Shuguo Pan
Southeast University
83 Papers
144 Citations
Shuguo Pan is an academic researcher from Southeast University. The author has contributed to research in topics: GNSS applications & Computer science. The author has an hindex of 10, co-authored 58 publications. Previous affiliations of Shuguo Pan include Chinese Ministry of Education.
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Papers
Combined GPS and BDS for single-frequency continuous RTK positioning through real-time estimation of differential inter-system biases
TL;DR: An inter-system differencing model for combined GPS and BDS single-frequency RTK positioning through real-time estimation of DISBs is presented and can effectively improve the positioning accuracy and reliability, especially for severely obstructed situations with only a few satellites observed.
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A method of GPS/BDS/GLONASS combined RTK positioning for middle-long baseline with partial ambiguity resolution
Wang Gao,Chengfa Gao,Shuguo Pan +2 more
TL;DR: In this article, a GPS/BDS/GLONASS combined real-time kinematic (RTK) positioning method for middle-long baseline is proposed, where a two-step AR strategy is adopted, where wide-lane and ionosphere-free observation model are used respectively.
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Multipath extraction and mitigation for bridge deformation monitoring using a single-difference model
TL;DR: It is evident that ambiguity resolution has significant improvement with applying multipath mitigation, contributing to more accurate and reliable ambiguity results in high-precision deformation monitoring.
47
Inter-System Differencing between GPS and BDS for Medium-Baseline RTK Positioning
TL;DR: Experimental results show that with the inter-system differencing model, the accuracy and reliability of RTK positioning can be effectively improved, especially for the obstructed environments with a small number of satellites available.
40
Improving Ambiguity Resolution for Medium Baselines Using Combined GPS and BDS Dual/Triple-Frequency Observations
TL;DR: The results indicate that, compared to the single triple-frequency BDS system, the combined system can significantly enhance the AR model strength, and thus improve AR performance for medium baselines with a 75.7% reduction of initialization time on average.