Probability of Resolution of MUSIC and g-MUSIC: An Asymptotic Approach
Autor: | David Schenck, Marius Pesavento, Xavier Mestre |
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Rok vydání: | 2022 |
Předmět: |
Signal Processing (eess.SP)
Performances analysis Iterative methods Covariance matrices Covariance matrix Eigenvalue and eigenfunctions Direction of arrival estimation FOS: Electrical engineering electronic engineering information engineering Cost functions Random variables Electrical Engineering and Systems Science - Signal Processing Electrical and Electronic Engineering Central Limit Theorem Multiple signal classification Eigenvalues and eigenfunctions Stochastic systems Signal resolution Cost-function Direction of arrival Behavioral science Signal classification Computer Science::Sound Probability of resolution Signal Processing Behavioral research G-multiple signal classification |
Zdroj: | IEEE Transactions on Signal Processing. 70:3566-3581 |
ISSN: | 1941-0476 1053-587X |
DOI: | 10.1109/tsp.2022.3178820 |
Popis: | In this article, the outlier production mechanism of the conventional Multiple Signal Classification (MUSIC) and the g-MUSIC Direction-of-Arrival (DoA) estimation technique is investigated using tools from Random Matrix Theory (RMT). A general Central Limit Theorem (CLT) is derived that allows to analyze the asymptotic stochastic behavior of eigenvector-based cost functions in the asymptotic regime where the number of snapshots and the number of antennas increase without bound at the same rate. Furthermore, this CLT is used to provide an accurate prediction of the resolution capabilities of the MUSIC and the g-MUSIC DoA estimation method. The finite dimensional distribution of the MUSIC and the g-MUSIC cost function is shown to be asymptotically jointly Gaussian distributed in the asymptotic regime. Comment: This work has been accepted for publication in the IEEE Transactions on Signal Processing. Copyright may be transferred without notice, after which this version may no longer be accessible |
Databáze: | OpenAIRE |
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