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 proceedingConference 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 languageEnglish (US)
Title of host publicationOCEANS 2022 Hampton Roads
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781665468091
DOIs
StatePublished - 2022
Event2022 OCEANS Hampton Roads, OCEANS 2022 - Hampton Roads, United States
Duration: Oct 17 2022Oct 20 2022

Publication series

NameOceans Conference Record (IEEE)
Volume2022-October
ISSN (Print)0197-7385

Conference

Conference2022 OCEANS Hampton Roads, OCEANS 2022
Country/TerritoryUnited States
CityHampton Roads
Period10/17/2210/20/22

All Science Journal Classification (ASJC) codes

  • Oceanography
  • Ocean Engineering

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