24 citations as recorded by crossref.

1. Undersampled Dynamic X-Ray Tomography With Dimension Reduction Kalman Filter J. Hakkarainen et al. https://doi.org/10.1109/TCI.2019.2896527
2. Damage Detection of Building Structures using AEKF(Adaptive Extended Kalman Filter) D. Yun et al. https://doi.org/10.7734/COSEIK.2019.32.1.45
3. Optimal parameterization of Kalman filter based three-phase dynamic state estimator for active distribution networks D. Ćetenović & A. Ranković https://doi.org/10.1016/j.ijepes.2018.04.008
4. An adaptive method for tuning process noise covariance matrix in EKF-based three-phase distribution system state estimation D. Ćetenović et al. https://doi.org/10.1016/j.ijepes.2021.107192
5. Structural Damage Identification with a Tuning-free Hybrid Extended Kalman Filter D. Yun et al. https://doi.org/10.1080/10168664.2020.1797614
6. An Augmented Multiple Imputation Particle Filter for River State Estimation With Missing Observation Z. Ismail & N. Jalaludin https://doi.org/10.3389/frobt.2021.788125
7. A robust dynamic state estimation for droop controlled islanded microgrids H. Farag et al. https://doi.org/10.1016/j.epsr.2016.05.030
8. Robust Data Assimilation in River Flow and Stage Estimation Based on Multiple Imputation Particle Filter Z. Ismail & N. Jalaludin https://doi.org/10.1109/ACCESS.2019.2949616
9. Sensor System and Observer Algorithm Co-Design For Modern Internal Combustion Engine Air Management Based on H2 Optimization X. Zhang et al. https://doi.org/10.3389/fmech.2021.611992
10. Estimation of ship roll damping and natural frequency using an extended Kalman filter applied to URANS output Y. Ozdemir https://doi.org/10.1007/s12046-023-02232-x
11. Noise Covariances Identification by MDM: Weighting, Recursion, and Implementation O. Kost et al. https://doi.org/10.1016/j.ifacol.2024.08.552
12. Parameter estimation using the extended Kalman filter applied to the roll decay experiment Y. Ozdemir https://doi.org/10.1080/17445302.2025.2543484
13. Noise covariance matrices in state‐space models: A survey and comparison of estimation methods—Part I J. Duník et al. https://doi.org/10.1002/acs.2783
14. Analysis of bias in an Ebola epidemic model by extended Kalman filter approach D. Ndanguza et al. https://doi.org/10.1016/j.matcom.2017.05.005
15. Adaptive Kalman Filter for Real-Time Visual Object Tracking Based on Autocovariance Least Square Estimation J. Li et al. https://doi.org/10.3390/app14031045
16. Prognostics for lithium-ion batteries using a two-phase gamma degradation process model C. Lin et al. https://doi.org/10.1016/j.ress.2021.107797
17. Iterative Filtering and Smoothing with Optical Flow Prediction Models J. Hakkarainen et al. https://doi.org/10.1137/24M1689120
18. Extracranial Estimation of Neural Mass Model Parameters Using the Unscented Kalman Filter L. Escuain-Poole et al. https://doi.org/10.3389/fams.2018.00046
19. Implementation of an Adaptive Bias‐Aware Extended Kalman Filter for Sea‐Ice Data Assimilation in the HARMONIE‐AROME Numerical Weather Prediction System Y. Batrak https://doi.org/10.1029/2021MS002533
20. Accurate SOC estimation of ternary lithium-ion batteries by HPPC test-based extended Kalman filter I. Monirul et al. https://doi.org/10.1016/j.est.2024.112304
21. Bayesian non-parametric methods for dynamic state-noise covariance matrix estimation: Application to target tracking C. Magnant et al. https://doi.org/10.1016/j.sigpro.2016.02.013
22. Measurement Difference Method: A Universal Tool for Noise Identification O. Kost et al. https://doi.org/10.1109/TAC.2022.3160679
23. Correcting Structural Bias in Dynamical Models of Infectious Disease Using a Bayesian State-Space Framework M. Amadi et al. https://doi.org/10.1007/s11538-026-01730-3
24. Unobservable Systems: No Problem for Noise Identification O. Kost et al. https://doi.org/10.1109/TAC.2025.3607601