  We consider the problem of making a quick decision in favor of one of two
possible physical signal models while the numerical measurements are acquired
by sensing devices featuring minimal digitization complexity. Therefore, the
digital data streams available for statistical processing are binary and
exhibit temporal and spatial dependencies. To handle the intractable
multivariate binary data model, we first consider sequential tests for
exponential family distributions. Within this generic probabilistic framework,
we identify adaptive approximations for the log-likelihood ratio and the
Kullback-Leibler divergence. The results allow designing sequential detectors
for binary radio systems and analyzing their average run-time along classical
arguments of Wald. In particular, the derived tests exploit the spatio-temporal
correlation structure of the analog sensor signals engraved into the binary
measurements. As an application, we consider the specification of binary
sensing architectures for cognitive radio and GNSS spectrum monitoring where
our results characterize the sequential detection latency as a function of the
temporal oversampling and the number of antennas. Finally, we evaluate the
efficiency of the proposed algorithms and illustrate the accuracy of our
analysis via Monte-Carlo simulations.
