Name
Title | ||
|---|---|---|
Approximate Bayesian Smoothing with Unknown Process and Measurement Noise Covariances. |
Abstract | ||
|---|---|---|
We present an adaptive smoother for linear state-space models with unknown process and measurement noise covariances. The proposed method utilizes the variational Bayes technique to perform approximate inference. The resulting smoother is computationally efficient, easy to implement, and can be applied to high dimensional linear systems. The performance of the algorithm is illustrated on a target ... |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1109/LSP.2015.2490543 | IEEE Signal Processing Letters |
Keywords | Field | DocType |
Smoothing methods,Covariance matrices,Noise,Kalman filters,Approximation methods,Bayes methods | Signal processing,Mathematical optimization,Pattern recognition,Linear system,Approximate inference,Kalman filter,Smoothing,Artificial intelligence,Mathematics,Bayes' theorem,Bayesian probability | Journal |
Volume | Issue | ISSN |
22 | 12 | 1070-9908 |
Citations | PageRank | References |
8 | 0.57 | 10 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Tohid Ardeshiri | 1 | 27 | 7.14 |
| Emre Özkan | 2 | 94 | 10.54 |
| Umut Orguner | 3 | 548 | 40.11 |
| Fredrik Gustafsson | 4 | 2287 | 281.33 |