Multi-Constellation GNSS-IR Building Model Rectification Using a Smartphone in Urban Areas
Mingda Ye, Guohao Zhang, and Li-Ta Hsu, Department of Aeronautical and Aviation Engineering, The Hong Kong Polytechnic University
Date/Time: Wednesday, Sep. 18, 9:20 a.m.
Urban modeling constitutes a critical component of smart city development, requiring the acquisition of geometric data related to urban environments and with special emphasis on building boundaries. Traditional urban modeling relies on Light Detection and Ranging (LiDAR), photogrammetry, and mobile mapping systems (MMS), with an accuracy of up to 0.5 meters (Ahmadi et al., 2010; dos Santos et al., 2019). However, these methods are expensive and labor-intensive, necessitating a more economical and precise alternative for large-scale modeling and timely updates.
In the past decades, GNSS remote sensing, particularly GNSS-IR, has been widely used for its all-weather capability, precision, and cost-effectiveness (Wang & Morton, 2020; Zavorotny et al., 2014; Li et al., 2021). By analyzing interference patterns from reflected GNSS signals, GNSS-IR can estimate vertical distances to surfaces like snow or sea at decimeter to centimeter accuracy, demonstrating its utility in environmental monitoring (Anderson, 2000; Tabibi et al., 2017; Ye et al., 2022). In our previous work, we introduced the GNSS-IR to range building facades in urban areas and experimentally demonstrated that the perpendicular distance from the receiver to the building facades can be measured at the centimeter level using a smartphone (Ye et al., 2024). However, there are several practical issues unanalyzed, e.g., the strategy of multi-constellation combined GNSS-IR ranging, the shortest observation duration required to retrieve a ranging result, and the effect of receiver position error.
In this study, a multi-constellation combined GNSS-IR method is developed to efficiently implement facade ranging using GPS, GLONASS, Galileo, and BDS observations. A systematic analysis is conducted on the performance of multi-constellation combined GNSS-IR in the rectification of building models in dense urban areas. The shortest observation duration of multipath satellites needed for retrieving an accurate GNSS-IR measurement is also investigated. Preliminary experimental results are obtained based on smartphone GNSS data in a dense urban area. One facade is detected by 6 satellites with a measured ranging error of 0.03 m, while another facade is detected by 3 satellites with a measured ranging error of 0.04 m. However, there is one facade detected by only 1 satellite, and the ranging error was larger, reaching 0.26 m. The result shows that multi-constellation combined GNSS-IR can retrieve facade distances at the centimeter level when the number of available observations is sufficient.
Detailed methodology and preliminary results can be found in the uploaded Expanded Abstract.
References
Ahmadi, S., Zoej, M. V., Ebadi, H., Moghaddam, H. A., & Mohammadzadeh, A. (2010). Automatic urban building boundary extraction from high resolution aerial images using an innovative model of active contours. International Journal of Applied Earth Observation and Geoinformation, 12(3), 150-157.
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dos Santos, R. C., Galo, M., & Carrilho, A. C. (2019). Extraction of building roof boundaries from LiDAR data using an adaptive alpha-shape algorithm. IEEE Geoscience and Remote Sensing Letters, 16(8), 1289-1293.
Li, Y., Yu, K., Jin, T., Chang, X., Wang, Q., & Li, J. (2021). Development of a GNSS-IR instrument based on low-cost positioning chips and its performance evaluation for estimating the reflector height. Gps Solutions, 25, 1-12.
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Wang, Y., & Morton, Y. J. (2020). Coherent GNSS reflection signal processing for high-precision and high-resolution spaceborne applications. IEEE Transactions on Geoscience and Remote Sensing, 59(1), 831-842.
Ye, M., Jin, S., & Jia, Y. (2022). Ten-Minute Sea-Level Variations From Combined Multi-GNSS Multipath Reflectometry Based on a Weighted Iterative Least-Square Method. IEEE Transactions on Geoscience and Remote Sensing, 60, 1-10.
Ye, M., Zhang, G., & Hsu, L.-T. (2024). Building Model Rectification Using GNSS Reflectometry. IEEE Geoscience and Remote Sensing Letters.
Zavorotny, V. U., Gleason, S., Cardellach, E., & Camps, A. (2014). Tutorial on remote sensing using GNSS bistatic radar of opportunity. IEEE Geoscience and Remote Sensing Magazine, 2(4), 8-45.
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