An eigenvector technique for detecting the number of emitters in a cluster

Author

Lee, Harry ; Li, Fu

Author_Institution

Atlantic Aerosp. Electron. Corp., Waltham, MA, USA

Volume

42

Issue

9

fYear

1994

fDate

9/1/1994 12:00:00 AM

Firstpage

2380

Lastpage

2388

Abstract

The paper introduces a new algorithm for estimating the number of sources in a cluster of closely spaced sources. The algorithm is based on consideration of the eigenvectors of the sample covariance matrix and is designated as the eigenvector detection technique (EDT). It is shown by examples that the EDT can reliably detect sources that number at lower signal-to-noise ratios (SNRs) than either the minimum description length (MDL) or Akaike information criterion (AIC) algorithms. The paper also presents a performance analysis of the EDT. Results include a “theoretical” expression for detection threshold SNR and a “theoretical” curve of probability of detection versus SNR for the technique; all analysis results show good agreement with simulation results

Keywords

eigenvalues and eigenfunctions; matrix algebra; parameter estimation; signal detection; EDT; closely spaced sources; detection threshold; eigenvector detection technique; eigenvector technique; number of emitters; performance analysis; probability of detection; sample covariance matrix; Additive noise; Algorithm design and analysis; Analytical models; Clustering algorithms; Covariance matrix; Eigenvalues and eigenfunctions; Frequency estimation; Navigation; Performance analysis; Signal to noise ratio;

fLanguage

English

Journal_Title

Signal Processing, IEEE Transactions on

Publisher

ieee

ISSN

1053-587X

Type

jour

DOI

10.1109/78.317859

Filename

317859