NDLI: Localisation of mixed near-field and far-field sources using the largest aperture sparse linear array

Content Provider	IET Digital Library
Author	Ebrahimi, Ali Akbar Abutalebi, Hamid Reza Karimi, Mahmood
Abstract	In some applications, the signals received by an array are a mixture of signals emitted by both far-field and near-field sources. This study develops a new cumulant-based multiple signal classification (MUSIC) algorithm for source localisation using a new structural sparse array for scenarios where both far-field and near-field sources coexist. The key feature of this algorithm is that it utilises fourth-order cumulants to compute the virtual covariance matrix and constructs a new special cumulant matrix to acquire the largest number of virtual sensors and the largest array aperture for a given number of sensors. The authors provide a geometric proof to justify the utilisation of the proposed sparse linear array and compute the effective aperture of the array. The proposed algorithm increases resolution ability, direction of arrival (DOA) and range estimation accuracy, and the number of sources to be localised. Moreover, the new method has the main advantage that it does not use the information of all sensors; so that it provides somewhat low computational complexity while it uses many actual and virtual sensors. Monte Carlo simulations are provided to demonstrate the effectiveness of the proposed method.
Starting Page	155
Ending Page	162
Page Count	8
ISSN	17519675
Volume Number	12
e-ISSN	17519683
Issue Number	Issue 2, Apr (2018)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-spr/12/2
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-spr.2017.0063
Journal	IET Signal Processing
Publisher Date	2017-09-05
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Algebra Computational Complexity Covariance Matrices Cumulant-based Multiple Signal Classification Algorithm Direction of Arrival Estimation Higher Order Statistics Largest Aperture Sparse Linear Array Monte Carlo Method Monte Carlo Simulation Method Range Estimation Accuracy Signal Classification Signal Processing And Detection Source Localisation Structural Sparse Array Virtual Covariance Matrix
Content Type	Text
Resource Type	Article
Subject	Signal Processing Electrical and Electronic Engineering

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