysis and Data Centers, and tracking station managers for generating high-quality data and products and for making them available to the GNSS community in a timely and reliable way. The authors want to thank the Kartographie und Geodäsie Agency (BKG) for developing and free-available use of BNC software for real-time and post-process PPP computations. The reviewers are kindly acknowledged for their contribution to improving the manuscript with their valuable comments and suggestions. es_ES
dc.language Inglés es_ES
dc.publisher Springer-Verlag es_ES
dc.relation.ispartof GPS Solutions es_ES
dc.rights Reconocimiento (by) es_ES
dc.subject Precise point positioning es_ES
dc.subject Real-time positioning es_ES
dc.subject Support vector regression es_ES
dc.subject Autoregressive integrated moving average es_ES
dc.subject Clock corrections es_ES
dc.subject.classification INGENIERIA CARTOGRAFICA, GEODESIA Y FOTOGRAMETRIA es_ES
dc.title SVR and ARIMA models as machine learning solutions for solving the latency problem in real-time clock corrections es_ES
dc.type Artículo es_ES
dc.identifier.doi 10.1007/s10291-022-01270-y es_ES
dc.rights.accessRights Abierto es_ES
dc.contributor.affiliation Universitat Politècnica de València. Escuela Técnica Superior de Ingeniería Geodésica, Cartográfica y Topográfica - Escola Tècnica Superior d'Enginyeria Geodèsica, Cartogràfica i Topogràfica es_ES
dc.description.bibliographicCitation Qafisheh, M.; Martín Furones, ÁE.; Capilla, RM.; Anquela Julián, AB. (2022). SVR and ARIMA models as machine learning solutions for solving the latency problem in real-time clock corrections. GPS Solutions. 26(3):1-14. https://doi.org/10.1007/s10291-022-01270-y es_ES
dc.description.accrualMethod S es_ES
dc.relation.publisherversion https://doi.org/10.1007/s10291-022-01270-y es_ES
dc.description.upvformatpinicio 1 es_ES
dc.description.upvformatpfin 14 es_ES
dc.type.version info:eu-repo/semantics/publishedVersion es_ES
dc.description.volume 26 es_ES
dc.description.issue 3 es_ES
dc.relation.pasarela S\467580 es_ES
dc.contributor.funder Universitat Politècnica de València es_ES
dc.description.references Asari K, Saito M, Mikami I (2019) Capturing method of millimeter displacement in centimeter class PPP-RTK measured data. In: Proceedings of the ION GNSS 2019, Pacific PNT Meeting, Honolulu, Hawaii, April 2019, pp 367–375 es_ES
dc.description.references Box GEP, Jenkins GM, Reinsel GC (2011) Time series analysis: forecasting and control, vol 734. Wiley, New York es_ES
dc.description.references Brownlee J (2017) Introduction to time series forecasting with python: how to prepare data and develop models to predict the future. In: Brownlee J (ed) Machine learning mastery es_ES
dc.description.references Cai C, Gao Y (2013) Modeling and assessment of combined GPS/GLONASS precise point positioning. GPS Solut 17(2):223–236 es_ES
dc.description.references Clarkson KL, Hazan E, Woodruff DP (2012) Sublinear optimization for machine learning. J ACM. https://doi.org/10.1145/2371656.2371658 es_ES
dc.description.references Daly IDK (1990) Characterization of NAVSTAR GPS and GLONASS on-board clocks. In: IEEE symposium on position location and navigation. A decade of excellence in the navigation sciences, Las Vegas, NV, USA, March 1990 es_ES
dc.description.references Dow JM, Neilan RE, Rizos C (2009) The international GNSS service in a changing landscape of global navigation satellite systems. J Geod 83(3–4):191–198 es_ES
dc.description.references Drucker H, Surges CJC, Kaufman L, Smola A, Vapnik V (1997) Support vector regression machines. Adv Neural Inf Process Syst 1:155–161 es_ES
dc.description.references El-Mowafy A (2019a) Impact of predicting real-time clock corrections during their outages on precise point positioning. Surv Rev 51(365):183–192 es_ES
dc.description.references El-Mowafy A (2019b) Predicting real-time orbit and clock corrections for positioning using GPS, GLONASS, and QZSS in natural hazard warning systems. J Appl Geod 13(2):69–79 es_ES
dc.description.references El-Mowafy A, Deo M, Kubo N (2017) Maintaining real-time precise point positioning during outages of orbit and clock corrections. GPS Solut 21(3):937–947 es_ES
