Abstract
High-accuracy GNSS positioning in urban environments is important for applications like safe autonomous driving, however, dynamic errors in complex urban environments limit positioning performances. Recently, deep learning-based (DL) approaches can obtain better GNSS positioning solutions in complex urban environments than model-based ones. However, DL-based approaches simply concentrate one-view GNSS observations as inputs, which are insufficient to model vehicle states accurately, and temporally continuous observations are highly correlated, leading to inaccurate positioning correction results. To solve the challenge, we propose a Sparse Representation-based Multiview Deep Reinforcement Learning model for positioning correction, which employs attention-based multiview fusion to process multiview observations, and uses sparse representation to alleviate disturbances from highly correlated observations. To represent the vehicle state sufficiently, we build a multiview positioning correction environment, and develop an attention-weighted multiview fusion module to fuse temporal features as belief states based on adaptively learned attention weights. To effectively process redundant and correlated multiview features, we impose the ℓ1 norm regularizer to learn sparse hidden representations and improve the precision of value estimation. Finally, we construct a sparse representation-driven multiview actor-critic positioning correction model to achieve high-accuracy GNSS positioning in complex urban environments. We validate performances in both Google Smartphone Decimeter Challenge (GSDC) datasets and our collected GNSS datasets in the Guangzhou area (GZGNSS). Experimental results show that our algorithm can improve localization performances with 27% improvements from WLS+KF in GSDC trajectories, 16% from RTK, and 6% from DL-based methods in GZGNSS trajectories.
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The data that support the findings of this study are available from the corresponding author upon reasonable request.
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Acknowledgements
This research is supported in part by National Natural Science Foundation of China under Grants 62203122, 62273106, 62320106008, 62373114, in part by Guang Dong Basic and Applied Basic Research Foundation 2023A1515011159, 2023A1515011480, in part by China Postdoctoral Science Foundation funded project 2022M720840, and in part by National Key Research and Development Plan-Strategic Scientific and Technological Innovation Cooperation Key Project under Grant 2023YFE0209400.
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Conceptualization: Haoli Zhao, Zhenni Li; Methodology: Haoli Zhao, Zhenni Li; Formal analysis and Investigation: Haoli Zhao; Software: Haoli Zhao, Qianming Wang; Data Curation: Haoli Zhao, Qianming Wang; Writing original draft preparation: Haoli Zhao; Writing review and editing: Haoli Zhao, Zhenni Li, Kan Xie, Ci Chen; Funding acquisition: Haoli Zhao, Zhenni Li, Shengli Xie, Ming Liu; Supervision: Zhenni Li, Shengli Xie, Ming Liu.
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Zhao, H., Li, Z., Wang, Q. et al. Improving performances of GNSS positioning correction using multiview deep reinforcement learning with sparse representation. GPS Solut 28, 98 (2024). https://doi.org/10.1007/s10291-024-01626-6
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DOI: https://doi.org/10.1007/s10291-024-01626-6