Atomic Decomposition by Basis Pursuit
SIAM Journal on Scientific Computing
Dictionary learning for sparse approximations with the majorization method
IEEE Transactions on Signal Processing
Sampling theorems for signals from the union of finite-dimensional linear subspaces
IEEE Transactions on Information Theory
IEEE Transactions on Information Theory
IEEE Transactions on Information Theory
Signal Recovery From Random Measurements Via Orthogonal Matching Pursuit
IEEE Transactions on Information Theory
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Compressive Sensing (CS) has developed as an enticing alternative to the traditional process of signal acquisition. For a length-N signal with sparsity K, merely M = O(K logN) ≪ N random linear projections (measurements) can be used for robust reconstruction in polynomial time. Sparsity is a powerful and simple signal model; yet, richer models that impose additional structure on the sparse nonzeros of a signal have been studied theoretically and empirically from the CS perspective. In this work, we introduce and study a sparse signal model for streams of pulses, i.e., S-sparse signals convolved with an unknown F-sparse impulse response. Our contributions are threefold: (i) we geometrically model this set of signals as an infinite union of subspaces; (ii) we derive a sufficient number of random measurements M required to preserve the metric information of this set. In particular this number is linear merely in the number of degrees of freedom of the signal S + F, and sublinear in the sparsity K = SF; (iii) we develop an algorithm that performs recovery of the signal from M measurements and analyze its performance under noise and model mismatch. Numerical experiments on synthetic and real data demonstrate the utility of our proposed theory and algorithm. Our method is amenable to diverse applications such as the high-resolution sampling of neuronal recordings and ultra-wideband (UWB) signals.