段階的非凸性ファクタグラフ最適化によるGNSS外れ値の軽減
GNSS Outlier Mitigation Via Graduated Non-Convexity Factor Graph Optimization
GNSS測位の外れ値（マルチパスやNLOS）を抑えるため、Geman McClure関数と粗から細への緩和を用いた段階的非凸性ファクタグラフ最適化（FGO-GNC）を提案し、香港の都市峡谷データで有効性を検証した論文。
著者: Weisong Wen, Guohao Zhang, Li-Ta Hsu
分類: eess.SP, cs.RO
原文アブストラクト
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.
