Uncertain Inference Using Ordinal Classification in Deep Networks for Acoustic Localization

Steven Whitaker; Zach Dekraker; Andrew Barnard; Timothy C. Havens; George D. Anderson

doi:10.1109/IJCNN52387.2021.9533605

Uncertain Inference Using Ordinal Classification in Deep Networks for Acoustic Localization

Steven Whitaker, Zach Dekraker, Andrew Barnard, Timothy C. Havens, George D. Anderson

Graduate Program in Acoustics

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

4 Scopus citations

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.

Original language	English (US)
Title of host publication	IJCNN 2021 - International Joint Conference on Neural Networks, Proceedings
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9780738133669
DOIs	https://doi.org/10.1109/IJCNN52387.2021.9533605
State	Published - Jul 18 2021
Event	2021 International Joint Conference on Neural Networks, IJCNN 2021 - Virtual, Shenzhen, China Duration: Jul 18 2021 → Jul 22 2021

Publication series

Name	Proceedings of the International Joint Conference on Neural Networks
Volume	2021-July

Conference

Conference	2021 International Joint Conference on Neural Networks, IJCNN 2021
Country/Territory	China
City	Virtual, Shenzhen
Period	7/18/21 → 7/22/21

All Science Journal Classification (ASJC) codes

Software
Artificial Intelligence

Access to Document

10.1109/IJCNN52387.2021.9533605

Cite this

Whitaker, S., Dekraker, Z., Barnard, A., Havens, T. C., & Anderson, G. D. (2021). Uncertain Inference Using Ordinal Classification in Deep Networks for Acoustic Localization. In IJCNN 2021 - International Joint Conference on Neural Networks, Proceedings (Proceedings of the International Joint Conference on Neural Networks; Vol. 2021-July). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/IJCNN52387.2021.9533605

@inproceedings{6d30ab2122114920bcce7abcc36c9205,

title = "Uncertain Inference Using Ordinal Classification in Deep Networks for Acoustic Localization",

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.",

author = "Steven Whitaker and Zach Dekraker and Andrew Barnard and Havens, {Timothy C.} and Anderson, {George D.}",

note = "Funding Information: This work was funded by the U.S. Naval Undersea Warfare Center and Naval Engineering Education Consortium (NEEC), grant #N00174-19-1-0004. Publisher Copyright: {\textcopyright} 2021 IEEE.; 2021 International Joint Conference on Neural Networks, IJCNN 2021 ; Conference date: 18-07-2021 Through 22-07-2021",

year = "2021",

month = jul,

day = "18",

doi = "10.1109/IJCNN52387.2021.9533605",

language = "English (US)",

series = "Proceedings of the International Joint Conference on Neural Networks",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

booktitle = "IJCNN 2021 - International Joint Conference on Neural Networks, Proceedings",

address = "United States",

}

Whitaker, S, Dekraker, Z, Barnard, A, Havens, TC & Anderson, GD 2021, Uncertain Inference Using Ordinal Classification in Deep Networks for Acoustic Localization. in IJCNN 2021 - International Joint Conference on Neural Networks, Proceedings. Proceedings of the International Joint Conference on Neural Networks, vol. 2021-July, Institute of Electrical and Electronics Engineers Inc., 2021 International Joint Conference on Neural Networks, IJCNN 2021, Virtual, Shenzhen, China, 7/18/21. https://doi.org/10.1109/IJCNN52387.2021.9533605

Uncertain Inference Using Ordinal Classification in Deep Networks for Acoustic Localization. / Whitaker, Steven; Dekraker, Zach; Barnard, Andrew et al.
IJCNN 2021 - International Joint Conference on Neural Networks, Proceedings. Institute of Electrical and Electronics Engineers Inc., 2021. (Proceedings of the International Joint Conference on Neural Networks; Vol. 2021-July).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

TY - GEN

T1 - Uncertain Inference Using Ordinal Classification in Deep Networks for Acoustic Localization

AU - Whitaker, Steven

AU - Dekraker, Zach

AU - Barnard, Andrew

AU - Havens, Timothy C.

AU - Anderson, George D.

PY - 2021/7/18

Y1 - 2021/7/18

N2 - 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.

AB - 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.

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U2 - 10.1109/IJCNN52387.2021.9533605

DO - 10.1109/IJCNN52387.2021.9533605

M3 - Conference contribution

AN - SCOPUS:85116415887

T3 - Proceedings of the International Joint Conference on Neural Networks

BT - IJCNN 2021 - International Joint Conference on Neural Networks, Proceedings

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2021 International Joint Conference on Neural Networks, IJCNN 2021

Y2 - 18 July 2021 through 22 July 2021

ER -

Whitaker S, Dekraker Z, Barnard A, Havens TC, Anderson GD. Uncertain Inference Using Ordinal Classification in Deep Networks for Acoustic Localization. In IJCNN 2021 - International Joint Conference on Neural Networks, Proceedings. Institute of Electrical and Electronics Engineers Inc. 2021. (Proceedings of the International Joint Conference on Neural Networks). doi: 10.1109/IJCNN52387.2021.9533605

Uncertain Inference Using Ordinal Classification in Deep Networks for Acoustic Localization

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All Science Journal Classification (ASJC) codes

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