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Abstract

Reducing data volume and improving signal-to-noise ratio (SNR) is of great importance for echoes from submerged targets, affected by serious marine environment noise. The echo from a target is made of its response to the incident wave, with the superposition of highlights (sub-echoes from main constituents of the target). Each of these highlights can be seen as a block, and the echo therefore has a block-sparse feature. This paper proposes a Compressive Sensing method to leverage Prior Information (CSPI), in which knowledge of the incident wave and the block-sparse feature are leveraged into the dictionary structure and signal reconstruction. CSPI is illustrated with simulations and field measurements of backscattering for a 1:20 model of the Benchmark Target Strength Simulation Submarine (BeTSSi-Sub). For simulated signals with different noise levels, CSPI can reconstruct an almost invisible signal (original SNR = 0dB), and improve SNR by up to 13dB (for an original SNR of 4dB) down to a still significant SNR of 7dB (for an original SNR of 0dB). For field measurements, CSPI can obtain the same SNR as the original signal using only 13% of the data, increasing SNR to 15dB using 30% data, and increasing with the compression ratio.
Original languageEnglish
Pages (from-to)1406-1415
Number of pages11
JournalJournal of the Acoustical Society of America (JASA)
Volume144
Issue number3
Early online date14 Sep 2018
Publication statusE-pub ahead of print - 14 Sep 2018

Fingerprint

Signal to noise ratio
echoes
signal to noise ratios
Signal reconstruction
dictionaries
marine environments
Signal-to-noise Ratio
compression ratio
Backscattering
Glossaries
backscattering
simulation

Keywords

  • acoustics
  • compressive sensing
  • scaled experiments
  • simulations
  • backscattering
  • sonar

ASJC Scopus subject areas

  • Acoustics and Ultrasonics
  • Ocean Engineering

Cite this

Compressive sensing method to leverage prior information for submerged target echoes. / Sun, Tongjing; Blondel, Philippe; Jia, Bing; Gao, Enwei.

In: Journal of the Acoustical Society of America (JASA), Vol. 144, No. 3, 14.09.2018, p. 1406-1415.

Research output: Contribution to journalArticle

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AB - Reducing data volume and improving signal-to-noise ratio (SNR) is of great importance for echoes from submerged targets, affected by serious marine environment noise. The echo from a target is made of its response to the incident wave, with the superposition of highlights (sub-echoes from main constituents of the target). Each of these highlights can be seen as a block, and the echo therefore has a block-sparse feature. This paper proposes a Compressive Sensing method to leverage Prior Information (CSPI), in which knowledge of the incident wave and the block-sparse feature are leveraged into the dictionary structure and signal reconstruction. CSPI is illustrated with simulations and field measurements of backscattering for a 1:20 model of the Benchmark Target Strength Simulation Submarine (BeTSSi-Sub). For simulated signals with different noise levels, CSPI can reconstruct an almost invisible signal (original SNR = 0dB), and improve SNR by up to 13dB (for an original SNR of 4dB) down to a still significant SNR of 7dB (for an original SNR of 0dB). For field measurements, CSPI can obtain the same SNR as the original signal using only 13% of the data, increasing SNR to 15dB using 30% data, and increasing with the compression ratio.

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