A selection procedure for the number of signals in presence of colored noise

Pinyuen Chen, Lifang Hsu

Research output: Contribution to journalArticle

Abstract

Ranking and selection theory is used to estimate the number of signals present in colored noise. The data structure follows the well-known MUSIC (MUltiple SIgnal Classification) model. We deal with the eigenvalues of a covariance matrix, using the MUSIC model and colored noise. The data matrix can be written as the product of two matrices. The first matrix is the sample covariance matrix of the observed vectors. The second matrix is the inverse of the sample covariance matrix of reference vectors. 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 our theory.

Original languageEnglish (US)
Pages (from-to)1741-1757
Number of pages17
JournalCommunications in Statistics - Theory and Methods
Volume38
Issue number10
DOIs
StatePublished - Jun 1 2009

Keywords

  • Eigenvalue
  • Ranking and selection
  • Signal processing

ASJC Scopus subject areas

  • Statistics and Probability

Fingerprint Dive into the research topics of 'A selection procedure for the number of signals in presence of colored noise'. Together they form a unique fingerprint.

  • Cite this