Decentralized Detection in Censoring Sensor Networks under Correlated Observations. (18th March 2010)

• Record Type: Journal Article
• Authors: Abu-Romeh Abu-Romeh, Abdullah S. Abdullah S.; Jones Jones, Douglas L. Douglas L.
• Other Names: Mecklenbräuker Mecklenbräuker Christoph Christoph Academic Editor.
• Is Part Of: EURASIP journal on advances in signal processing. Volume 2010(2010)
• Publication Date: 2010-03-18
• Subjects: Signal processing -- Periodicals; Traitement du signal; Signal processing; Periodicals; 621.3822
• DOI: 10.1155/2010/838921
• Languages: English
• ISSNs: 1687-6172

Abstract

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.