PPP Parameter Estimation Algorithm Based on Hybrid Filtering

Precise Point Positioning (PPP) is a technology that utilizes the Global Navigation Satellite System (GNSS) for high accuracy positioning. Starting from the quality control of PPP, this paper focuses on the convergence speed and positioning error of PPP, and the article proposes an improved optimization algorithm for parameter estimation in the data processing stage. A hybrid filtering algorithm is proposed for the re-convergence caused by data interruptions during data processing and the dispersion of localization results caused by abnormal observations. The algorithm combines Previous Data-Constrained Kalman Filter (PD-CKF) and Robust Adaptive Kalman Filter (RAKF), and takes the actual localization results of JFNG station as an example to confirm the new algorithm's. The simulation results show that the improved algorithm can effectively avoid the phenomenon of re-convergence caused by data interruption, and can dynamically adapt to the changes of the observation data by adaptively adjusting the filter parameters, so as to improve the system's effect of anti-differential.