Name
Abstract | ||
|---|---|---|
This article proposes the problem of joint state estimation and correlation identification for data fusion with unknown and time-varying correlation under the Bayesian learning framework. The considered data correlation is represented by the randomly weighted sum of positive semi-definite matrices, where the random weights depict at least three kinds of unknown correlation across single-sensor measurement components, multisensor measurements, and local estimates. Based on the variational Bayesian mechanism, the joint posterior distribution of the state and weights is derived in a closed-form iterative manner, through minimizing the Kullback–Leibler divergence. The three-case simulation shows the superiority of the proposed method in the root-mean-square error of estimation and identification. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1109/TCYB.2021.3049769 | IEEE Transactions on Cybernetics |
Keywords | DocType | Volume |
Data fusion,joint estimation and identification,unknown correlation,variational Bayesian | Journal | 52 |
Issue | ISSN | Citations |
8 | 2168-2267 | 0 |
PageRank | References | Authors |
0.34 | 27 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Wanying Zhang | 1 | 0 | 0.34 |
| Yan Liang | 2 | 158 | 14.45 |
| Henry Leung | 3 | 1309 | 151.88 |
| Feng Yang | 4 | 18 | 4.42 |