Robust factor graph optimisation method for shipborne GNSS/INS integrated navigation system

Abstract

Robust Global Navigation Satellite System (GNSS) factors are introduced into a factor graph optimisation based integrated navigation system to address the challenge of occluded GNSS signals during ship navigation, which leads to increased errors in positioning results. To enhance the robustness of the GNSS tracking loop, a vector tracking method is applied to receiver tracking loop. Then, the Mahalanobis distance was employed to assess the pseudorange residual and identify and reject signals that exhibit anomalies. Specifically, the pseudorange residual is computed as the difference between the predicted pseudorange of the GNSS receiver and the measured pseudorange. Using the historical information in the window, robust GNSS factors were constructed for use in the factor graph. The robust factor graph optimisation method for a shipborne GNSS/Inertial Navigation System integrated navigation system was implemented by constructing robust GNSS factors and Inertial Measurement Unit factors. The experimental results confirm that the positioning accuracy of the proposed method is superior to those of the factor graph optimization and extended Kalman filter.

A Robust Position Estimation Method in the Integrated Navigation System via Factor Graph

“…The study in [20] combines the advantages of the sliding window and chi-square to allow faults to be estimated repeatedly to improve GNSS integrity. Similarly, the work in [21] uses the history information within a sliding window to fit new observations, reducing the effect of coarseness. However, all these solutions have a certain degree of time delay [22].…”