Title
Blind Deconvolution with Additional Autocorrelations via Convex Programs.
Abstract
In this work we characterize all ambiguities of the linear (aperiodic) one-dimensional convolution on two fixed finite-dimensional complex vector spaces. It will be shown that the convolution ambiguities can be mapped one-to-one to factorization ambiguities in the $z-$domain, which are generated by swapping the zeros of the input signals. We use this polynomial description to show a deterministic version of a recently introduced masked Fourier phase retrieval design. In the noise-free case a (convex) semi-definite program can be used to recover exactly the input signals if they share no common factors (zeros). Then, we reformulate the problem as deterministic blind deconvolution with prior knowledge of the autocorrelations. Numerically simulations show that our approach is also robust against additive noise.
Year
Venue
Field
2017
arXiv: Information Theory
Vector space,Mathematical optimization,Phase retrieval,Blind deconvolution,Polynomial,Convolution,Fourier transform,Factorization,Aperiodic graph,Mathematics
DocType
Volume
Citations 
Journal
abs/1701.04890
4
PageRank 
References 
Authors
0.41
10
4
Name
Order
Citations
PageRank
Philipp Walk1407.77
Peter Jung215423.80
Götz E. Pfander352.11
Babak Hassibi48737778.04