Sparse linear arrays; directions of arrival estimation; least squares estimator
Résumé :
[en] Sparse linear arrays (SLAs), such as nested and co-prime arrays,
have the attractive capability of providing enhanced degrees of freedom
by exploiting the co-array model. Accordingly, co-array-based
Direction of Arrivals (DoAs) estimation has recently gained considerable
interest in array processing. The literature has suggested
applying MUSIC on an augmented sample covariance matrix for
co-array-based DoAs estimation. In this paper, we propose a Least
Squares (LS) estimator for co-array-based DoAs estimation employing
the covariance fitting method as an alternative to MUSIC. We
show that the proposed LS estimator provides consistent estimates
of DoAs of identifiable sources for SLAs. Additionally, an analytical
expression for the large sample performance of the proposed
estimator is derived. Numerical results illustrate the finite sample
behavior in relation to the derived analytical expression. Moreover,
the performance of the proposed LS estimator is compared to the
co-array-based MUSIC.
Disciplines :
Ingénierie électrique & électronique
Auteur, co-auteur :
SEDIGHI, Saeid ; University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT)
SHANKAR, Bhavani ; University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT)
Maleki, Sina
OTTERSTEN, Björn ; University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT)
Co-auteurs externes :
no
Langue du document :
Anglais
Titre :
CONSISTENT LEAST SQUARES ESTIMATOR FOR CO-ARRAY-BASED DOA ESTIMATION
Date de publication/diffusion :
juillet 2018
Nom de la manifestation :
The Tenth IEEE Sensor Array and Multichannel Signal Processing Workshop
Date de la manifestation :
08-07-2018 to 11-07-2018
Manifestation à portée :
International
Titre de l'ouvrage principal :
IEEE Sensor Array and Multichannel Signal Processing Workshop (SAM)
