Asymptotic performance of optimal gain-and-phase estimators of sensor arrays

Author

Cheng, Qi ; Hua, Yingbo ; Stoica, Petre

Author_Institution

Sch. of Electr. Eng., Univ. of Western Sydney Nepean, Kingswood, NSW, Australia

Volume

48

Issue

12

fYear

2000

fDate

12/1/2000 12:00:00 AM

Firstpage

3587

Lastpage

3590

Abstract

For estimating angles of arrival, there are three well known algorithms: weighted noise subspace fitting (WNSF), unconditional maximum likelihood (UML), and conditional maximum likelihood (CML). These algorithms can also be used for estimating/calibrating the gains-and-phases of sensor arrays, assuming the angles of arrival are known. We show that the WNSF algorithm with an optimal weight has the same statistical efficiency as the UML algorithm but more efficient than the CML algorithm. This conclusion was known for angles of arrival estimation and is now confirmed for gains-and-phases calibration. Computationally, the WNSF algorithm is shown to be more attractive than the other two as it can be implemented via a quadratic minimization procedure for arbitrarily shaped arrays.

Keywords

array signal processing; calibration; direction-of-arrival estimation; maximum likelihood estimation; optimisation; phase estimation; angles of arrival estimation; asymptotic performance; conditional maximum likelihood algorithm; data model; gain calibration; optimal gain estimator; optimal phase estimator; optimal weight; phase calibration; quadratic minimization; sensor arrays; statistical efficiency; unconditional maximum likelihood algorithm; weighted noise subspace fitting algorithm; Australia Council; Calibration; Maximum likelihood estimation; Minimization methods; Multiple signal classification; Phase estimation; Phased arrays; Sensor arrays; Signal processing algorithms; Unified modeling language;

fLanguage

English

Journal_Title

Signal Processing, IEEE Transactions on

Publisher

ieee

ISSN

1053-587X

Type

jour

DOI

10.1109/78.887058

Filename

887058