Compressive Shift Retrieval
Abstract
The classical shift retrieval problem considers two signals in vector form that are related by a shift. The problem is of great importance in many applications and is typically solved by maximizing the cross-correlation between the two signals. Inspired by compressive sensing, in this paper, we seek to estimate the shift directly from compressed signals. We show that under certain conditions, the shift can be recovered using fewer samples and less computation compared to the classical setup. Of particular interest is shift estimation from Fourier coefficients. We show that under rather mild conditions only one Fourier coefficient suffices to recover the true shift.
Keywords
Cite
@article{arxiv.1303.4996,
title = {Compressive Shift Retrieval},
author = {Henrik Ohlsson and Yonina C. Eldar and Allen Y. Yang and S. Shankar Sastry},
journal= {arXiv preprint arXiv:1303.4996},
year = {2015}
}
Comments
Submitted to IEEE Transactions on Signal Processing. Accepted to the 38th International Conference on Acoustics, Speech, and Signal Processing (ICASSP), Vancouver, Canada, May 2013