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1. • • The enhanced adaptive Kalman filtering (EAKF) based on dynamic recursive nominal covariance estimation (DNRCE) and modified variational Bayesian inference is presented and has good adaptive performance for inaccurate and time‐varying noise covariance matrices.
     www.semanticscholar.org/paper/A-New-Adaptive-Kalman-Filter-with-Inaccurate-Noise-Xu-Wu/4b905bc22e51fb4faae347b3d97af2e8efbcba3f

     A New Adaptive Kalman Filter with Inaccurate Noise Statistics

2. People also ask

   Can Kalman filter improve Gaussian state-space model with inaccurate noise statistics?

   • In this paper, a new adaptive Kalman filter is proposed for a linear Gaussian state-space model with inaccurate noise statistics based on the variational Bayesian (VB) approach.

   A New Adaptive Kalman Filter with Inaccurate Noise Statistics

   link.springer.com/article/10.1007/s00034-019-01053-w

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   Is vbakf a Bayesian adaptive Kalman filter for Gaussian state-space models?

   • Novel Adaptive Kalman Filter With Inaccurate Process and Measurement Noise Covariance Matrices Abstract—In this paper, a novel variational Bayesian (VB)-based adaptive Kalman filter (VBAKF) for linear Gaussian state-space models with inaccurate process and measurement noise covari- ance matrices is proposed.

   Novel Adaptive Kalman Filter With Inaccurate Process and Measurement

   www.researchgate.net/profile/Yulong-Huang-4/publication/319495563_A_Novel_Adaptive_Kalman_Filter_With_Inaccurate_Process_and_Measurement_Noise_Covariance_Matrices/links/5fc17cde299bf104cf86d9a9/A-Novel-Adaptive-Kalman-Filter-With-Inaccurate-Process-and-Measurement-Noise-Covariance-Matrices.pdf?origin=publication_detail

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   Can Bayesian Expectation-maximization adaptive Kalman filter improve dynamic state estimation?

   • We propose a new residual-based Bayesian expectation-maximization adaptive Kalman filter (RBEMAKF) to improve dynamic state estimation with inaccurate and time-varying PMNCMs, which can serve as a general AKF framework for more extensions.

   A novel residual-based Bayesian expectation–maximization adaptive Kal…

   www.sciencedirect.com/science/article/pii/S0263224124008224

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   How to analyze noise composition in a Kalman filter?

   • The first method is to analyze the noise composition from the mechanism level and describe the noise by using an interpretable mathematical model. The Kalman filter usually adopts a mechanism model as a system model in the estimation process.

   State of art on state estimation: Kalman filter driven by machine learnin…

   www.sciencedirect.com/science/article/pii/S1367578823000731

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   Does Kalman filter performance degrade if inaccurate noise statistics are used?

   • The performance of Kalman filter degrades severely when inaccurate mean vectors and covariance matrices of state and measurement noises are used. Unfortunately, accurate noise statistics may be unavailable in some engineering applications, such as target tracking.

   A New Adaptive Kalman Filter with Inaccurate Noise Statistics

   link.springer.com/article/10.1007/s00034-019-01053-w

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   How accurate is Kalman filtering?

   • Simulation results show that the proposed filter has better estimation accuracy than existing state-of-the-art adaptive Kalman filters. Kalman filtering has been extensively applied in many science and engineering disciplines, such as target tracking, control, signal processing, communication, navigation and robotics .

   A New Adaptive Kalman Filter with Inaccurate Noise Statistics

   link.springer.com/article/10.1007/s00034-019-01053-w

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3. link.springer.com › article › 10A New Adaptive Kalman Filter with Inaccurate Noise Statistics

   link.springer.com › article › 10

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   Feb 14, 2019 · To address the filtering problem of a linear Gaussian state-space model with inaccurate noise statistics, a large number of adaptive Kalman filters (AKFs) have been proposed based on different methods.

   • Author: Dingjie Xu, Zhemin Wu, Yulong Huang

   • Publish Year: 2019

4. ieeexplore.ieee.org › document › 8025799A Novel Adaptive Kalman Filter With Inaccurate Process and ...

   ieeexplore.ieee.org › document › 8025799

   Sep 5, 2017 · In this paper, a novel variational Bayesian (VB)-based adaptive Kalman filter (VBAKF) for linear Gaussian state-space models with inaccurate process and measurement noise covariance matrices is proposed.

   • Author: Yulong Huang, Yonggang Zhang, Zhemin Wu, Ning Li, Jonathon Chambers

   • Publish Year: 2018

5. Videos

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     Kalman Filter for Beginners, Part 1 - Recursive Filters & MATLAB Examples

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6. link.springer.com › content › pdfA New Adaptive Kalman Filter with Inaccurate Noise Statistics

   link.springer.com › content › pdf

   In this paper, a new adaptive Kalman filter is proposed for a linear Gaussian state-space model with inaccurate noise statistics based on the variational Bayesian (VB) approach.

   • Author: Dingjie Xu, Zhemin Wu, Yulong Huang

   • Publish Year: 2019

7. www.sciencedirect.com › science › articleState of art on state estimation: Kalman filter driven by ...

   www.sciencedirect.com › science › article

   Jan 1, 2023 · The advancement of deep neural networks and their integration with Kalman filter can improve the accuracy, efficiency, and adaptability of state estimation in complex systems such as autonomous vehicles, robotics, healthcare, and real-time systems.

8. www.sciencedirect.com › science › articleInteracting Multiple Model Adaptive Robust Kalman Filter for ...

   www.sciencedirect.com › science › article

   1 day ago · The Kalman filter, serving as a recursive estimation technique for real-time applications, has found extensive application in various domains including navigation and target tracking [1]. Based on explicit system parameters and the assumption of Gaussian noise, the Kalman filter can provide the optimal estimate of the state.

9. www.sciencedirect.com › science › articleA novel residual-based Bayesian expectation–maximization ...

   www.sciencedirect.com › science › article

   Aug 1, 2024 · We propose a new residual-based Bayesian expectation-maximization adaptive Kalman filter (RBEMAKF) to improve dynamic state estimation with inaccurate and time-varying PMNCMs, which can serve as a general AKF framework for more extensions.

10. www.researchgate.net › profile › Yulong-Huang-4A Novel Adaptive Kalman Filter With Inaccurate Process and ...

    www.researchgate.net › profile › Yulong-Huang-4

    The Kalman filter is optimal in terms of minimum mean square error (MMSE) for linear Gaussian state-space model (1)–(2) with accurate Qk and Rk. However, the use of wrong/inaccurate Qk and Rk...