Convergence analysis of clipped input adaptive filters applied to system identification

Convergence analysis of clipped input adaptive filters applied to system identification

One of the efficient solutions for the identification of long finite-impulse response systems is the three-level clipped input LMS/RLS (CLMS/CRLS) adaptive filter. In this paper, we first derive the convergence behavior of the CLMS and CRLS algorithms for both time-invariant and time-varying system...

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Hauptverfasser: Bekrani, M., Khong, A. W. H.
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Sprache:eng
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Adaptive filter
Clipping
Convergence rate
Misalignment
System identification
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description One of the efficient solutions for the identification of long finite-impulse response systems is the three-level clipped input LMS/RLS (CLMS/CRLS) adaptive filter. In this paper, we first derive the convergence behavior of the CLMS and CRLS algorithms for both time-invariant and time-varying system identification. In addition, we employ results arising from this analysis to derive the optimal step-size and forgetting factor for CLMS and CRLS. We show that these optimal step-size and forgetting factor allow the algorithms to achieve a low steady-state misalignment.
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subjects Adaptive filter
Clipping
Convergence rate
Misalignment
System identification
title Convergence analysis of clipped input adaptive filters applied to system identification
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