Spectral Compressive Sensing with Polar Interpolation
Abstract
Existing approaches to compressive sensing of frequency-sparse signals focuses on signal recovery rather than spectral estimation. Furthermore, the recovery performance is limited by the coherence of the required sparsity dictionaries and by the discretization of the frequency parameter space. In this paper, we introduce a greedy recovery algorithm that leverages a band-exclusion function and a polar interpolation function to address these two issues in spectral compressive sensing. Our algorithm is geared towards line spectral estimation from compressive measurements and outperforms most existing approaches in fidelity and tolerance to noise.
Cite
@article{arxiv.1303.2799,
title = {Spectral Compressive Sensing with Polar Interpolation},
author = {Karsten Fyhn and Hamid Dadkhahi and Marco F. Duarte},
journal= {arXiv preprint arXiv:1303.2799},
year = {2013}
}
Comments
v1: 5 pages, 2 figures, accepted for publication at ICASSP 2013. v2,v3: This version corrects minor typos in Algorithm 1 from the published version