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Estimating the Frequency of a Clustered Signal

Authors Xue Chen, Eric Price

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LIPIcs.ICALP.2019.36.pdf
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Author Details

Xue Chen
  • Northwestern University, Evanston, IL, USA
Eric Price
  • The University of Texas at Austin, USA

Funding

  • Chen, Xue: Supported by research funding from Northwestern University. Part of this work was done while the author was in the University of Texas at Austin supported by NSF Grant CCF-1526952 and a Simons Investigator Award (#409864, David Zuckerman).
  • Price, Eric: Supported in part by NSF Award CCF-1751040 (CAREER).

Acknowledgements

We thank Daniel Kane and Zhao Song for many helpful discussions. We also thank the anonymous referee for the detailed feedback and comments.

Cite AsGet BibTex

Xue Chen and Eric Price. Estimating the Frequency of a Clustered Signal. In 46th International Colloquium on Automata, Languages, and Programming (ICALP 2019). Leibniz International Proceedings in Informatics (LIPIcs), Volume 132, pp. 36:1-36:13, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2019)
https://doi.org/10.4230/LIPIcs.ICALP.2019.36

Abstract

We consider the problem of locating a signal whose frequencies are "off grid" and clustered in a narrow band. Given noisy sample access to a function g(t) with Fourier spectrum in a narrow range [f_0 - Delta, f_0 + Delta], how accurately is it possible to identify f_0? We present generic conditions on g that allow for efficient, accurate estimates of the frequency. We then show bounds on these conditions for k-Fourier-sparse signals that imply recovery of f_0 to within Delta + O~(k^3) from samples on [-1, 1]. This improves upon the best previous bound of O(Delta + O~(k^5))^{1.5}. We also show that no algorithm can do better than Delta + O~(k^2). In the process we provide a new O~(k^3) bound on the ratio between the maximum and average value of continuous k-Fourier-sparse signals, which has independent application.

Subject Classification

ACM Subject Classification
  • Theory of computation → Design and analysis of algorithms
  • Theory of computation → Streaming, sublinear and near linear time algorithms
Keywords
  • sublinear algorithms
  • Fourier transform

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References

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