Name
Abstract | ||
|---|---|---|
A slide window variational adaptive Kalman filter is presented in this brief based on adaptive learning of inaccurate state and measurement noise covariance matrices, which is composed of the forward Kalman filtering, the backward Kalman smoothing, and the online estimates of noise covariance matrices. By imposing an approximation on the smoothing posterior distribution of slide window state vectors, the posterior distributions of noise covariance matrices can be analytically updated as inverse Wishart distributions by exploiting the variational Bayesian method, which avoids the fixed-point iterations and achieves good computational efficiency. Simulation comparisons demonstrate that the proposed method has better filtering accuracy and consistency than the existing cutting-edge method. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1109/TCSII.2020.2995714 | IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS II-EXPRESS BRIEFS |
Keywords | DocType | Volume |
Covariance matrices, Microsoft Windows, Kalman filters, Smoothing methods, Probability density function, Noise measurement, Bayes methods, Adaptive filter, Kalman filter, unknown noise statistics, variational Bayesian, Kalman smoother | Journal | 67 |
Issue | ISSN | Citations |
12 | 1549-7747 | 0 |
PageRank | References | Authors |
0.34 | 0 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yulong Huang | 1 | 186 | 21.07 |
| Fengchi Zhu | 2 | 0 | 0.34 |
| Guangle Jia | 3 | 5 | 2.45 |
| Yonggang Zhang | 4 | 87 | 16.11 |