Journal article
Convolutional Autoencoding of Small Targets in the Littoral Sonar Acoustic Backscattering Domain
Journal of marine science and engineering, Vol.11(1), p.21
12/23/2022
DOI: 10.3390/jmse11010021
Abstract
Automated target recognition is an important task in the littoral warfare domain, as distinguishing mundane objects from mines can be a matter of life and death. This is initial work towards the application of convolutional autoencoding to the littoral sonar space, with goals of disentangling the reflection noise prevalent in underwater acoustics and allowing recognition of the shape and material of targets. The autoencoders were trained on magnitude Fourier transforms of the TREX13 dataset. Clusters in the encoding space representing the known variable of measurement distance between the target and the sensor were found. An encoding vector space of around 16 dimensions appeared sufficient, and the space was shown to generalize well to unseen data.
Details
- Title: Subtitle
- Convolutional Autoencoding of Small Targets in the Littoral Sonar Acoustic Backscattering Domain
- Creators
- Timothy J. LinhardtAnanya Sen GuptaMatthew Bays
- Resource Type
- Journal article
- Publication Details
- Journal of marine science and engineering, Vol.11(1), p.21
- DOI
- 10.3390/jmse11010021
- ISSN
- 2077-1312
- eISSN
- 2077-1312
- Grant note
- DOI: 10.13039/100000006, name: Office of Naval Research, award: N00174-20-1-0016
- Language
- English
- Date published
- 12/23/2022
- Academic Unit
- Electrical and Computer Engineering
- Record Identifier
- 9984354958402771
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