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
N1 - Funding Information:
This work is supported in part by the US Office of Naval Research under grants N00014-18-1-2820 and N00014-22-1-2620 and by the Strategic Environmental Research and Development Program (SERDP). It is based upon work supported by the Humphreys Engineer Center Support Activity under Contract Numbers W912HQ-22-C-0011. Kurdila is supported by the Applied Research Laboratory under an Eric Walker Graduate Fellowship.
Publisher Copyright:
© 2022 IEEE.
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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U2 - 10.1109/OCEANS47191.2022.9977385
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 -