  Distributed compressive sensing is a framework considering jointly sparsity
within signal ensembles along with multiple measurement vectors (MMVs). The
current theoretical bound of performance for MMVs, however, is derived to be
the same with that for single MV (SMV) because no assumption about signal
ensembles is made.
  In this work, we propose a new concept of inducing the factor called
"Euclidean distances between signals" for the performance analysis of MMVs. The
novelty is that the size of signal ensembles will be taken into consideration
in our analysis to theoretically show that MMVs indeed exhibit better
performance than SMV. Although our concept can be broadly applied to CS
algorithms with MMVs, the case study conducted on a well-known greedy solver
called simultaneous orthogonal matching pursuit (SOMP) will be explored in this
paper. We show that the performance of SOMP, when incorporated with our concept
by modifying the steps of support detection and signal estimations, will be
improved remarkably, especially when the Euclidean distances between signals
are short. The performance of modified SOMP is verified to meet our theoretical
prediction.
