• DocumentCode
    1671898
  • Title

    Distributed detection and Uniformly Most Powerful tests

  • Author

    Rogers, Uri ; Hao Chen

  • Author_Institution
    Dept. of Electr. & Comput. Eng., Boise State Univ., Boise, ID, USA
  • fYear
    2013
  • Firstpage
    4256
  • Lastpage
    4260
  • Abstract
    Uniformly Most Powerful (UMP) centralized detection for the composite binary hypothesis problem has been well researched. This paper extends the UMP methodology to the parallel distributed detection problem, when the local observations are independently distributed. A collection of general theorems and corollaries define sufficient conditions for the existence of a UMP parallel Distributed Detection (UMP-DD) under one set of fusion rules. These same conditions under another set of general fusion rules result in at least a Locally Most Powerful Distributed Detection (LMP-DD) rule. The subtleties of these conclusions are explored using informative examples that highlight the strengths of this approach and introduce new groups of UMP-DD tests.
  • Keywords
    signal detection; UMP DD tests; UMP centralized detection; UMP parallel distributed detection problem; composite binary hypothesis problem; general fusion rules; locally most powerful distributed detection rule; uniformly most powerful centralized detection; uniformly most powerful tests; Gaussian noise; Optimization; Probability density function; Random variables; Testing; Vectors; Composite Hypothesis Testing; Distributed Detection; Log-concave; Uniformly Most Powerful Test;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Acoustics, Speech and Signal Processing (ICASSP), 2013 IEEE International Conference on
  • Conference_Location
    Vancouver, BC
  • ISSN
    1520-6149
  • Type

    conf

  • DOI
    10.1109/ICASSP.2013.6638462
  • Filename
    6638462