Abstract
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.
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Data availability
The datasets generated and analyzed during the current study are available from the corresponding author on reasonable request.
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This work was in part supported by the National Natural Science Foundation of China under Grant 61871033.
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Chang, Y., Wang, Y., Shen, Y. et al. A new fuzzy strong tracking cubature Kalman filter for INS/GNSS. GPS Solut 25, 120 (2021). https://doi.org/10.1007/s10291-021-01148-5
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DOI: https://doi.org/10.1007/s10291-021-01148-5