Compressive Sampling Using EM Algorithm

TL;DR

This paper introduces an EM algorithm-based method for reconstructing signals in compressive sampling, offering improved performance over traditional approaches through simulation studies.

Contribution

It proposes a novel EM algorithm-based reconstruction technique for compressive sampling, enhancing computational efficiency and accuracy compared to existing methods.

Findings

The new EM-based approach outperforms conventional methods in simulations.

The naive EM approach has computational challenges that are addressed by the proposed modification.

Simulation results demonstrate improved signal recovery accuracy.

Abstract

Conventional approaches of sampling signals follow the celebrated theorem of Nyquist and Shannon. Compressive sampling, introduced by Donoho, Romberg and Tao, is a new paradigm that goes against the conventional methods in data acquisition and provides a way of recovering signals using fewer samples than the traditional methods use. Here we suggest an alternative way of reconstructing the original signals in compressive sampling using EM algorithm. We first propose a naive approach which has certain computational difficulties and subsequently modify it to a new approach which performs better than the conventional methods of compressive sampling. The comparison of the different approaches and the performance of the new approach has been studied using simulated data.