Jazz: A companion to music for frequency estimation with missing data
Qiuwei Li, Shuang Li, Hassan Mansour, Michael B. Wakin, Dehui Yang, Zhihui Zhu
Abstract
Frequency estimation is a classical problem in signal processing, with applications ranging from sensor array processing to wireless communications and structural health monitoring. Modern algorithms based on atomic norm minimization can cope with missing data but incur a high computational cost. To recover missing data from an ensemble of frequency-sparse signals, we propose a computationally efficient low-rank tensor completion algorithm that exploits the fact that each signal in the ensemble can be associated with a Toeplitz matrix. We name our algorithm JAZZ in the spirit of the classical MUSIC algorithm for frequency estimation and in tribute to the random, improvisational nature of jazz music.
BibTeX
@inproceedings{icassp2017_jazzacompanionto,
title = {Jazz: A companion to music for frequency estimation with missing data},
author = {Qiuwei Li and Shuang Li and Hassan Mansour and Michael B. Wakin and Dehui Yang and Zhihui Zhu},
booktitle = {ICASSP 2017},
year = {2017}
}