Superimposed event detection by sequential Monte Carlo methods

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Superimposed event detection by sequential Monte Carlo methods [Ardişik Monte Carlo yöntemiyle bindirilmiş olay sezimi].pdf (1.15 MB)

Date

2007

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Source Title

Proceedings of the 15th Signal Processing and Communications Applications, IEEE 2007

Print ISSN

2165-0608

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IEEE

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Language

Turkish

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Conference Paper

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Abstract

In this paper, we consider the detection of rare events by applying particle filtering. We model the rare event as an AR signal superposed on a background signal. The activation and deactivation times of the AR-signal are unknown. We solve the online detection problem of this superpositional rare 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.

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Keywords

Argon , Management science , Mathematical models , Signal filtering and prediction , Signal processing , State space methods , Event detection , Numerical experiments , On-line detection , Particle Filtering , Rare events , Sequential Monte Carlo methods , State spaces , Monte Carlo methods

Citation

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http://hdl.handle.net/11693/27059

item.page.isversionof

http://dx.doi.org/10.1109/SIU.2007.4298796

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Department of Electrical and Electronics Engineering