Abstract

Compressive sensing can guarantee the recovery accuracy of suitably constrained signals by using sampling rates much lower than the Nyquist limit. This is a leap from signal sampling to information sampling. The measurement matrix is key to implementation but limited in the acquisition systems. This article presents the critical elements of the direct under-sampling—compressive sensing (DUS–CS) method, constructing the under-sampling measurement matrix, combined with a priori information sparse representation and reconstruction, and we show how it can be physically implemented using dedicated hardware. To go beyond the Nyquist constraints, we show how to design and adjust the sampling time of the A/D circuit and how to achieve low-speed random non-uniform direct under-sampling. We applied our method to data measured with different compression ratios (volume ratios of collected data to original data). It is shown that DUS-CS works well when the SNR is 3 dB, 0 dB, −3 dB, and −5 dB and the compression ratio is 50%, 20%, and 10%, and this is validated with both simulation and actual measurements. The method we propose provides an effective way for compressed sensing theory to move toward practical field applications that use underwater echo signals.
Original languageEnglish
Article number4596
Pages (from-to)1-14
Number of pages14
JournalApplied Sciences
Volume9
Issue number21
DOIs
Publication statusPublished - 29 Oct 2019

Keywords

  • underwater acoustics
  • compressive sensing
  • instrumentation
  • sonar
  • Under-sampling
  • Compressive sensing
  • Echo signals
  • Measurement matrix

ASJC Scopus subject areas

  • Ocean Engineering
  • Acoustics and Ultrasonics
  • Engineering(all)
  • Instrumentation
  • Materials Science(all)
  • Fluid Flow and Transfer Processes
  • Process Chemistry and Technology
  • Computer Science Applications

Cite this

Direct Under-Sampling Compressive Sensing Method for Underwater Echo Signals and Physical Implementation. / Sun, Tongjing; Li, Ji; Blondel, Philippe.

In: Applied Sciences, Vol. 9, No. 21, 4596, 29.10.2019, p. 1-14.

Research output: Contribution to journalArticle

@article{9fb4e21171534ba88c0fba1406426a34,
title = "Direct Under-Sampling Compressive Sensing Method for Underwater Echo Signals and Physical Implementation",
abstract = "Compressive sensing can guarantee the recovery accuracy of suitably constrained signals by using sampling rates much lower than the Nyquist limit. This is a leap from signal sampling to information sampling. The measurement matrix is key to implementation but limited in the acquisition systems. This article presents the critical elements of the direct under-sampling—compressive sensing (DUS–CS) method, constructing the under-sampling measurement matrix, combined with a priori information sparse representation and reconstruction, and we show how it can be physically implemented using dedicated hardware. To go beyond the Nyquist constraints, we show how to design and adjust the sampling time of the A/D circuit and how to achieve low-speed random non-uniform direct under-sampling. We applied our method to data measured with different compression ratios (volume ratios of collected data to original data). It is shown that DUS-CS works well when the SNR is 3 dB, 0 dB, −3 dB, and −5 dB and the compression ratio is 50{\%}, 20{\%}, and 10{\%}, and this is validated with both simulation and actual measurements. The method we propose provides an effective way for compressed sensing theory to move toward practical field applications that use underwater echo signals.",
keywords = "underwater acoustics, compressive sensing, instrumentation, sonar, Under-sampling, Compressive sensing, Echo signals, Measurement matrix",
author = "Tongjing Sun and Ji Li and Philippe Blondel",
note = "Article prepared by lead author during her stay with me at Bath with grant from Chinese Scholarship Council",
year = "2019",
month = "10",
day = "29",
doi = "10.3390/app9214596",
language = "English",
volume = "9",
pages = "1--14",
journal = "Applied Sciences",
issn = "2076-3417",
publisher = "MDPI",
number = "21",

}

TY - JOUR

T1 - Direct Under-Sampling Compressive Sensing Method for Underwater Echo Signals and Physical Implementation

AU - Sun, Tongjing

AU - Li, Ji

AU - Blondel, Philippe

N1 - Article prepared by lead author during her stay with me at Bath with grant from Chinese Scholarship Council

PY - 2019/10/29

Y1 - 2019/10/29

N2 - Compressive sensing can guarantee the recovery accuracy of suitably constrained signals by using sampling rates much lower than the Nyquist limit. This is a leap from signal sampling to information sampling. The measurement matrix is key to implementation but limited in the acquisition systems. This article presents the critical elements of the direct under-sampling—compressive sensing (DUS–CS) method, constructing the under-sampling measurement matrix, combined with a priori information sparse representation and reconstruction, and we show how it can be physically implemented using dedicated hardware. To go beyond the Nyquist constraints, we show how to design and adjust the sampling time of the A/D circuit and how to achieve low-speed random non-uniform direct under-sampling. We applied our method to data measured with different compression ratios (volume ratios of collected data to original data). It is shown that DUS-CS works well when the SNR is 3 dB, 0 dB, −3 dB, and −5 dB and the compression ratio is 50%, 20%, and 10%, and this is validated with both simulation and actual measurements. The method we propose provides an effective way for compressed sensing theory to move toward practical field applications that use underwater echo signals.

AB - Compressive sensing can guarantee the recovery accuracy of suitably constrained signals by using sampling rates much lower than the Nyquist limit. This is a leap from signal sampling to information sampling. The measurement matrix is key to implementation but limited in the acquisition systems. This article presents the critical elements of the direct under-sampling—compressive sensing (DUS–CS) method, constructing the under-sampling measurement matrix, combined with a priori information sparse representation and reconstruction, and we show how it can be physically implemented using dedicated hardware. To go beyond the Nyquist constraints, we show how to design and adjust the sampling time of the A/D circuit and how to achieve low-speed random non-uniform direct under-sampling. We applied our method to data measured with different compression ratios (volume ratios of collected data to original data). It is shown that DUS-CS works well when the SNR is 3 dB, 0 dB, −3 dB, and −5 dB and the compression ratio is 50%, 20%, and 10%, and this is validated with both simulation and actual measurements. The method we propose provides an effective way for compressed sensing theory to move toward practical field applications that use underwater echo signals.

KW - underwater acoustics

KW - compressive sensing

KW - instrumentation

KW - sonar

KW - Under-sampling

KW - Compressive sensing

KW - Echo signals

KW - Measurement matrix

UR - http://www.scopus.com/inward/record.url?scp=85075246037&partnerID=8YFLogxK

U2 - 10.3390/app9214596

DO - 10.3390/app9214596

M3 - Article

VL - 9

SP - 1

EP - 14

JO - Applied Sciences

JF - Applied Sciences

SN - 2076-3417

IS - 21

M1 - 4596

ER -