Theoretische Grenzen und algorithmische Verfahren verteilter komprimierender Abtastung

Drittmittelfinanzierte Einzelförderung


Details zum Projekt

Projektleiter/in:
Prof. Dr. Hermann Schulz-Baldes
Prof. Dr.-Ing. Ralf Müller


Beteiligte FAU-Organisationseinheiten:
Professur für Informationsübertragung
Professur für Mathematik

Mittelgeber: DFG-Einzelförderung / Sachbeihilfe (EIN-SBH)
Projektstart: 01.01.2019
Projektende: 31.12.2021


Abstract (fachliche Beschreibung):

The theoretical limits of distributed compressive sensing are studied by
tools from both information theory and statistical physics. The investigations
cover both noise-free and noisy distributed compressive sensing. The theoretical insights
are utilized to design approximate message passing algorithms for joint recovery of large distributed compressive sensing networks with feasible computational complexity. These algo-
rithms enable us to verify the non-rigorous results obtained by the replica method from statistical mechanics, and also, to propose theoretically optimal approaches for sampling and low complexity. The proposed research will lead to improved performance of reconstruction algorithms for distributed compressive sensing, e.g. higher compression rates and/or higher fidelity of reconstruction.

Zuletzt aktualisiert 2019-20-03 um 09:04