Title :
Some properties of optimal thresholds in decentralized detection
Author :
Irving, William W. ; Tsitsiklis, John N.
Author_Institution :
Dept. of Electr. Eng., MIT, Cambridge, MA, USA
fDate :
4/1/1994 12:00:00 AM
Abstract :
A decentralized Bayesian hypothesis testing problem is considered. It is analytically demonstrated that for the known signal in the Gaussian noise binary hypothesis problem, when there are two sensors with statistically independent identically distributed Gaussian observations (conditioned on the true hypothesis), there is no loss in optimality in using the same decision rule at both sensors. Also, a multiple hypothesis problem is considered; some structure is analytically established for an optimal set of decision rules
Keywords :
Bayes methods; optimisation; probability; sensor fusion; signal detection; Gaussian noise binary hypothesis; decentralized Bayesian hypothesis testing problem; decentralized detection; decision rules; distributed Gaussian observations; distributed sensor systems; optimal thresholds; sensor fusion; signal processing; Control systems; Design engineering; Design methodology; Linear programming; Output feedback; Polynomials; Process control; Robust control; Robustness; Uncertain systems;
Journal_Title :
Automatic Control, IEEE Transactions on
