A new fuzzy strong tracking cubature Kalman filter for INS/GNSS

To further enhance the positioning accuracy and stability of the INS/GNSS integrated navigation system, we present a new fuzzy strong tracking cubature Kalman filter (FSTCKF) algorithm for data fusion. A fuzzy logic controller is designed for the strong tracking cubature Kalman filter (STCKF), which aims at strengthening the filter's ability to identify and respond to the dynamics. Chi-square tests are separately conducted on the innovation vector in order to reveal the dynamic properties inside the velocity and position states. Thereafter, parallel fuzzy inferences are conducted to generate a time-varying smoothing factor matrix, which helps the STCKF obtain the multiple fading factors distributed to each state variable. Numerical simulations and real data testing results demonstrate the superiority and robustness of the proposed FSTCKF algorithm. Not only can the proposed algorithm maintain the accuracy and stability in steady conditions, but further increase the dynamic tracking ability as well. Finally, the positioning performances of the INS/GNSS can be improved.