One-Bit Compressed Sensing by Greedy Algorithms

TL;DR

This paper introduces STrMP, a simple and efficient greedy algorithm for recovering sparse signals from one-bit measurements, combining consistent reconstruction with OMP principles.

Contribution

The paper proposes STrMP, a novel greedy algorithm that simplifies implementation and improves efficiency for one-bit compressed sensing recovery.

Findings

STrMP is faster and more accurate than existing algorithms.

It requires solving only convex, unconstrained subproblems per iteration.

Numerical experiments validate its effectiveness.

Abstract

Sign truncated matching pursuit (STrMP) algorithm is presented in this paper. STrMP is a new greedy algorithm for the recovery of sparse signals from the sign measurement, which combines the principle of consistent reconstruction with orthogonal matching pursuit (OMP). The main part of STrMP is as concise as OMP and hence STrMP is simple to implement. In contrast to previous greedy algorithms for one-bit compressed sensing, STrMP only need to solve a convex and unconstraint subproblem at each iteration. Numerical experiments show that STrMP is fast and accurate for one-bit compressed sensing compared with other algorithms.