Near-field multiple source localization by passive sensor array

Author

Huang, Yung-Dar ; Barkat, Mourad

Author_Institution

Dept. of Electr. Eng., State Univ. of New York, Stony Brook, NY, USA

Volume

39

Issue

7

fYear

1991

fDate

7/1/1991 12:00:00 AM

Firstpage

968

Lastpage

975

Abstract

The localization of multiple near-field sources in a spatially white Gaussian noise environment is studied. A modified two-dimensional (2-D) version of the multiple signal classification (MUSIC) algorithm is used to localize the signal sources; range and bearing. A global-optimum maximum likelihood searching approach to localize these sources is discussed. It is shown that in the single source situation, the covariances of both the 2-D MUSIC estimator and the maximum likelihood estimator (MLE) approach the Cramer-Rao lower bound as the number of snapshots increases to infinity. In the multiple source situation, it is observed that for a high signal-to-noise ratio (SNR) and a large number of snapshots, the root mean square errors (RMSEs) of both localization techniques are relatively small. However, for low SNR and/or small number of snapshots, the performance of the MLE is much superior that of the modified 2-D MUSIC

Keywords

parameter estimation; signal detection; signal processing; white noise; 2-D MUSIC estimator; Cramer-Rao lower bound; MLE; SNR; array processing; bearing; global-optimum maximum likelihood searching; multiple signal classification; multiple source localization; near-field sources; passive sensor array; range; root mean square errors; signal-to-noise ratio; spatially white Gaussian noise; Antenna arrays; Calibration; Maximum likelihood estimation; Multiple signal classification; Position measurement; Root mean square; Sensor arrays; Sensor phenomena and characterization; Shape; Signal to noise ratio;

fLanguage

English

Journal_Title

Antennas and Propagation, IEEE Transactions on

Publisher

ieee

ISSN

0018-926X

Type

jour

DOI

10.1109/8.86917

Filename

86917