{"title":"State Estimation Method Using Median of Multiple Candidates for Observation Signals Including Outliers","categories":["Research"],"author_url":"https://blog.hatena.ne.jp/control_eng_ch/","html":"<iframe src=\"https://hatenablog-parts.com/embed?url=https%3A%2F%2Fblog.control-theory.com%2Fentry%2F2026%2F03%2F04%2F084603\" title=\"State Estimation Method Using Median of Multiple Candidates for Observation Signals Including Outliers - \u5236\u5fa1\u5de5\u5b66\u30d6\u30ed\u30b0 / Control Engineering Blog\" class=\"embed-card embed-blogcard\" scrolling=\"no\" frameborder=\"0\" style=\"display: block; width: 100%; height: 190px; max-width: 500px; margin: 10px 0px;\"></iframe>","url":"https://blog.control-theory.com/entry/2026/03/04/084603","width":"100%","blog_title":"\u5236\u5fa1\u5de5\u5b66\u30d6\u30ed\u30b0 / Control Engineering Blog","image_url":null,"type":"rich","author_name":"control_eng_ch","blog_url":"https://blog.control-theory.com/","version":"1.0","published":"2026-03-04 08:46:03","description":"This article explains the MCV (Median of Candidate Vectors) observer, a state estimation method robust to sensor outliers. Multiple estimation candidates are created along the time axis, and the median operation selects one unaffected by outliers. Observer gains are designed via LMI optimization.","provider_url":"https://hatena.blog","provider_name":"Hatena Blog","height":"190"}