934 research outputs found
Xampling: Signal Acquisition and Processing in Union of Subspaces
We introduce Xampling, a unified framework for signal acquisition and
processing of signals in a union of subspaces. The main functions of this
framework are two. Analog compression that narrows down the input bandwidth
prior to sampling with commercial devices. A nonlinear algorithm then detects
the input subspace prior to conventional signal processing. A representative
union model of spectrally-sparse signals serves as a test-case to study these
Xampling functions. We adopt three metrics for the choice of analog
compression: robustness to model mismatch, required hardware accuracy and
software complexities. We conduct a comprehensive comparison between two
sub-Nyquist acquisition strategies for spectrally-sparse signals, the random
demodulator and the modulated wideband converter (MWC), in terms of these
metrics and draw operative conclusions regarding the choice of analog
compression. We then address lowrate signal processing and develop an algorithm
for that purpose that enables convenient signal processing at sub-Nyquist rates
from samples obtained by the MWC. We conclude by showing that a variety of
other sampling approaches for different union classes fit nicely into our
framework.Comment: 16 pages, 9 figures, submitted to IEEE for possible publicatio
Bayesian Compressive Sensing of Sparse Signals with Unknown Clustering Patterns
We consider the sparse recovery problem of signals with an unknown clustering pattern in the context of multiple measurement vectors (MMVs) using the compressive sensing (CS) technique. For many MMVs in practice, the solution matrix exhibits some sort of clustered sparsity pattern, or clumpy behavior, along each column, as well as joint sparsity across the columns. In this paper, we propose a new sparse Bayesian learning (SBL) method that incorporates a total variation-like prior as a measure of the overall clustering pattern in the solution. We further incorporate a parameter in this prior to account for the emphasis on the amount of clumpiness in the supports of the solution to improve the recovery performance of sparse signals with an unknown clustering pattern. This parameter does not exist in the other existing algorithms and is learned via our hierarchical SBL algorithm. While the proposed algorithm is constructed for the MMVs, it can also be applied to the single measurement vector (SMV) problems. Simulation results show the effectiveness of our algorithm compared to other algorithms for both SMV and MMVs
Towards a Mathematical Theory of Super-Resolution
This paper develops a mathematical theory of super-resolution. Broadly
speaking, super-resolution is the problem of recovering the fine details of an
object---the high end of its spectrum---from coarse scale information
only---from samples at the low end of the spectrum. Suppose we have many point
sources at unknown locations in and with unknown complex-valued
amplitudes. We only observe Fourier samples of this object up until a frequency
cut-off . We show that one can super-resolve these point sources with
infinite precision---i.e. recover the exact locations and amplitudes---by
solving a simple convex optimization problem, which can essentially be
reformulated as a semidefinite program. This holds provided that the distance
between sources is at least . This result extends to higher dimensions
and other models. In one dimension for instance, it is possible to recover a
piecewise smooth function by resolving the discontinuity points with infinite
precision as well. We also show that the theory and methods are robust to
noise. In particular, in the discrete setting we develop some theoretical
results explaining how the accuracy of the super-resolved signal is expected to
degrade when both the noise level and the {\em super-resolution factor} vary.Comment: 48 pages, 12 figure
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