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

• Published in: Journal of marine science and engineering Vol. 12; no. 10; p. 1886
• Main Authors: Christensen, Andrew, Sen Gupta, Ananya, Kirsteins, Ivars
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.10.2024
• 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
• ISSN: 2077-1312; 2077-1312
• DOI: 10.3390/jmse12101886

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.