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GNSS Outlier Mitigation via Graduated Non-Convexity Factor Graph Optimization.
- Source :
- IEEE Transactions on Vehicular Technology; Jan2022, Vol. 71 Issue 1, p297-310, 14p
- Publication Year :
- 2022
-
Abstract
- Accurate and globally referenced global navigation satellite system (GNSS) based vehicular positioning can be achieved in outlier-free open areas. However, the performance of GNSS can be significantly degraded by outlier measurements, such as multipath effects and non-line-of-sight (NLOS) receptions arising from signal reflections of buildings. Inspired by the advantage of batch historical data in resisting outlier measurements, in this paper, we propose a graduated non-convexity factor graph optimization (FGO-GNC) to improve the GNSS positioning performance, where the impact of GNSS outliers is mitigated by estimating the optimal weightings of GNSS measurements. Different from the existing local solutions, the proposed FGO-GNC employs the non-convex Geman McClure (GM) function to globally estimate the weightings of GNSS measurements via a coarse-to-fine relaxation. The effectiveness of the proposed method is verified through several challenging datasets collected in urban canyons of Hong Kong using automobile level and low-cost smartphone level GNSS receivers. [ABSTRACT FROM AUTHOR]
- Subjects :
- GLOBAL Positioning System
Subjects
Details
- Language :
- English
- ISSN :
- 00189545
- Volume :
- 71
- Issue :
- 1
- Database :
- Complementary Index
- Journal :
- IEEE Transactions on Vehicular Technology
- Publication Type :
- Academic Journal
- Accession number :
- 154862290
- Full Text :
- https://doi.org/10.1109/TVT.2021.3130909