Compressive Sampling Using EM Algorithm
This work offers an incremental improvement in compressive sampling, a domain-specific technique for signal processing.
The paper tackles the problem of signal reconstruction in compressive sampling by proposing an EM algorithm-based approach, which outperforms conventional methods in simulated data.
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.