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

Christian Schou Oxvig, Thomas Arildsen

Publikation: Konferencebidrag uden forlag/tidsskriftPosterForskning

Resumé

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

Fingerprint

Message passing
Compressed sensing
Glossaries

Citer dette

Oxvig, C. S., & Arildsen, T. (2018). Generalised Approximate Message Passing for Non-I.I.D. Sparse Signals. Poster præsenteret på international Traveling Workshop on Interactions between low-complexity data models and Sensing Techniques, Marseille, Frankrig. https://doi.org/10.5281/zenodo.1690664
Oxvig, Christian Schou ; Arildsen, Thomas. / Generalised Approximate Message Passing for Non-I.I.D. Sparse Signals. Poster præsenteret på international Traveling Workshop on Interactions between low-complexity data models and Sensing Techniques, Marseille, Frankrig.
@conference{a40661c02aa4454bbe8bffcd52142827,
title = "Generalised Approximate Message Passing for Non-I.I.D. Sparse Signals",
abstract = "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.",
keywords = "compressed sensing, signal processing, estimation theory",
author = "Oxvig, {Christian Schou} and Thomas Arildsen",
year = "2018",
month = "11",
day = "22",
doi = "10.5281/zenodo.1690664",
language = "English",
note = "null ; Conference date: 21-11-2018 Through 23-11-2018",
url = "https://sites.google.com/view/itwist18",

}

Oxvig, CS & Arildsen, T 2018, 'Generalised Approximate Message Passing for Non-I.I.D. Sparse Signals', international Traveling Workshop on Interactions between low-complexity data models and Sensing Techniques, Marseille, Frankrig, 21/11/2018 - 23/11/2018. https://doi.org/10.5281/zenodo.1690664

Generalised Approximate Message Passing for Non-I.I.D. Sparse Signals. / Oxvig, Christian Schou; Arildsen, Thomas.

2018. Poster præsenteret på international Traveling Workshop on Interactions between low-complexity data models and Sensing Techniques, Marseille, Frankrig.

Publikation: Konferencebidrag uden forlag/tidsskriftPosterForskning

TY - CONF

T1 - Generalised Approximate Message Passing for Non-I.I.D. Sparse Signals

AU - Oxvig, Christian Schou

AU - Arildsen, Thomas

PY - 2018/11/22

Y1 - 2018/11/22

N2 - 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.

AB - 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.

KW - compressed sensing

KW - signal processing

KW - estimation theory

U2 - 10.5281/zenodo.1690664

DO - 10.5281/zenodo.1690664

M3 - Poster

ER -

Oxvig CS, Arildsen T. Generalised Approximate Message Passing for Non-I.I.D. Sparse Signals. 2018. Poster præsenteret på international Traveling Workshop on Interactions between low-complexity data models and Sensing Techniques, Marseille, Frankrig. https://doi.org/10.5281/zenodo.1690664