  We tackle distributed detection of a non-cooperative target with a Wireless
Sensor Network (WSN). When the target is present, sensors observe an (unknown)
deterministic signal with attenuation depending on the distance between the
sensor and the (unknown) target positions, embedded in symmetricand unimodal
noise. The Fusion Center (FC) receives quantized sensor observations through
error-prone Binary Symmetric Channels (BSCs) and is in charge of performing a
more-accurate global decision. The resulting problem is a two-sided parameter
testing with nuisance parameters (i.e. the target position) present only under
the alternative hypothesis. After introducing the Generalized Likelihood Ratio
Test (GLRT) for the problem, we develop a novel fusion rule corresponding to a
Generalized Rao (G-Rao) test, based on Davies' framework, to reduce the
computational complexity. Also, a rationale for threshold-optimization is
proposed and confirmed by simulations. Finally, the aforementioned rules are
compared in terms of performance and computational complexity.
