Further Results on the Optimality of the Likelihood-Ratio Test for Local Sensor Decision Rules in the Presence of Nonideal Channels
In this paper, we consider the design of local decision rules for distributed detection systems where decisions from peripheral detectors are transmitted over dependent nonideal channels. Under the conditional independence assumption among multiple sensor observations, we show that the optimal detection performance can be achieved by employing likelihood-ratio quantizers (LRQ) as local decision rules under both the Bayesian criterion and Neyman-Pearson (NP) criterion even for the cases where the channels between the fusion center and local sensors are dependent and noisy. This work generalizes the previous work where independence among such channels was assumed. A person-by-person optimization (PBPO) procedure to obtain the solution is presented along with an illustrative example.
distributed detection likelihood-ratio quantizers (lrqs) bayesian criterion neyman-pearson (np) criterion sensor networks