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Descripción
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| This paper proposes a computationally efficient nonlinear filter that approximates the posterior probability density function (PDF) as a Gaussian mixture. The novelty of this filter lies in the update step. If the likelihood has a bounded support made up of different regions, we can use a modified prior PDF, which is a mixture, that meets Bayes? rule exactly. The central idea of this paper is that a Kalman filter applied to each component of the modified prior mixture can improve the approximation to the posterior provided by the Kalman filter. In practice, bounded support is not necessary. | |
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Internacional
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Si |
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Nombre congreso
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15th International Conference on Information Fusion, FUSION 2012 |
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Tipo de participación
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960 |
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Lugar del congreso
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Singapore |
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Revisores
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Si |
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ISBN o ISSN
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978-1-4673-0417-7 |
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DOI
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Fecha inicio congreso
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07/09/2012 |
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Fecha fin congreso
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12/09/2012 |
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Desde la página
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479 |
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Hasta la página
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486 |
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Título de las actas
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Proceedings of 15th International Conference on Information Fusion 2012 |