dc.description.references Enge P, Misra P (2011) Global positioning system: signals, measurements, and performance—revised, 2nd edn. Ganga-Jamuna Press, Nanded es_ES
dc.description.references Ge M, Gendt G, Rothacher M, Shi C, Liu J (2008) Resolution of GPS carrier-phase ambiguities in Precise Point Positioning (PPP) with daily observations. J Geod 82(7):389–399. https://doi.org/10.1007/s00190-007-0187-4 es_ES
dc.description.references Ge M, Chen J, Dousa J, Gendt G, Wickert J (2012) A computationally efficient approach for estimating high-rate satellite clock corrections in realtime. GPS Solut 16(1):9–17. https://doi.org/10.1007/s10291-011-0206-z es_ES
dc.description.references Georg Weber G, Mervart L, Stürze A, Stöcker D (2016) BNC Ntrip Client (BNC). A toolkit for retrieving, decoding, converting and processing real-time GNSS data streams. https://software.rtcm-ntrip.org/export/HEAD/ntrip/trunk/BNC/src/bnchelp.html. Accessed Jan 2020 es_ES
dc.description.references Grayson B, Penna NT, Mills JP, Grant DS (2018) GPS precise point positioning for UAV photogrammetry. Photogramm Rec 33(164):427–447 es_ES
dc.description.references Grinter T, Roberts C (2013) Real time precise point positioning: are we there yet? In: Proceedings of the international global navigation satellite systems society. IGNSS symposium 2013, Outrigger Gold Coast, Qld, Australia es_ES
dc.description.references Guyon I, Boser B, Vapnik V (1993) Automatic capacity tuning of very large VC-dimension classifiers. Adv Neural Inf Process Syst 5:147–155 es_ES
dc.description.references Hadas T, Bosy J (2014) IGS RTS precise orbits and clocks verification and quality degradation over time. GPS Solut 19(1):93–105. https://doi.org/10.1007/s10291-014-0369-5 es_ES
dc.description.references Hadas T, Bosy J (2015) IGS RTS precise orbits and clocks verification and quality. GPS Solut 19(1):93–105 es_ES
dc.description.references Hauschild A, Montenbruck O, Steigenberger P (2013) Short-term analysis of GNSS clocks. GPS Solut 17(3):295–307 es_ES
dc.description.references Henkel P, Psychas D, Günther C, Hugentobler U (2018) Estimation of satellite position, clock and phase bias corrections. J Geod 92(10):1199–1217 es_ES
dc.description.references Hofmann-Wellenhof B, Lichtenegger H, Wasle E (2008) GNSS, global navigation satellite systems: GPS, GLONASS, GALILEO & more. Springer, New York es_ES
dc.description.references Huang G, Zhang Q (2012) Real-time estimation of satellite clock offset using adaptively robust Kalman filter with classified adaptive factors. GPS Solut 16(4):531–539. https://doi.org/10.1007/s10291-012-0254-z es_ES
dc.description.references Huang GW, Zhang Q, Xu GC (2014) Real-time clock offset prediction with an improved model. GPS Solut 18(1):95–104. https://doi.org/10.1007/s10291-013-0313-0 es_ES
dc.description.references Hyndman RJ, Athanasopoulos G (2018). Forecasting: principles and practice. Online-open-access-textbooks es_ES
dc.description.references Johnston G, Riddell A, Hausler G (2017) The international GNSS service. In: Teunissen P, Montenbruck O (eds) Handbook of global navigation satellite systems. Springer, New York, pp 967–982. https://doi.org/10.1007/978-3-319-42928-1_33 es_ES
dc.description.references Kim M, Kim J (2017) GA-ARMA model for predicting IGS RTS corrections. Int J Aerosp Eng. https://doi.org/10.1155/2017/6316590 es_ES
dc.description.references Kouba J, Héroux P (2001) Precise point positioning using IGS orbit and clock products. GPS Solut 5(2):12–28. https://doi.org/10.1007/PL00012883 es_ES
dc.description.references Kwiatkowski D, Phillips PCB, Schmidt P, Shin Y (1992) Testing the null hypothesis of stationarity against the alternative of a unit root. J Econom 54(1–3):159–178 es_ES
dc.description.references Leick A, Rapoport L, Tatarnikov D (2015) GPS satellite surveying, 4th edn. Wiley, Hoboken es_ES
dc.description.references Li X, Ge M, Dai X, Ren X, Fritsche M, Wickert J, Schuh H (2015) Accuracy and reliability of multi-GNSS real-time precise positioning: GPS, GLONASS, BeiDou, and Galileo. J Geod 89(6):607–635 es_ES
