Constant Modulus Blind Adaptive Beamforming Based on Unscented Kalman Filtering
An unscented Kalman filter-based constant modulus adaptation algorithm (UKF-CMA) is proposed for blind uniform linear beamforming. The proposed algorithm is obtained by first developing a model of the constant modulus (CM) criterion and then fitting that model into the Kalman filter-style state spac...
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| Veröffentlicht in: | IEEE signal processing letters 2015-04, Vol.22 (4), p.474-478 |
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| container_title | IEEE signal processing letters |
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| creator | Bhotto, Md Zulfiquar Ali Bajic, Ivan V. |
| description | An unscented Kalman filter-based constant modulus adaptation algorithm (UKF-CMA) is proposed for blind uniform linear beamforming. The proposed algorithm is obtained by first developing a model of the constant modulus (CM) criterion and then fitting that model into the Kalman filter-style state space model by using an auxiliary parameter. The proposed algorithm does not require a priori information about the process noise and measurement noise covariance matrices and hence it can be applied readily. Simulation results demonstrate that the proposed algorithm offers improved performance compared to the recursive least square-based CM (RLS-CMA) and least-mean square-based CM (LMS-CMA) algorithms for adaptive blind beamforming. |
| doi_str_mv | 10.1109/LSP.2014.2362932 |
| format | Article |
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The proposed algorithm is obtained by first developing a model of the constant modulus (CM) criterion and then fitting that model into the Kalman filter-style state space model by using an auxiliary parameter. The proposed algorithm does not require a priori information about the process noise and measurement noise covariance matrices and hence it can be applied readily. Simulation results demonstrate that the proposed algorithm offers improved performance compared to the recursive least square-based CM (RLS-CMA) and least-mean square-based CM (LMS-CMA) algorithms for adaptive blind beamforming.</description><identifier>ISSN: 1070-9908</identifier><identifier>EISSN: 1558-2361</identifier><identifier>DOI: 10.1109/LSP.2014.2362932</identifier><identifier>CODEN: ISPLEM</identifier><language>eng</language><publisher>New York: IEEE</publisher><subject>Adaptation models ; Algorithms ; Array signal processing ; Beamforming ; Blind beamforming ; Blinds ; Computer simulation ; constant modulus ; Constants ; Criteria ; Direction-of-arrival estimation ; Fittings ; Kalman filters ; Noise ; Noise measurement ; Signal processing algorithms ; state space model ; unscented Kalman filter ; Vectors</subject><ispartof>IEEE signal processing letters, 2015-04, Vol.22 (4), p.474-478</ispartof><rights>Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) Apr 2015</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c324t-813e71165d96956053f41426f9ce3841b1747b0d74249bc2474f4aaa2767e6d93</citedby><cites>FETCH-LOGICAL-c324t-813e71165d96956053f41426f9ce3841b1747b0d74249bc2474f4aaa2767e6d93</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/6922488$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>314,777,781,793,27905,27906,54739</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/6922488$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc></links><search><creatorcontrib>Bhotto, Md Zulfiquar Ali</creatorcontrib><creatorcontrib>Bajic, Ivan V.</creatorcontrib><title>Constant Modulus Blind Adaptive Beamforming Based on Unscented Kalman Filtering</title><title>IEEE signal processing letters</title><addtitle>LSP</addtitle><description>An unscented Kalman filter-based constant modulus adaptation algorithm (UKF-CMA) is proposed for blind uniform linear beamforming. The proposed algorithm is obtained by first developing a model of the constant modulus (CM) criterion and then fitting that model into the Kalman filter-style state space model by using an auxiliary parameter. The proposed algorithm does not require a priori information about the process noise and measurement noise covariance matrices and hence it can be applied readily. Simulation results demonstrate that the proposed algorithm offers improved performance compared to the recursive least square-based CM (RLS-CMA) and least-mean square-based CM (LMS-CMA) algorithms for adaptive blind beamforming.