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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Bibliographic Details
Published in:IEEE transactions on signal processing 1998-05, Vol.46 (5), p.1443-1451
Main Authors: Yardimci, Y., Cetin, A.E., Cadzow, J.A.
Format: Article
Language:English
Subjects:
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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Summary: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