Name
Abstract | ||
|---|---|---|
In this paper, we propose a novel modified distributed Kalman algorithm, which is a diffusion strategy that the state estimation is more precise while the system model is time-varying. Our focus is on the missing data gathered by a set of sensor nodes that may obtain incomplete information because of the harsh environment. Simulation results evaluate the performance of the proposed distributed Kalman filtering algorithm. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1007/978-3-030-22808-8_28 | ADVANCES IN NEURAL NETWORKS - ISNN 2019, PT II |
Keywords | Field | DocType |
Diffusion estimation, Kalman filtering, Missing data | Kalman filtering algorithm,Computer science,Algorithm,Kalman filter,Missing data,Complete information,System model | Conference |
Volume | ISSN | Citations |
11555 | 0302-9743 | 0 |
PageRank | References | Authors |
0.34 | 0 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Shuangyi Xiao | 1 | 0 | 0.34 |
| Nankun Mu | 2 | 35 | 5.41 |
| Feng Chen | 3 | 17 | 5.08 |