High-density urban environments severely impair smartphone Global Navigation Satellite System(GNSS)positioning due to Non-Line-of-Sight(NLOS)signals and limited satellite visibility,leading to reduced accuracy and con...High-density urban environments severely impair smartphone Global Navigation Satellite System(GNSS)positioning due to Non-Line-of-Sight(NLOS)signals and limited satellite visibility,leading to reduced accuracy and continuity.Three-Dimensional Map-aided(3DMA)GNSS methods partially solve the problems but still much rely on noisy pseudorange measurements,while the resolution of carrier-phase ambiguities remain challenging,limiting their robustness in complex urban areas.To overcome these challenges,this study introduces a novel Factor Graph Optimization(FGO)framework that tightly integrates 3D map constraints with multiple GNSS observations.First,a Shadow Matching(SDM)scoring strategy is proposed by incorporating Time-Diferenced Carrier Phase(TDCP)constraints.Second,a map-matching probability approach is applied to identify a unique candidate road segment,thereby reducing solution ambiguity.Third,a Random Sample Consensus(RANSAC)-based region growing clustering algorithm is designed to manage multimodal high-score points and ensure unique clustering.Finally,a factor graph model is constructed that fuses pseudorange,Doppler,and TDCP observations with 3D map constraints,signifcantly enhancing positioning accuracy and stability.Field experiments in typical urban scenarios show that the proposed method outperforms existing SDM techniques such as road constraint and region-growing clustering,as well as advanced GNSS optimization frameworks,in terms of both positioning accuracy and trajectory continuity.Specifcally,the proportion of horizontal positioning errors within 3 m and 5 m reached 76.7%and 93.1%,respectively,substantially exceeding those achieved by the advanced GNSS multi-source fusion framework(63.4%and 79.3%).展开更多
基金supported in part by the National Natural Science Foundation of China(Grants 42394060,42394065 and 42274020)the Science and Technology Planning Project of Jiangsu Province(Grant BE2023692)+2 种基金supported by the Fundamental Research Funds for the Central Universities(Grants 2025-00046)the Postgraduate Research&Practice Innovation Program of Jiangsu Province(Grant KYCX25_2890)the Graduate Innovation Program of China University of Mining and Technology(Grant 2025WLKXJ200).
文摘High-density urban environments severely impair smartphone Global Navigation Satellite System(GNSS)positioning due to Non-Line-of-Sight(NLOS)signals and limited satellite visibility,leading to reduced accuracy and continuity.Three-Dimensional Map-aided(3DMA)GNSS methods partially solve the problems but still much rely on noisy pseudorange measurements,while the resolution of carrier-phase ambiguities remain challenging,limiting their robustness in complex urban areas.To overcome these challenges,this study introduces a novel Factor Graph Optimization(FGO)framework that tightly integrates 3D map constraints with multiple GNSS observations.First,a Shadow Matching(SDM)scoring strategy is proposed by incorporating Time-Diferenced Carrier Phase(TDCP)constraints.Second,a map-matching probability approach is applied to identify a unique candidate road segment,thereby reducing solution ambiguity.Third,a Random Sample Consensus(RANSAC)-based region growing clustering algorithm is designed to manage multimodal high-score points and ensure unique clustering.Finally,a factor graph model is constructed that fuses pseudorange,Doppler,and TDCP observations with 3D map constraints,signifcantly enhancing positioning accuracy and stability.Field experiments in typical urban scenarios show that the proposed method outperforms existing SDM techniques such as road constraint and region-growing clustering,as well as advanced GNSS optimization frameworks,in terms of both positioning accuracy and trajectory continuity.Specifcally,the proportion of horizontal positioning errors within 3 m and 5 m reached 76.7%and 93.1%,respectively,substantially exceeding those achieved by the advanced GNSS multi-source fusion framework(63.4%and 79.3%).
