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Approximate maximum likelihood estimation of two closely spaced sources

Vincent, François and Besson, Olivier and Chaumette, Eric Approximate maximum likelihood estimation of two closely spaced sources. (2014) Signal Processing, 97. 83-90. ISSN 0165-1684

(Document in English)

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Official URL: http://dx.doi.org/10.1016/j.sigpro.2013.10.017


The performance of the majority of high resolution algorithms designed for either spectral analysis or Direction-of-Arrival (DoA) estimation drastically degrade when the amplitude sources are highly correlated or when the number of available snapshots is very small and possibly less than the number of sources. Under such circumstances, only Maximum Likelihood (ML) or ML-based techniques can still be effective. The main drawback of such optimal solutions lies in their high computational load. In this paper we propose a computationally efficient approximate ML estimator, in the case of two closely spaced signals, that can be used even in the single snapshot case. Our approach relies on Taylor series expansion of the projection onto the signal subspace and can be implemented through 1-D Fourier transforms. Its effectiveness is illustrated in complicated scenarios with very low sample support and possibly correlated sources, where it is shown to outperform conventional estimators.

Item Type:Article
Additional Information:Thanks to Elsevier editor. The definitive version is available at http://www.sciencedirect.com The original PDF of the article can be found at Signal Processing website: http://www.sciencedirect.com/science/journal/01651684/97/supp/C
HAL Id:hal-00905319
Audience (journal):International peer-reviewed journal
Uncontrolled Keywords:
Institution:Université de Toulouse > Institut Supérieur de l'Aéronautique et de l'Espace - ISAE-SUPAERO (FRANCE)
French research institutions > Office National d'Etudes et Recherches Aérospatiales - ONERA (FRANCE)
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Deposited On:18 Nov 2013 09:18

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