Name
Abstract | ||
|---|---|---|
In this paper, linear distributed estimation is revisited on the basis of the hypothesizing distributed Kalman filter and equations for a flexible application of the algorithm are derived. We propose a new approximation for the mean-squared-error matrix and present techniques for automatically improving the hypothesis about the global measurement model. Utilizing these extensions, the precision of the filter is improved so that it asymptotically yields optimal results for time-invariant models. Pseudo-code for the implementation of the algorithm is provided and the lossless inclusion of out-of-sequence measurements is discussed. An evaluation demonstrates the effect of the new extensions and compares the results to state-of-the-art methods. |
Year | Venue | Keywords |
|---|---|---|
2013 | Fusion | Kalman filters,estimation theory,mean square error methods,approximation,distributed Kalman filtering,linear distributed estimation,mean squared error matrix,out-of-sequence measurements,pseudo-code,time-invariant models,Distributed Estimation,Kalman Filtering,Sensor-networks,Track-to-Track Fusion (T2TF) |
Field | DocType | Citations |
Alpha beta filter,Computer science,Control theory,Artificial intelligence,Estimation theory,Ensemble Kalman filter,Invariant extended Kalman filter,Extended Kalman filter,Fast Kalman filter,Algorithm,Minimum mean square error,Kalman filter,Machine learning | Conference | 2 |
PageRank | References | Authors |
0.41 | 6 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Marc Reinhardt | 1 | 67 | 7.03 |
| Benjamin Noack | 2 | 168 | 23.73 |
| Uwe D. Hanebeck | 3 | 944 | 133.52 |