# GNSS-Based Monitoring Methods for Mining Headframes
In contemporary GNSS research, notwithstanding the substantial advancements achieved in positioning, navigation, and monitoring through GNSS technology, several limitations remain, particularly concerning the enhancement of monitoring accuracy and the application of the technology in complex environments. These challenges primarily arise from intrinsic signal errors, including signal blockage, multipath effects, atmospheric delay errors, and satellite geometry distribution [13]. To mitigate these issues, researchers have undertaken extensive experimental investigations aimed at correcting and enhancing GNSS performance. For instance, in complex environments, multi-system integration has been utilized to enhance positioning accuracy and reliability. [PERSON] et al. demonstrated this approach by combining GNSS with inertial navigation systems and incorporating a Cascade-LSTM neural network to predict pseudo-velocity measurements, effectively bridging GNSS signal gaps during interruptions [14]. Furthermore, the development of cost-effective, high-performance hardware constitutes a crucial avenue for the advancement of Global Navigation Satellite Systems (GNSS). [PERSON] et al. demonstrated the feasibility of using Garmin handheld GNSS devices for geodetic measurements by employing relative methods and precise point positioning (PPP) technology to assess their static and kinematic positioning capabilities. They concluded that these devices could potentially replace expensive GNSS receivers in certain regions [15]. Additionally, the utilization of low Earth orbit (LEO) satellites to supplement traditional GNSS signals is gaining traction as a viable approach to address signal limitations. [PERSON] et al. successfully integrated LEO satellites with conventional GNSS satellites, thereby alleviating dynamic stress on receivers and enhancing the overall performance of the system [16]. In the context of deformation monitoring data acquisition using Global Navigation Satellite System (GNSS) technology, the investigation of data processing and analysis methodologies is crucial for ensuring the accuracy and reliability of the monitoring data. This, in turn, provides a foundational basis for the structural safety monitoring and assessment of large structures, such as headframes. Commonly employed techniques, including Kalman filtering, Gaussian filtering, and wavelet transform, are utilized to clean and denoise the monitoring data, thereby enhancing the reliability of the analysis outcomes. [PERSON] and [PERSON] utilized Kalman filter technology augmented with a shaping filter, which improved the real-time deformation monitoring accuracy of GPS coordinate time series and effectively addressed data containing colored noise [17]. Additionally, [PERSON] et al. introduced an enhanced wavelet threshold denoising method, which was shown to be effective in eliminating high-frequency noise while preserving data details, as demonstrated through simulation analysis and engineering case studies [18]. [PERSON] and colleagues conducted a study on the application of spatiotemporal Kalman filtering in deformation analysis. They compared the similarities and differences among three typical spatiotemporal Kalman filtering models, examining both their fundamental principles and experimental applications. Their analysis focused on the models' applicability in deformation analysis and their effectiveness in spatiotemporal filtering denoising, data interpolation, and deformation prediction [19]. [PERSON] and [PERSON] investigated a sliding filtering model tailored for GNSS deformation monitoring. Their research aimed to enhance the model's robustness against anomalies during periods of rapid local deformation while improving precision in processing stable deformation. They employed the minimum description length criterion to assess the effectiveness of various models in deformation monitoring [20].