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In this paper we tackle distributed detection of a non-cooperative targetwith a Wireless Sensor Network (WSN). When the target is present, sensorsobserve an unknown random signal with amplitude attenuation depending on thedistance between the sensor and the target (unknown) positions, embedded inwhite Gaussian noise. The Fusion Center (FC) receives sensors decisions througherror-prone Binary Symmetric Channels (BSCs) and is in charge of performing a(potentially) more-accurate global decision. The resulting problem is aone-sided testing with nuisance parameters present only under thetarget-present hypothesis. We first focus on fusion rules based on GeneralizedLikelihood Ratio Test (GLRT), Bayesian and hybrid approaches. Then, aimed atreducing the computational complexity, we develop fusion rules based ongeneralizations of the well-known Locally-Optimum Detection (LOD) framework.Finally, all the proposed rules are compared in terms of performance andcomplexity.

Distributed Detection of a Non-cooperative Target via Generalized Locally-optimum Approaches