Robust Time-Varying Parameter Proportionate Affine-Projection-Like Algorithm for Sparse System Identification
| Autor: | Xiangping Zeng, Pucha Song, Liping Zhao, Wei Quan, Haiquan Zhao |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
0209 industrial biotechnology
Noise (signal processing) Computer science Applied Mathematics System identification Score 02 engineering and technology Function (mathematics) Variance (accounting) Exponential function Matrix (mathematics) 020901 industrial engineering & automation Rate of convergence Signal Processing Algorithm |
| Zdroj: | Circuits, Systems, and Signal Processing. 40:3395-3416 |
| ISSN: | 1531-5878 0278-081X |
| Popis: | Due to its low computational burden, the affine-projection-like (APL) adaptive filtering algorithm has been extensively studied for colored signal input. Recently, a robust APL algorithm was designed by adopting the M-estimate cost function in impulsive noise environment; however, its convergence rate is very slow for sparse system identification. This paper proposed a proportionate APL M-estimate (PAPLM) algorithm, which is derived by using the proportionate matrix to heighten the convergence rate. To maintain good steady-state performance of the PAPLM algorithm, a time-varying parameter PAPLM (TV-PAPLM) algorithm is proposed, which uses a modified exponential function to adjust the time-varying parameter according to the ratio of the mean square score function to the system noise variance. Moreover, the steady-state excess mean-square error performance of PAPLM algorithm is analyzed and obtained in detail. Simulation results reveal that the proposed PAPLM and TV-PAPLM algorithms achieve fast convergence rate and good steady-state performance in sparse system identification. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|
Full text from SpringerLink