Framework for online superimposed event detection by sequential Monte Carlo methods

In this paper, we consider online separation and detection of superimposed events by applying particle filtering. We concentrate on a model where a background process, represented by a ID-signal, is superimposed by an auto-regressive (AR) 'event signal', but the proposed approach is applic...

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Bibliographic Details
Published in2008 IEEE International Conference on Acoustics, Speech and Signal Processing pp. 2125 - 2128
Main Authors Urfalioglu, O., Kuruoglu, E.E., Cetin, A.E.
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.03.2008
Subjects
Bayesian methods
Bayesian Statistics
Conditional Density
Event detection
Filtering
Hidden Markov models
Importace Sampling
Monte Carlo methods
Particle filters
SIR
State estimation
State-space methods
Statistics
Stochastic processes
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Summary:In this paper, we consider online separation and detection of superimposed events by applying particle filtering. We concentrate on a model where a background process, represented by a ID-signal, is superimposed by an auto-regressive (AR) 'event signal', but the proposed approach is applicable in a more general setting. The activation and deactivation times of the event-signal are assumed to be unknown. We solve the online detection problem of this superpositional event by extending the state space dimension by one. The additional parameter of the state represents the AR-signal, which is zero when deactivated. Numerical experiments demonstrate the effectiveness of our approach.
ISBN:9781424414833
1424414830
ISSN:1520-6149
2379-190X
DOI:10.1109/ICASSP.2008.4518062