Sparse linear arrays; directions of arrival estimation; least squares estimator
Abstract :
[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 :
Electrical & electronics engineering
Author, co-author :
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)
External co-authors :
no
Language :
English
Title :
CONSISTENT LEAST SQUARES ESTIMATOR FOR CO-ARRAY-BASED DOA ESTIMATION
Publication date :
July 2018
Event name :
The Tenth IEEE Sensor Array and Multichannel Signal Processing Workshop
Event date :
08-07-2018 to 11-07-2018
Audience :
International
Main work title :
IEEE Sensor Array and Multichannel Signal Processing Workshop (SAM)
