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DOA estimation in partially correlated noise using low-rank/sparse matrix decomposition
KTH, School of Electrical Engineering (EES), Signal Processing. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre. Sharif Univ. of Tech, Iran.
KTH, School of Electrical Engineering (EES), Signal Processing. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.ORCID iD: 0000-0002-6855-5868
KTH, School of Electrical Engineering (EES), Signal Processing. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
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2014 (English)In: 2014 IEEE 8th Sensor Array and Multichannel Signal Processing Workshop (SAM), IEEE Computer Society, 2014, 373-376 p.Conference paper, Published paper (Refereed)
Abstract [en]

We consider the problem of direction-of-arrival (DOA) estimation in unknown partially correlated noise environments where the noise covariance matrix is sparse. A sparse noise covariance matrix is a common model for a sparse array of sensors consisted of several widely separated subarrays. Since interelement spacing among sensors in a subarray is small, the noise in the subarray is in general spatially correlated, while, due to large distances between subarrays, the noise between them is uncorrelated. Consequently, the noise covariance matrix of such an array has a block diagonal structure which is indeed sparse. Moreover, in an ordinary nonsparse array, because of small distance between adjacent sensors, there is noise coupling between neighboring sensors, whereas one can assume that non-adjacent sensors have spatially uncorrelated noise which makes again the array noise covariance matrix sparse. Utilizing some recently available tools in low-rank/sparse matrix decomposition, matrix completion, and sparse representation, we propose a novel method which can resolve possibly correlated or even coherent sources in the aforementioned partly correlated noise. In particular, when the sources are uncorrelated, our approach involves solving a second-order cone programming (SOCP), and if they are correlated or coherent, one needs to solve a computationally harder convex program. We demonstrate the effectiveness of the proposed algorithm by numerical simulations and comparison to the Cramer-Rao bound (CRB).

Place, publisher, year, edition, pages
IEEE Computer Society, 2014. 373-376 p.
Series
Proceedings of the IEEE Sensor Array and Multichannel Signal Processing Workshop, ISSN 2151-870X
Keyword [en]
Communication channels (information theory), Convex programming, Cramer-Rao bounds, Direction of arrival, Signal processing, White noise, Cramer-rao bound (CRB), Direction-of-arrival estimation, Inter-element spacing, Matrix decomposition, Noise covariance matrix, Second-order cone programming, Sparse representation, Uncorrelated noise
National Category
Signal Processing
Identifiers
URN: urn:nbn:se:kth:diva-144612DOI: 10.1109/SAM.2014.6882419ISI: 000360273100094Scopus ID: 2-s2.0-84907387277ISBN: 978-147991481-4 (print)OAI: oai:DiVA.org:kth-144612DiVA: diva2:714347
Conference
2014 IEEE 8th Sensor Array and Multichannel Signal Processing Workshop, SAM 2014, A Coruna, Spain, 22 June 2014 through 25 June 2014
Note

QC 20141219

Available from: 2014-04-27 Created: 2014-04-27 Last updated: 2015-09-18Bibliographically approved

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Jansson, Magnus

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