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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| Published in | Journal of marine science and engineering Vol. 12; no. 10; p. 1886 |
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| Main Authors | , , |
| Format | Journal Article |
| Language | English |
| Published |
Basel
MDPI AG
01.10.2024
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| Subjects | |
| Online Access | Get full text |
| ISSN | 2077-1312 2077-1312 |
| DOI | 10.3390/jmse12101886 |
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| Abstract | 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. |
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| AbstractList | 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. |
| Audience | Academic |
| Author | Kirsteins, Ivars Sen Gupta, Ananya Christensen, Andrew |
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| SubjectTerms | 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 |
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| Title | Underwater Small Target Classification Using Sparse Multi-View Discriminant Analysis and the Invariant Scattering Transform |
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