Design Issues and Challenges of an FPGA-based Orthogonal Matching Pursuit Implementation for Compressive Sensing Reconstruction

Abstract

Compressive sensing (CS) is as an evolving research area in signal processing due to the advantages offered for signal compression. Based on the sparsity of signals, CS allows the sampling of sparse signals under the sub-Nyquist rate, and yet promises a reliable data recovery. To date, the implementation of practical applications of CS in hardware platforms, especially in real-time applications, still faces challenging issues due to the high computational complexity of its algorithms, hence leading to high power-consuming processes. There are several CS reconstruction approaches, and orthogonal matching pursuit (OMP) is one of the best and popular algorithms implemented. However, this algorithm faces two (2) major process issues: optimisation and the least square problem. Due to OMP’s significant contribution, this paper presents an overview of the design issues and challenges of OMP algorithm implementation for CS reconstruction. The field-programmable gate array (FPGA) as a viable hardware solution for OMP implementation is reviewed and discussed based on reconstruction time, signal size, number of measurements, sparsity and features.