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The usefulness of imaging sensors in motion prediction
It is shown that the response of an image-enhanced tracking loop is superior to that attainable from conventional trackers without imaging capability. Because of the non-Gaussian nature of the problem and the nonlinearities in the motion model, it is difficult to make inclusive generalizations about...
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| Format: | Conference Proceeding |
| Language: | English |
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| container_end_page | 725 vol.2 |
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| container_start_page | 721 |
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| creator | Sworder, D.D. Kent, M. Hutchins, R.G. Singer, P.A. |
| description | It is shown that the response of an image-enhanced tracking loop is superior to that attainable from conventional trackers without imaging capability. Because of the non-Gaussian nature of the problem and the nonlinearities in the motion model, it is difficult to make inclusive generalizations about the relative performance. It is evident though that an imager may help to achieve good quiescent performance without a severe tuning penalty. This exchange is difficult to make with conventional algorithms. Two modified linear Gauss Markov procedures are shown to have similar performance, and in this application, increasing the order of the estimator to shape the ersatz Gaussian disturbance is not worth the additional complexity. The image-based architecture provides 40% better quiescent performance with a somewhat less pronounced improvement during a turn.< > |
| doi_str_mv | 10.1109/ACSSC.1992.269101 |
| format | conference_proceeding |
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Because of the non-Gaussian nature of the problem and the nonlinearities in the motion model, it is difficult to make inclusive generalizations about the relative performance. It is evident though that an imager may help to achieve good quiescent performance without a severe tuning penalty. This exchange is difficult to make with conventional algorithms. Two modified linear Gauss Markov procedures are shown to have similar performance, and in this application, increasing the order of the estimator to shape the ersatz Gaussian disturbance is not worth the additional complexity. The image-based architecture provides 40% better quiescent performance with a somewhat less pronounced improvement during a turn.< ></description><identifier>ISSN: 1058-6393</identifier><identifier>ISBN: 0818631600</identifier><identifier>ISBN: 9780818631603</identifier><identifier>EISSN: 2576-2303</identifier><identifier>DOI: 10.1109/ACSSC.1992.269101</identifier><language>eng</language><publisher>IEEE Comput. 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Because of the non-Gaussian nature of the problem and the nonlinearities in the motion model, it is difficult to make inclusive generalizations about the relative performance. It is evident though that an imager may help to achieve good quiescent performance without a severe tuning penalty. This exchange is difficult to make with conventional algorithms. Two modified linear Gauss Markov procedures are shown to have similar performance, and in this application, increasing the order of the estimator to shape the ersatz Gaussian disturbance is not worth the additional complexity. The image-based architecture provides 40% better quiescent performance with a somewhat less pronounced improvement during a turn.< ></description><subject>Acceleration</subject><subject>Aircraft</subject><subject>Gas detectors</subject><subject>Hardware</subject><subject>Image sensors</subject><subject>Predictive models</subject><subject>Radar tracking</subject><subject>Sensor phenomena and characterization</subject><subject>Sensor systems</subject><subject>Target tracking</subject><issn>1058-6393</issn><issn>2576-2303</issn><isbn>0818631600</isbn><isbn>9780818631603</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>1992</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><recordid>eNotj81OwzAQhC1-JELpA8DJL5Cwu46d-FhFUJAqcWg5V068LkZtUsXtgbcnqMxl5vBpNCPEI0KBCPZ50azXTYHWUkHGIuCVyEhXJicF6lrcQ421UWgAbkSGoOvcKKvuxDylb5hUaix1lQmz-WJ5ThzO-55TkkOQ8eB2sd_JxH0axiRjLw_DKQ69PI7sY_cXH8RtcPvE83-fic_Xl03zlq8-lu_NYpVHrMpTjuiq4Mix6rqanXOKSvS2hdYbBG4Ntb4kRd5qojbYrjJugr0OjJagUzPxdOmNzLw9jtO28Wd7eax-AalwSKQ</recordid><startdate>1992</startdate><enddate>1992</enddate><creator>Sworder, D.D.</creator><creator>Kent, M.</creator><creator>Hutchins, R.G.</creator><creator>Singer, P.A.</creator><general>IEEE Comput. Soc. Press</general><scope>6IE</scope><scope>6IL</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIL</scope></search><sort><creationdate>1992</creationdate><title>The usefulness of imaging sensors in motion prediction</title><author>Sworder, D.D. ; Kent, M. ; Hutchins, R.G. ; Singer, P.A.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-i174t-11a7fa2ae3cc8eaaa3241d9b0bd610eb62bd4232d9522bf9c76aae3d5fe1920c3</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>1992</creationdate><topic>Acceleration</topic><topic>Aircraft</topic><topic>Gas detectors</topic><topic>Hardware</topic><topic>Image sensors</topic><topic>Predictive models</topic><topic>Radar tracking</topic><topic>Sensor phenomena and characterization</topic><topic>Sensor systems</topic><topic>Target tracking</topic><toplevel>online_resources</toplevel><creatorcontrib>Sworder, D.D.</creatorcontrib><creatorcontrib>Kent, M.</creatorcontrib><creatorcontrib>Hutchins, R.G.</creatorcontrib><creatorcontrib>Singer, P.A.</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 Xplore</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>Sworder, D.D.</au><au>Kent, M.</au><au>Hutchins, R.G.</au><au>Singer, P.A.</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>The usefulness of imaging sensors in motion prediction</atitle><btitle>[1992] Conference Record of the Twenty-Sixth Asilomar Conference on Signals, Systems & Computers</btitle><stitle>ACSSC</stitle><date>1992</date><risdate>1992</risdate><spage>721</spage><epage>725 vol.2</epage><pages>721-725 vol.2</pages><issn>1058-6393</issn><eissn>2576-2303</eissn><isbn>0818631600</isbn><isbn>9780818631603</isbn><abstract>It is shown that the response of an image-enhanced tracking loop is superior to that attainable from conventional trackers without imaging capability. Because of the non-Gaussian nature of the problem and the nonlinearities in the motion model, it is difficult to make inclusive generalizations about the relative performance. It is evident though that an imager may help to achieve good quiescent performance without a severe tuning penalty. This exchange is difficult to make with conventional algorithms. Two modified linear Gauss Markov procedures are shown to have similar performance, and in this application, increasing the order of the estimator to shape the ersatz Gaussian disturbance is not worth the additional complexity. The image-based architecture provides 40% better quiescent performance with a somewhat less pronounced improvement during a turn.< ></abstract><pub>IEEE Comput. Soc. Press</pub><doi>10.1109/ACSSC.1992.269101</doi></addata></record> |
| fulltext | fulltext_linktorsrc |
| identifier | ISSN: 1058-6393 |
| ispartof | [1992] Conference Record of the Twenty-Sixth Asilomar Conference on Signals, Systems & Computers, 1992, p.721-725 vol.2 |
| issn | 1058-6393 2576-2303 |
| language | eng |
| recordid | cdi_ieee_primary_269101 |
| source | IEEE Xplore All Conference Series |
| subjects | Acceleration Aircraft Gas detectors Hardware Image sensors Predictive models Radar tracking Sensor phenomena and characterization Sensor systems Target tracking |
| title | The usefulness of imaging sensors in motion prediction |
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