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A bias-compensated MUSIC for small number of samples

Vincent, François and Pascal, Frédéric and Besson, Olivier A bias-compensated MUSIC for small number of samples. (2017) Signal Processing, 138. 117-120. ISSN 0165-1684

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Official URL: https://doi.org/10.1016/j.sigpro.2017.03.015

Abstract

The multiple signal classification (MUSIC) method is known to be asymptotically efficient, yet with a small number of snapshots its performance degrades due to bias in MUSIC localization function. In this communication, starting from G-MUSIC which improves over MUSIC in low sample support, a high signal to noise ratio approximation of the G-MUSIC localization function is derived. This approximation results in closed-form expressions of the weights applied to each eigenvector of the sample covariance matrix. A new method which consists in minimizing this simplified G-MUSIC localization function is thus in- troduced, and referred to as sG-MUSIC. Interestingly enough, this sG-MUSIC criterion can be interpreted as a bias correction of the conventional MUSIC localization function. Numerical simulations indicate that sG-MUSIC incur only a marginal loss in terms of mean square error of the direction of arrival estimates, as compared to G-MUSIC, and performs better than MUSIC

Item Type:Article
Audience (journal):International peer-reviewed journal
Uncontrolled Keywords:
Institution:French research institutions > Centre National de la Recherche Scientifique - CNRS (FRANCE)
Université de Toulouse > Institut Supérieur de l'Aéronautique et de l'Espace - ISAE-SUPAERO (FRANCE)
Other partners > Université Paris-Saclay (FRANCE)
Other partners > CentraleSupélec (FRANCE)
Laboratory name:
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Deposited On:22 Oct 2020 12:29

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