- Title
- Asymptotic properties of subband identification
- Creator
- Marelli, Damián; Fu, Minyue
- Relation
- IEEE Transactions on Signal Processing Vol. 51, p. 3128-3142
- Publisher Link
- http://dx.doi.org/10.1109/TSP.2003.819008
- Publisher
- Institute of Electrical and Electronics Engineers (IEEE)
- Resource Type
- journal article
- Date
- 2003
- Description
- The purpose of the paper is to study the asymptotic properties (i.e., strong convergence and asymptotic convergence rate) of the subband identification method in every subband and in the overall method. The study of strong convergence aims to answer the question whether the "best possible" model is retrieved, on the limit, with probability one. The study of the asymptotic convergence rate aims to give an expression that quantifies how fast the model approaches the "best possible" value as the number of samples goes to infinity. To do this, we need to generalize existing results for fullband identification. In the process of doing so, we come up with a new notion of ergodicity, which we call strong ergodicity. Strongly ergodic signals not only satisfy the assumptions required for our analysis but also enjoy an interesting property, which is that strong ergodicity is invariant under a number of transformations. In particular, the subband components of a strongly ergodic signal are guaranteed to be strongly ergodic, therefore, ergodic, which is not true for an ergodic signal in general.
- Subject
- analysis filterbank; asymptotic convergence rate; asymptotic properties; convergence of numerical methods; identification; signal processing; strong ergodicity; synthesis filterbank
- Identifier
- uon:774
- Identifier
- http://hdl.handle.net/1959.13/26024
- Identifier
- ISSN:1053-587X
- Rights
- Copyright © 2003 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.
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