A Performance Analysis of Reconstruction Algorithms for an Analog-to-Information Converter Architecture

Abstract

Compressed Sensing (CS) is a technique that allows the sampling of sparse signals with sub-Nyquist sampling rates. A device that implements the CS technique is the Analog-to-Information Converter (AIC), which compresses the input signal while sampling it. This paper presents CS principles and performance analysis of different reconstruction algorithms for a dual-tone sine signal applied to an adapted version of the Random Modulation Pre-Integrator (RMPI) AIC.