Compressive sensing (CS) is a new approach to simultaneous sensing and compression

that enables a potentially large reduction in the sampling and computation

costs for acquisition of signals having a sparse or compressible representation in some

basis. The CS literature has focused almost exclusively on problems involving single

signals in one or two dimensions. However, many important applications involve distributed

networks or arrays of sensors. In other applications, the signal is inherently

multidimensional and sensed progressively along a subset of its dimensions examples

include hyperspectral imaging and video acquisition. Initial work proposed joint sparsity

models for signal ensembles that exploit both intra- and inter-signal correlation

structures. Joint sparsity models enable a reduction in the total number of compressive

mea-Compressive sensing (CS) is a new approach to simultaneous sensing and compression

that enables a potentially large reduction in the sampling and computation

costs for acquisition of signals having a sparse or compressible representation in some

basis. The CS literature has focused almost exclusively on problems involving single

signals in one or two dimensions. However, many important applications involve distributed

networks or arrays of sensors. In other applications, the signal is inherently

multidimensional and sensed progressively along a subset of its dimensions　examples

include hyperspectral imaging and video acquisition. Initial work proposed joint sparsity

models for signal ensembles that exploit both intra- and inter-signal correlation

structures. Joint sparsity models enable a reduction in the total number of compressive

mea