Oversampled Adaptive Sensing

Conference contribution
(Original article)


Publication Details

Author(s): Müller R, Bereyhi A, Mecklenbräuker CF
Publisher: IEEE
Publication year: 2018
Language: English


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 Authors / FAU Editors

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


External institutions with authors

Technische Universität Wien


How to cite

APA:
Müller, R., Bereyhi, A., & Mecklenbräuker, C.F. (2018). Oversampled Adaptive Sensing. In Proceedings of the Information Theory and Applications Workshop (ITA). 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: 

Last updated on 2019-21-07 at 07:28