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

Research output: Contribution to conference without publisher/journalPosterResearch

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.
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Details

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.
Original languageEnglish
Publication date22 Nov 2018
DOI
Publication statusPublished - 22 Nov 2018
Publication categoryResearch
Peer-reviewedNo
Eventinternational Traveling Workshop on Interactions between low-complexity data models and Sensing Techniques - Centre International de Rencontres Mathématiques, Marseille, France
Duration: 21 Nov 201823 Nov 2018
Conference number: 4
https://sites.google.com/view/itwist18

Workshop

Workshopinternational Traveling Workshop on Interactions between low-complexity data models and Sensing Techniques
Number4
LocationCentre International de Rencontres Mathématiques
CountryFrance
CityMarseille
Period21/11/201823/11/2018
Internet address

    Research areas

  • compressed sensing, signal processing, estimation theory
ID: 291008368