Effective Improvement of Under-Modeling Frequency-Domain Kalman Filter
| Autor: | Kai Chen, Wenzhi Fan, Jiancheng Tao, Jing Lu |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
Computer science
Applied Mathematics 020206 networking & telecommunications 02 engineering and technology Kalman filter computer.software_genre Adaptive filter Robustness (computer science) Control theory Frequency domain Signal Processing 0202 electrical engineering electronic engineering information engineering Electrical and Electronic Engineering Audio signal processing computer |
| Zdroj: | IEEE Signal Processing Letters. 26:342-346 |
| ISSN: | 1558-2361 1070-9908 |
| DOI: | 10.1109/lsp.2019.2890965 |
| Popis: | The frequency-domain Kalman filter (FKF) has been utilized in many audio signal processing applications due to its fast convergence speed and robustness. However, the performance of the FKF in under-modeling situations has not been investigated. This letter presents an analysis of the steady-state behavior of the commonly used diagonalized FKF and reveals that it suffers from a biased solution in under-modeling scenarios. An effective improvement of the FKF is proposed, having the benefits of the guaranteed optimal steady-state behavior at the cost of a very limited increase of computational burden. The convergence behavior of the proposed algorithm is also analyzed. Computer simulations are conducted to validate the improved performance of the proposed method. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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