Performance analysis of normalized least mean p-norm lattice algorithm for alpha-stable processes

QSpace/Manakin Repository

Performance analysis of normalized least mean p-norm lattice algorithm for alpha-stable processes

Show simple item record


dc.contributor.author Kahaei, M.H.
dc.contributor.author Boashash, B
dc.contributor.author Deriche, M
dc.date.accessioned 2012-10-18T11:23:27Z
dc.date.available 2012-10-18T11:23:27Z
dc.date.issued 1999
dc.identifier.citation M. H. Kahaei, B. Boashash, and M. Deriche, "Performance analysis of normalized least mean p-norm lattice algorithm for alpha-stable processes," in Proc. of Signal Processing and Its Applications, 1999. ISSPA '99. Proceedings of the Fifth International Symposium on, 1999, pp. 387-390 vol.1. en_US
dc.identifier.other doi: 10.1109/isspa.1999.818193
dc.identifier.uri http://hdl.handle.net/10576/10876
dc.description This paper presents an adaptive gradient-based algorithm using the lattice structure for parameter estimation of AR alpha-stable processes. (Additional details can be found in the comprehensive book on Time-Frequency Signal Analysis and Processing (see http://www.elsevier.com/locate/isbn/0080443354). In addition, the most recent upgrade of the original software package that calculates Time-Frequency Distributions and Instantaneous Frequency estimators can be downloaded from the web site: www.time-frequency.net. This was the first software developed in the field, and it was first released publicly in 1987 at the 1st ISSPA conference held in Brisbane, Australia, and then continuously updated). en_US
dc.description.abstract The existence of impulsive noise with alpha-stable distributions has been addressed in many applications. In this paper, we present two new adaptive algorithms using the lattice structure for a-stable processes. In these algorithms fractional lower order moments of residual errors are used to update the filter coefficients. Based on the empirical results, the proposed algorithms show superior convergence speed over presently available techniques in parameter estimation of astable processes. The problem of misalignment of the mean of the estimated parameters with respect to the true values is also addressed. en_US
dc.language.iso en en_US
dc.publisher IEEE en_US
dc.subject alpha-stable distributions en_US
dc.subject FIR lattice filters en_US
dc.subject adaptive algorithms en_US
dc.subject adaptive gradient-based algorithm en_US
dc.subject alpha-stable processes en_US
dc.subject convergence speed en_US
dc.subject filter coefficients updating en_US
dc.subject ractional lower order moments en_US
dc.subject impulsive noise en_US
dc.subject lattice structure en_US
dc.subject noise suppression en_US
dc.subject normalized least mean p-norm lattice algorithm en_US
dc.subject parameter estimation en_US
dc.subject performance analysis en_US
dc.subject residual errors en_US
dc.title Performance analysis of normalized least mean p-norm lattice algorithm for alpha-stable processes en_US
dc.type Article en_US

Files in this item

Files Size Format View
Boashash-Kahaei ... alpha-stable processes.pdf 378.3Kb PDF View/Open

This item appears in the following Collection(s)

Show simple item record

Search QSpace


Advanced Search

Browse

My Account