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Distributed compressive sensing is a framework considering jointly sparsitywithin signal ensembles along with multiple measurement vectors (MMVs). Thecurrent theoretical bound of performance for MMVs, however, is derived to bethe same with that for single MV (SMV) because no assumption about signalensembles 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. Thenovelty is that the size of signal ensembles will be taken into considerationin our analysis to theoretically show that MMVs indeed exhibit betterperformance than SMV. Although our concept can be broadly applied to CSalgorithms with MMVs, the case study conducted on a well-known greedy solvercalled simultaneous orthogonal matching pursuit (SOMP) will be explored in thispaper. We show that the performance of SOMP, when incorporated with our conceptby modifying the steps of support detection and signal estimations, will beimproved remarkably, especially when the Euclidean distances between signalsare short. The performance of modified SOMP is verified to meet our theoreticalprediction.

Distributed Compressive Sensing: Performance Analysis with Diverse Signal Ensembles