Conference proceeding
Sonar Target Response Feature Extraction Using Neighbourhood Component Analysis
OCEANS 2023 - MTS/IEEE U.S. Gulf Coast, pp.1-4
09/25/2023
DOI: 10.23919/OCEANS52994.2023.10337244
Abstract
In the field of autonomous sonar target recognition, researchers have long grappled with the challenge of creating reliable representations of sonar target features. The features crucial for distinguishing sonar targets undergo nonlinear changes over time due to unknown, complicated target scattering physics. It becomes essential for any feature extraction algorithm to mitigate these effects while still understanding why features were chosen for improving discrimination. This work investigates using the supervised dimensionality technique, Neighborhood Component Analysis (NCA), for feature extraction. More specifically, we introduce a novel algorithm that uses a cascade of NCA blocks to extract features from sonar target responses with multiple beams. The effectiveness of feature extraction is evaluated with the popular classifiers k -nearest neighbors k NN) and support vector machines (SVM). A subset of the labeled examples from the Malta Plateau database were used to evaluate the proposed algorithm.
Details
- Title: Subtitle
- Sonar Target Response Feature Extraction Using Neighbourhood Component Analysis
- Creators
- Andrew Christensen - University of IowaAnanya Sen Gupta - University of Iowa,Electrical Computer Engineering,Iowa City,IowaIvars Kirsteins - Naval Undersea Warfare Center
- Resource Type
- Conference proceeding
- Publication Details
- OCEANS 2023 - MTS/IEEE U.S. Gulf Coast, pp.1-4
- Publisher
- The Marine Technology Society (MTS)
- DOI
- 10.23919/OCEANS52994.2023.10337244
- Grant note
- University of Iowa (10.13039/100008893) N00014-21-1-2420 / Office of Naval Research (10.13039/100000006)
- Language
- English
- Date published
- 09/25/2023
- Academic Unit
- Electrical and Computer Engineering
- Record Identifier
- 9984533280002771
Metrics
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