Robust Decentralized Hypothesis Testing

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


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.


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.


  1. [AVJ08b]
    S. Appadwedula, V. V. Veeravalli, and D. L. Jones, “Decentralized detection with censoring sensors.” IEEE Transactions on Signal Processing, vol. 56, no. 4, pp. 1362–1373, 2008.MathSciNetCrossRefGoogle Scholar
  2. [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
  3. [Cov69]
    T. M. Cover, “Hypothesis testing with finite statistics,” Ann. Math. Statist., vol. 40, no. 3, pp. 828–835, 06 1969.Google Scholar
  4. [GZ13a]
    G. Gül and A. M. Zoubir, “Robust detection under communication constraints,” in Proc. IEEE 14th Int. Workshop on Advances in Wireless Communications (SPAWC), Darmstadt, Germany, June 2013, pp. 410–414.Google Scholar
  5. [HC70]
    M. E. Hellman and T. M. Cover, “Learning with finite memory,” Ann. Math. Statist., vol. 41, no. 3, pp. 765–782, 06 1970.Google Scholar
  6. [HS68]
    P. J. Huber, “Robust confidence limits,” Z. Wahrcheinlichkeitstheorie verw. Gebiete, vol. 10, pp. 269—278, 1968.Google Scholar
  7. [Tsi93]
    J. N. Tsitsiklis, “Decentralized detection,” in In Advances in Statistical Signal Processing. JAI Press, 1993, pp. 297–344.Google Scholar
  8. [Var96]
    P. K. Varshney, Distributed detection and data fusion, 1st ed.   Secaucus, NJ, USA: Springer-Verlag New York, Inc., 1996.Google Scholar
  9. [VP94]
    V. V. Veeravalli, T. Basar and H. V. Poor, “Minimax robust decentralized detection,” IEEE Trans. Inform. Theory, vol. 40, pp. 35–40, Jan 1994.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