Underwater Small Target Classification Using Sparse Multi-View Discriminant Analysis and the Invariant Scattering Transform

Sonar automatic target recognition (ATR) systems suffer from complex acoustic scattering, background clutter, and waveguide effects that are ever-present in the ocean. Traditional signal processing techniques often struggle to distinguish targets when noise and complicated target geometries are intr...

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Bibliographic Details
Published in:Journal of marine science and engineering 2024-10, Vol.12 (10), p.1886
Main Authors: Christensen, Andrew, Sen Gupta, Ananya, Kirsteins, Ivars
Format: Article
Language:English
Subjects:
Accuracy
Acoustic scattering
Acoustics
Algorithms
Analysis
Automatic target recognition
Background noise
Classification
Clutter
convolutional networks
Datasets
Deep learning
Discriminant analysis
Feature extraction
Invariants
Machine learning
multi-view
Neural networks
Noise reduction
Ocean bottom
Signal processing
small targets
Sonar
sparsity
Submarines
Support vector machines
Target recognition
Waveguides
Wavelet analysis
wavelets
Citations: Items that this one cites
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Summary:Sonar automatic target recognition (ATR) systems suffer from complex acoustic scattering, background clutter, and waveguide effects that are ever-present in the ocean. Traditional signal processing techniques often struggle to distinguish targets when noise and complicated target geometries are introduced. Recent advancements in machine learning and wavelet theory offer promising directions for extracting informative features from sonar return data. This work introduces a feature extraction and dimensionality reduction technique using the invariant scattering transform and Sparse Multi-view Discriminant Analysis for identifying highly informative features in the PONDEX09/PONDEX10 datasets. The extracted features are used to train a support vector machine classifier that achieves an average classification accuracy of 97.3% using six unique targets.
ISSN:2077-1312
2077-1312
DOI:10.3390/jmse12101886