A practical high-dimensional Sparse Fourier Transform

Shaogang Wang, Vishal M. Patel, Athina Petropulu

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

As compared to the FFT, the recently introduced Sparse Fourier Transform (SFT) achieves substantial reduction in the complexity of detecting frequencies in signals that are sparse in the frequency domain. However, the SFT requires the significant frequencies to be on the grid and the exact sparsity of the signal to be known. In this paper, we propose a framework that overcomes these issues. Our method makes use of a pre-permutation window to confine the leakage within finite frequency bins and the Neyman-Pearson criterion to detect weak signals without knowing the exact signal sparsity. Various numerical experiments and an application to radar target detection demonstrate the advantages of the proposed method.

Original languageEnglish (US)
Title of host publication2017 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2017 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages4341-4345
Number of pages5
ISBN (Electronic)9781509041176
DOIs
StatePublished - Jun 16 2017
Event2017 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2017 - New Orleans, United States
Duration: Mar 5 2017Mar 9 2017

Publication series

NameICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
ISSN (Print)1520-6149

Other

Other2017 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2017
CountryUnited States
CityNew Orleans
Period3/5/173/9/17

All Science Journal Classification (ASJC) codes

  • Software
  • Signal Processing
  • Electrical and Electronic Engineering

Keywords

  • Multi-dimensional signal processing
  • detection and estimation
  • sparse Fourier transform

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