- Title
- Performance analysis for subband identification
- Creator
- Marelli, Damian; Fu, Minyue
- Relation
- IEEE Transactions on Signal Processing Vol. 52, Issue 1, p. 142-134
- Publisher Link
- http://dx.doi.org/10.1109/TSP.2003.820083
- Publisher
- Institute of Electrical and Electronics Engineers (IEEE)
- Resource Type
- journal article
- Date
- 2004
- Description
- The so-called subband identification method has been introduced recently as an alternative method for identification of finite-impulse response systems with large tap sizes. It is known that this method can be more numerically efficient than the classical system identification method. However, no results are available to quantify its advantages. This paper offers a rigorous study of the performance of the subband method. More precisely, we aim to compare the performance of the subband identification method with the classical (fullband) identification method. The comparison is done in terms of the asymptotic residual error, asymptotic convergence rate, and computational cost when the identification is carried out using the prediction error method, and the optimization is done using the least-squares method. It is shown that by properly choosing the filterbanks, the number of parameters in each subband, the number of subbands, and the downsampling factor, the two identification methods can have compatible asymptotic residual errors and convergence rate. However, for applications where a high order model is required, the subband method is more numerically efficient. We study two types of subband identification schemes: one using critical sampling and another one using oversampling. The former is simpler to use and easier to understand, whereas the latter involves more design problems but offers further computational savings.
- Subject
- FIR filters; adaptive filters; last mean squares method; signal sampling
- Identifier
- http://hdl.handle.net/1959.13/26030
- Identifier
- uon:775
- Identifier
- ISSN:1053-587x
- Rights
- Copyright © 2004 IEEE. Reprinted from IEEE Transactions on Signal Processing. This material is posted here with permission of the IEEE. Such permission of the IEEE does not in any way imply IEEE endorsement of any of the University of Newcastle's products or services. Internal or personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution must be obtained from the IEEE by writing to pubs-permissions@ieee.org. By choosing to view this document, you agree to all provisions of the copyright laws protecting it.
- Language
- eng
- Full Text
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