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—In the context of compressed sensing (CS), both Subspace Pursuit (SP) and Compressive Sampling Matching Pursuit (CoSaMP) are very important iterative greedy recovery algorithms which could reduce the recovery complexity greatly comparing with the well-known ℓ1-minimization. Restricted isometry property (RIP) and restricted isometry constant (RIC) of(More)
—For a measurement matrix in compressed sensing, its spark (or the smallest number of columns that are linearly dependent) is an important performance parameter. The matrix with spark greater than 2k guarantees the exact recovery of k-sparse signals under an l0-optimization, and the one with large spark may perform well under approximate algorithms of the(More)
—We propose a new iterative greedy algorithm for reconstructions of sparse signals with or without noisy perturbations in compressed sensing. The proposed algorithm, called subspace thresholding pursuit (STP) in this paper, is a simple combination of subspace pursuit and iterative hard thresholding. Firstly, STP has the theoretical guarantee comparable to(More)
We introduce a general framework to deterministically construct binary measurement matrices for compressed sensing. The proposed matrices are composed of (circulant) permutation submatrix blocks and zero submatrix blocks, thus making their hardware realization convenient and easy. Firstly, using the famous Johnson bound for binary constant weight codes, we(More)