Research on AIS algorithm based on factor graph inference in Doppler localization

Abstract In this paper, an adaptive incremental smoothing (AIS) algorithm based on factor graph inference is proposed to solve the problem of improving the Doppler positioning accuracy of low-Earth orbit communication satellites. Traditional methods such as iterative least squares (ILS) algorithm and Extended Kalman filter (EKF) have certain limitations in real-time Doppler localization. The AIS algorithm uses factor graph inference to identify interference factors, and adopts adaptive smoothing to reduce the impact of estimation errors and promote adaptive noise estimation. The ground station Starlink satellite constellation static positioning was taken as an example, and the simulation was carried out on the STK software simulation platform to verify the feasibility and effectiveness of the algorithm. The experimental results show that the positioning accuracy of the AIS algorithm based on factor graph inference is significantly improved compared with the ILS and EKF algorithms in real-time Doppler positioning solutions. The aim of this research is to improve the positioning accuracy of low-orbit satellites and provide a reference for practical applications.