Abstract
In this paper, we study the effect of filter variability in compressive sensing systems. Compressive sensing entails projecting the signal on a set of random signals, which is done by means of mixers and low pass filters. When reconstructing the signal, it is important to have a model for the sensing hardware, hence, the need to mitigate variability effects on the reconstruction process. In order to do that, there is a need for quantifying the effect of variability on reconstruction in order to be able to design compressive sensing systems that are robust to variability.
| Original language | English (US) |
|---|---|
| Title of host publication | 2014 IEEE 12th International New Circuits and Systems Conference, NEWCAS 2014 |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| Pages | 201-204 |
| Number of pages | 4 |
| ISBN (Print) | 9781479948857 |
| DOIs | |
| State | Published - Oct 22 2014 |
| Externally published | Yes |