Talks and Poster Presentations (with Proceedings-Entry):
S. C. Birgmeier, N. Görtz:
"Robust Approximate Message Passing for Nonzero-mean Sensing Matrices";
Talk: IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2019),
Brighton, UK;
05-12-2019
- 05-17-2019; in: "Proceedings ICASSP 2019",
IEEE (ed.);
(2019),
ISSN: 2379-190x;
4898
- 4902.
English abstract:
The standard Approximate Message Passing (AMP) algorithm efficiently recovers a sparse signal from a small number of noisy linear
measurements. It requires the measurement matrix to be zero-mean,
however. Even small deviations from this requirement cause it to
diverge. In this paper, we show how mean-removal can be combined with standard Bayesian AMP to achieve signal recovery. Furthermore, a modified Bayesian AMP algorithm is presented, which achieves performance comparable to AMP in the zero-mean measurement matrix regime even for large mean. Simulation results and state evolution for both techniques are provided.
German abstract:
The standard Approximate Message Passing (AMP) algorithm efficiently recovers a sparse signal from a small number of noisy linear
measurements. It requires the measurement matrix to be zero-mean,
however. Even small deviations from this requirement cause it to
diverge. In this paper, we show how mean-removal can be combined with standard Bayesian AMP to achieve signal recovery. Furthermore, a modified Bayesian AMP algorithm is presented, which achieves performance comparable to AMP in the zero-mean measurement matrix regime even for large mean. Simulation results and state evolution for both techniques are provided.
Keywords:
Signal Processing / Compressed Sensing / Approximate Message Passing
"Official" electronic version of the publication (accessed through its Digital Object Identifier - DOI)
http://dx.doi.org/10.1109/ICASSP.2019.8682504
Created from the Publication Database of the Vienna University of Technology.