Title :
Framework for online superimposed event detection by sequential Monte Carlo methods
Author :
Urfalioglu, O. ; Kuruoglu, Ercan Engin ; Çetin, A. Enis
Author_Institution :
Dept. of Electr. & Electron. Eng., Bilkent Univ., Ankara, Turkey
fDate :
March 31 2008-April 4 2008
Abstract :
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.
Keywords :
Monte Carlo methods; particle filtering (numerical methods); state-space methods; auto-regressive event signal; online separation; particle filtering; sequential Monte Carlo methods; state space dimension; superimposed event detection; Bayesian methods; Event detection; Filtering; Hidden Markov models; Monte Carlo methods; Particle filters; State estimation; State-space methods; Statistics; Stochastic processes; Bayesian Statistics; Conditional Density; Event detection; Importace Sampling; SIR;
Conference_Titel :
Acoustics, Speech and Signal Processing, 2008. ICASSP 2008. IEEE International Conference on
Conference_Location :
Las Vegas, NV
Print_ISBN :
978-1-4244-1483-3
Electronic_ISBN :
1520-6149
DOI :
10.1109/ICASSP.2008.4518062
