Name
Title | ||
|---|---|---|
Kalman vs H∞ filter in terms of convergence and accuracy: Application to carrier frequency offset estimation |
Abstract | ||
|---|---|---|
H∞ filtering is more and more used in the field of recursive estimation in signal processing. The purpose of this communication is to compare Kalman filtering and H∞ filtering by considering their Ricatti-type equations. Our contribution is twofold: firstly, we show that the H∞ filter can be seen as a Kalman filter with a model-noise covariance matrix that depends on the noise attenuation level and varies in time. Hence, this can explain the convergence properties of the H∞ filter when estimating parameters. The convergence and accuracy properties of both Kalman and H∞ filters are then illustrated by the estimation of a carrier frequency offset in a mobile communication system. |
Year | Venue | Keywords |
|---|---|---|
2012 | Signal Processing Conference | H∞ filters,Kalman filters,Riccati equations,covariance matrices,mobile communication,recursive estimation,signal processing,H∞ filtering,Kalman filtering,Ricatti-type equations,carrier frequency offset estimation,mobile communication system,model-noise covariance matrix,recursive estimation,signal processing,H∞ filter,Kalman filter,carrier frequency offset,extended H∞ filter,extended Kalman filter |
Field | DocType | ISSN |
Signal processing,Extended Kalman filter,Alpha beta filter,Fast Kalman filter,Control theory,Filter (signal processing),Kalman filter,Invariant extended Kalman filter,Ensemble Kalman filter,Mathematics | Conference | 2219-5491 |
ISBN | Citations | PageRank |
978-1-4673-1068-0 | 1 | 0.39 |
References | Authors | |
3 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Héctor Poveda | 1 | 8 | 2.61 |
| Eric Grivel | 2 | 136 | 33.92 |
| Guillaume Ferré | 3 | 36 | 13.29 |
| Nicolai Christov | 4 | 8 | 5.92 |