ROBUSTIFYING SIGNAL SUBSPACE METHODS WITH GROUP SLOPE
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Authors
Oh, Micah Y.
Subjects
acoustic
underwater
signal
interference
slope
regression
noise
simulation
sparsity
underwater
signal
interference
slope
regression
noise
simulation
sparsity
Advisors
Bassett, Robert L.
Date of Issue
2022-12
Date
Publisher
Monterey, CA; Naval Postgraduate School
Language
Abstract
Isolating and identifying specific acoustic signals in an underwater acoustic environment is challenging due to the presence of noise and potentially interfering signals. We construct an optimization problem that includes a time-sparse interference term to account for the unique form of interfering signals and use this optimization problem to detect audio samples contaminated with interference. To enforce sparsity in our interference term, we use the Group Sorted L-One Penalized Estimation norm as a sparsity-inducing penalty. Applying this estimator to more than thirty cases of simulated and real-world acoustic data demonstrates its ability in more than 90% of those cases to detect interference in acoustic data. A standard runtime of about one second for a ten-second data sample allows our contributions to be used for interference detection in real time.
Type
Thesis
Description
Series/Report No
Department
Operations Research (OR)
Organization
Identifiers
NPS Report Number
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Citation
Distribution Statement
Approved for public release. Distribution is unlimited.
Rights
This publication is a work of the U.S. Government as defined in Title 17, United States Code, Section 101. Copyright protection is not available for this work in the United States.