Reference : CONSISTENT LEAST SQUARES ESTIMATOR FOR CO-ARRAY-BASED DOA ESTIMATION

	Document type :	Scientific congresses, symposiums and conference proceedings : Paper published in a book
	Discipline(s) :	Engineering, computing & technology : Electrical & electronics engineering
	To cite this reference:	http://hdl.handle.net/10993/37677

Title :	CONSISTENT LEAST SQUARES ESTIMATOR FOR CO-ARRAY-BASED DOA ESTIMATION
Language :	English
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) > >]
Publication date :	Jul-2018
Main document title :	IEEE Sensor Array and Multichannel Signal Processing Workshop (SAM)
Peer reviewed :	Yes
Audience :	International
Event name :	The Tenth IEEE Sensor Array and Multichannel Signal Processing Workshop
Event date :	08-07-2018 to 11-07-2018
Keywords :	[en] 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.
Target :	Researchers ; Professionals ; Students
Permalink :	http://hdl.handle.net/10993/37677
DOI :	10.1109/SAM.2018.8448477
Other URL :	https://ieeexplore.ieee.org/abstract/document/8448477
FnR project :	FnR ; FNR11228830 > Saeid Sedighi > > Compressive Sensing for Ranging and Detection in Automotive Applications > 15/02/2017 > 14/02/2021 > 2016

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