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Nonlinear filtering using measurements affected by stochastic, set-theoretic and association uncertainty

Research output: Contribution in Book/Report/ProceedingsPaper

Published
Publication date5/07/2011
Host publication2011 Proceedings of the 14th International Conference on Information Fusion (FUSION 2011)
Place of PublicationUSA
Pages1069-1076
Number of pages8
ISBN (Electronic)978-0-9824438-3-5
<mark>Original language</mark>English
EventInternational Conference on Information Fusion - Chicago, United States

Conference

ConferenceInternational Conference on Information Fusion
CountryUnited States
CityChicago
Period5/07/118/07/11

Conference

ConferenceInternational Conference on Information Fusion
CountryUnited States
CityChicago
Period5/07/118/07/11

Abstract

The problem is sequential Bayesian detection and estimation of nonlinear dynamic stochastic systems using measurements affected by three sources of uncertainty: stochastic, set-theoretic and data association uncertainty. Following Mahler’s framework for information fusion, the paper develops the optimal Bayes filter for this problem in the form of the Bernoulli filter for interval measurements, implemented as a particle filter. The numerical results demonstrate the filter performance: it detects the presence of targets reliably, and using a sufficient number of particles, the support of its posterior spatial PDF is guaranteed to include the true target state.

Bibliographic note

IEEE Catalog Number: CFP11FUS-CDR