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dc.contributor.authorWu, WRen_US
dc.contributor.authorKundu, Aen_US
dc.date.accessioned2014-12-08T15:02:35Z-
dc.date.available2014-12-08T15:02:35Z-
dc.date.issued1996-06-01en_US
dc.identifier.issn1053-587Xen_US
dc.identifier.urihttp://dx.doi.org/10.1109/78.506611en_US
dc.identifier.urihttp://hdl.handle.net/11536/1243-
dc.description.abstractThe Kalman filter is the optimal recursive filter, although its optimality can only be claimed under the Gaussian noise environment, In this paper, we consider the problem of recursive filtering with non-Gaussian noises, One of the most promising schemes, which was proposed by Masreliez, uses the nonlinear score function as the correction term in the state estimate, Unfortunately, the score function cannot be easily implemented except for simple cases, In this paper, a new method for efficient evaluation of the score function is developed, The method employs an adaptive normal expansion to expand the score function followed by truncation of the higher order terms, Consequently, the score function can be approximated by a few central moments, The normal expansion is made adaptive by using the concept of conjugate recentering and the saddle point method, It is shown that the approximation is satisfactory, and the method is simple and practically feasible, Experimental results are reported to demonstrate the effectiveness of the new algorithm.en_US
dc.language.isoen_USen_US
dc.titleRecursive filtering with non-Gaussian noisesen_US
dc.typeArticleen_US
dc.identifier.doi10.1109/78.506611en_US
dc.identifier.journalIEEE TRANSACTIONS ON SIGNAL PROCESSINGen_US
dc.citation.volume44en_US
dc.citation.issue6en_US
dc.citation.spage1454en_US
dc.citation.epage1468en_US
dc.contributor.department電信工程研究所zh_TW
dc.contributor.departmentInstitute of Communications Engineeringen_US
dc.identifier.wosnumberWOS:A1996UU13400012-
dc.citation.woscount8-
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