A new adaptive recursive RLS-based fast-array IIR filter for active noise and vibration control systems

Montazeri, Allahyar and Poshtan, Javad (2011) A new adaptive recursive RLS-based fast-array IIR filter for active noise and vibration control systems. Signal Processing, 91 (1). pp. 98-113. ISSN 0165-1684

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Abstract

Infinite impulse response filters have not been used extensively in active noise and vibration control applications. The problems are mainly due to the multimodal error surface and instability of adaptive IIR filters used in such applications. Considering these, in this paper a new adaptive recursive RLS-based fast-array IIR filter for active noise and vibration control applications is proposed. At first an RLS-based adaptive IIR filter with computational complexity of order O(n(2)) is derived, and a sufficient condition for its stability is proposed by applying passivity theorem on the equivalent feedback representation of this adaptive algorithm. In the second step, to reduce the computational complexity of the algorithm to the order of O(n) as well as to improve its numerical stability, a fast array implementation of this adaptive IIR filter is derived. This is accomplished by extending the existing results of fast-array implementation of adaptive FIR filters to adaptive IIR filters. Comparison of the performance of the fast-array algorithm with that of Erikson's FuLMS and SHARF algorithms confirms that the proposed algorithm has faster convergence rate and ability to reach a lower minimum mean square error which is of great importance in active noise and vibration control applications.

Item Type:
Journal Article
Journal or Publication Title:
Signal Processing
Subjects:
?? LMS ALGORITHMAFFINEACTIVE NOISE AND VIBRATION CONTROLADAPTIVE IIR FILTERSCONVERGENCE ANALYSISSOUND REPRODUCTION SYSTEMSRECURSIVE LEAST SQUARECANCELLATIONLEAST-SQUARES ALGORITHMSSTABILITYFAST ARRAY ALGORITHM ??
ID Code:
65606
Deposited By:
Deposited On:
12 Jul 2013 10:25
Refereed?:
Yes
Published?:
Published
Last Modified:
18 Sep 2023 00:43