Estimating the number of signals in presence of colored noise
Pinyuen Chen - Syracuse University, Gerard J. Genello - Air Force Research Laboratory, Michael C. Wicks - Air Force Research Laboratory
Wed, 28 April 2004, 3:20 PM - 4:10 PM
In this paper, statistical ranking and selection theory is used to
estimate the number of signals present in colored noise. The data
structure follows the well-known MUltiple SIgnal Classification
(MUSIC) model. We are dealing with the eigen-analyses of a matrix,
using the MUSIC model and colored noise. The data matrix can be
written as the product of a covariance matrix and the inverse of
second covariance matrix. We propose a multi-step selection procedure
to construct a confidence interval on the number of signals present
in a data set. Properties of this procedure will be stated and proved.
Those properties will be used to compute the required parameters
(procedure constants). Numerical examples are given to illustrate
Dr. Pinyuen Chen - Syracuse University
Pinyuen Chen received his Ph. D. in statistics at UC Santa Barbara under the guidance of Milton Sobel in 1982. His research area includes statistical ranking and selection, multivariate analysis, and statistical signal processing. He is a Professor and the Director of Interdisciplinary Statistics Program at Syracuse University.