An Iterative Dictionary Learning-Based Algorithm for DOA Estimation

This letter proposes a dictionary learning algorithm for solving the grid mismatch problem in direction of arrival (DOA) estimation from both the array sensor data and from the sign of the array sensor data. Discretization of the grid in the sparsity-based DOA estimation algorithms is a problem, whi...

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Veröffentlicht in:	IEEE communications letters 2016-09, Vol.20 (9), p.1784-1787
Hauptverfasser:	Zamani, Hojatollah, Zayyani, Hadi, Marvasti, Farrokh
Format:	Artikel
Sprache:	eng
Schlagworte:	Algorithms Compressed sensing Cost function Dictionaries dictionary learning Direction of arrival Direction-of-arrival estimation Estimation Sensor arrays Sensors sign of the measurements steepest-descent
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creator	Zamani, Hojatollah Zayyani, Hadi Marvasti, Farrokh
description	This letter proposes a dictionary learning algorithm for solving the grid mismatch problem in direction of arrival (DOA) estimation from both the array sensor data and from the sign of the array sensor data. Discretization of the grid in the sparsity-based DOA estimation algorithms is a problem, which leads to a bias error. To compensate this bias error, a dictionary learning technique is suggested, which is based on minimizing a suitable cost function. We also propose an algorithm for the estimation of DOA from the sign of the measurements. It extends the iterative method with adaptive thresholding algorithm to the 1-b compressed sensing framework. Simulation results show the effectiveness of the dictionary learning-based algorithms in comparison with the counterpart algorithms in DOA estimation both from the sensors' data and from the sign of the sensors' data.
doi_str_mv	10.1109/LCOMM.2016.2587674
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Discretization of the grid in the sparsity-based DOA estimation algorithms is a problem, which leads to a bias error. To compensate this bias error, a dictionary learning technique is suggested, which is based on minimizing a suitable cost function. We also propose an algorithm for the estimation of DOA from the sign of the measurements. It extends the iterative method with adaptive thresholding algorithm to the 1-b compressed sensing framework. 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Discretization of the grid in the sparsity-based DOA estimation algorithms is a problem, which leads to a bias error. To compensate this bias error, a dictionary learning technique is suggested, which is based on minimizing a suitable cost function. We also propose an algorithm for the estimation of DOA from the sign of the measurements. It extends the iterative method with adaptive thresholding algorithm to the 1-b compressed sensing framework. Simulation results show the effectiveness of the dictionary learning-based algorithms in comparison with the counterpart algorithms in DOA estimation both from the sensors' data and from the sign of the sensors' data.</description><subject>Algorithms</subject><subject>Compressed sensing</subject><subject>Cost function</subject><subject>Dictionaries</subject><subject>dictionary learning</subject><subject>Direction of arrival</subject><subject>Direction-of-arrival estimation</subject><subject>Estimation</subject><subject>Sensor arrays</subject><subject>Sensors</subject><subject>sign of the measurements</subject><subject>steepest-descent</subject><issn>1089-7798</issn><issn>1558-2558</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2016</creationdate><recordtype>article</recordtype><sourceid>RIE</sourceid><recordid>eNo9kM1OwzAQhC0EEqXwAnCJxDllbcexfQxpgUqpeoGzZZxNSdUmxU6R-va4tOKwP4eZndVHyD2FCaWgn6pyuVhMGNB8woSSucwuyIgKoVIW22XcQelUSq2uyU0IawBQTNARKYsumQ_o7dD-YDJt3dD2nfWHpELru7Zbpc82YJ0Um1Xv2-FrmzS9T6bLIpmFod3ao_yWXDV2E_DuPMfk42X2Xr6l1fJ1XhZV6niWDanikmY1zV0ugAnggiJjijUAwjHktKZCUcRP0CzLnaitwlrL3EktYjnNx-TxdHfn--89hsGs-73vYqShioPOtJY8qthJ5XwfgsfG7Hx81B8MBXOEZf5gmSMsc4YVTQ8nU4uI_wYpQGgp-S9q32Og</recordid><startdate>201609</startdate><enddate>201609</enddate><creator>Zamani, Hojatollah</creator><creator>Zayyani, Hadi</creator><creator>Marvasti, Farrokh</creator><general>IEEE</general><general>The Institute of Electrical and Electronics Engineers, Inc. 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Discretization of the grid in the sparsity-based DOA estimation algorithms is a problem, which leads to a bias error. To compensate this bias error, a dictionary learning technique is suggested, which is based on minimizing a suitable cost function. We also propose an algorithm for the estimation of DOA from the sign of the measurements. It extends the iterative method with adaptive thresholding algorithm to the 1-b compressed sensing framework. Simulation results show the effectiveness of the dictionary learning-based algorithms in comparison with the counterpart algorithms in DOA estimation both from the sensors' data and from the sign of the sensors' data.</abstract><cop>New York</cop><pub>IEEE</pub><doi>10.1109/LCOMM.2016.2587674</doi><tpages>4</tpages></addata></record>
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subjects	Algorithms Compressed sensing Cost function Dictionaries dictionary learning Direction of arrival Direction-of-arrival estimation Estimation Sensor arrays Sensors sign of the measurements steepest-descent
title	An Iterative Dictionary Learning-Based Algorithm for DOA Estimation
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