Abstract
Comparison of manifold representations of sonar data
The Journal of the Acoustical Society of America, Vol.153(3_supplement), pp.A178-A178
03/01/2023
DOI: 10.1121/10.0018580
Abstract
There are many challenges in active sonar target recognition due to the dynamic nature of the environment, unknown target geometries, and various clutter present within the ocean. These factors combine and entangle the target features within the received response. This research assumes the informative and discriminatory acoustic target features lie on a low dimensional manifold and can be extracted using statistical and machine learning techniques. Linear techniques, such as principal component analysis and linear discriminant analysis, are useful for dimension reduction but will typically not accurately capture the underlying non-linearity within a dataset. Non-linear manifold learning techniques, such as T-distributed stochastic neighbor embedding and uniform manifold approximation and projection, are relatively recent techniques that create low-dimensional embeddings in an unsupervised fashion and can capture the non-linearity. Previously, persistent braid features have been reported in real sonar data1. Our objective here is to do comparison between these different manifold representations for both simulated data with a known ground truth and experimental active sonar data. [1] A. Sen Gupta, B. Kubicek, A. Christensen, and I. Kirsteins, “Geometric feature representation in active sonar signal processing,” OCEANS 2022—Chennai, 2022, pp. 1–5. [Work supported by NDSEG 2021 and ONR Grant No. N00014-21-1-2420].
Details
- Title: Subtitle
- Comparison of manifold representations of sonar data
- Creators
- Bernice Kubicek - University of IowaAnanya Sen GuptaIvars Kirsteins - Naval Undersea Warfare Center
- Resource Type
- Abstract
- Publication Details
- The Journal of the Acoustical Society of America, Vol.153(3_supplement), pp.A178-A178
- DOI
- 10.1121/10.0018580
- ISSN
- 0001-4966
- eISSN
- 1520-8524
- Language
- English
- Date published
- 03/01/2023
- Academic Unit
- Electrical and Computer Engineering
- Record Identifier
- 9984410800602771
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