GNSSFormer: Enhancing GNSS Single Point Positioning Performance Based on Transformer for Smartphone

Jinkun Li, Chundi Xiu, Anlan Yu, Zhiqing Hong, Feng Wang, Haotian Wang, Yuchen Wang, James Chakwizira, Dongkai Yang

Abstract: The global navigation satellite system (GNSS) provides continuous high-precision positioning, enabling many applications such as vehicle navigation and pedestrian monitoring. However, in challenging environments such as urban canyons, positioning accuracy is significantly degraded due to multipath and nonline-of-sight (NLOS) issues. To tackle this issue, we introduce a single point positioning (SPP) framework based on pseudorange correction, including three modules: feature extraction, pseudorange correction, and positioning model. Heavy pseudorange error is the primary cause of inaccurate localization, and, in particular, we introduce GNSSFormer, a Transformer-based model to obtain the pseudorange correction values. After preprocessing raw observations of the GNSS, we designed a pseudorange correction model, GNSSFormer, containing a temporal Transformer block and a multisatellite joint spatial Transformer block. The GNSSFormer extracts multiple features related to both satellites and receivers, learning the complex global relationships between these features and the pseudorange errors to derive correction values. Subsequently, an extended Kalman filter (EKF)-based Rauch–Tung–Striebel (RTS) smoothing SPP algorithm is employed to determine the location. Validation demonstrates that the GNSSFormer significantly enhances SPP performance compared to state-of-the-art algorithms, improving positioning accuracy at least by 29.42% and 28.40% on two open-source datasets and 18.91% on a real-world dataset.

External IDs:dblp:journals/iotj/LiXYHWWWCY25