Morphological Component Analysis of Long-Duration Ringdown from Elastic Objects Imaged with the Sediment Volume Search Sonar

Hannah R. Kurdila; Geoff Goehle; Daniel C. Brown

doi:10.1109/OCEANS47191.2022.9977385

Morphological Component Analysis of Long-Duration Ringdown from Elastic Objects Imaged with the Sediment Volume Search Sonar

Hannah R. Kurdila
, Geoff Goehle
, Daniel C. Brown

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

Abstract

A common problem in signal processing is decomposing a signal comprised of several components into its constituent parts. This paper uses Morphological Component Analysis (MCA) to decompose experimentally collected Sediment Volume Search Sonar (SVSS) data into short-duration and longduration components. The SVSS is a synthetic aperture sonar (SAS) system designed for detection of ordnance at shallow water depths. In the implementation of MCA, Enveloped Sinusoid Parseval (ESP) frames are used to represent the signal components with sparse representations obtained via the Split Augmented Lagrangian Shrinkage Algorithm (SALSA). Ultimately, we are able to isolate late-time ringing of metallic objects both on top of and buried beneath sediment and generate sonar imagery using the two separated components to demonstrate the isolation.

Original language	English (US)
Title of host publication	OCEANS 2022 Hampton Roads
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9781665468091
DOIs	https://doi.org/10.1109/OCEANS47191.2022.9977385
State	Published - 2022
Event	2022 OCEANS Hampton Roads, OCEANS 2022 - Hampton Roads, United States Duration: Oct 17 2022 → Oct 20 2022

Publication series

Name	Oceans Conference Record (IEEE)
Volume	2022-October
ISSN (Print)	0197-7385

Conference

Conference	2022 OCEANS Hampton Roads, OCEANS 2022
Country/Territory	United States
City	Hampton Roads
Period	10/17/22 → 10/20/22

All Science Journal Classification (ASJC) codes

Oceanography
Ocean Engineering

Access to Document

10.1109/OCEANS47191.2022.9977385

Cite this

@inproceedings{69402e7c818d4584943388e2eab793df,

title = "Morphological Component Analysis of Long-Duration Ringdown from Elastic Objects Imaged with the Sediment Volume Search Sonar",

abstract = "A common problem in signal processing is decomposing a signal comprised of several components into its constituent parts. This paper uses Morphological Component Analysis (MCA) to decompose experimentally collected Sediment Volume Search Sonar (SVSS) data into short-duration and longduration components. The SVSS is a synthetic aperture sonar (SAS) system designed for detection of ordnance at shallow water depths. In the implementation of MCA, Enveloped Sinusoid Parseval (ESP) frames are used to represent the signal components with sparse representations obtained via the Split Augmented Lagrangian Shrinkage Algorithm (SALSA). Ultimately, we are able to isolate late-time ringing of metallic objects both on top of and buried beneath sediment and generate sonar imagery using the two separated components to demonstrate the isolation.",

author = "Kurdila, \{Hannah R.\} and Geoff Goehle and Brown, \{Daniel C.\}",

note = "Publisher Copyright: {\textcopyright} 2022 IEEE.; 2022 OCEANS Hampton Roads, OCEANS 2022 ; Conference date: 17-10-2022 Through 20-10-2022",

year = "2022",

doi = "10.1109/OCEANS47191.2022.9977385",

language = "English (US)",

series = "Oceans Conference Record (IEEE)",

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

booktitle = "OCEANS 2022 Hampton Roads",

address = "United States",

}

Kurdila, HR, Goehle, G & Brown, DC 2022, Morphological Component Analysis of Long-Duration Ringdown from Elastic Objects Imaged with the Sediment Volume Search Sonar. in OCEANS 2022 Hampton Roads. Oceans Conference Record (IEEE), vol. 2022-October, Institute of Electrical and Electronics Engineers Inc., 2022 OCEANS Hampton Roads, OCEANS 2022, Hampton Roads, United States, 10/17/22. https://doi.org/10.1109/OCEANS47191.2022.9977385

Morphological Component Analysis of Long-Duration Ringdown from Elastic Objects Imaged with the Sediment Volume Search Sonar. / Kurdila, Hannah R.; Goehle, Geoff ; Brown, Daniel C.
OCEANS 2022 Hampton Roads. Institute of Electrical and Electronics Engineers Inc., 2022. (Oceans Conference Record (IEEE); Vol. 2022-October).

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

TY - GEN

T1 - Morphological Component Analysis of Long-Duration Ringdown from Elastic Objects Imaged with the Sediment Volume Search Sonar

AU - Kurdila, Hannah R.

AU - Goehle, Geoff

AU - Brown, Daniel C.

PY - 2022

Y1 - 2022

N2 - A common problem in signal processing is decomposing a signal comprised of several components into its constituent parts. This paper uses Morphological Component Analysis (MCA) to decompose experimentally collected Sediment Volume Search Sonar (SVSS) data into short-duration and longduration components. The SVSS is a synthetic aperture sonar (SAS) system designed for detection of ordnance at shallow water depths. In the implementation of MCA, Enveloped Sinusoid Parseval (ESP) frames are used to represent the signal components with sparse representations obtained via the Split Augmented Lagrangian Shrinkage Algorithm (SALSA). Ultimately, we are able to isolate late-time ringing of metallic objects both on top of and buried beneath sediment and generate sonar imagery using the two separated components to demonstrate the isolation.

AB - A common problem in signal processing is decomposing a signal comprised of several components into its constituent parts. This paper uses Morphological Component Analysis (MCA) to decompose experimentally collected Sediment Volume Search Sonar (SVSS) data into short-duration and longduration components. The SVSS is a synthetic aperture sonar (SAS) system designed for detection of ordnance at shallow water depths. In the implementation of MCA, Enveloped Sinusoid Parseval (ESP) frames are used to represent the signal components with sparse representations obtained via the Split Augmented Lagrangian Shrinkage Algorithm (SALSA). Ultimately, we are able to isolate late-time ringing of metallic objects both on top of and buried beneath sediment and generate sonar imagery using the two separated components to demonstrate the isolation.

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UR - https://www.scopus.com/pages/publications/85145773826#tab=citedBy

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DO - 10.1109/OCEANS47191.2022.9977385

M3 - Conference contribution

AN - SCOPUS:85145773826

T3 - Oceans Conference Record (IEEE)

BT - OCEANS 2022 Hampton Roads

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2022 OCEANS Hampton Roads, OCEANS 2022

Y2 - 17 October 2022 through 20 October 2022

ER -

Morphological Component Analysis of Long-Duration Ringdown from Elastic Objects Imaged with the Sediment Volume Search Sonar

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