eprintid: 1421172 rev_number: 63 eprint_status: archive userid: 608 dir: disk0/01/42/11/72 datestamp: 2014-03-18 09:28:38 lastmod: 2021-12-20 00:45:55 status_changed: 2015-03-17 16:34:32 type: proceedings_section metadata_visibility: show item_issues_count: 0 creators_name: Wang, L title: Kinematic GNSS Shadow Matching Using Particle Filters ispublished: pub divisions: UCL divisions: B04 divisions: C05 abstract: Student Paper Award Winner. The poor performance of GNSS user equipment in urban canyons is a well-known problem and is particularly inaccurate in the cross-street direction. However, the accuracy in this direction greatly affects many applications, including vehicle lane identification and high-accuracy pedestrian navigation. Shadow matching was proposed to help solve this problem by using information derived from 3D models of buildings. Though users of GNSS positioning typically move, previous research has focused on static shadow-matching positioning. In this paper, for the first time, kinematic shadow-matching positioning is tackled. Kalman filter based shadow matching is proposed and also, in order to overcome some of its predicted limitations, a particle filter is proposed to better solve the problem. date: 2014-09-12 publisher: The Institute of Navigation official_url: http://www.ion.org/publications/browse.cfm?proceedingsID=85 vfaculties: VENG oa_status: green full_text_type: other primo: open primo_central: open_green verified: verified_manual elements_source: Manually entered elements_id: 935421 lyricists_name: Wang, Lei lyricists_id: LWANG11 full_text_status: public place_of_pub: Manassas, US pagerange: 1907 - 1919 event_title: ION GNSS+ 2014 event_location: Tampa, Florida event_dates: 2014-09-08 - 2014-09-12 book_title: Proceedings of the 27th International Technical Meeting of The Satellite Division of the Institute of Navigation (ION GNSS+ 2014) citation: Wang, L; (2014) Kinematic GNSS Shadow Matching Using Particle Filters. In: Proceedings of the 27th International Technical Meeting of The Satellite Division of the Institute of Navigation (ION GNSS+ 2014). (pp. 1907 - 1919). The Institute of Navigation: Manassas, US. Green open access document_url: https://discovery.ucl.ac.uk/id/eprint/1421172/1/GNSS14-0181.pdf