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

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 th...

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Bibliographic Details
Published in14th International Conference on Information Fusion pp. 1 - 8
Main Authors Ristic, B., Gning, A., Mihaylova, L.
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.07.2011
Subjects
Approximation methods
Atmospheric measurements
Bernoulli filter
interval measurements
Measurement uncertainty
Noise
Noise measurement
particle filters
Particle measurements
random sets
Sequential Bayesian estimation
Uncertainty
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Summary: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.
ISBN:9781457702679
1457702673