Cooperative adaptive estimation of distributed noncircular complex signals
The problem of distributed (cooperative) adaptive estimation of complex signals is addressed using augmented statistics and widely linear modelling, which enables optimal second order estimation of complex signals with both circular (rotation invariant) and noncircular (rotation dependent) distribut...
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| creator | Dini, D. H. Mandic, D. P. |
| description | The problem of distributed (cooperative) adaptive estimation of complex signals is addressed using augmented statistics and widely linear modelling, which enables optimal second order estimation of complex signals with both circular (rotation invariant) and noncircular (rotation dependent) distributions. The widely linear distributed augmented complex Kalman filter (D-ACKF) and recursive least squares (D-ACRLS) algorithms are introduced, and shown to allow for a unified treatment of the generality of complex valued signals. Further, the D-ACKF proposed here avoids the typical assumption that the observation noises at different nodes in the network are uncorrelated; thus providing enhanced performance in realworld scenarios. |
| doi_str_mv | 10.1109/ACSSC.2012.6489281 |
| format | Conference Proceeding |
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H.</creatorcontrib><creatorcontrib>Mandic, D. P.</creatorcontrib><title>Cooperative adaptive estimation of distributed noncircular complex signals</title><title>2012 Conference Record of the Forty Sixth Asilomar Conference on Signals, Systems and Computers (ASILOMAR)</title><addtitle>ACSSC</addtitle><description>The problem of distributed (cooperative) adaptive estimation of complex signals is addressed using augmented statistics and widely linear modelling, which enables optimal second order estimation of complex signals with both circular (rotation invariant) and noncircular (rotation dependent) distributions. The widely linear distributed augmented complex Kalman filter (D-ACKF) and recursive least squares (D-ACRLS) algorithms are introduced, and shown to allow for a unified treatment of the generality of complex valued signals. Further, the D-ACKF proposed here avoids the typical assumption that the observation noises at different nodes in the network are uncorrelated; thus providing enhanced performance in realworld scenarios.</description><subject>complex circularity</subject><subject>distributed diffusion estimation</subject><subject>distributed recursive least squares (RLS)</subject><subject>Kalman filter</subject><subject>sensor networks</subject><subject>Widely linear model</subject><issn>1058-6393</issn><issn>2576-2303</issn><isbn>9781467350501</isbn><isbn>1467350508</isbn><isbn>1467350494</isbn><isbn>9781467350495</isbn><isbn>1467350516</isbn><isbn>9781467350518</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2012</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><sourceid>RIE</sourceid><recordid>eNotkNtKxDAYhOMJrGtfQG_6Aq1_zs3lUjyy4MXq9ZI2fyTSbUraFX17i-7VDDPwMQwhNxQqSsHcrZvttqkYUFYpURtW0xNyRYXSXIIw4pRkTGpVMg78jORG18dOAj0nGQVZl4obfknyafoEgAWqjBEZeWliHDHZOXxhYZ0d_wxOc9gvWRyK6AsXpjmF9jCjK4Y4dCF1h96moov7scfvYgofg-2na3LhF8H8qCvy_nD_1jyVm9fH52a9KQPVci5rxoBz6dEJxbRxLVqQ2KLwSggrGWoHdUcVOFBeoreCS8sUV9ByTwXlK3L7zw2IuBvTsjT97I638F-1G1L9</recordid><startdate>201211</startdate><enddate>201211</enddate><creator>Dini, D. H.</creator><creator>Mandic, D. P.</creator><general>IEEE</general><scope>6IE</scope><scope>6IH</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIO</scope></search><sort><creationdate>201211</creationdate><title>Cooperative adaptive estimation of distributed noncircular complex signals</title><author>Dini, D. H. ; Mandic, D. P.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-i175t-8220335fed46279dbea05ebe4f644a52e7d08c160d06f5efa435a26360b3f1413</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2012</creationdate><topic>complex circularity</topic><topic>distributed diffusion estimation</topic><topic>distributed recursive least squares (RLS)</topic><topic>Kalman filter</topic><topic>sensor networks</topic><topic>Widely linear model</topic><toplevel>online_resources</toplevel><creatorcontrib>Dini, D. H.</creatorcontrib><creatorcontrib>Mandic, D. P.</creatorcontrib><collection>IEEE Electronic Library (IEL) Conference Proceedings</collection><collection>IEEE Proceedings Order Plan (POP) 1998-present by volume</collection><collection>IEEE Xplore All Conference Proceedings</collection><collection>IEEE/IET Electronic Library (IEL)</collection><collection>IEEE Proceedings Order Plans (POP) 1998-present</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Dini, D. H.</au><au>Mandic, D. P.</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>Cooperative adaptive estimation of distributed noncircular complex signals</atitle><btitle>2012 Conference Record of the Forty Sixth Asilomar Conference on Signals, Systems and Computers (ASILOMAR)</btitle><stitle>ACSSC</stitle><date>2012-11</date><risdate>2012</risdate><spage>1518</spage><epage>1522</epage><pages>1518-1522</pages><issn>1058-6393</issn><eissn>2576-2303</eissn><isbn>9781467350501</isbn><isbn>1467350508</isbn><eisbn>1467350494</eisbn><eisbn>9781467350495</eisbn><eisbn>1467350516</eisbn><eisbn>9781467350518</eisbn><abstract>The problem of distributed (cooperative) adaptive estimation of complex signals is addressed using augmented statistics and widely linear modelling, which enables optimal second order estimation of complex signals with both circular (rotation invariant) and noncircular (rotation dependent) distributions. The widely linear distributed augmented complex Kalman filter (D-ACKF) and recursive least squares (D-ACRLS) algorithms are introduced, and shown to allow for a unified treatment of the generality of complex valued signals. Further, the D-ACKF proposed here avoids the typical assumption that the observation noises at different nodes in the network are uncorrelated; thus providing enhanced performance in realworld scenarios.</abstract><pub>IEEE</pub><doi>10.1109/ACSSC.2012.6489281</doi><tpages>5</tpages></addata></record> |
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| ispartof | 2012 Conference Record of the Forty Sixth Asilomar Conference on Signals, Systems and Computers (ASILOMAR), 2012, p.1518-1522 |
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| language | eng |
| recordid | cdi_ieee_primary_6489281 |
| source | IEEE Electronic Library (IEL) Conference Proceedings |
| subjects | complex circularity distributed diffusion estimation distributed recursive least squares (RLS) Kalman filter sensor networks Widely linear model |
| title | Cooperative adaptive estimation of distributed noncircular complex signals |
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