{"id":1014,"date":"2007-09-06T05:43:58","date_gmt":"2007-09-06T09:43:58","guid":{"rendered":"http:\/\/mseas.net16.net\/?p=1014"},"modified":"2021-08-16T20:52:08","modified_gmt":"2021-08-17T00:52:08","slug":"non-linear-optimization-of-autonomous-undersea-vehicle-sampling-strategies-for-oceanographic-data-assimilation","status":"publish","type":"post","link":"https:\/\/mseas.mit.edu\/?p=1014","title":{"rendered":"Non-linear Optimization of Autonomous Undersea Vehicle Sampling Strategies for Oceanographic Data-Assimilation"},"content":{"rendered":"The problem of how to optimally deploy a suite of sensors to estimate the oceanographic\r\nenvironment is addressed. An optimal way to estimate (nowcast) and predict (forecast)\r\nthe ocean environment is to assimilate measurements from dynamic and uncertain regions\r\ninto a dynamical ocean model. In order to determine the sensor deployment strategy\r\nthat optimally samples the regions of uncertainty, a Genetic Algorithm (GA) approach\r\nis presented. The scalar cost function is defined as a weighted combination of a sensor\r\nsuite&#8217;s sampling of the ocean variability, ocean dynamics, transmission loss sensitivity,\r\nmodeled temperature uncertainty (and others). The benefit of the GA approach is that the\r\nuser can determine &#8220;optimal&#8221; via a weighting of constituent cost functions, which can\r\ninclude ocean dynamics, acoustics, cost, time, etc. A numerical example with three gliders,\r\ntwo powered AUVs, and three moorings is presented to illustrate the optimization\r\napproach in the complex shelfbreak region south of New England.","protected":false},"excerpt":{"rendered":"<p>The problem of how to optimally deploy a suite of sensors to estimate the oceanographic environment is addressed. An optimal way to estimate (nowcast) and predict (forecast) the ocean environment is to assimilate measurements from dynamic and uncertain regions into a dynamical ocean model. In order to determine the sensor deployment strategy that optimally samples [&hellip;]<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[31,183,33,35,5,185,194,63,64],"tags":[],"class_list":["post-1014","post","type-post","status-publish","format-standard","hentry","category-uncertainty-quantification-and-reduced-order-modeling","category-science-of-autonomy","category-uncertainty-quantification-and-predictions","category-optimal-path-planning","category-publications","category-adaptive-sampling","category-papers-in-refereed-journals-adaptive-sampling","category-papers-in-refereed-journals-optimal-path-planning","category-papers-in-refereed-journals-uncertainty-quantification-and-predictions"],"_links":{"self":[{"href":"https:\/\/mseas.mit.edu\/index.php?rest_route=\/wp\/v2\/posts\/1014","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/mseas.mit.edu\/index.php?rest_route=\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/mseas.mit.edu\/index.php?rest_route=\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/mseas.mit.edu\/index.php?rest_route=\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/mseas.mit.edu\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=1014"}],"version-history":[{"count":6,"href":"https:\/\/mseas.mit.edu\/index.php?rest_route=\/wp\/v2\/posts\/1014\/revisions"}],"predecessor-version":[{"id":5726,"href":"https:\/\/mseas.mit.edu\/index.php?rest_route=\/wp\/v2\/posts\/1014\/revisions\/5726"}],"wp:attachment":[{"href":"https:\/\/mseas.mit.edu\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=1014"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/mseas.mit.edu\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=1014"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/mseas.mit.edu\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=1014"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}