Title :
Transform domain CPtNLMS algorithms
Author :
Wagner, Kevin T. ; Doroslovacki, Milos I.
Author_Institution :
Radar Div., Naval Res. Lab., Washington, DC, USA
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;
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
DOI :
10.1109/CISS.2013.6552258
