  Fusion frames are collection of subspaces which provide a redundant
representation of signal spaces. They generalize classical frames by replacing
frame vectors with frame subspaces. This paper considers the sparse recovery of
a signal from a fusion frame. We use a block sparsity model for fusion frames
and then show that sparse signals under this model can be compressively sampled
and reconstructed in ways similar to standard Compressed Sensing (CS). In
particular we invoke a mixed l1/l2 norm minimization in order to reconstruct
sparse signals. In our work, we show that assuming a certain incoherence
property of the subspaces and the apriori knowledge of it allows us to improve
recovery when compared to the usual block sparsity case.