dc.description.references Maciuk K (2019) Satellite clock stability analysis depending on the reference clock type. Arab J Geosci. https://doi.org/10.1007/s12517-018-4069-2 es_ES
dc.description.references Martín A, Anquela AB, Dimas-Pagés A, Cos-Gayón F (2015a) Validation of performance of real-time kinematic PPP. A possible tool for deformation monitoring. Measurement 69:95–108. https://doi.org/10.1016/j.measurement.2015.03.026 es_ES
dc.description.references Martín A, Hadas T, Dimas A, Anquela AB, Berné JL (2015b) Influence of real-time products latency on kinematic PPP results. In: 5th international colloquium scientific and fundamental aspects of the galileo programme. Braunschweig, Germany es_ES
dc.description.references Nie Z, Liu F, Gao Y (2020) Real-time precise point positioning with a low-cost dual-frequency GNSS device. GPS Solut. https://doi.org/10.1007/s10291-019-0922-3 es_ES
dc.description.references Piccolo D (1990) A distance measure for classifying ARIMA models. J Time Ser Anal 11(2):153–164. https://doi.org/10.1111/j.1467-9892.1990.tb00048.x es_ES
dc.description.references Qafisheh MWA, Martín A, Torres-Sospedra J (2020) Support vector regression machine learning tool to predict GNSS clock corrections in real-time PPP technique. ICL-GNSS 2020 WiP proceedings, June 2–4, 2020, Tampere, Finland. http://ceur-ws.org/Vol-2626/paper11.pdf es_ES
dc.description.references Sanz J, Juan JM, Zornoza J Hernández-Pajares M (2013) GNSS data processing. Volume I: fudamentals and algorithms. Ed. ESA communications. https://gssc.esa.int/navipedia/GNSS_Book/ESA_GNSS-Book_TM-23_Vol_I.pdf es_ES
dc.description.references Schlitzer G (1995) Testing the stationarity of economic time series: further Monte Carlo evidence. Ric Econ 49(2):125–144 es_ES
dc.description.references Senior KL, Ray JR, Beard RL (2008) Characterization of periodic variations in the GPS satellite clocks. GPS Solut 12(3):211–225. https://doi.org/10.1007/s10291-008-0089-9 es_ES
dc.description.references Smola AJ, Schölkopf B (2004) A tutorial on support vector regression. Stat Comput 14(3):199–222. https://doi.org/10.1023/B:STCO.0000035301.49549.88 es_ES
dc.description.references Sneeuw N, Novák P, Crespi M, Sansò F (2012) Analysing time series of GNSS residuals by means of AR (I) MA processes. In: VII Hotine-Marussi symposium on mathematical geodesy, international association of geodesy symposia 137. Springer, Berlin es_ES
dc.description.references Teunissen PJG, Khodabandeh A (2015) Review and principles of PPP-RTK methods. J Geod 89(3):217–240 es_ES
dc.description.references Van Le H, Nishio M (2019) Structural change monitoring of a cable-stayed bridge by time-series modeling of the global thermal deformation acquired by GPS. J Civ Struct Health Monit 9(5):689–701. https://doi.org/10.1007/s13349-019-00360-9 es_ES
dc.description.references Weber G, Mervart L (2007) The BKG ntrip client (BNC). EUREF symposium 2007, London es_ES
dc.description.references Xin J, Zhou J, Yang SX, Li X, Wang Y (2018) Bridge structure deformation prediction based on GNSS data using Kalman-ARIMA-GARCH model. Sens. https://doi.org/10.3390/s18010298 es_ES
dc.description.references Yang H, Xu C, Gao Y (2019) Analysis of GPS satellite clock prediction performance with different update intervals and application to real-time PPP. Surv Rev 51(364):43–52. https://doi.org/10.1080/00396265.2017.1359473 es_ES
dc.description.references Ye Q, Szeto WY, Wong SC (2012) Short-term traffic speed forecasting based on data recorded at irregular intervals. IEEE Transact Intell Transp Syst 13(4):1727–1737. https://doi.org/10.1109/TITS.2012.2203122 es_ES
dc.description.references Ye S, Zhao L, Song J, Chen D, Jiang W (2018) Analysis of estimated satellite clock biases and their effects on precise point positioning. GPS Solut. https://doi.org/10.1007/s10291-017-0680-z es_ES
dc.description.references Zumberge JF, Heflin M, Jefferson DC, Watkins M, Webb F (1997) Precise point positioning for the efficient and robust analysis of GPS data from large networks. J Geophys Res. https://doi.org/10.1029/96JB03860 es_ES


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