GNSS Outlier Mitigation Via Graduated Non-Convexity Factor Graph Optimization

Weisong Wen, Guohao Zhang, Li Ta Hsu

Research output: Journal article publicationJournal articleAcademic researchpeer-review

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.

Original languageEnglish
JournalIEEE Transactions on Vehicular Technology
DOIs
Publication statusAccepted/In press - 26 Nov 2021

Keywords

  • Adaptive tunning
  • Estimation
  • Factor graph optimization
  • Global navigation satellite system
  • GNSS
  • Graduated non-convexity
  • Navigation
  • NLOS
  • Nonconvex Geman McClure
  • Position measurement
  • Receivers
  • Satellites
  • Three-dimensional displays
  • Urban canyons
  • Weight measurement

ASJC Scopus subject areas

  • Automotive Engineering
  • Aerospace Engineering
  • Electrical and Electronic Engineering
  • Applied Mathematics

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