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Robust Decentralized Hypothesis Testing

  • Gökhan Gül
Chapter
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 414)

Abstract

In this chapter, the concept of robust hypothesis testing is extended from a single sensor to multiple sensors. Decentralized sensor networks are studied, where each sensor shares only a summary of its observed data with its neighbors and/or with the fusion center. First, a parallel network topology, as illustrated in Fig. 6.1, is considered and later the results are generalized to arbitrary sensors networks, different tests, e.g. the Neyman–Pearson test and centralized sensor networks. The motivation behind the design of robust decentralized networks is that they fulfill two important requirements for any detection problem that is intended to be realized in practice: high detection accuracy due to multiple sensors and reliability due to robust hypothesis testing.

Keywords

Decision Maker Sensor Network Decision Rule Error Probability Fusion Center 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Institut für Nachrichtentechnik, Fachbereich Elektro- und Informationstechnik (ETIT)Technische Universität DarmstadtDarmstadtGermany

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