</description><subject>Adaptation models</subject><subject>Algorithms</subject><subject>Array signal processing</subject><subject>Beamforming</subject><subject>Blind beamforming</subject><subject>Blinds</subject><subject>Computer simulation</subject><subject>constant modulus</subject><subject>Constants</subject><subject>Criteria</subject><subject>Direction-of-arrival estimation</subject><subject>Fittings</subject><subject>Kalman filters</subject><subject>Noise</subject><subject>Noise measurement</subject><subject>Signal processing algorithms</subject><subject>state space model</subject><subject>unscented Kalman filter</subject><subject>Vectors</subject><issn>1070-9908</issn><issn>1558-2361</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2015</creationdate><recordtype>article</recordtype><sourceid>RIE</sourceid><recordid>eNpdkE1LAzEQhoMoWKt3wUvAi5etmWw2H8e2WBUrFbTnkO5mZctuUpNdwX9vSosHTzMvPO8wPAhdA5kAEHW_fH-bUAJsQnNOVU5P0AiKQmYpwmnaiSCZUkSeo4sYt4QQCbIYodXcu9gb1-NXXw3tEPGsbVyFp5XZ9c23xTNrutqHrnGfeGairbB3eO1iaV2fwotpO-Pwoml7GxJzic5q00Z7dZxjtF48fMyfsuXq8Xk-XWZlTlmfScitAOBFpbgqOCnymgGjvFalzSWDDQgmNqQSjDK1KSkTrGbGGCq4sLxS-RjdHe7ugv8abOx116Sf2tY464eogTNKiSzoHr39h279EFz6LlHAlWSS7SlyoMrgYwy21rvQdCb8aCB6b1gnw3pvWB8Np8rNodJYa_9wrihlUua_qQJ0wA</recordid><startdate>201504</startdate><enddate>201504</enddate><creator>Bhotto, Md Zulfiquar Ali</creator><creator>Bajic, Ivan V.</creator><general>IEEE</general><general>The Institute of Electrical and Electronics Engineers, Inc. (IEEE)</general><scope>97E</scope><scope>RIA</scope><scope>RIE</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7SC</scope><scope>7SP</scope><scope>8FD</scope><scope>JQ2</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope><scope>F28</scope><scope>FR3</scope></search><sort><creationdate>201504</creationdate><title>Constant Modulus Blind Adaptive Beamforming Based on Unscented Kalman Filtering</title><author>Bhotto, Md Zulfiquar Ali ; Bajic, Ivan V.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c324t-813e71165d96956053f41426f9ce3841b1747b0d74249bc2474f4aaa2767e6d93</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2015</creationdate><topic>Adaptation models</topic><topic>Algorithms</topic><topic>Array signal processing</topic><topic>Beamforming</topic><topic>Blind beamforming</topic><topic>Blinds</topic><topic>Computer simulation</topic><topic>constant modulus</topic><topic>Constants</topic><topic>Criteria</topic><topic>Direction-of-arrival estimation</topic><topic>Fittings</topic><topic>Kalman filters</topic><topic>Noise</topic><topic>Noise measurement</topic><topic>Signal processing algorithms</topic><topic>state space model</topic><topic>unscented Kalman filter</topic><topic>Vectors</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Bhotto, Md Zulfiquar Ali</creatorcontrib><creatorcontrib>Bajic, Ivan V.</creatorcontrib><collection>IEEE All-Society Periodicals Package (ASPP) 2005-present</collection><collection>IEEE All-Society Periodicals Package (ASPP) 1998-Present</collection><collection>IEEE Electronic Library (IEL)</collection><collection>CrossRef</collection><collection>Computer and Information Systems Abstracts</collection><collection>Electronics & Communications Abstracts</collection><collection>Technology Research Database</collection><collection>ProQuest Computer Science Collection</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Computer and Information Systems Abstracts Academic</collection><collection>Computer and Information Systems Abstracts Professional</collection><collection>ANTE: Abstracts in New Technology & Engineering</collection><collection>Engineering Research Database</collection><jtitle>IEEE signal processing letters</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Bhotto, Md Zulfiquar Ali</au><au>Bajic, Ivan V.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Constant Modulus Blind Adaptive Beamforming Based on Unscented Kalman Filtering</atitle><jtitle>IEEE signal processing letters</jtitle><stitle>LSP</stitle><date>2015-04</date><risdate>2015</risdate><volume>22</volume><issue>4</issue><spage>474</spage><epage>478</epage><pages>474-478</pages><issn>1070-9908</issn><eissn>1558-2361</eissn><coden>ISPLEM</coden><abstract>An unscented Kalman filter-based constant modulus adaptation algorithm (UKF-CMA) is proposed for blind uniform linear beamforming. The proposed algorithm is obtained by first developing a model of the constant modulus (CM) criterion and then fitting that model into the Kalman filter-style state space model by using an auxiliary parameter. The proposed algorithm does not require a priori information about the process noise and measurement noise covariance matrices and hence it can be applied readily. Simulation results demonstrate that the proposed algorithm offers improved performance compared to the recursive least square-based CM (RLS-CMA) and least-mean square-based CM (LMS-CMA) algorithms for adaptive blind beamforming.</abstract><cop>New York</cop><pub>IEEE</pub><doi>10.1109/LSP.2014.2362932</doi><tpages>5</tpages></addata></record> |
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| ispartof | IEEE signal processing letters, 2015-04, Vol.22 (4), p.474-478 |
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| subjects | Adaptation models Algorithms Array signal processing Beamforming Blind beamforming Blinds Computer simulation constant modulus Constants Criteria Direction-of-arrival estimation Fittings Kalman filters Noise Noise measurement Signal processing algorithms state space model unscented Kalman filter Vectors |
| title | Constant Modulus Blind Adaptive Beamforming Based on Unscented Kalman Filtering |
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