Statistical Shipping Noise Characterization and Mitigation for Underwater Acoustic Communications

Lazar Atanackovic, Ruoyu Zhang, Lutz Lampe, Roee DIamant

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Achieving high data rate robust communication in shallow and harbour underwater acoustic (UA) environments can be a demanding challenge in the presence of shipping noise. Noise generated from nearby passing ships can lead to impulsive agitations which impair UA communication systems. Utilizing the assumption that impulse noise exhibits sparsity, we realize a compressed sensing (CS) based framework for noise estimation exploiting the pilot sub-carriers of UA orthogonal frequency-division modulation systems. Under the CS framework, we propose the use of a empirical Bayesian approach which first characterizes the statistical properties of shipping noise prior to conceiving an estimate. In addition, we invoke the K-SVD algorithm for dictionary learning. K-SVD iteratively forms a sparse representation for the class of shipping noise signals, which is later used for noise estimation. Numerical results show that the empirical Bayesian based signal recovery algorithm yields the best performance for interference estimation.

Original languageAmerican English
Title of host publicationOCEANS 2019 - Marseille, OCEANS Marseille 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728114507
DOIs
StatePublished - Jun 2019
Event2019 OCEANS - Marseille, OCEANS Marseille 2019 - Marseille, France
Duration: 17 Jun 201920 Jun 2019

Publication series

NameOCEANS 2019 - Marseille, OCEANS Marseille 2019
Volume2019-June

Conference

Conference2019 OCEANS - Marseille, OCEANS Marseille 2019
Country/TerritoryFrance
CityMarseille
Period17/06/1920/06/19

Keywords

  • Underwater acoustic (UA) communications
  • compressed sensing (CS)
  • dictionary learning (DL)
  • impulse noise
  • shipping noise

All Science Journal Classification (ASJC) codes

  • Oceanography
  • Automotive Engineering
  • Management, Monitoring, Policy and Law
  • Water Science and Technology
  • Instrumentation

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