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Distributed detection with dependent observations is always a challenging problem. The problemof detectionwith shared information has many applications when sensors have overlapped measurements, e.g., when distributed detection is performed in a security system where sensors have overlapped coverages. For this shared information scenario, we investigate the distributed detection problem in parallel...
In this paper, we present a unifying framework for distributed detection with dependent or independent observations. This novel framework utilizes an expanded hierarchical model by introducing a hidden variable. Facilitated by this new framework, we identify several classes of distributed detection problems with conditionally dependent observations whose optimal sensor signaling structure resembles...
Distributed detection with conditionally independent observations at local sensors is well understood. The problem becomes significantly more complicated when dependence is present among sensor observations. In this paper, we attempt to make progress in our understanding of the dependent observation case. Toward this end, we present a new hierarchical model by introducing a hidden or latent variable;...
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...
Stochastic-resonance (SR), a nonlinear physical phenomenon in which the performance of some nonlinear systems can be enhanced by adding suitable noise, has been observed and applied in many areas. However, it has not been shown whether or not this phenomenon plays a role in distributed detection. It seems counterintuitive that adding additional noise to the received decisions at the fusion center...
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