427 research outputs found
Structured random measurements in signal processing
Compressed sensing and its extensions have recently triggered interest in
randomized signal acquisition. A key finding is that random measurements
provide sparse signal reconstruction guarantees for efficient and stable
algorithms with a minimal number of samples. While this was first shown for
(unstructured) Gaussian random measurement matrices, applications require
certain structure of the measurements leading to structured random measurement
matrices. Near optimal recovery guarantees for such structured measurements
have been developed over the past years in a variety of contexts. This article
surveys the theory in three scenarios: compressed sensing (sparse recovery),
low rank matrix recovery, and phaseless estimation. The random measurement
matrices to be considered include random partial Fourier matrices, partial
random circulant matrices (subsampled convolutions), matrix completion, and
phase estimation from magnitudes of Fourier type measurements. The article
concludes with a brief discussion of the mathematical techniques for the
analysis of such structured random measurements.Comment: 22 pages, 2 figure
Simultaneous Sparse Approximation Using an Iterative Method with Adaptive Thresholding
This paper studies the problem of Simultaneous Sparse Approximation (SSA).
This problem arises in many applications which work with multiple signals
maintaining some degree of dependency such as radar and sensor networks. In
this paper, we introduce a new method towards joint recovery of several
independent sparse signals with the same support. We provide an analytical
discussion on the convergence of our method called Simultaneous Iterative
Method with Adaptive Thresholding (SIMAT). Additionally, we compare our method
with other group-sparse reconstruction techniques, i.e., Simultaneous
Orthogonal Matching Pursuit (SOMP), and Block Iterative Method with Adaptive
Thresholding (BIMAT) through numerical experiments. The simulation results
demonstrate that SIMAT outperforms these algorithms in terms of the metrics
Signal to Noise Ratio (SNR) and Success Rate (SR). Moreover, SIMAT is
considerably less complicated than BIMAT, which makes it feasible for practical
applications such as implementation in MIMO radar systems
Matrix Completion in Colocated MIMO Radar: Recoverability, Bounds & Theoretical Guarantees
It was recently shown that low rank matrix completion theory can be employed
for designing new sampling schemes in the context of MIMO radars, which can
lead to the reduction of the high volume of data typically required for
accurate target detection and estimation. Employing random samplers at each
reception antenna, a partially observed version of the received data matrix is
formulated at the fusion center, which, under certain conditions, can be
recovered using convex optimization. This paper presents the theoretical
analysis regarding the performance of matrix completion in colocated MIMO radar
systems, exploiting the particular structure of the data matrix. Both Uniform
Linear Arrays (ULAs) and arbitrary 2-dimensional arrays are considered for
transmission and reception. Especially for the ULA case, under some mild
assumptions on the directions of arrival of the targets, it is explicitly shown
that the coherence of the data matrix is both asymptotically and approximately
optimal with respect to the number of antennas of the arrays involved and
further, the data matrix is recoverable using a subset of its entries with
minimal cardinality. Sufficient conditions guaranteeing low matrix coherence
and consequently satisfactory matrix completion performance are also presented,
including the arbitrary 2-dimensional array case.Comment: 19 pages, 7 figures, under review in Transactions on Signal
Processing (2013
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