7 research outputs found
Manifold Gradient Descent Solves Multi-Channel Sparse Blind Deconvolution Provably and Efficiently
Multi-channel sparse blind deconvolution, or convolutional sparse coding,
refers to the problem of learning an unknown filter by observing its circulant
convolutions with multiple input signals that are sparse. This problem finds
numerous applications in signal processing, computer vision, and inverse
problems. However, it is challenging to learn the filter efficiently due to the
bilinear structure of the observations with the respect to the unknown filter
and inputs, as well as the sparsity constraint. In this paper, we propose a
novel approach based on nonconvex optimization over the sphere manifold by
minimizing a smooth surrogate of the sparsity-promoting loss function. It is
demonstrated that manifold gradient descent with random initializations will
provably recover the filter, up to scaling and shift ambiguity, as soon as the
number of observations is sufficiently large under an appropriate random data
model. Numerical experiments are provided to illustrate the performance of the
proposed method with comparisons to existing ones.Comment: accepted by IEEE Transactions on Information Theor
Identifiability Conditions for Compressive Multichannel Blind Deconvolution
In applications such as multi-receiver radars and ultrasound array systems,
the observed signals can often be modeled as a linear convolution of an unknown
signal which represents the transmit pulse and sparse filters which describe
the sparse target scenario. The problem of identifying the unknown signal and
the sparse filters is a sparse multichannel blind deconvolution (MBD) problem
and is in general ill-posed. In this paper, we consider the identifiability
problem of sparse-MBD and show that, similar to compressive sensing, it is
possible to identify the sparse filters from compressive measurements of the
output sequences. Specifically, we consider compressible measurements in the
Fourier domain and derive identifiability conditions in a deterministic setup.
Our main results demonstrate that -sparse filters can be identified from
Fourier measurements from only two coprime channels. We also show that
measurements per channel are necessary. The sufficient condition sharpens
as the number of channels increases asymptotically in the number of channels,
it suffices to acquire on the order of Fourier samples per channel. We also
propose a kernel-based sampling scheme that acquires Fourier measurements from
a commensurate number of time samples. We discuss the gap between the
sufficient and necessary conditions through numerical experiments including
comparing practical reconstruction algorithms. The proposed compressive MBD
results require fewer measurements and fewer channels for identifiability
compared to previous results, which aids in building cost-effective receivers.Comment: 13 pages, 5 figure