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翻訳と辞書　辞書検索 [ 開発暫定版 ] スポンサード リンク Covariance intersection ： ウィキペディア英語版 Covariance intersection Covariance intersection is an algorithm for combining two or more estimates of state variables in a Kalman filter when the correlation between them is unknown. ==Specification== Items of information a and b are known and are to be fused into information item c. We know a and b have mean/covariance $\backslash hat\; a$, $A$ and $\backslash hat\; b$, $B$, but the cross correlation is not known. The covariance intersection update gives mean and covariance for c as : $C^\; =\; \backslash omega\; A^\; +\; (1-\backslash omega)\; B^\; \backslash ,\; ,$ : $\backslash hat\; c\; =\; C(\backslash omega\; A^\; \backslash hat\; a\; +\; (1-\backslash omega)B^\; \backslash hat\; b)\; \backslash ,\; .$ where ''ω'' is computed to minimize a selected norm, e.g., logdet or trace. While it is necessary to solve an optimization problem for higher dimensions, closed-form solutions exist for lower dimensions. CI can be used in place of the conventional Kalman update equations to ensure that the resulting estimate is conservative, regardless of the correlation between the two estimates, with covariance strictly non-increasing according to the chosen measure. The use of a fixed measure is necessary for rigor to ensure that a sequence of updates does not cause the filtered covariance to increase.〔 抄文引用元・出典: フリー百科事典『 ウィキペディア（Wikipedia）』 ■ウィキペディアで「Covariance intersection」の詳細全文を読む スポンサード リンク 翻訳と辞書 : 翻訳のためのインターネットリソース Copyright(C) kotoba.ne.jp 1997-2016. All Rights Reserved.