Low-level multi-INT sensor fusion using entropic measures of dependence
An information-theoretic method of low-level multi-INT sensor fusion is presented, the end product of which is the entropic map, i.e. a collection of Gaussian clusters of information relevant to a given target signature formed over a geographical basis. The method is designed to be computationally e...
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| creator | Deignan, P. B. Wong, M. A. Douglass, A. B. |
| description | An information-theoretic method of low-level multi-INT sensor fusion is presented, the end product of which is the entropic map, i.e. a collection of Gaussian clusters of information relevant to a given target signature formed over a geographical basis. The method is designed to be computationally efficient with minimal side-information. To that end, an unbiased estimate of information from finite data is derived along with a data-dependent, information-optimal measurement partition. A method for the determination of the information-optimal sensor suite is given for a possibly geographically dependent target signature. Finally, it is shown that a multi-relational entropic measure of dependence can be superior to suboptimal error-based techniques of estimation of multiple sensor measurements of a real process. |
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B. ; Wong, M. A. ; Douglass, A. B.</creator><creatorcontrib>Deignan, P. B. ; Wong, M. A. ; Douglass, A. B.</creatorcontrib><description>An information-theoretic method of low-level multi-INT sensor fusion is presented, the end product of which is the entropic map, i.e. a collection of Gaussian clusters of information relevant to a given target signature formed over a geographical basis. The method is designed to be computationally efficient with minimal side-information. To that end, an unbiased estimate of information from finite data is derived along with a data-dependent, information-optimal measurement partition. A method for the determination of the information-optimal sensor suite is given for a possibly geographically dependent target signature. 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B.</creatorcontrib><creatorcontrib>Wong, M. A.</creatorcontrib><creatorcontrib>Douglass, A. B.</creatorcontrib><title>Low-level multi-INT sensor fusion using entropic measures of dependence</title><title>14th International Conference on Information Fusion</title><addtitle>ICIF</addtitle><description>An information-theoretic method of low-level multi-INT sensor fusion is presented, the end product of which is the entropic map, i.e. a collection of Gaussian clusters of information relevant to a given target signature formed over a geographical basis. The method is designed to be computationally efficient with minimal side-information. To that end, an unbiased estimate of information from finite data is derived along with a data-dependent, information-optimal measurement partition. A method for the determination of the information-optimal sensor suite is given for a possibly geographically dependent target signature. Finally, it is shown that a multi-relational entropic measure of dependence can be superior to suboptimal error-based techniques of estimation of multiple sensor measurements of a real process.</description><subject>Algorithm design and analysis</subject><subject>Entropic map</subject><subject>Entropy</subject><subject>Estimation</subject><subject>Mutual information</subject><subject>Optimization</subject><subject>resource management</subject><subject>Sensor fusion</subject><subject>Traffic control</subject><isbn>9781457702679</isbn><isbn>1457702673</isbn><isbn>0982443838</isbn><isbn>9780982443828</isbn><isbn>098244382X</isbn><isbn>9780982443835</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2011</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><sourceid>RIE</sourceid><recordid>eNotjs1KxDAURiMiqGOfwE1eoJDc_N6lDDoODOOm-yG2NxJp09K0im9vQb_FObvDd8XuBXrQWnnlr1mFzkttnBNgHd6yqpRPsc1aBO3u2OE0ftc9fVHPh7VfUn08N7xQLuPM41rSmPnG_MEpL_M4pZYPFMo6U-Fj5B1NlDvKLT2wmxj6QtW_d6x5eW72r_Xp7XDcP53qhGKplRAyYngHaRVoKUEZZcJ2DSlqiVZZj62KLQKAb01HThjb-RiMihGA1I49_mUTEV2mOQ1h_rkYdM6CU78SO0Z1</recordid><startdate>201107</startdate><enddate>201107</enddate><creator>Deignan, P. 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B.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-i90t-3001f9ab2163241123535a0269ef41963689c3fc92228c5de7056d8fa53ff22e3</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2011</creationdate><topic>Algorithm design and analysis</topic><topic>Entropic map</topic><topic>Entropy</topic><topic>Estimation</topic><topic>Mutual information</topic><topic>Optimization</topic><topic>resource management</topic><topic>Sensor fusion</topic><topic>Traffic control</topic><toplevel>online_resources</toplevel><creatorcontrib>Deignan, P. B.</creatorcontrib><creatorcontrib>Wong, M. A.</creatorcontrib><creatorcontrib>Douglass, A. B.</creatorcontrib><collection>IEEE Electronic Library (IEL) Conference Proceedings</collection><collection>IEEE Proceedings Order Plan All Online (POP All Online) 1998-present by volume</collection><collection>IEEE Xplore All Conference Proceedings</collection><collection>IEEE Electronic Library (IEL)</collection><collection>IEEE Proceedings Order Plans (POP All) 1998-Present</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Deignan, P. B.</au><au>Wong, M. A.</au><au>Douglass, A. B.</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>Low-level multi-INT sensor fusion using entropic measures of dependence</atitle><btitle>14th International Conference on Information Fusion</btitle><stitle>ICIF</stitle><date>2011-07</date><risdate>2011</risdate><spage>1</spage><epage>7</epage><pages>1-7</pages><isbn>9781457702679</isbn><isbn>1457702673</isbn><eisbn>0982443838</eisbn><eisbn>9780982443828</eisbn><eisbn>098244382X</eisbn><eisbn>9780982443835</eisbn><abstract>An information-theoretic method of low-level multi-INT sensor fusion is presented, the end product of which is the entropic map, i.e. a collection of Gaussian clusters of information relevant to a given target signature formed over a geographical basis. The method is designed to be computationally efficient with minimal side-information. To that end, an unbiased estimate of information from finite data is derived along with a data-dependent, information-optimal measurement partition. A method for the determination of the information-optimal sensor suite is given for a possibly geographically dependent target signature. Finally, it is shown that a multi-relational entropic measure of dependence can be superior to suboptimal error-based techniques of estimation of multiple sensor measurements of a real process.</abstract><pub>IEEE</pub><tpages>7</tpages></addata></record> |
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| source | IEEE Electronic Library (IEL) Conference Proceedings |
| subjects | Algorithm design and analysis Entropic map Entropy Estimation Mutual information Optimization resource management Sensor fusion Traffic control |
| title | Low-level multi-INT sensor fusion using entropic measures of dependence |
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