Construction of high-precision satellite positioning model and research on fault feedback mechanism based on improved FP-Growth algorithm

To address accuracy degradation and delayed fault response in satellite positioning under complex environments, this study proposes a positioning model integrating an improved FP-Growth (Frequent Pattern Growth) algorithm with a real-time fault feedback mechanism. The model employs a Bidirectional Frequent Pattern Tree (Bi-FPT) to optimize storage and traversal efficiency, incorporates dynamic support thresholds and item utility values to enhance the mining of key positioning features, and establishes a library of association rules linking location states to fault modes, thereby enabling rapid fault diagnosis. To evaluate the model’s performance, experiments were conducted using a dataset containing one million measured Global Positioning System (GPS)/BeiDou data points. Horizontal and vertical Root Mean Square Error (RMSE) served as the metrics for positioning accuracy, alongside statistics on fault recognition rates and response times. The results demonstrate a horizontal positioning RMSE of 0.8 m and a vertical positioning RMSE of 1.2 m, with a positioning accuracy of 89.7%, a fault recognition rate of 85%, and a system response time of only 0.8 s. Relative to the traditional FP-Growth algorithm, the proposed method reduces horizontal RMSE from 1.5 m to 0.8 m, shortens fault response time from 1.6 s to 0.8 s, and increases fault recognition rate from 75% to 85%. Notably, the model maintains high reliability and rapid feedback capabilities even in challenging scenarios such as urban canyons, demonstrating significant value for engineering applications. Not applicable.