Superimposed event detection by sequential Monte Carlo methods
Enis Çetin V.A.
2007 IEEE 15th Signal Processing and Communications Applications, SIU
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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.
Signal filtering and prediction
State space methods
Sequential Monte Carlo methods
Monte Carlo methods