Oversampled Adaptive Sensing

Beitrag bei einer Tagung
(Originalarbeit)


Details zur Publikation

Autor(en): Müller R, Bereyhi A, Mecklenbräuker CF
Verlag: IEEE
Jahr der Veröffentlichung: 2018
Sprache: Englisch


Abstract

We develop a Bayesian framework for sensing which adapts the sensing time
and/or basis functions to the instantaneous sensing quality measured in terms
of the expected posterior mean-squared error. For sparse Gaussian sources a
significant reduction in average sensing time and/or mean-squared error is
achieved in comparison to non-adaptive sensing. For compression ratio 3, a
sparse 10% Gaussian source and equal average sensing times, the proposed method
gains about 2 dB over the performance bound of optimum compressive sensing,
about 3 dB over non-adaptive 3-fold oversampled orthogonal sensing and about 6
to 7 dB to LASSO-based recovery schemes while enjoying polynomial time
complexity.

We utilize that in the presence of Gaussian noise the mean-squared error
conditioned on the current observation is proportional to the derivative of the
conditional mean estimate with respect to this observation.


FAU-Autoren / FAU-Herausgeber

Bereyhi, Ali
Lehrstuhl für Digitale Übertragung
Müller, Ralf Prof. Dr.-Ing.
Professur für Informationsübertragung


Autor(en) der externen Einrichtung(en)
Technische Universität Wien


Zitierweisen

APA:
Müller, R., Bereyhi, A., & Mecklenbräuker, C.F. (2018). Oversampled Adaptive Sensing. San Diego, US: IEEE.

MLA:
Müller, Ralf, Ali Bereyhi, and Christoph F. Mecklenbräuker. "Oversampled Adaptive Sensing." Proceedings of the Information Theory and Applications Workshop (ITA), San Diego IEEE, 2018.

BibTeX: 

Zuletzt aktualisiert 2018-22-09 um 14:38