Random Triggering-Based Sub-Nyquist Sampling System for Sparse Multiband Signal

We propose a novel random triggering-based modulated wideband compressive sampling (RT-MWCS) method to facilitate efficient realization of sub-Nyquist rate compressive sampling systems for sparse wideband signals. Under the assumption that the signal is repetitively (not necessarily periodically) tr...

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Bibliographic Details
Published in:IEEE transactions on instrumentation and measurement 2017-07, Vol.66 (7), p.1789-1797
Main Authors: Zhao, Yijiu, Hu, Yu Hen, Liu, Jingjing
Format: Article
Language:English
Subjects:
Broadband
Clocks
Compressive sampling (CS)
Computer architecture
Converters
Fourier transforms
Frequency measurement
Hardware
Modulation
random demodulation
random triggering
Sampling
Signal quality
Signal reconstruction
sparse multiband signal
Wideband
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Summary:We propose a novel random triggering-based modulated wideband compressive sampling (RT-MWCS) method to facilitate efficient realization of sub-Nyquist rate compressive sampling systems for sparse wideband signals. Under the assumption that the signal is repetitively (not necessarily periodically) triggered, RT-MWCS uses random modulation to obtain measurements of the signal at randomly chosen positions. It uses multiple measurement vector method to estimate the nonzero supports of the signal in the frequency domain. Then, the signal spectrum is solved using least square estimation. The distinct ability of estimating sparse multiband signal is facilitated with the use of level triggering and time-to-digital converter devices previously used in random equivalent sampling scheme. Compared to the existing compressive sampling (CS) techniques, such as modulated wideband converter (MWC), RT-MWCS is with simple system architecture and can be implemented with one channel at the cost of more sampling time. Experimental results indicate that, for sparse multiband signal with unknown spectral support, RT-MWCS requires a sampling rate much lower than Nyquist rate, while giving great quality of signal reconstruction.
ISSN:0018-9456
1557-9662
DOI:10.1109/TIM.2017.2665983