Robust maximum correlation entropy Kalman filtering algorithm based on S-functions under impulse noise
Author:
Funder
the National Natural Science Foundation of China under Grant
Publisher
Springer Science and Business Media LLC
Link
https://link.springer.com/content/pdf/10.1007/s11760-024-03135-y.pdf
Reference26 articles.
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2. Lee, D., Vukovich, G., Lee, R.: Robust unscented Kalman filter for nanosat attitude estimation. Int. J. Control Autom. Syst. (2017). https://doi.org/10.1007/s12555-016-0498-4
3. Khalkhali, M., Vahedian, A., Sadoghi Yazdi, H.: Vehicle tracking with Kalman filter using online situation assessment. Robot. Auton. Syst. 131, 103596 (2020). https://doi.org/10.1016/j.robot.2020.103596
4. Lu, T., Watanabe, Y., Yamada, S., Takada, H.: Comparative evaluation of Kalman filters and motion models in vehicular state estimation and path prediction. J. Navig. (2021). https://doi.org/10.1017/S0373463321000370
5. Khalkhali, M., Vahedian, A., Sadoghi Yazdi, H.: Situation assessment-augmented interactive Kalman filter for multi-vehicle tracking. IEEE Trans. Intell. Transp. Syst. (2021). https://doi.org/10.1109/TITS.2021.3050878
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