Name
Abstract | ||
|---|---|---|
The identification of transversal filters is important for numerous applications. In acoustical applications a first order Markov model is often used to describe the time-variant nature of transversal filters. The Kalman filter is the optimal unbiased estimator for such a Markov model. It inherently calculates the uncertainty of the current state estimate by its state error covariance matrix. In contrast to the broadband Kalman filter the covariance matrix of the exact Kalman filter depends on properties of the input signal. The single step covariance update of the exact Kalman filter is a discrete-time algebraic Riccati equation. We propose a solution for the steady state covariance matrix, which depends on the process parameters of the Markov model as well as properties of the input signal. It is derived based on the eigendecomposition of the covariance matrix and the autocorrelation matrix of the input signal. The proposed algorithm converges in few iterations and gives accurate results. We show how this result can be used to predict the steady state performance of the Kalman filter for system identification through numerical examples. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1109/LSP.2020.3029703 | IEEE SIGNAL PROCESSING LETTERS |
Keywords | DocType | Volume |
Kalman filter, system identification, acoustic signal processing | Journal | 27 |
Issue | ISSN | Citations |
99 | 1070-9908 | 0 |
PageRank | References | Authors |
0.34 | 0 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Johannes Fabry | 1 | 2 | 1.81 |
| Stefan Kuhl | 2 | 0 | 1.69 |
| Peter Jax | 3 | 159 | 19.11 |