Compressive Sensing for streaming signals using the Streaming Greedy Pursuit

Abstract

Compressive Sensing (CS) has recently emerged as significant signal processing framework to acquire and reconstruct sparse signals at rates significantly below the Nyquist rate. However, most of the CS development to-date has focused on finite-length signals and representations. In this paper we present a new CS framework and a greedy reconstruction algorithm, the Streaming Greedy Pursuit (SGP), explicitly designed for streaming applications and signals of unknown length. Our sampling framework is designed to be causal and implementable using existing hardware architectures. Furthermore, our reconstruction algorithm provides explicit computational guarantees, which makes it appropriate for real-time system implementations. Our experimental results on very long signals demonstrate the good performance of the SGP and validate our approach.