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DOA estimation in p...
DOA estimation in partially correlated noise using low-rank/sparse matrix decomposition
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- Malek Mohammadi, Mohammadreza (författare)
- KTH,Signalbehandling,ACCESS Linnaeus Centre,Sharif Univ. of Tech, Iran
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- Jansson, Magnus (författare)
- KTH,Signalbehandling,ACCESS Linnaeus Centre
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- Owrang, Arash (författare)
- KTH,Signalbehandling,ACCESS Linnaeus Centre
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Koochakzadeh, Ali (författare)
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Babaie-Zadeh, Massoud (författare)
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(creator_code:org_t)
- IEEE Computer Society, 2014
- 2014
- Engelska.
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Ingår i: 2014 IEEE 8th Sensor Array and Multichannel Signal Processing Workshop (SAM). - : IEEE Computer Society. - 9781479914814 ; , s. 373-376
- Relaterad länk:
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https://urn.kb.se/re...
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https://doi.org/10.1...
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Abstract
Ämnesord
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- 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).
Ämnesord
- TEKNIK OCH TEKNOLOGIER -- Elektroteknik och elektronik -- Signalbehandling (hsv//swe)
- ENGINEERING AND TECHNOLOGY -- Electrical Engineering, Electronic Engineering, Information Engineering -- Signal Processing (hsv//eng)
Nyckelord
- 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
Publikations- och innehållstyp
- ref (ämneskategori)
- kon (ämneskategori)
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