Enhancing the Precision of Smartphone GNSS Positioning Using Machine Learning
Author:
Affiliation:
1. Computer Science and Design, Singapore University of Technology and Design, Singapore
Publisher
ACM
Link
https://dl.acm.org/doi/pdf/10.1145/3591156.3591177
Reference14 articles.
1. Quality assessment of GNSS observations from an Android N smartphone and positioning performance analysis using time-differenced filtering approach
2. Quality analysis of multi-GNSS raw observations and a velocity-aided positioning approach based on smartphones
3. Paziewski J , Sieradzki R , Baryla R. Signal characterization and assessment of code GNSS positioning with low-power consumption smartphones. GPS solutions , 2019 , 23(4): 1-12. Paziewski J, Sieradzki R, Baryla R. Signal characterization and assessment of code GNSS positioning with low-power consumption smartphones. GPS solutions, 2019, 23(4): 1-12.
4. On the feasibility of resolving Android GNSS carrier-phase ambiguities
5. Raw GNSS observations from Android smartphones: characteristics and short-baseline RTK positioning performance
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