Multi-frequency and multi-system GNSS positioning error modeling and correction based on machine learning in biomechanical context
Abstract
Positioning error modeling and correction in multi-frequency and multi-system GNSS is vital. Conventional methods have limitations in complex scenarios. Here, the RBF neural network algorithm is harnessed. GNSS and dual frequency data are integrated via multi-source feature extraction. K-means determines the RBF center to capture data traits. OLS optimizes the model. Through learning from extensive raw data, real-time error prediction and correction occur, resolving accuracy-complexity issues. In biomechanics, GNSS has great potential. In rehabilitation, it can precisely locate patients during outdoor mobility exercises. For example, for those recovering from orthopedic surgeries, GNSS tracks movement paths. This data correlates with biomechanical parameters like joint angles and muscle forces during walking or running. Understanding how patients’ biomechanics change in different outdoor terrains and distances helps design personalized rehab plans. In sports, it monitors athletes’ outdoor training. Analyzing position data alongside biomechanical metrics like sprint acceleration and body rotation during maneuvers refines training techniques. Experimentally, compared to RF, LSTM, and SVM, the RBF neural network’s MSE dropped by 20.1%, 30.3%, and 44.4% respectively. Execution time reduced by 37.5%, 84.1%, and 64.7%. This enhanced GNSS method thus offers new prospects for biomechanical research and applications.
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Copyright (c) 2025 Qi Liu, Jian Zhao, Yuran Chen, Jiangshun Yu, Shan Wu, Sirui Wu
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