Abstract
To identify or localize a target, multisensor analysis has to be able to recognize one situation out of a set of possibilities. To do so, it uses measurements of more or less doubtful origin and prior knowledge that is understood to be often poorly defined, and whose validity is moreover difficult to evaluate under real observation conditions. The present synthesis proposes a generic modeling of this type of information, in the form of mass sets of the theory of evidence, with closer attention being paid to the most common case where the data originates from statistical processes. On the one hand robust target classification procedures can be achieved by applying appropriate decision criteria to these mass sets, on the other hand they can be integrated rigorously into a target tracking process, to reflect the origin of the localization measurements better. In all cases, the solutions found are placed in relation to those of the main competitive approaches currently used.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
A. Appriou: Formulation et traitement de l’incertain en analyse multi-senseurs. Conférence invitée, 14ème Colloque GRETSI, Juan-les-Pins, 13–16 septembre 1993.
A. Appriou: Probabilités et incertitude en fusion de données multi-senseurs. Revue Scientifique et Technique de la Défense, n°11, 1991–1, pp 27–40.
A. Appriou: Intérêt des théories de l’incertain en fusion de données. Conférence invitée, Colloque International sur le Radar, Paris, 24–28 avril 1989.
G. Shafer: A mathematical theory of evidence. Princeton University Press, Princeton, New Jersey, 1976.
R.R. Yager: Entropy and specificity in a mathematical theory of evidence. International Journal General Systems, Vol. 9, 1983, pp 249–260.
R.R. Yager: A general approach to decision making with evidential knowledge. Uncertainty in Artificial Intelligence, L.N. Kanal & J.F. Lemmer éd., Elsevier Science Publishers, B.V. North-Holland, 1986.
D. Dubois, H. Prade: Théorie des possibilités: application à la représentation des connaissances en informatique“, Masson, Paris, 1988.
F. Janez, A. Appriou: Théorie de l’évidence et cadres de discernement non exhaustifs. Revue Traitement du Signal, Vol. 13, n° 2, 1996.
F. Janez, A. Appriou: Fusion of sources defined on different non-exhaustive frames. IPMU’ 96, Granada, Spain, July 1–5 1996.
V. Nimier, A. Appriou: Utilisation de la théorie de Dempster-Shafer pour la fusion d’informations. GRETSI, 15ème Colloque sur le Traitement du Signal et des Images, Juan-les Pins, 18–22 septembre 1995.
M.C. Perron-Gitton: Apport d’une approche neuro-floue dans un contexte de fusion de données basé sur la théorie de l’évidence. IPMU’ 94, Paris, 4–8 juillet 1994.
A. Appriou: Multiple signal tracking processes. Aerospace Science and Technology, n° 2, February 1997.
Y. Bar Shalom, T.E. Fortmann Tracking and data association. Academic Press, New York, 1988.
D. Lerro, Y. Bar Shalom: Interacting multiple model tracking with target amplitude feature. IEEE Transactions on Aerospace and Electronic Systems, Vol. 29, n° 2, april 1993.
J. Dezert: Vers un nouveau concept de navigation autonome d’engin. Un lien entre le filtrage à association probabiliste de données et la théorie de l’évidence. Thèse de doctorat, Université Paris-XI, 27 septembre 1990, ONERA.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 1998 Springer-Verlag Berlin Heidelberg
About this chapter
Cite this chapter
Appriou, A. (1998). Uncertain Data Aggregation in Classification and Tracking Processes. In: Bouchon-Meunier, B. (eds) Aggregation and Fusion of Imperfect Information. Studies in Fuzziness and Soft Computing, vol 12. Physica, Heidelberg. https://doi.org/10.1007/978-3-7908-1889-5_13
Download citation
DOI: https://doi.org/10.1007/978-3-7908-1889-5_13
Publisher Name: Physica, Heidelberg
Print ISBN: 978-3-662-11073-7
Online ISBN: 978-3-7908-1889-5
eBook Packages: Springer Book Archive