  We consider a multi-object detection problem over a sensor network (SNET)
with limited range sensors. This problem complements the widely considered
decentralized detection problem where all sensors observe the same object.
While the necessity for global collaboration is clear in the decentralized
detection problem, the benefits of collaboration with limited range sensors is
unclear and has not been widely explored. In this paper we develop a
distributed detection approach based on recent development of the false
discovery rate (FDR). We first extend the FDR procedure and develop a
transformation that exploits complete or partial knowledge of either the
observed distributions at each sensor or the ensemble (mixture) distribution
across all sensors. We then show that this transformation applies to
multi-dimensional observations, thus extending FDR to multi-dimensional
settings. We also extend FDR theory to cases where distributions under both
null and positive hypotheses are uncertain. We then propose a robust
distributed algorithm to perform detection. We further demonstrate scalability
to large SNETs by showing that the upper bound on the communication complexity
scales linearly with the number of sensors that are in the vicinity of objects
and is independent of the total number of sensors. Finally, we deal with
situations where the sensing model may be uncertain and establish robustness of
our techniques to such uncertainties.
