Uncertain Inference Using Ordinal Classification in Deep Networks for Acoustic Localization
Document Type
Conference Proceeding
Publication Date
9-20-2021
Department
Department of Electrical and Computer Engineering; Department of Computer Science
Abstract
Highly-reverberate underwater environments pose challenges for conventional localization techniques due to the highly non-linear nature of reflective surfaces, multi-path, and scattering fields. In this paper, we compare different machine learning methods for passive localization and tracking of single, non-stationary, underwater acoustic sources using multiple underwater acoustic vector sensors. We incorporate ordinal classification for localization in a novel approach to acoustic localization and compare the results with other standard methods. Realworld experiments demonstrate that both categorical and ordinal classification using deep LSTM networks significantly reduce localization error.
Publication Title
Proceedings of the International Joint Conference on Neural Networks
ISBN
9780738133669
Recommended Citation
Whitaker, S.,
Dekraker, Z.,
Barnard, A.,
Havens, T. C.,
&
Anderson, G.
(2021).
Uncertain Inference Using Ordinal Classification in Deep Networks for Acoustic Localization.
Proceedings of the International Joint Conference on Neural Networks,
2021-July.
http://doi.org/10.1109/IJCNN52387.2021.9533605
Retrieved from: https://digitalcommons.mtu.edu/michigantech-p/15426