Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/81174
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dc.contributorInterdisciplinary Division of Aeronautical and Aviation Engineering-
dc.creatorXu, B-
dc.creatorJia, Q-
dc.creatorLuo, Y-
dc.creatorHsu, LT-
dc.date.accessioned2019-08-16T08:57:11Z-
dc.date.available2019-08-16T08:57:11Z-
dc.identifier.urihttp://hdl.handle.net/10397/81174-
dc.language.isoenen_US
dc.publisherMolecular Diversity Preservation International (MDPI)en_US
dc.rights© 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).en_US
dc.rightsThe following publication Xu, B.; Jia, Q.; Luo, Y.; Hsu, L.-T. Intelligent GPS L1 LOS/Multipath/NLOS Classifiers Based on Correlator-, RINEX- and NMEA-Level Measurements. Remote Sens. 2019, 11, 1851, 1-23 is available at https://dx.doi.org/10.3390/rs11161851en_US
dc.subjectGlobal positioning system (GPS)en_US
dc.subjectSoftware-defined receiver (SDR)en_US
dc.subjectSignal classificationen_US
dc.subjectNon-line-of-sight (NLOS)en_US
dc.subjectMultipathen_US
dc.subjectSupport vector machine (SVM)en_US
dc.subjectUrban environmenten_US
dc.titleIntelligent GPS L1 LOS/Multipath/NLOS classifiers based on correlator-, RINEX- and NMEA-level measurementsen_US
dc.typeJournal/Magazine Articleen_US
dc.identifier.spage1en_US
dc.identifier.epage23en_US
dc.identifier.volume11en_US
dc.identifier.issue16en_US
dc.identifier.doi10.3390/rs11161851en_US
dcterms.abstractThis paper proposes to use a correlator-level global positioning system (GPS) line-of-sight/multipath/non-line-of-sight (LOS/MP/NLOS) signal reception classifier to improve positioning performance in an urban environment. Conventional LOS/MP/NLOS classifiers, referred to as national marine electronics association (NMEA)-level and receiver independent exchange format (RINEX)-level classifiers, are usually performed using attributes extracted from basic observables or measurements such as received signal strength, satellite elevation angle, code pseudorange, etc. The NMEA/RINEX-level classification rate is limited because the complex signal propagation in urban environment is not fully manifested in these end attributes. In this paper, LOS/MP/NLOS features were extracted at the baseband signal processing stage. Multicorrelator is implemented in a GPS software-defined receiver (SDR) and exploited to generate features from the autocorrelation function (ACF). A robust LOS/MP/NLOS classifier using a supervised machine learning algorithm, support vector machine (SVM), is then trained. It is also proposed that the Skymask and code pseudorange double difference observable are used to label the real signal type. Raw GPS intermediate frequency data were collected in urban areas in Hong Kong and were postprocessed using a self-developed SDR, which can easily output correlator-level LOS/MP/NLOS features. The SDR measurements were saved in the file with the format of NMEA and RINEX. A fair comparison among NMEA-, RINEX-, and correlator-level classifiers was then carried out on a common ground. Results show that the correlator-level classifier improves the metric of F1 score by about 25% over the conventional NMEA- and RINEX-level classifiers for testing data collected at different places to that of training data. In addition to this finding, correlator-level classifier is found to be more feasible in practical applications due to its less dependency on surrounding scenarios compared with the NMEA/RINEX-level classifiers.-
dcterms.accessRightsopen accessen_US
dcterms.bibliographicCitationRemote sensing, 8 Aug. 2019, v. 11, no. 16, 1851, p. 1-23-
dcterms.isPartOfRemote sensing-
dcterms.issued2019-08-08-
dc.identifier.eissn2072-4292en_US
dc.identifier.artn1851en_US
dc.description.validate201908 bcrcen_US
dc.description.oaVersion of Recorden_US
dc.identifier.FolderNumbera0353-n01en_US
dc.description.pubStatusPublisheden_US
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