ACCURATE REAL-TIME TRACKING IN GEOLOGY: A DATA-DRIVEN APPROACH
DOI:
https://doi.org/10.47390/ts-v3i10y2025No3Keywords:
Real-Time Tracking, Geological Monitoring, Data-Driven System, Machine Learning, Big Data.Abstract
Accurately tracking the position of machinery like tunnel borers or robots in underground environments is a major challenge. Standard GPS fails in these settings, and sensor systems like inertial navigation units accumulate large positioning errors over time. This paper presents a novel, data-driven solution to this problem. The core of our system is a powerful data processing framework that fuses these vast, real-time sensor datasets. Using a machine learning model, we correct the drift in the motion sensors, enabling highly accurate, real-time geological positioning.
References
1. Kaplan, E. D., & Hegarty, C. J. (Eds.). (2017). Understanding GPS/GNSS: principles and applications. Artech house.
2. Улжаев, Э., Убайдуллаев, У., & Хонтураев, С. (2025). ТЕХНОЛОГИИ ОПРЕДЕЛЕНИЯ КООРДИНАТ С ПОМОЩЬЮ ДРОНОВ. Techscience. uz-Texnika fanlarining dolzarb masalalari, 3(5), 25-29.
3. Groves, P. D. (2013). Principles of GNSS, inertial, and multisensor integrated navigation systems. Artech house.
4. Хонтураев, С. (2025). ПРИМЕНЕНИЕ ДРОНОВ В СОВРЕМЕННОЙ ГЕОПРОСТРАНСТВЕННОЙ КАРТОГРАФИИ. Techscience. uz-Texnika fanlarining dolzarb masalalari, 3(4), 29-32.
5. Julier, S. J., & Uhlmann, J. K. (2004). "Unscented filtering and nonlinear estimation." Proceedings of the IEEE, 92(3), 401-422.
6. Khalilov, D., Bozorova, S., Khonturaev, S., Khoitkulov, A., Sotvoldieva, D., & Toshmatov, S. (2024). Self-learning system and methods of selection of weight coefficients of neural network. In E3S Web of Conferences (Vol. 508, p. 04011). EDP Sciences.
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