Direct Under-Sampling Compressive Sensing Method for Underwater Echo Signals and Physical Implementation
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...
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doaj-a3b57f742d354bb49fa866fa3add0f742020-11-24T21:56:45ZengMDPI AGApplied Sciences2076-34172019-10-01921459610.3390/app9214596app9214596Direct Under-Sampling Compressive Sensing Method for Underwater Echo Signals and Physical ImplementationTongjing Sun0Ji Li1Philippe Blondel2Department of Automation, Hangzhou Dianzi University, Xiasha Higher Education Zone, Hangzhou 310018, ChinaDepartment of Automation, Hangzhou Dianzi University, Xiasha Higher Education Zone, Hangzhou 310018, ChinaDepartment of Physics, University of Bath, Claverton Down, Bath BA2 7AY, UKCompressive 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.https://www.mdpi.com/2076-3417/9/21/4596compressive sensingunder-samplingmeasurement matrixecho signals |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Tongjing Sun Ji Li Philippe Blondel |
spellingShingle |
Tongjing Sun Ji Li Philippe Blondel Direct Under-Sampling Compressive Sensing Method for Underwater Echo Signals and Physical Implementation Applied Sciences compressive sensing under-sampling measurement matrix echo signals |
author_facet |
Tongjing Sun Ji Li Philippe Blondel |
author_sort |
Tongjing Sun |
title |
Direct Under-Sampling Compressive Sensing Method for Underwater Echo Signals and Physical Implementation |
title_short |
Direct Under-Sampling Compressive Sensing Method for Underwater Echo Signals and Physical Implementation |
title_full |
Direct Under-Sampling Compressive Sensing Method for Underwater Echo Signals and Physical Implementation |
title_fullStr |
Direct Under-Sampling Compressive Sensing Method for Underwater Echo Signals and Physical Implementation |
title_full_unstemmed |
Direct Under-Sampling Compressive Sensing Method for Underwater Echo Signals and Physical Implementation |
title_sort |
direct under-sampling compressive sensing method for underwater echo signals and physical implementation |
publisher |
MDPI AG |
series |
Applied Sciences |
issn |
2076-3417 |
publishDate |
2019-10-01 |
description |
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. |
topic |
compressive sensing under-sampling measurement matrix echo signals |
url |
https://www.mdpi.com/2076-3417/9/21/4596 |
work_keys_str_mv |
AT tongjingsun directundersamplingcompressivesensingmethodforunderwaterechosignalsandphysicalimplementation AT jili directundersamplingcompressivesensingmethodforunderwaterechosignalsandphysicalimplementation AT philippeblondel directundersamplingcompressivesensingmethodforunderwaterechosignalsandphysicalimplementation |
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