Minimax Decentralized Hypothesis Testing

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


The source of uncertainty in hypothesis testing can either be the distribution functions conditioned on each hypothesis, or the a priori probabilities as introduced in Chap. 2. In this chapter, implications of the uncertainty caused by an unknown a priori probability is exploited for decentralized detection networks with (DDN-WF) and without a fusion center (DDN-WoF), which are illustrated in Figs. 7.1 and 7.2.


Decision Maker Sensor Network Error Probability Detection Performance 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.


  1. [Tsi93]
    S. Barbarossa and G. Scutari, “Decentralized detection,” in In Advances in Statistical Signal Processing. JAI Press, 1993, pp. 297–344.Google Scholar
  2. [BS07]
    S. Barbarossa and G. Scutari, “Bio-inspired sensor network design,” Signal Processing Magazine, IEEE, vol. 24, no. 3, pp. 26–35, May 2007.Google Scholar
  3. [CS11]
    F. S. Cattivelli and A. H. Sayed, “Distributed detection over adaptive networks using diffusion adaptation,” IEEE Transactions on Signal Processing, vol. 59, no. 5, pp. 1917–1932, May 2011.Google Scholar
  4. [CK92]
    M. Cherikh and P. B. Kantor, “Counterexamples in distributed detection,” IEEE Transactions on Information Theory, vol. 38, no. 1, pp. 162–165, 1992.CrossRefGoogle Scholar
  5. [Che52]
    H. Chernoff, “A measure of asymptotic efficiency for tests of a hypothesis based on the sums of observations,” Annals of Mathematical Statistics, vol. 23, pp. 409–507, 1952.MathSciNetzbMATHGoogle Scholar
  6. [FCFZ09]
    P. Frasca, R. Carli, F. Fagnani, and S. Zampieri, “Average consensus on networks with quantized communication,” International Journal of Robust and Nonlinear Control, vol. 19, pp. 1787–1816, 2009.MathSciNetCrossRefzbMATHGoogle Scholar
  7. [Lev08]
    B. C. Levy, Principles of Signal Detection and Parameter Estimation, 1st ed. Springer Publishing Company, Incorporated, 2008.CrossRefGoogle Scholar
  8. [Tsi88]
    J. N. Tsitsiklis, “Decentralized detection by a large numb. of sensors,” Mathematics of Control, Signals, and Systems, pp. 167–182, 1988.Google Scholar
  9. [Var96]
    P. K. Varshney, Distributed detection and data fusion, 1st ed. Secaucus, NJ, USA: Springer-Verlag New York, Inc., 1996.Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

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

Personalised recommendations