5,440 research outputs found
Info-Greedy sequential adaptive compressed sensing
We present an information-theoretic framework for sequential adaptive
compressed sensing, Info-Greedy Sensing, where measurements are chosen to
maximize the extracted information conditioned on the previous measurements. We
show that the widely used bisection approach is Info-Greedy for a family of
-sparse signals by connecting compressed sensing and blackbox complexity of
sequential query algorithms, and present Info-Greedy algorithms for Gaussian
and Gaussian Mixture Model (GMM) signals, as well as ways to design sparse
Info-Greedy measurements. Numerical examples demonstrate the good performance
of the proposed algorithms using simulated and real data: Info-Greedy Sensing
shows significant improvement over random projection for signals with sparse
and low-rank covariance matrices, and adaptivity brings robustness when there
is a mismatch between the assumed and the true distributions.Comment: Preliminary results presented at Allerton Conference 2014. To appear
in IEEE Journal Selected Topics on Signal Processin
Adaptive Non-uniform Compressive Sampling for Time-varying Signals
In this paper, adaptive non-uniform compressive sampling (ANCS) of
time-varying signals, which are sparse in a proper basis, is introduced. ANCS
employs the measurements of previous time steps to distribute the sensing
energy among coefficients more intelligently. To this aim, a Bayesian inference
method is proposed that does not require any prior knowledge of importance
levels of coefficients or sparsity of the signal. Our numerical simulations
show that ANCS is able to achieve the desired non-uniform recovery of the
signal. Moreover, if the signal is sparse in canonical basis, ANCS can reduce
the number of required measurements significantly.Comment: 6 pages, 8 figures, Conference on Information Sciences and Systems
(CISS 2017) Baltimore, Marylan
Non-adaptive pooling strategies for detection of rare faulty items
We study non-adaptive pooling strategies for detection of rare faulty items.
Given a binary sparse N-dimensional signal x, how to construct a sparse binary
MxN pooling matrix F such that the signal can be reconstructed from the
smallest possible number M of measurements y=Fx? We show that a very low number
of measurements is possible for random spatially coupled design of pools F. Our
design might find application in genetic screening or compressed genotyping. We
show that our results are robust with respect to the uncertainty in the matrix
F when some elements are mistaken.Comment: 5 page
Compressive sensing based Bayesian sparse channel estimation for OFDM communication systems: high performance and low complexity
In orthogonal frequency division modulation (OFDM) communication systems,
channel state information (CSI) is required at receiver due to the fact that
frequency-selective fading channel leads to disgusting inter-symbol
interference (ISI) over data transmission. Broadband channel model is often
described by very few dominant channel taps and they can be probed by
compressive sensing based sparse channel estimation (SCE) methods, e.g.,
orthogonal matching pursuit algorithm, which can take the advantage of sparse
structure effectively in the channel as for prior information. However, these
developed methods are vulnerable to both noise interference and column
coherence of training signal matrix. In other words, the primary objective of
these conventional methods is to catch the dominant channel taps without a
report of posterior channel uncertainty. To improve the estimation performance,
we proposed a compressive sensing based Bayesian sparse channel estimation
(BSCE) method which can not only exploit the channel sparsity but also mitigate
the unexpected channel uncertainty without scarifying any computational
complexity. The propose method can reveal potential ambiguity among multiple
channel estimators that are ambiguous due to observation noise or correlation
interference among columns in the training matrix. Computer simulations show
that propose method can improve the estimation performance when comparing with
conventional SCE methods.Comment: 24 pages,16 figures, submitted for a journa
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