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

fYear

2008

fDate

March 31 2008-April 4 2008

Firstpage

2125

Lastpage

2128

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;

fLanguage

English

Publisher

ieee

Conference_Titel

Acoustics, Speech and Signal Processing, 2008. ICASSP 2008. IEEE International Conference on

Conference_Location

Las Vegas, NV

ISSN

1520-6149

Print_ISBN

978-1-4244-1483-3

Electronic_ISBN

1520-6149

Type

conf

DOI

10.1109/ICASSP.2008.4518062

Filename

4518062