A fundamental algorithm for the power estimation of closely separated spectral sources

Author

Silverstein, Seth D.

Author_Institution

GE Corporate Res. & Dev., Schenectady, NY, USA

fYear

1992

fDate

7-9 Oct 1992

Firstpage

420

Lastpage

423

Abstract

A new algorithm is based upon division of the eigenstructure of the sample covariance into approximate signal and noise subspaces due to fluctuations caused by finite data samples. These fluctuation effects are calculated using stochastic perturbation theoretic techniques. These results show that the moments of the MUSIC null spectrum can be approximated by linear functionals of the source SNR and the number of snapshots. All theoretical predictions are in accord with the simulation results. A featured simulation demonstrates accurate source power estimates for three sources separated by sub-Rayleigh resolution spatial frequencies with a weak source of 0 dB SNR sandwiched between two much larger sources of 40 and 20 dB

Keywords

array signal processing; eigenvalues and eigenfunctions; fluctuations; functional equations; parameter estimation; perturbation techniques; stochastic processes; closely separated spectral sources; eigenstructure; fluctuations; fundamental algorithm; linear functionals; power estimation; sample covariance; simulation; stochastic perturbation theoretic techniques; sub-Rayleigh resolution spatial frequencies; Covariance matrix; Direction of arrival estimation; Fluctuations; Frequency estimation; Multiple signal classification; Phased arrays; Research and development; Signal to noise ratio; Spatial resolution; Vectors;

fLanguage

English

Publisher

ieee

Conference_Titel

Statistical Signal and Array Processing, 1992. Conference Proceedings., IEEE Sixth SP Workshop on

Conference_Location

Victoria, BC

Print_ISBN

0-7803-0508-6

Type

conf

DOI

10.1109/SSAP.1992.246874

Filename

246874