Optimised projections for generalised distributed compressed sensing

Different signals from the various sensors of the same scene form an ensemble. Distributed compressed sensing (DCS) rests on a new concept called the joint sparsity of the ensemble. JSM-1 is a model that describes the joint sparsity by one dictionary. Previously, the generalisation of JSM-1 was proposed where the signal ensemble depends on two dictionaries. Its compressed sensing (CS) version is considered: generalised DCS (GDCS). Instead of using random projections (random Gaussian (rGauss)), a gradient method with Barzilai-Borwein stepsize (GBB) is developed to optimise the projections in the GDCS. It enhances the reconstruction performance of the GDCS. It is verified by some experiments on the synthesised signals.