Statistical Shipping Noise Characterization and Mitigation for Underwater Acoustic Communications

Lazar Atanackovic; Ruoyu Zhang; Lutz Lampe; Roee DIamant

doi:10.1109/OCEANSE.2019.8867520

Statistical Shipping Noise Characterization and Mitigation for Underwater Acoustic Communications

Lazar Atanackovic, Ruoyu Zhang, Lutz Lampe, Roee DIamant

Department of Marine Technologies

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-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 language	English
Title of host publication	OCEANS 2019 - Marseille, OCEANS Marseille 2019
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9781728114507
DOIs	https://doi.org/10.1109/OCEANSE.2019.8867520
State	Published - Jun 2019
Event	2019 OCEANS - Marseille, OCEANS Marseille 2019 - Marseille, France Duration: 17 Jun 2019 → 20 Jun 2019

Publication series

Name	OCEANS 2019 - Marseille, OCEANS Marseille 2019
Volume	2019-June

Conference

Conference	2019 OCEANS - Marseille, OCEANS Marseille 2019
Country/Territory	France
City	Marseille
Period	17/06/19 → 20/06/19

Bibliographical note

Funding Information:
This work is supported by the NATO Science for Peace and Security Programme.

Publisher Copyright:
© 2019 IEEE.

Keywords

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

ASJC Scopus subject areas

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

Access to Document

10.1109/OCEANSE.2019.8867520

Cite this

Atanackovic, L., Zhang, R., Lampe, L., & DIamant, R. (2019). Statistical Shipping Noise Characterization and Mitigation for Underwater Acoustic Communications. In OCEANS 2019 - Marseille, OCEANS Marseille 2019 [8867520] (OCEANS 2019 - Marseille, OCEANS Marseille 2019; Vol. 2019-June). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/OCEANSE.2019.8867520

@inproceedings{6ee2a8f4a88f4627ad0262cbf92994e9,

title = "Statistical Shipping Noise Characterization and Mitigation for Underwater Acoustic Communications",

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.",

keywords = "Underwater acoustic (UA) communications, compressed sensing (CS), dictionary learning (DL), impulse noise, shipping noise",

author = "Lazar Atanackovic and Ruoyu Zhang and Lutz Lampe and Roee DIamant",

note = "Funding Information: This work is supported by the NATO Science for Peace and Security Programme. Publisher Copyright: {\textcopyright} 2019 IEEE.; 2019 OCEANS - Marseille, OCEANS Marseille 2019 ; Conference date: 17-06-2019 Through 20-06-2019",

year = "2019",

month = jun,

doi = "10.1109/OCEANSE.2019.8867520",

language = "English",

series = "OCEANS 2019 - Marseille, OCEANS Marseille 2019",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

booktitle = "OCEANS 2019 - Marseille, OCEANS Marseille 2019",

address = "United States",

}

Atanackovic, L, Zhang, R, Lampe, L & DIamant, R 2019, Statistical Shipping Noise Characterization and Mitigation for Underwater Acoustic Communications. in OCEANS 2019 - Marseille, OCEANS Marseille 2019., 8867520, OCEANS 2019 - Marseille, OCEANS Marseille 2019, vol. 2019-June, Institute of Electrical and Electronics Engineers Inc., 2019 OCEANS - Marseille, OCEANS Marseille 2019, Marseille, France, 17/06/19. https://doi.org/10.1109/OCEANSE.2019.8867520

Statistical Shipping Noise Characterization and Mitigation for Underwater Acoustic Communications. / Atanackovic, Lazar; Zhang, Ruoyu; Lampe, Lutz et al.

OCEANS 2019 - Marseille, OCEANS Marseille 2019. Institute of Electrical and Electronics Engineers Inc., 2019. 8867520 (OCEANS 2019 - Marseille, OCEANS Marseille 2019; Vol. 2019-June).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Statistical Shipping Noise Characterization and Mitigation for Underwater Acoustic Communications

AU - Atanackovic, Lazar

AU - Zhang, Ruoyu

AU - Lampe, Lutz

AU - DIamant, Roee

PY - 2019/6

Y1 - 2019/6

N2 - 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.

AB - 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.

KW - Underwater acoustic (UA) communications

KW - compressed sensing (CS)

KW - dictionary learning (DL)

KW - impulse noise

KW - shipping noise

UR - http://www.scopus.com/inward/record.url?scp=85087551917&partnerID=8YFLogxK

U2 - 10.1109/OCEANSE.2019.8867520

DO - 10.1109/OCEANSE.2019.8867520

M3 - Conference contribution

AN - SCOPUS:85087551917

T3 - OCEANS 2019 - Marseille, OCEANS Marseille 2019

BT - OCEANS 2019 - Marseille, OCEANS Marseille 2019

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2019 OCEANS - Marseille, OCEANS Marseille 2019

Y2 - 17 June 2019 through 20 June 2019

ER -

Statistical Shipping Noise Characterization and Mitigation for Underwater Acoustic Communications

Abstract

Publication series

Conference

Bibliographical note

Keywords

ASJC Scopus subject areas

Access to Document

Other files and links

Fingerprint

Cite this