{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,5]],"date-time":"2026-05-05T06:31:53Z","timestamp":1777962713044,"version":"3.51.4"},"reference-count":45,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2020,3,17]],"date-time":"2020-03-17T00:00:00Z","timestamp":1584403200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["41774027"],"award-info":[{"award-number":["41774027"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["41704008"],"award-info":[{"award-number":["41704008"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2016YFB0502101"],"award-info":[{"award-number":["2016YFB0502101"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012226","name":"Fundamental Research Funds for the Central Universities","doi-asserted-by":"publisher","award":["KYCX19_0067"],"award-info":[{"award-number":["KYCX19_0067"]}],"id":[{"id":"10.13039\/501100012226","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>In urban areas, the accuracy and reliability of global navigation satellite systems (GNSS) vehicle positioning decline due to substantial non-line-of-sight (NLOS) signals and multipath effects. Recently, positioning enhancement approaches with supervised GNSS signal type classification based on 3D building model-aided labelling have received widespread attention. Despite the reduced computing costs and improved real-time performance, the strict requirements of 3D building models on accuracy and timeliness limit the popularization of the technology to some extent. Meanwhile, the diversity of anomalous observation sources is beyond the reach of NLOS\/multipath detection methods. This paper attempts to construct an alternative framework for quality identification of GNSS observations combining clustering-based anomaly detection and supervised classification, in which the hierarchical density-based spatial clustering of applications with noise (HDBSCAN) algorithm is used to label the offline dataset as normal and anomalous observations without the aid of 3D building models, and the supervised classifier in the online system learns the classification rule for real-time anomaly detection. The experimental results based on the measured vehicle GPS\/BeiDou data show that after excluding anomalous observations the single point positioning accuracy of the offline dataset is improved by 87.0%, 45.9%, and 69.6% in the east, north, and up directions, respectively, and the positioning accuracy of two online datasets is improved by 48.4%\/45.7%, 39.6%\/63.3%, and 49.6%\/49.1% in the three directions. Through a large number of comparative experiments and discussion on key issues, it is certified that the proposed method is highly feasible and has great potential in the practical application of urban GNSS vehicle positioning.<\/jats:p>","DOI":"10.3390\/rs12060971","type":"journal-article","created":{"date-parts":[[2020,3,18]],"date-time":"2020-03-18T08:13:27Z","timestamp":1584519207000},"page":"971","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":33,"title":["Anomaly Detection for Urban Vehicle GNSS Observation with a Hybrid Machine Learning System"],"prefix":"10.3390","volume":"12","author":[{"given":"Yan","family":"Xia","sequence":"first","affiliation":[{"name":"School of Instrument Science and Engineering, Southeast University, Nanjing 210096, China"},{"name":"Key Laboratory of Micro-Inertial Instrument and Advanced Navigation Technology, Ministry of Education, Nanjing 210096, China"},{"name":"Nottingham Geospatial Institute, The University of Nottingham, Nottingham NG7 2TU, UK"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shuguo","family":"Pan","sequence":"additional","affiliation":[{"name":"School of Instrument Science and Engineering, Southeast University, Nanjing 210096, China"},{"name":"Key Laboratory of Micro-Inertial Instrument and Advanced Navigation Technology, Ministry of Education, Nanjing 210096, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2440-8054","authenticated-orcid":false,"given":"Xiaolin","family":"Meng","sequence":"additional","affiliation":[{"name":"Nottingham Geospatial Institute, The University of Nottingham, Nottingham NG7 2TU, UK"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wang","family":"Gao","sequence":"additional","affiliation":[{"name":"School of Instrument Science and Engineering, Southeast University, Nanjing 210096, China"},{"name":"Key Laboratory of Micro-Inertial Instrument and Advanced Navigation Technology, Ministry of Education, Nanjing 210096, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Fei","family":"Ye","sequence":"additional","affiliation":[{"name":"School of Instrument Science and Engineering, Southeast University, Nanjing 210096, China"},{"name":"Key Laboratory of Micro-Inertial Instrument and Advanced Navigation Technology, Ministry of Education, Nanjing 210096, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4145-3941","authenticated-orcid":false,"given":"Qing","family":"Zhao","sequence":"additional","affiliation":[{"name":"School of Transportation, Southeast University, Nanjing 210096, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xingwang","family":"Zhao","sequence":"additional","affiliation":[{"name":"Nottingham Geospatial Institute, The University of Nottingham, Nottingham NG7 2TU, UK"},{"name":"School of Geodesy and Geomatics, Anhui University of Science and Technology, Huainan 232001, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2020,3,17]]},"reference":[{"key":"ref_1","unstructured":"Groves, P.D., Jiang, Z., Wang, L., and Ziebart, M.K. 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