  We consider the problem of sequential binary hypothesis testing with a
distributed sensor network in a non-Gaussian noise environment. To this end, we
present a general formulation of the Consensus + Innovations Sequential
Probability Ratio Test (CISPRT). Furthermore, we introduce two different
concepts for robustifying the CISPRT and propose four different algorithms,
namely, the Least-Favorable-Density-CISPRT, the Median-CISPRT, the M-CISPRT,
and the Myriad-CISPRT. Subsequently, we analyze their suitability for different
binary hypothesis tests before verifying and evaluating their performance in a
shift-in-mean and a shift-in-variance scenario.
