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

Christian Schou Oxvig, Thomas Arildsen

Research output: Contribution to conference without publisher/journalPaper without publisher/journalResearchpeer-review

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Abstract

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.
Original languageEnglish
Publication date22 Nov 2018
Number of pages3
Publication statusPublished - 22 Nov 2018
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

Keywords

  • compressed sensing
  • signal processing
  • estimation theory

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