Transform domain CPtNLMS algorithms

Author

Wagner, Kevin T. ; Doroslovacki, Milos I.

Author_Institution

Radar Div., Naval Res. Lab., Washington, DC, USA

fYear

2013

fDate

20-22 March 2013

Firstpage

1

Lastpage

4

Abstract

The concept of self-orthogonalizing adaptation is extended from the least mean square algorithm to the general case of complex proportionate type normalized least mean square algorithms. The derived algorithm requires knowledge of the input signal´s covariance matrix. Implementation of the algorithm using a fixed transform such as the discrete cosine transform or discrete wavelet transform is presented for applications in which the input signal´s covariance matrix is unknown.

Keywords

adaptive filters; covariance matrices; discrete cosine transforms; discrete wavelet transforms; least mean squares methods; complex proportionate type normalized least mean square algorithm; discrete cosine transform; discrete wavelet transform; input signal covariance matrix; least mean square algorithm; self-orthogonalizing adaptative filter; transform domain CPtNLMS Algorithm; Convergence; Covariance matrices; Discrete cosine transforms; Least mean square algorithms; Least squares approximations; Vectors; Adaptive filtering; convergence; least mean square algorithms;

fLanguage

English

Publisher

ieee

Conference_Titel

Information Sciences and Systems (CISS), 2013 47th Annual Conference on

Conference_Location

Baltimore, MD

Print_ISBN

978-1-4673-5237-6

Electronic_ISBN

978-1-4673-5238-3

Type

conf

DOI

10.1109/CISS.2013.6552258

Filename

6552258