We propose two compressive multiplexers for the efficient sampling of ensembles of correlated signals. We show that we can acquire correlated ensembles, taking advantage of their (a priori-unknown) correlation structure, at a sub-Nyquist rate using simple modulation and filtering architectures. We recast the reconstruction of the ensemble as a low-rank matrix recovery problem from generalized […]
Read MoreThe recently developed theory of Compressive sensing (CS) has shown that sparse signals can be reconstructed from a much smaller number of measurements than their bandwidth suggests. In this paper we present a sampling scheme to acquire ensembles of correlated signals at a sub-Nyquist rate. The sampling architecture uses simple analog building blocks including analog […]
Read MoreTrained generative models have shown remark-able performance as priors for inverse problems in imaging for example, Generative Adversarial Network priors permit recovery of test images from 5-10x fewer measurements than sparsity priors. Unfortunately, these models may be unable to represent any particular image because of architectural choices, mode collapse, and bias in the training […]
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We consider the bilinear inverse problem of recovering two vectors,??and?, in??from their entrywise product. For the case where the vectors have known signs and belong to known subspaces, we introduce the convex program BranchHull, which is posed in the natural parameter space and does not require an approximate solution or initialization in order to be […]
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This note considers the problem of blind identification of a linear, time-invariant (LTI) system when the input signals are unknown, but belong to sufficiently diverse, known subspaces. This problem can be recast as the recovery of a rank-1 matrix, and is effectively relaxed using a semidefinite program (SDP). We show that exact recovery of both […]
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This paper considers recovering L-dimensional vectors w, and x 1 , x 2 , ? , x N from their circular convolutions y_{n} = w * x_{n} , n = 1, 2, 3, ? , N. The vector wis assumed to be S-sparse in a known basis that is spread out in the Fourier domain, […]
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This letter considers the blind separation of convolutive mixtures in a multi-in-multi-out (MIMO) communication system. Multiple source signals are transmitted simultaneously over a shared communication medium (modeled as linear convolutive channels) to multiple receivers. We recast the joint recovery of the source signals, and the channel impulse responses as a block-rank-one matrix recovery problem, which […]
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Blind deconvolution refers to recovering signals by observing only their convolution. Blind deconvolution is a fundamental problem in signal processing, wireless communication, and systems theory. This problem arises in the context of blind channel estimation in wireless communications, passive imaging: for example, to estimate earth layer prole for oil exploration, seismic interferometry, radar imaging, and many other applications&
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