Presentation 2001/10/18
Statistical Properties of a Memoryless Nonlinear Gradient Algorithm for an Adaptive Constrained IIR Notch Filter
Naoko Tani, Yegui Xiao,
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Abstract(in English) Gradient-type algorithm for adaptive IIR notch filters are very attractive in terms of both performance and conputational requirements in real-life applications. This paper presents, in detail, a statistical analysis of a memoryless nonlinear gradient algorithm (MNG) developed recently for a well-known second-order adaptive IIR notch filter with constrained poles and zeros. This analysis is based on a proper use of Taylor series expansion and nonlinearization of filter output signals. Closed form expression for the steady-state bias is first derived, which is valid for both fast and slow adaptations. Analytical expression for the estimation mean square error (MSE) of the MNG is then derived for very slow adaptation. Extensive simulations are performed to support the analytical findings.
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Keyword(in English) Adaptive IIR notch filters / Statistical properties / Memoryless nonlinear gradient algorithm
Paper # DSP2001-105,ICD2001-110,IE2001-89
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Committee DSP
Conference Date 2001/10/18(1days)
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Registration To Digital Signal Processing (DSP)
Language JPN
Title (in Japanese) (See Japanese page)
Sub Title (in Japanese) (See Japanese page)
Title (in English) Statistical Properties of a Memoryless Nonlinear Gradient Algorithm for an Adaptive Constrained IIR Notch Filter
Sub Title (in English)
Keyword(1) Adaptive IIR notch filters
Keyword(2) Statistical properties
Keyword(3) Memoryless nonlinear gradient algorithm
1st Author's Name Naoko Tani
1st Author's Affiliation Faculty of Human Life and Environmental Science, Hiroshima Prefectural Women's University()
2nd Author's Name Yegui Xiao
2nd Author's Affiliation Faculty of Human Life and Environmental Science, Hiroshima Prefectural Women's University
Date 2001/10/18
Paper # DSP2001-105,ICD2001-110,IE2001-89
Volume (vol) vol.101
Number (no) 383
Page pp.pp.-
#Pages 8
Date of Issue