Generalised Approximate Message Passing for Non-I.I.D. Sparse Signals

Christian Schou Oxvig, Thomas Arildsen

Publikation: Konferencebidrag uden forlag/tidsskriftPaper uden forlag/tidsskriftForskningpeer review

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Abstrakt

Generalised approximate message passing (GAMP) is an approximate Bayesian estimation algorithm for signals observed through a linear transform with a possibly non-linear subsequent measurement model. By leveraging prior information about the observed signal, such as sparsity in a known dictionary, GAMP can for example reconstruct signals from under-determined measurements -- known as compressed sensing. In the sparse signal setting, most existing signal priors for GAMP assume the input signal to have i.i.d. entries. Here we present sparse signal priors for GAMP to estimate non-i.d.d. signals through a non-uniform weighting of the input prior, for example allowing GAMP to support model-based compressed sensing.
OriginalsprogEngelsk
Publikationsdato22 nov. 2018
Antal sider3
StatusUdgivet - 22 nov. 2018
Begivenhedinternational Traveling Workshop on Interactions between low-complexity data models and Sensing Techniques - Centre International de Rencontres Mathématiques, Marseille, Frankrig
Varighed: 21 nov. 201823 nov. 2018
Konferencens nummer: 4
https://sites.google.com/view/itwist18

Workshop

Workshopinternational Traveling Workshop on Interactions between low-complexity data models and Sensing Techniques
Nummer4
LokationCentre International de Rencontres Mathématiques
LandFrankrig
ByMarseille
Periode21/11/201823/11/2018
Internetadresse

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