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Line Spectral Estimation Based on Compressed Sensing with Deterministic Sub-Nyquist Sampling

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Abstract

As an alternative to the traditional sampling theory, compressed sensing allows acquiring much smaller amount of data, still estimating the spectra of frequency-sparse signals accurately. However, the previous methods focus more on single measurement vector and default to using impractical random sampling. In this paper, we employ a deterministic and simple sampling scheme, that is, using three sub-Nyquist channels with pairwise coprime undersampling ratios. A multi-task model is utilized to unite the three-channel samples and address the multiple measurement vector problem. The model is solved by the proposed complex-valued multi-task algorithm based on variational Bayesian inference. Simulations show that this method is feasible and robust at quite low sampling rates.

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Authors and Affiliations

  1. School of Electronic Information, Wuhan University, Wuhan, China

    Shan Huang, Hong Sun, Haijian Zhang & Lei Yu

Authors
  1. Shan Huang
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  2. Hong Sun
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  3. Haijian Zhang
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  4. Lei Yu
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Corresponding author

Correspondence to Haijian Zhang.

Additional information

This work was supported by the National Natural Science Foundation of China with Grant Number 61501335.

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Huang, S., Sun, H., Zhang, H. et al. Line Spectral Estimation Based on Compressed Sensing with Deterministic Sub-Nyquist Sampling. Circuits Syst Signal Process 37, 1777–1788 (2018). https://doi.org/10.1007/s00034-017-0633-3

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  • DOI: https://doi.org/10.1007/s00034-017-0633-3

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