Robust direction-of-arrival estimation in non-Gaussian noise

A nonlinearly weighted least-squares method is developed for robust modeling of sensor array data, weighting functions for various observation noise scenarios are determined using maximum likelihood estimation theory. The computational complexity of the new method is comparable with the standard lea...

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Veröffentlicht in:	IEEE transactions on signal processing 1998-05, Vol.46 (5), p.1443-1451
Hauptverfasser:	Yardimci, Y., Cetin, A.E., Cadzow, J.A.
Format:	Artikel
Sprache:	eng
Schlagworte:	Applied sciences Bayesian methods Covariance matrix Degradation Detection, estimation, filtering, equalization, prediction Direction of arrival estimation Exact sciences and technology Information, signal and communications theory Mean square error methods Noise robustness Sensor arrays Signal and communications theory Signal processing algorithms Signal to noise ratio Signal, noise Speech processing Telecommunications and information theory
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Beschreibung
Zusammenfassung:	A nonlinearly weighted least-squares method is developed for robust modeling of sensor array data, weighting functions for various observation noise scenarios are determined using maximum likelihood estimation theory. The computational complexity of the new method is comparable with the standard least-squares estimation procedures. Simulation examples of direction-of-arrival estimation are presented.
ISSN:	1053-587X 1941-0476
DOI:	10.1109/78.668808