Title :
Fast algorithms for Brownian matrices
Author_Institution :
Laboratoire Des Signaux et Systemes, Gif-Sur-Yvette, France
fDate :
4/1/1983 12:00:00 AM
Abstract :
Brownian motion is one of the most common models used to represent nonstationary signals. The covariance matrix of a discrete-time Brownian motion has a very particular structure, and is called a Brownian matrix. This note presents a number of results concerning linear problems appearing in digital signal processing with Brownian matrices. In particular, it is shown that fast algorithms used for Toeplitz matrices are simpler and faster for Brownian matrices. Examples are given to illustrate the different results presented in the note.
Keywords :
Acoustics; Brownian motion; Covariance matrix; Digital signal processing; Equations; Random variables; Signal processing; Signal processing algorithms; Symmetric matrices; White noise;
Journal_Title :
Acoustics, Speech and Signal Processing, IEEE Transactions on
DOI :
10.1109/TASSP.1983.1164078