Name
Abstract | ||
|---|---|---|
A new heavy-tailed robust Kalman filter is presented to address the issue that the linear stochastic state-space model has heavy-tailed noise with time-varying process bias. The one-step predicted probability density function (PDF) is modeled as the Student’s-t-inverse-Wishart distribution, and the likelihood PDF is modeled as the Student’s-t distribution. To acquire the approximate joint posterior PDF, the conjugate prior distributions of the state vector and auxiliary variables are set as the Gaussian, the inverse-Wishart, the Gaussian-Gamma, and the Gamma distributions, respectively. A new Gaussian hierarchical state-space model is presented by introducing auxiliary variables. Based on the proposed Gaussian hierarchical state-space model, the parameters of the proposed heavy-tailed robust filter are jointly inferred using the approach of the variational Bayesian. The simulation illustrates that the time-varying process bias is adaptively real-time estimated in this paper. In comparison with the existing cutting-edge filters, the presented heavy-tailed robust filter obtains higher accuracy. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1007/s00034-021-01866-8 | Circuits, Systems, and Signal Processing |
Keywords | DocType | Volume |
Kalman filter, Variational Bayesian, Heavy-tailed process and Heavy-tailed measurement noises, Time-varying process bias, Linear systems | Journal | 41 |
Issue | ISSN | Citations |
4 | 0278-081X | 0 |
PageRank | References | Authors |
0.34 | 17 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jiang, Zi-hao | 1 | 0 | 0.34 |
| Zhou, Wei-dong | 2 | 0 | 0.34 |
| Guangle Jia | 3 | 5 | 2.45 |
| Shan, Cheng-hao | 4 | 0 | 0.34 |
| Hou, Liang | 5 | 0 | 0.34 |