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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| Main Authors: | , |
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| Other Authors: | |
| Format: | Conference or Workshop Item |
| Language: | English |
| Published: |
2013
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| Subjects: | |
| Online Access: | https://hdl.handle.net/10356/97401 http://hdl.handle.net/10220/13162 |
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| Institution: | Nanyang Technological University |
| Language: | English |
| Summary: | 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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