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

Christian Schou Oxvig, Thomas Arildsen

Publikation: Konferencebidrag uden forlag/tidsskriftPosterForskning

Abstrakt

Generalised approximate message passing (GAMP) is an approximate Bayesian estimation algorithm for signals observed through a linear transform with a possibly non-linear measurement model.
By leveraging prior information about the observed signal, such as sparsity in a known dictionary, GAMP enables reconstructing 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.
We present sparse signal priors to estimate non-identically distributed signals through a non-uniform weighting, e.g. enabling model-based compressed sensing with GAMP.
OriginalsprogEngelsk
Publikationsdato22 nov. 2018
DOI
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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