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arXiv · 1607.06226

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

Shan Huang·Hong Sun·Haijian Zhang·Lei Yu
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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, compressed sensing usually requires random sampling in data acquisition, which is difficult to implement in hardware. In this paper, we propose a deterministic and simple sampling scheme, that is, sampling at three sub-Nyquist rates which have coprime undersampled ratios. This sampling method turns out to be valid through numerical experiments. A complex-valued multitask algorithm based on variational Bayesian inference is proposed to estimate the spectra of frequency-sparse signals after sampling. Simulations show that this method is feasible and robust at quite low sampling rates.

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Shan Huang, Hong Sun, Haijian Zhang, Lei Yu. 2016-07-21. Line Spectral Estimation Based on Compressed Sensing with Deterministic Sub-Nyquist Sampling. https://arxiv.org/abs/1607.06226

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