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    Estimador competitivo de bajo coste computacional para deconvolución dispersa

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    URI: http://hdl.handle.net/10902/3444
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    Author
    Vía Rodríguez, JavierAutoridad Unican; Pérez Blanco, David José; Santamaría Caballero, Luis IgnacioAutoridad Unican; Pantaleón Prieto, Carlos J.
    Date
    2003-09
    Derechos
    © 2003 URSI España
    Publicado en
    URSI 2003, XVIII Simposium Nacional de la Unión Científica Internacional de Radio, La Coruña
    Abstract:
    This paper presents an efficient algorithm for computing the maximum a posteriori (MAP) estimate of a Bernouilli- Gaussian sequence distorted by a linear filter and corrupted by noise. The computational effort for the MAP estimator increases exponentially with the number of samples and is generally unfeasible. The approach used by Kormylo and Mendel in their pioneering work on the Single Most-Likely Replacement algorithm (SMLR) has been the sub-optimal reference to evaluate the efficiency of this new estimation method in computational effort and detection probability. This algorithm reduces drastically the SMLR computational load and allows real-time processing, because only few samples of the observation data are required by employing a windowing strategy. An extensive Monte Carlo analysis, using synthetic data, has been performed to compare the behaviour of the proposed estimators.
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    UNIVERSIDAD DE CANTABRIA

    Repositorio realizado por la Biblioteca Universitaria utilizando DSpace software
    Contact Us | Send Feedback
    Metadatos sujetos a:licencia de Creative Commons Reconocimiento 3.0 España