1,181 research outputs found
One-bit compressive sensing with norm estimation
Consider the recovery of an unknown signal from quantized linear
measurements. In the one-bit compressive sensing setting, one typically assumes
that is sparse, and that the measurements are of the form
. Since such
measurements give no information on the norm of , recovery methods from
such measurements typically assume that . We show that if one
allows more generally for quantized affine measurements of the form
, and if the vectors
are random, an appropriate choice of the affine shifts allows
norm recovery to be easily incorporated into existing methods for one-bit
compressive sensing. Additionally, we show that for arbitrary fixed in
the annulus , one may estimate the norm up to additive error from
such binary measurements through a single evaluation of the inverse Gaussian
error function. Finally, all of our recovery guarantees can be made universal
over sparse vectors, in the sense that with high probability, one set of
measurements and thresholds can successfully estimate all sparse vectors
within a Euclidean ball of known radius.Comment: 20 pages, 2 figure
Signal Recovery From 1-Bit Quantized Noisy Samples via Adaptive Thresholding
In this paper, we consider the problem of signal recovery from 1-bit noisy
measurements. We present an efficient method to obtain an estimation of the
signal of interest when the measurements are corrupted by white or colored
noise. To the best of our knowledge, the proposed framework is the pioneer
effort in the area of 1-bit sampling and signal recovery in providing a unified
framework to deal with the presence of noise with an arbitrary covariance
matrix including that of the colored noise. The proposed method is based on a
constrained quadratic program (CQP) formulation utilizing an adaptive
quantization thresholding approach, that further enables us to accurately
recover the signal of interest from its 1-bit noisy measurements. In addition,
due to the adaptive nature of the proposed method, it can recover both fixed
and time-varying parameters from their quantized 1-bit samples.Comment: This is a pre-print version of the original conference paper that has
been accepted at the 2018 IEEE Asilomar Conference on Signals, Systems, and
Computer
Quantization and Compressive Sensing
Quantization is an essential step in digitizing signals, and, therefore, an
indispensable component of any modern acquisition system. This book chapter
explores the interaction of quantization and compressive sensing and examines
practical quantization strategies for compressive acquisition systems.
Specifically, we first provide a brief overview of quantization and examine
fundamental performance bounds applicable to any quantization approach. Next,
we consider several forms of scalar quantizers, namely uniform, non-uniform,
and 1-bit. We provide performance bounds and fundamental analysis, as well as
practical quantizer designs and reconstruction algorithms that account for
quantization. Furthermore, we provide an overview of Sigma-Delta
() quantization in the compressed sensing context, and also
discuss implementation issues, recovery algorithms and performance bounds. As
we demonstrate, proper accounting for quantization and careful quantizer design
has significant impact in the performance of a compressive acquisition system.Comment: 35 pages, 20 figures, to appear in Springer book "Compressed Sensing
and Its Applications", 201
Hamming Compressed Sensing
Compressed sensing (CS) and 1-bit CS cannot directly recover quantized
signals and require time consuming recovery. In this paper, we introduce
\textit{Hamming compressed sensing} (HCS) that directly recovers a k-bit
quantized signal of dimensional from its 1-bit measurements via invoking
times of Kullback-Leibler divergence based nearest neighbor search.
Compared with CS and 1-bit CS, HCS allows the signal to be dense, takes
considerably less (linear) recovery time and requires substantially less
measurements (). Moreover, HCS recovery can accelerate the
subsequent 1-bit CS dequantizer. We study a quantized recovery error bound of
HCS for general signals and "HCS+dequantizer" recovery error bound for sparse
signals. Extensive numerical simulations verify the appealing accuracy,
robustness, efficiency and consistency of HCS.Comment: 33 pages, 8 figure
Quantized Compressed Sensing for Partial Random Circulant Matrices
We provide the first analysis of a non-trivial quantization scheme for
compressed sensing measurements arising from structured measurements.
Specifically, our analysis studies compressed sensing matrices consisting of
rows selected at random, without replacement, from a circulant matrix generated
by a random subgaussian vector. We quantize the measurements using stable,
possibly one-bit, Sigma-Delta schemes, and use a reconstruction method based on
convex optimization. We show that the part of the reconstruction error due to
quantization decays polynomially in the number of measurements. This is in line
with analogous results on Sigma-Delta quantization associated with random
Gaussian or subgaussian matrices, and significantly better than results
associated with the widely assumed memoryless scalar quantization. Moreover, we
prove that our approach is stable and robust; i.e., the reconstruction error
degrades gracefully in the presence of non-quantization noise and when the
underlying signal is not strictly sparse. The analysis relies on results
concerning subgaussian chaos processes as well as a variation of McDiarmid's
inequality.Comment: 15 page
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