  Distributed compressed sensing is concerned with representing an ensemble of
jointly sparse signals using as few linear measurements as possible. Two novel
joint reconstruction algorithms for distributed compressed sensing are
presented in this paper. These algorithms are based on the idea of using one of
the signals as side information; this allows to exploit joint sparsity in a
more effective way with respect to existing schemes. They provide gains in
reconstruction quality, especially when the nodes acquire few measurements, so
that the system is able to operate with fewer measurements than is required by
other existing schemes. We show that the algorithms achieve better performance
with respect to the state-of-the-art.
