Name
Abstract | ||
|---|---|---|
theoretical analysis is presented of the correction step of the Kalman filter (KF) and its various approximations for the case of a nonlinear measurement equation with additive Gaussian noise. The KF is based on a Gaussian approximation to the joint density of the state and the measurement. The analysis metric is the Kullback-Leibler divergence of this approximation from the true joint density. The purpose of the analysis is to provide a quantitative tool for understanding and assessing the performance of the KF and its variants in nonlinear scenarios. This is illustrated using a numerical example. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1109/TSP.2013.2279367 | IEEE Transactions on Signal Processing |
Keywords | Field | DocType |
AWGN,Kalman filters,approximation theory,filtering theory,Gaussian approximation,Kalman filter approximation analysis,Kullback-Leibler divergence,additive Gaussian noise,nonlinear measurement equation,true joint density,Bayesian filtering,Kalman filtering,Kullback-Leibler divergence,nonlinear measurement | Mathematical optimization,Extended Kalman filter,Fast Kalman filter,Filtering problem,Kalman filter,Ensemble Kalman filter,Invariant extended Kalman filter,Additive white Gaussian noise,Gaussian noise,Mathematics | Journal |
Volume | Issue | ISSN |
61 | 22 | 1053-587X |
Citations | PageRank | References |
14 | 0.74 | 9 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Mark R. Morelande | 1 | 195 | 24.96 |
| Angel F. Garcia-Fernandez | 2 | 131 | 18.15 |