Adaptive matching pursuit for off-grid compressed sensing

TL;DR

This paper introduces AMP-CTLS, an adaptive method for off-grid compressed sensing that iteratively updates the dictionary to accurately recover signals sparse in continuous parameter spaces.

Contribution

It proposes a novel adaptive matching pursuit algorithm with constrained total least squares to address grid mismatch in off-grid compressed sensing.

Findings

AMP-CTLS effectively reduces grid mismatch errors.

Numerical experiments show improved signal recovery accuracy.

The method converges reliably in various scenarios.

Abstract

Compressive sensing (CS) can effectively recover a signal when it is sparse in some discrete atoms. However, in some applications, signals are sparse in a continuous parameter space, e.g., frequency space, rather than discrete atoms. Usually, we divide the continuous parameter into finite discrete grid points and build a dictionary from these grid points. However, the actual targets may not exactly lie on the grid points no matter how densely the parameter is grided, which introduces mismatch between the predefined dictionary and the actual one. In this article, a novel method, namely adaptive matching pursuit with constrained total least squares (AMP-CTLS), is proposed to find actual atoms even if they are not included in the initial dictionary. In AMP-CTLS, the grid and the dictionary are adaptively updated to better agree with measurements. The convergence of the algorithm is discussed, and numerical experiments demonstrate the advantages of AMP-CTLS.