Name
Title | ||
|---|---|---|
A Novel Heavy-Tailed Mixture Distribution Based Robust Kalman Filter for Cooperative Localization |
Abstract | ||
|---|---|---|
In cooperative localization for autonomous underwater vehicles (AUVs), the practical stochastic noise may be heavy-tailed, and nonstationary distributed because of acoustic speed variation, multipath effect of acoustic channel, and changeable underwater environment. To address such noise, a novel heavy-tailed mixture (HTM) distribution is first proposed in this article, and then expressed as a hierarchical Gaussian form by employing a categorical distributed auxiliary vector. Based on that, a novel HTM distribution based robust Kalman filter is proposed, where the one-step prediction, and measurement likelihood probability density functions are, respectively, modeled as an HTM distribution, and a Normal-Gamma-inverse Wishart distribution. The proposed filter is verified by a lake experiment about cooperative localization for AUVs. Compared with the cutting-edge filter, the proposed filter has been improved by 50.27% in localization error but no more than twice computational time is required. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1109/TII.2020.3015001 | IEEE Transactions on Industrial Informatics |
Keywords | DocType | Volume |
Autonomous underwater vehicles (AUVs),cooperative localization,heavy-tailed and nonstationary noises,Kalman filter (KF),variational Bayesian (VB) | Journal | 17 |
Issue | ISSN | Citations |
5 | 1551-3203 | 3 |
PageRank | References | Authors |
0.39 | 0 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Mingming Bai | 1 | 6 | 1.79 |
| Yulong Huang | 2 | 186 | 21.07 |
| Yonggang Zhang | 3 | 87 | 16.11 |
| Feng Chen | 4 | 17 | 5.08 |