Open Access Research Article

Decentralized Detection in Censoring Sensor Networks under Correlated Observations

AbdullahS Abu-Romeh* and DouglasL Jones

Author Affiliations

Coordinated Science Laboratory, University of Illinois at Urbana-Champaign, 1308 W Main St., Urbana, 61801, USA

For all author emails, please log on.

EURASIP Journal on Advances in Signal Processing 2010, 2010:838921  doi:10.1155/2010/838921

Published: 18 March 2010

Abstract

The majority of optimal rules derived for different decentralized detection application scenarios are based on an assumption that the sensors' observations are statistically independent. Deriving the optimal decision rule in the canonical decentralized setting with correlated observations was shown to be complicated even for the simple case of two sensors. We introduce an alternative suboptimal rule to deal with correlated observations in decentralized detection with censoring sensors using a modified generalized likelihood ratio test (mGLRT). In the censoring scheme, sensors either send or do not send their complete observations to the fusion center. Using ML estimation to estimate the censored values, the decentralized problem is converted to a centralized problem. Our simulation results indicate that, when sensor observations are correlated, the mGLRT gives considerably better performance in terms of probability of detection than does the optimal decision rule derived for uncorrelated observations.