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Student's t-Kernel-Based Maximum Correntropy Kalman Filter
| Content Provider | MDPI |
|---|---|
| Author | Huang, Hongliang Zhang, Hai |
| Copyright Year | 2022 |
| Description | The state estimation problem is ubiquitous in many fields, and the common state estimation method is the Kalman filter. However, the Kalman filter is based on the mean square error criterion, which can only capture the second-order statistics of the noise and is sensitive to large outliers. In many areas of engineering, the noise may be non-Gaussian and outliers may arise naturally. Therefore, the performance of the Kalman filter may deteriorate significantly in non-Gaussian noise environments. To improve the accuracy of the state estimation in this case, a novel filter named Student’s t kernel-based maximum correntropy Kalman filter is proposed in this paper. In addition, considering that the fixed-point iteration method is used to solve the optimal estimated state in the filtering algorithm, the convergence of the algorithm is also analyzed. Finally, comparative simulations are conducted and the results demonstrate that with the proper parameters of the kernel function, the proposed filter outperforms the other conventional filters, such as the Kalman filter, Huber-based filter, and maximum correntropy Kalman filter. |
| Starting Page | 1683 |
| e-ISSN | 14248220 |
| DOI | 10.3390/s22041683 |
| Journal | Sensors |
| Issue Number | 4 |
| Volume Number | 22 |
| Language | English |
| Publisher | MDPI |
| Publisher Date | 2022-02-21 |
| Access Restriction | Open |
| Subject Keyword | Sensors Industrial Engineering Kalman Filter Student's T Kernel Function Maximum Correntropy Criterion Fixed-point Iteration Method Convergence Analysis |
| Content Type | Text |
| Resource Type | Article |
