{"id":176,"date":"2015-05-04T11:51:15","date_gmt":"2015-05-04T10:51:15","guid":{"rendered":"http:\/\/babel.isa.uma.es\/kipr\/?p=176"},"modified":"2015-07-16T15:29:43","modified_gmt":"2015-07-16T14:29:43","slug":"analysis-of-the-deterioration-of-several-kalman-filters-depending-on-the-amount-of-uncertainty-in-the-observations-when-the-observation-model-is-non-linear","status":"publish","type":"post","link":"https:\/\/babel.isa.uma.es\/kipr\/?p=176","title":{"rendered":"Analysis of the deterioration of several Kalman Filters depending on the amount of uncertainty in the observations, when the observation model is non-linear"},"content":{"rendered":"<h4>Mark R. Morelande and \u00c1ngel F. Garc\u00eda-Fern\u00e1ndez, <strong>Analysis of Kalman Filter Approximations for Nonlinear Measurements<\/strong>, IEEE Transactions on signal processing, vol. 61, no. 22, 2013 <a href=\"http:\/\/doi.org\/10.1109\/TSP.2013.2279367\" target=\"_blank\">DOI: 10.1109\/TSP.2013.2279367<\/a>.<\/strong><\/p>\n<blockquote><p>A theoretical analysis is presented of the correction step  of  the  Kalman filter  (KF)  and  its  various  approximations for the case of a nonlinear measurement equation with additive Gaussian noise. The KF is based on a Gaussian app roximation to the joint density of the state and the measurement. The analysis metric is the Kullback-Leibler divergence of this approximation from  the  true  joint  density.  The  purpose  of  the  analysis  is  to provide a quantitative tool for understanding and assessing the performance of the  KF and its variants in nonlinear scenarios. This is illustrated using a numerical example.<\/p><\/blockquote>\n","protected":false},"excerpt":{"rendered":"<p>Mark R. Morelande and \u00c1ngel F. Garc\u00eda-Fern\u00e1ndez, Analysis of Kalman Filter Approximations for Nonlinear Measurements, IEEE Transactions on signal processing, <span class=\"ellipsis\">&hellip;<\/span> <span class=\"more-link-wrap\"><a href=\"https:\/\/babel.isa.uma.es\/kipr\/?p=176\" class=\"more-link\"><span>Read More &rarr;<\/span><\/a><\/span><\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[41],"tags":[29,80,4,16,6],"class_list":["post-176","post","type-post","status-publish","format-standard","hentry","category-probability-and-statistics","tag-bayesian-estimation","tag-kalman-filters","tag-probabilistic-sensor-model","tag-recursive-bayesian-estimation","tag-useful-for-teaching"],"_links":{"self":[{"href":"https:\/\/babel.isa.uma.es\/kipr\/index.php?rest_route=\/wp\/v2\/posts\/176"}],"collection":[{"href":"https:\/\/babel.isa.uma.es\/kipr\/index.php?rest_route=\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/babel.isa.uma.es\/kipr\/index.php?rest_route=\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/babel.isa.uma.es\/kipr\/index.php?rest_route=\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/babel.isa.uma.es\/kipr\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=176"}],"version-history":[{"count":1,"href":"https:\/\/babel.isa.uma.es\/kipr\/index.php?rest_route=\/wp\/v2\/posts\/176\/revisions"}],"predecessor-version":[{"id":177,"href":"https:\/\/babel.isa.uma.es\/kipr\/index.php?rest_route=\/wp\/v2\/posts\/176\/revisions\/177"}],"wp:attachment":[{"href":"https:\/\/babel.isa.uma.es\/kipr\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=176"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/babel.isa.uma.es\/kipr\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=176"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/babel.isa.uma.es\/kipr\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=176"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}