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
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;
Conference_Titel :
Acoustics, Speech and Signal Processing (ICASSP), 2013 IEEE International Conference on
Conference_Location :
Vancouver, BC
DOI :
10.1109/ICASSP.2013.6638462