Name
Abstract | ||
|---|---|---|
This letter deals with recursive filtering for dynamic systems where an explicit process model is not easily devisable. Most Bayesian filters assume the availability of such an explicit process model, and thus may require additional assumptions or fail to properly leverage all available information. In contrast, we propose a filter that employs a purely residual-based modeling of the available inf... |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/LRA.2017.2776340 | IEEE Robotics and Automation Letters |
Keywords | Field | DocType |
Hidden Markov models,Kalman filters,Robot sensing systems,Bayes methods,Mathematical model,Mobile communication | Alpha beta filter,Extended Kalman filter,Control theory,Recursive Bayesian estimation,Adaptive filter,Kernel adaptive filter,Recursive filter,Invariant extended Kalman filter,Ensemble Kalman filter,Mathematics | Journal |
Volume | Issue | ISSN |
3 | 1 | 2377-3766 |
Citations | PageRank | References |
3 | 0.40 | 7 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Michael Blösch | 1 | 427 | 31.24 |
| M. Burri | 2 | 343 | 18.62 |
| Hannes Sommer | 3 | 74 | 6.81 |
| Roland Siegwart | 4 | 7640 | 551.49 |
| Marco Hutter | 5 | 460 | 58.00 |