Factor Graph Optimization for Urban Environment GNSS Positioning and Robust Performance Analysis

  Objectives  BeiDou satellite navigation system (BeiDou)/global navigation satellite system(GNSS) can provide continuous and reliable high-precision navigation and positioning service in open-sky, yet in urban environment, suffering from outliers and cycle slips caused by severe multi-path and non-line-of-sight signals, the navigation and positioning capability remains inadequate. Compared with the extended Kalman filter (EKF), factor graph optimization (FGO) can make full use of historical measurements and restrain the influence of anomalous data through constraints and redundant measurements inside the window.

  Methods  A GNSS positioning model based on sliding-window FGO is constructed, adopting posteriori residuals test as outlier detection method, and analysis of robust performance between EKF and FGO is presented in terms of minimum detectable bias, correct detection rate and positioning accuracy.

  Results  The result of urban environment experiment shows that the minimum detectable bias is reduced by 11.92%-32.56%, the correct detection rate is improved by 3.84%-10.47%, and the GNSS positioning accuracy is improved by 11.29%-25.99%.

  Conclusions  Overall, for Application of GNSS navigation and positioning in urban environment, FGO has better robustness and positioning accuracy, could replace EKF models based on single epoch observations.