Abstract
By minimizing the mean squared error, Kalman filter provides optimal estimates with exact system models and precise statistical information of the noises, and the filter may fail or be affected seriously with uncertain models and noises. In the dynamic positioning and navigation field, the filter with the adaptive factor or the fading factor was developed to overcome the drawbacks of the Kalman filter. However, the distinction between these two factors and their performances in the global navigation satellite system (GNSS) and the inertial navigation system (INS) integrated systems is rarely discussed and tested in the literature. The theoretical analysis is implemented between the filters with the adaptive factor and the fading factor, and the performance of both these two filters and the conventional Kalman filter is tested with actual data of the GNSS/INS integrated systems; then, multiple indexes are applied to compare performances of the filtering. For all experiments, the cubature Kalman filter is adopted, aiming at the high-dimensional nonlinear problems. Comparative results demonstrate that the influences of the modeling deviations are well controlled with the adaptive factor or the fading factor, and the filter with the adaptive factor is more likely to avoid the filter divergence and to achieve stable performance in the GNSS/INS integrated systems.
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Data supporting this research and used to obtain the results reported can be provided by the corresponding author (E-mail: jcddtg@163.com).
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Acknowledgements
This research was partially supported by the Natural Science Foundation of Henan Province (212300410198) and the National Natural Science Foundation of China (41774026).
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Jiang, C., Zhang, S., Li, H. et al. Performance evaluation of the filters with adaptive factor and fading factor for GNSS/INS integrated systems. GPS Solut 25, 130 (2021). https://doi.org/10.1007/s10291-021-01165-4
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DOI: https://doi.org/10.1007/s10291-021-01165-4