On the effect of filter variability in compressive sensing systems

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.