Name
Abstract | ||
|---|---|---|
The federated Kalman filter embodies an efficient and easy-to-implement solution for linear distributed estimation problems. Data from independent sensors can be processed locally and in parallel on different nodes without running the risk of erroneously ignoring possible dependencies. The underlying idea is to counteract the common process noise issue by inflating the joint process noise matrix. In this paper, the same trick is generalized to nonlinear models and non-Gaussian process noise. The probability density of the joint process noise is split into an exponential mixture of transition densities. By this means, the process noise is modeled to independently affect the local system models. The estimation results provided by the sensor devices can then be fused, just as if they were indeed independent. |
Year | Venue | Keywords |
|---|---|---|
2013 | Information Fusion | Gaussian distribution,Gaussian noise,Kalman filters,exponential distribution,matrix algebra,nonlinear estimation,nonlinear filters,sensor fusion,statistical distributions,common process noise,data processing,exponential transition density mixture,federated Kalman filter,joint process noise matrix,linear distributed estimation problem,nonGaussian process noise,nonlinear federated filter,nonlinear model,probability density,sensor fusion,Distributed Estimation,Federated Kalman Filter,Nonlinear Estimation |
Field | DocType | ISBN |
Extended Kalman filter,Control theory,Computer science,Filter (signal processing),Kalman filter,Sensor fusion,Ensemble Kalman filter,Invariant extended Kalman filter,Nonlinear filter,Gaussian noise | Conference | 978-605-86311-1-3 |
Citations | PageRank | References |
1 | 0.37 | 4 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Benjamin Noack | 1 | 4 | 1.48 |
| Julier, S.J. | 2 | 1971 | 192.03 |
| Marc Reinhardt | 3 | 4 | 1.15 |
| Uwe D. Hanebeck | 4 | 599 | 71.02 |