Sensor gain and phase estimation

Author

Cheng, Qi ; Hua, Yingbo

Author_Institution

School of Eleectr. Eng., Northern Territory Univ., Casuarina, NT, Australia

Volume

1

fYear

1997

fDate

2-5 Nov. 1997

Firstpage

274

Abstract

We present three algorithms for joint estimation of source angles and sensor gains and phases. These algorithms are based on the principles of weighted noise subspace fitting (WNSF), conditional maximum likelihood, and unconditional maximum likelihood. We study the statistical performances of the three algorithms assuming the source angles are known. The WNSF algorithm with an optimum weight is shown to be statistically the most efficient among the three and is implementable in an iterative quadratic fashion.

Keywords

array signal processing; direction-of-arrival estimation; maximum likelihood estimation; noise; optimisation; phase estimation; statistical analysis; WNSF algorithm; algorithms; array signal processing; conditional maximum likelihood; iterative quadratic implementation; maximum likelihood algorithms; optimum weight; optimum weighted noise subspace fitting; sensor gain estimation; sensor phase estimation; source angle estimation; statistical performance; unconditional maximum likelihood; Algorithm design and analysis; Covariance matrix; Data models; Iterative algorithms; Maximum likelihood estimation; Multiple signal classification; Phase estimation; Phased arrays; Sensor arrays; Signal processing algorithms;

fLanguage

English

Publisher

ieee

Conference_Titel

Signals, Systems & Computers, 1997. Conference Record of the Thirty-First Asilomar Conference on

Conference_Location

Pacific Grove, CA, USA

ISSN

1058-6393

Print_ISBN

0-8186-8316-3

Type

conf

DOI

10.1109/ACSSC.1997.680192

Filename

680192