A globally convergent approach for blind MIMO adaptive deconvolution
We address the deconvolution of MIMO linear mixtures. The approach is based on the construction of a hierarchical family of composite criteria involving CM criterion and second order statistics constraint. Although, the criteria are based on fourth order statistics, we give a complete proof of conve...
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| creator | Touzni, A. Fijalkow, I. Larimore, M. Treichler, J.R. |
| description | We address the deconvolution of MIMO linear mixtures. The approach is based on the construction of a hierarchical family of composite criteria involving CM criterion and second order statistics constraint. Although, the criteria are based on fourth order statistics, we give a complete proof of convergence of this structure. We show that each cost function leads to the restoration of one single source. Moreover the approach is naturally robust with respect to the channels order estimation. An adaptive algorithm is derived for the simultaneous estimation of all sources. |
| doi_str_mv | 10.1109/ICASSP.1998.681630 |
| format | Conference Proceeding |
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No.98CH36181)</title><addtitle>ICASSP</addtitle><description>We address the deconvolution of MIMO linear mixtures. The approach is based on the construction of a hierarchical family of composite criteria involving CM criterion and second order statistics constraint. Although, the criteria are based on fourth order statistics, we give a complete proof of convergence of this structure. We show that each cost function leads to the restoration of one single source. Moreover the approach is naturally robust with respect to the channels order estimation. An adaptive algorithm is derived for the simultaneous estimation of all sources.</description><subject>Adaptive signal processing</subject><subject>Convergence</subject><subject>Costs</subject><subject>Deconvolution</subject><subject>MIMO</subject><subject>Robustness</subject><subject>Signal restoration</subject><subject>Source separation</subject><subject>Statistics</subject><subject>Wireless communication</subject><issn>1520-6149</issn><issn>2379-190X</issn><isbn>9780780344280</isbn><isbn>0780344286</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>1998</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><sourceid>RIE</sourceid><recordid>eNotUNtqg0AUXHqBSuoP5Gl_QHvOXlzPY0gvCSSkkBb6FlbdTbdYFbWB_H0t6TAwDAzDMIzNEVJEoIf1crHfv6ZIlKdZjpmEKxYJaShBgo9rFpPJYaJUSuRwwyLUApIMFd2xeBi-YILSGoyO2OOCH-u2sHV95mXbnFx_dM3Ibdf1rS0_uW97XtShqfh2vd1xW9luDCfHK_eXbuufMbTNPbv1th5c_K8z9v789LZcJZvdyzR2kwQ0YkxkZrUSwk5GF448gERfWCU9aUNaQYlaW2HAkkaSTslCEhrpM1NWolRyxuaX3uCcO3R9-Lb9-XC5QP4C7c1Mlg</recordid><startdate>1998</startdate><enddate>1998</enddate><creator>Touzni, A.</creator><creator>Fijalkow, I.</creator><creator>Larimore, M.</creator><creator>Treichler, J.R.</creator><general>IEEE</general><scope>6IE</scope><scope>6IH</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIO</scope></search><sort><creationdate>1998</creationdate><title>A globally convergent approach for blind MIMO adaptive deconvolution</title><author>Touzni, A. ; Fijalkow, I. ; Larimore, M. ; Treichler, J.R.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-i172t-36a5422a1725be9f0031fba43f9579540c155a270a95193e43b39173f67cd2c43</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>1998</creationdate><topic>Adaptive signal processing</topic><topic>Convergence</topic><topic>Costs</topic><topic>Deconvolution</topic><topic>MIMO</topic><topic>Robustness</topic><topic>Signal restoration</topic><topic>Source separation</topic><topic>Statistics</topic><topic>Wireless communication</topic><toplevel>online_resources</toplevel><creatorcontrib>Touzni, A.</creatorcontrib><creatorcontrib>Fijalkow, I.</creatorcontrib><creatorcontrib>Larimore, M.</creatorcontrib><creatorcontrib>Treichler, J.R.</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 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>Touzni, A.</au><au>Fijalkow, I.</au><au>Larimore, M.</au><au>Treichler, J.R.</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>A globally convergent approach for blind MIMO adaptive deconvolution</atitle><btitle>Proceedings of the 1998 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP '98 (Cat. No.98CH36181)</btitle><stitle>ICASSP</stitle><date>1998</date><risdate>1998</risdate><volume>4</volume><spage>2385</spage><epage>2388 vol.4</epage><pages>2385-2388 vol.4</pages><issn>1520-6149</issn><eissn>2379-190X</eissn><isbn>9780780344280</isbn><isbn>0780344286</isbn><abstract>We address the deconvolution of MIMO linear mixtures. The approach is based on the construction of a hierarchical family of composite criteria involving CM criterion and second order statistics constraint. Although, the criteria are based on fourth order statistics, we give a complete proof of convergence of this structure. We show that each cost function leads to the restoration of one single source. Moreover the approach is naturally robust with respect to the channels order estimation. An adaptive algorithm is derived for the simultaneous estimation of all sources.</abstract><pub>IEEE</pub><doi>10.1109/ICASSP.1998.681630</doi><oa>free_for_read</oa></addata></record> |
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| ispartof | Proceedings of the 1998 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP '98 (Cat. No.98CH36181), 1998, Vol.4, p.2385-2388 vol.4 |
| issn | 1520-6149 2379-190X |
| language | eng |
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| source | IEEE Electronic Library (IEL) Conference Proceedings |
| subjects | Adaptive signal processing Convergence Costs Deconvolution MIMO Robustness Signal restoration Source separation Statistics Wireless communication |
| title | A globally convergent approach for blind MIMO adaptive deconvolution |
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