# GNSS Outlier Mitigation Via Graduated Non-Convexity Factor Graph Optimization

arXiv: https://arxiv.org/abs/2109.00667 · HTML: https://mlsanity.com/paper/2109.00667/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Sep 2021.

## Built on by (most cited first, 3 of 3)

- [Learning-based NLOS Detection and Uncertainty Prediction of GNSS Observations with Transformer-Enhanced LSTM Network](https://mlsanity.com/paper/2309.00480/) (2023)
- [Learning-based GNSS Uncertainty Quantification using Continuous-Time Factor Graph Optimization](https://mlsanity.com/paper/2503.04933/) (2025)
- [CredibleDFGO: Differentiable Factor Graph Optimization with Credibility Supervision](https://mlsanity.com/paper/2605.06100/) (2026)

## Abstract (arXiv)

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.