Computationally efficient multisensor fusion estimation algorithms / Seokhyoung Lee in Transactions of the ASME . Journal of dynamic systems, measurement, and control, Vol. 132 N° 2 (Mars/Avril 2010)  

Public ISBD [article]  inTransactions of the ASME . Journal of dynamic systems, measurement, and control > Vol. 132 N° 2 (Mars/Avril 2010) . - 04 p. Titre : Computationally efficient multisensor fusion estimation algorithms Type de document : texte imprimé Auteurs : Seokhyoung Lee, Auteur ; Vladimir Shin, Auteur Année de publication : 2010 Article en page(s) : 04 p. Note générale : Systèmes dynamiques Langues : Anglais (eng) Mots-clés : Approximation theory Covariance matrices Sensor fusion Index. décimale : 629.8 Résumé : This paper provides two computationally effective fusion estimation algorithms. The first algorithm is based on Cholesky factorization of a cross-covariance block matrix. This algorithm has low computational complexity and is equivalent to the standard composite fusion estimation algorithm as well. The second algorithm is based on a special approximation scheme for local cross-covariances. Such approximation is useful to compute matrix weights for fusion estimation in a multidimensional-multisensor environment. Subsequent computational analysis of the proposed fusion algorithms is presented with corresponding examples showing the low computational complexities of the new fusion estimation algorithms. DEWEY : 629.8 ISSN : 0022-0434 En ligne : http://asmedl.aip.org/getabs/servlet/GetabsServlet?prog=normal&id=JDSMAA00013200 [...] [article] Computationally efficient multisensor fusion estimation algorithms [texte imprimé] / Seokhyoung Lee, Auteur ; Vladimir Shin, Auteur . - 2010 . - 04 p. Systèmes dynamiques Langues : Anglais (eng) in Transactions of the ASME . Journal of dynamic systems, measurement, and control > Vol. 132 N° 2 (Mars/Avril 2010) . - 04 p. Mots-clés : Approximation theory Covariance matrices Sensor fusion Index. décimale : 629.8 Résumé : This paper provides two computationally effective fusion estimation algorithms. The first algorithm is based on Cholesky factorization of a cross-covariance block matrix. This algorithm has low computational complexity and is equivalent to the standard composite fusion estimation algorithm as well. The second algorithm is based on a special approximation scheme for local cross-covariances. Such approximation is useful to compute matrix weights for fusion estimation in a multidimensional-multisensor environment. Subsequent computational analysis of the proposed fusion algorithms is presented with corresponding examples showing the low computational complexities of the new fusion estimation algorithms. DEWEY : 629.8 ISSN : 0022-0434 En ligne : http://asmedl.aip.org/getabs/servlet/GetabsServlet?prog=normal&id=JDSMAA00013200 [...]

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