Name
Abstract | ||
|---|---|---|
Fueled by applications in sensor networks, these years have witnessed a surge of interest in distributed estimation and filtering. A new approach is hereby proposed for the Distributed Kalman Filter (DKF) by integrating a local covariance computation scheme. Compared to existing well-established DKF methods, the virtue of the present approach lies in accelerating the convergence of the state estimates to those of the Centralized Kalman Filter (CKF). Meanwhile, an algorithm is proposed that allows each node to compute the averaged measurement noise covariance matrix within a minimal discrete-time running steps in a distributed way. Both theoretical analysis and extensive numerical simulations are conducted to show the feasibility and superiority of the proposed method. |
Year | Venue | Field |
|---|---|---|
2017 | arXiv: Systems and Control | Convergence (routing),Extended Kalman filter,Mathematical optimization,Fast Kalman filter,Control theory,Computer science,Covariance intersection,Filter (signal processing),Kalman filter,Covariance matrix,Covariance |
DocType | Volume | Citations |
Journal | abs/1703.05438 | 0 |
PageRank | References | Authors |
0.34 | 0 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ye Yuan | 1 | 438 | 61.04 |
| Ling Shi | 2 | 1717 | 107.86 |
| Jun Liu | 3 | 215 | 20.63 |
| Zhiyong Chen | 4 | 164 | 19.68 |
| Hai-Tao Zhang | 5 | 401 | 37.71 |
| Goncalves, J. | 6 | 404 | 42.24 |