Multistep Knowledge-Aided Iterative ESPRIT: Design and Analysis

In this work, we propose a subspace-based algorithm for direction-of-arrival (DOA) estimation that iteratively reduces the disturbance factors of the estimated data covariance matrix and incorporates prior knowledge which is gradually obtained on line. An analysis of the mean squared error of the re...

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Veröffentlicht in:IEEE transactions on aerospace and electronic systems 2018-10, Vol.54 (5), p.2189-2201
Hauptverfasser: Pinto, Silvio F. B., de Lamare, Rodrigo C.
Format: Artikel
Sprache:eng
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Zusammenfassung:In this work, we propose a subspace-based algorithm for direction-of-arrival (DOA) estimation that iteratively reduces the disturbance factors of the estimated data covariance matrix and incorporates prior knowledge which is gradually obtained on line. An analysis of the mean squared error of the reshaped data covariance matrix is carried out along with comparisons between computational complexities of the proposed and existing algorithms. Simulations focusing on closely-spaced sources, where they are uncorrelated and correlated, illustrate the improvements achieved.
ISSN:0018-9251
1557-9603
DOI:10.1109/TAES.2018.2811098