Satellite Positioning Accuracy Improvement in Urban Canyons Through a New Weight Model Utilizing GPS Signal Strength Variability
Urban environments present substantial obstacles to GPS positioning accuracy, primarily due to multipath interference and limited satellite visibility. To address these challenges, we propose a novel weighting approach, referred to as the HK model, that enhances real-time GPS positioning performance...
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MDPI AG
2025-07-01
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| author | Hye-In Kim Kwan-Dong Park |
| author_facet | Hye-In Kim Kwan-Dong Park |
| author_sort | Hye-In Kim |
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| description | Urban environments present substantial obstacles to GPS positioning accuracy, primarily due to multipath interference and limited satellite visibility. To address these challenges, we propose a novel weighting approach, referred to as the HK model, that enhances real-time GPS positioning performance by leveraging the variability of the signal-to-noise ratio (SNR), without requiring auxiliary sensors. Analysis of 24 h observational datasets collected across diverse environments, including open-sky (OS), city streets (CS), and urban canyons (UC), demonstrates that multipath-affected non-line-of-sight (NLOS) signals exhibit significantly greater SNR variability than direct line-of-sight (LOS) signals. The HK model classifies received signals based on the standard deviation of their SNR and assigns corresponding weights during position estimation. Comparative performance evaluation indicates that relative to existing weighting models, the HK model improves 3D positioning accuracy by up to 22.4 m in urban canyon scenarios, reducing horizontal RMSE from 13.0 m to 4.7 m and vertical RMSE from 19.5 m to 6.9 m. In city street environments, horizontal RMSE is reduced from 11.6 m to 3.8 m. Furthermore, a time-sequential analysis at the TEHE site confirms consistent improvements in vertical positioning accuracy across all 24-hourly datasets, and in terms of horizontal accuracy, in 22 out of 24 cases. These results demonstrate that the HK model substantially surpasses conventional SNR- or elevation-based weighting techniques, particularly under severe multipath conditions frequently encountered in dense urban settings. |
| format | Article |
| id | doaj-art-474700424d18420eb5c5f5e7dca77bbd |
| institution | Kabale University |
| issn | 1424-8220 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | MDPI AG |
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| series | Sensors |
| spelling | doaj-art-474700424d18420eb5c5f5e7dca77bbd2025-08-20T03:36:30ZengMDPI AGSensors1424-82202025-07-012515467810.3390/s25154678Satellite Positioning Accuracy Improvement in Urban Canyons Through a New Weight Model Utilizing GPS Signal Strength VariabilityHye-In Kim0Kwan-Dong Park1PP-Solution Inc., 606 Seobusaet-gil #B-2311, Seoul 08504, Republic of KoreaPP-Solution Inc., 606 Seobusaet-gil #B-2311, Seoul 08504, Republic of KoreaUrban environments present substantial obstacles to GPS positioning accuracy, primarily due to multipath interference and limited satellite visibility. To address these challenges, we propose a novel weighting approach, referred to as the HK model, that enhances real-time GPS positioning performance by leveraging the variability of the signal-to-noise ratio (SNR), without requiring auxiliary sensors. Analysis of 24 h observational datasets collected across diverse environments, including open-sky (OS), city streets (CS), and urban canyons (UC), demonstrates that multipath-affected non-line-of-sight (NLOS) signals exhibit significantly greater SNR variability than direct line-of-sight (LOS) signals. The HK model classifies received signals based on the standard deviation of their SNR and assigns corresponding weights during position estimation. Comparative performance evaluation indicates that relative to existing weighting models, the HK model improves 3D positioning accuracy by up to 22.4 m in urban canyon scenarios, reducing horizontal RMSE from 13.0 m to 4.7 m and vertical RMSE from 19.5 m to 6.9 m. In city street environments, horizontal RMSE is reduced from 11.6 m to 3.8 m. Furthermore, a time-sequential analysis at the TEHE site confirms consistent improvements in vertical positioning accuracy across all 24-hourly datasets, and in terms of horizontal accuracy, in 22 out of 24 cases. These results demonstrate that the HK model substantially surpasses conventional SNR- or elevation-based weighting techniques, particularly under severe multipath conditions frequently encountered in dense urban settings.https://www.mdpi.com/1424-8220/25/15/4678GPSSNRmultipathweight modelurban canyons |
| spellingShingle | Hye-In Kim Kwan-Dong Park Satellite Positioning Accuracy Improvement in Urban Canyons Through a New Weight Model Utilizing GPS Signal Strength Variability Sensors GPS SNR multipath weight model urban canyons |
| title | Satellite Positioning Accuracy Improvement in Urban Canyons Through a New Weight Model Utilizing GPS Signal Strength Variability |
| title_full | Satellite Positioning Accuracy Improvement in Urban Canyons Through a New Weight Model Utilizing GPS Signal Strength Variability |
| title_fullStr | Satellite Positioning Accuracy Improvement in Urban Canyons Through a New Weight Model Utilizing GPS Signal Strength Variability |
| title_full_unstemmed | Satellite Positioning Accuracy Improvement in Urban Canyons Through a New Weight Model Utilizing GPS Signal Strength Variability |
| title_short | Satellite Positioning Accuracy Improvement in Urban Canyons Through a New Weight Model Utilizing GPS Signal Strength Variability |
| title_sort | satellite positioning accuracy improvement in urban canyons through a new weight model utilizing gps signal strength variability |
| topic | GPS SNR multipath weight model urban canyons |
| url | https://www.mdpi.com/1424-8220/25/15/4678 |
| work_keys_str_mv | AT hyeinkim satellitepositioningaccuracyimprovementinurbancanyonsthroughanewweightmodelutilizinggpssignalstrengthvariability AT kwandongpark satellitepositioningaccuracyimprovementinurbancanyonsthroughanewweightmodelutilizinggpssignalstrengthvariability |