Fast alternating projected gradient descent algorithms for recovering spectrally sparse signals

Myung Cho; Jian-Feng Cai; Suhui Liu; Yonina C. Eldar; Weiyu Xu

ICASSP 2016accepted0 citations

Fast alternating projected gradient descent algorithms for recovering spectrally sparse signals

Myung Cho, Jian-Feng Cai, Suhui Liu, Yonina C. Eldar, Weiyu Xu

Abstract

We propose fast algorithms that speed up or improve the performance of recovering spectrally sparse signals from un-derdetermined measurements. Our algorithms are based on a non-convex approach of using alternating projected gradient descent for structured matrix recovery. We apply this approach to two formulations of structured matrix recovery: Hankel and Toeplitz mosaic structured matrix, and Hankel structured matrix. Our methods provide better recovery performance, and faster signal recovery than existing algorithms, including atomic norm minimization.

BibTeX

@inproceedings{icassp2016_fastalternatingp,
  title = {Fast alternating projected gradient descent algorithms for recovering spectrally sparse signals},
  author = {Myung Cho and Jian-Feng Cai and Suhui Liu and Yonina C. Eldar and Weiyu Xu},
  booktitle = {ICASSP 2016},
  year = {2016}
}