Most papers dealing with data fusion try to use a single tool to provide the "best" estimation. But, as pointed out by Prade-Dubois (1988), most tools dealing with imperfect information aim at different purposes. In the previous paper (Strauss et al. (1996)), the Guess filter has been presented. This filter aims at using three different error theories together to obtain an estimation that combines robustness, accuracy, reliability and easy setup. The possibility theory handles precision, the statistical theory is used to reduce uncertainty, and the rough set theory allows a robust and easy computation of the resulting filter. In the Guess filter, data are represented as a possibility distribution, and because of this representation, data issued from different sensors can be combined at both high and low level. Fusion at a low-level takes advantage of redundancy to reduce the overall uncertainty and thus to increase accuracy. Fusion at a high-level reduces the influence of inadequacy in data modeling. This method has been implemented on a submarine robot. Experimental results are presented.


    Access

    Access via TIB

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Filtering and fusing compass and gyrometer data using Guess filter


    Contributors:
    Strauss, O. (author)


    Publication date :

    1997


    Size :

    7 Seiten, 11 Quellen




    Type of media :

    Conference paper


    Type of material :

    Print


    Language :

    English




    A new gyrometer for automotive applications

    Leblond,H. / Jupinet,F. / Colinot,J.P. et al. | Automotive engineering | 1990


    HRG TECHNOLOGY: A PROMISING GYROMETER SPACE EQUIPMENT

    Girard, O. / Caron, J. M. / Berthier, P. et al. | British Library Conference Proceedings | 2006



    Fusing and filtering arrogant classifiers

    Magnus, A.L. / Oxley, M.E. | IEEE | 2002


    Fusing and Filtering Arrogant Classifiers

    Magnus, A. / Oxley, M. / International Society of Information Fusion et al. | British Library Conference Proceedings | 2002