Estimating the number of signals in presence of colored noise
Pinyuen Chen, G.J. Genello, Michael C. Wicks · 2004
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 deal with the eigenanalyses 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 multistep selection procedure to construct a confidence interval on the number of signals present in a data set. Properties of this procedure are stated and proved. Those properties are used to compute the required parameters (procedure constants). Numerical examples are given to illustrate our theory.