Framework for online superimposed event detection by sequential Monte Carlo methods
ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
2125 - 2128
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In this paper, we consider online seperation and detection of superimposed events by applying particle filtering. We concentrate on a model where a background process, represented by a 1D-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. ©2008 IEEE.
Signal filtering and prediction
State space methods
Monte Carlo methods