Near-field multiple source localization by passive sensor array

• Published in: IEEE transactions on antennas and propagation Vol. 39; no. 7; pp. 968 - 975
• Main Authors: Huang, Y.-D., Barkat, M.
• Format: Journal Article
• Language: English
• Published: New York, NY IEEE 01.07.1991; Institute of Electrical and Electronics Engineers
• Subjects: Antenna arrays; Applied sciences; Calibration; Detection, estimation, filtering, equalization, prediction; Exact sciences and technology; Information, signal and communications theory; Maximum likelihood estimation; Multiple signal classification; Position measurement; Root mean square; Sensor arrays; Sensor phenomena and characterization; Shape; Signal and communications theory; Signal to noise ratio; Signal, noise; Telecommunications and information theory; Near field; Radiation source; Localization; Sensor array; Arrival angle; Signal estimation
• ISSN: 0018-926X; 1558-2221
• DOI: 10.1109/8.86917

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.< >