Please use this identifier to cite or link to this item: http://ir.futminna.edu.ng:8080/jspui/handle/123456789/10353
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dc.contributor.authorEyo, Etim E.-
dc.contributor.authorMusa, Tajul A.-
dc.contributor.authorIdris, Khairulnizam M.-
dc.contributor.authorOpaluwa, Y. D.-
dc.date.accessioned2021-07-18T10:08:19Z-
dc.date.available2021-07-18T10:08:19Z-
dc.date.issued2014-03-08-
dc.identifier.citationEyo E.E., Musa T.A., Idris K.M., Opaluwa Y.D. (2014) Reverse RTK Data Streaming for Low-Cost Landslide Monitoring. In: Abdul Rahman A., Boguslawski P., Anton F., Said M., Omar K. (eds) Geoinformation for Informed Decisions. Lecture Notes in Geoinformation and Cartography. Springer, Cham. https://doi.org/10.1007/978-3-319-03644-1_2en_US
dc.identifier.isbnPrint: 978-3-319-03643-4 and Online: 978-3-319-03644-1-
dc.identifier.urihttp://repository.futminna.edu.ng:8080/jspui/handle/123456789/10353-
dc.description.abstractThis chapter describes the preliminary study of real-time data streaming in support of the proposed low-cost landslide monitoring system using the Reverse Real-Time Kinematic (RRTK) technique. The RRTK algorithm was implemented by streaming raw Global Positioning System (GPS) data of both the reference and roving station(s) to the control centre for processing, and transmission of the position solution to the roving station. The main purpose of the data streaming was to investigate the quality of the measurements, for utilization in landslide modelling and analysis in near real-time. A novel methodology using a high pass filtering technique was implemented, to detect outliers in the observations. Also, the autocorrelation of GPS time series was investigated.en_US
dc.description.sponsorshipFlagship UTM Fund (i-Sejahtera—MAWAR), vote number 01G01en_US
dc.language.isoenen_US
dc.publisherSpringer International Publishing Switzerlanden_US
dc.subjectData streaming Landslide Monitoring Low-cost GPS High pass filter Autocorrelationen_US
dc.titleReverse RTK Data Streaming for Low-Cost Landslide Monitoringen_US
dc.typeBook chapteren_US
Appears in Collections:Surveying & Geoinformatics

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