| Article ID | Journal | Published Year | Pages | File Type |
|---|---|---|---|---|
| 6952122 | Digital Signal Processing | 2014 | 7 Pages |
Abstract
The main drawback of the recursive least p-norm (RLpN) adaptive-filtering algorithm is a poor tracking performance in the presence of abrupt changes in the model. In this paper, a new method to enhance tracking capability of the RLpN (ET-RLpN) algorithm is proposed, which uses the adaptive gain factor in the cross-correlation vector and the input-signal autocorrelation matrix to enhance tracking capability. Simulation results in system identification and echo cancellation applications are presented, which demonstrate that the ET-RLpN achieves improved tracking capability compared to the conventional RLpN and controlled adaptive combination of two RLpN filters (CAC-RLpN).
Related Topics
Physical Sciences and Engineering
Computer Science
Signal Processing
Authors
Sheng Zhang, Jiashu Zhang,