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A normalized robust mixed-norm adaptive algorithm for system identification
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DOI：10.1109/LSP.2003.819353.png)
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Abstract
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A normalized robust mixed-norm (NRMN) algorithm for system identification in the presence of impulsive noise is introduced. The standard robust mixed-norm (RMN) algorithm exhibits slow convergence, requires a stationary operating environment, and employs a constant step-size that needs to be determined a priori. To overcome. these limitations the proposed NRMN algorithm introduces a time-varying learning rate and, thus, no longer requires a stationary environment, a major drawback of the RMN algorithm. The proposed NRMN exhibits increased convergence rate and substantially reduces the steady-state coefficient error, as compared to the least mean square (LMS), normalized LMS (NLMS), least absoluted deviation (LAD), and RMN algorithm.
Keywords:
adaptive filtering
impulsive noise
mixed-norm adaptive filtering
normalized step-size
system identification
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