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