Automated vehicle detection in a nuclear facility using low-frequency acoustic sensors

Jason Hite; Kenneth Dayman; Nageswara Rao; Christopher Greulich; Satyabrata Sen; David Chichester; Andrew Nicholson; Dan Archer; Michael Willis; Irakli Garishvili; Andrew Rowe; James Ghawaly; Jared Johnson

doi:10.23919/FUSION45008.2020.9190452

Automated vehicle detection in a nuclear facility using low-frequency acoustic sensors

Jason Hite, Kenneth Dayman, Nageswara Rao, Christopher Greulich, Satyabrata Sen, David Chichester, Andrew Nicholson, Dan Archer, Michael Willis, Irakli Garishvili, Andrew Rowe, James Ghawaly, Jared Johnson

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

2 Scopus citations

Abstract

This article presents an analysis of the method of construction and results for a classifier intended to identify vehicles using low-frequency acoustic data collected by a distributed sensor network. This data is collected as part of a venture intended to explore data analytics and multisensor fusion techniques for the monitoring of activities at a test bed nuclear facility located at Oak Ridge National Laboratory in Oak Ridge, Tennessee. We describe the associated target signature and design a classifier based on a multilayer perceptron, followed by an analysis of its results. We discuss how overall accuracy is not the only consideration in constructing this classifier, and how for this application, it is actually desirable to operate at a lower level of accuracy in exchange for a reduction in the false alarm rate, as well as how this relates to the actual deployment of the classifier in practical use.

Original language	English
Title of host publication	Proceedings of 2020 23rd International Conference on Information Fusion, FUSION 2020
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9780578647098
DOIs	https://doi.org/10.23919/FUSION45008.2020.9190452
State	Published - Jul 2020
Event	23rd International Conference on Information Fusion, FUSION 2020 - Virtual, Pretoria, South Africa Duration: Jul 6 2020 → Jul 9 2020

Publication series

Name	Proceedings of 2020 23rd International Conference on Information Fusion, FUSION 2020

Conference

Conference	23rd International Conference on Information Fusion, FUSION 2020
Country/Territory	South Africa
City	Virtual, Pretoria
Period	07/6/20 → 07/9/20

Funding

This work was funded by the Office of Defense Nuclear Nonproliferation Research and Development (NA-22), within the US Department of Energy’s National Nuclear Security Administration. This manuscript has been authored by UT-Battelle, LLC, under contract DE-AC05-00OR22725 with the US Department of Energy (DOE). The US government retains and the publisher, by accepting the article for publication, acknowledges that the US government retains a nonexclusive, paid-up, irrevocable, worldwide license to publish or reproduce the published form of this manuscript, or allow others to do so, for US government purposes. DOE will provide public access to these results of federally sponsored research in accordance with the DOE Public Access Plan (http://energy.gov/downloads/doe-public-access-plan).

Keywords

Classification
Sensor networks
Vehicle detection

Access to Document

10.23919/FUSION45008.2020.9190452

Cite this

Hite, J., Dayman, K., Rao, N., Greulich, C., Sen, S., Chichester, D., Nicholson, A., Archer, D., Willis, M., Garishvili, I., Rowe, A., Ghawaly, J., & Johnson, J. (2020). Automated vehicle detection in a nuclear facility using low-frequency acoustic sensors. In Proceedings of 2020 23rd International Conference on Information Fusion, FUSION 2020 Article 9190452 (Proceedings of 2020 23rd International Conference on Information Fusion, FUSION 2020). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.23919/FUSION45008.2020.9190452

Hite, Jason ; Dayman, Kenneth ; Rao, Nageswara et al. / Automated vehicle detection in a nuclear facility using low-frequency acoustic sensors. Proceedings of 2020 23rd International Conference on Information Fusion, FUSION 2020. Institute of Electrical and Electronics Engineers Inc., 2020. (Proceedings of 2020 23rd International Conference on Information Fusion, FUSION 2020).

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title = "Automated vehicle detection in a nuclear facility using low-frequency acoustic sensors",

abstract = "This article presents an analysis of the method of construction and results for a classifier intended to identify vehicles using low-frequency acoustic data collected by a distributed sensor network. This data is collected as part of a venture intended to explore data analytics and multisensor fusion techniques for the monitoring of activities at a test bed nuclear facility located at Oak Ridge National Laboratory in Oak Ridge, Tennessee. We describe the associated target signature and design a classifier based on a multilayer perceptron, followed by an analysis of its results. We discuss how overall accuracy is not the only consideration in constructing this classifier, and how for this application, it is actually desirable to operate at a lower level of accuracy in exchange for a reduction in the false alarm rate, as well as how this relates to the actual deployment of the classifier in practical use.",

keywords = "Classification, Sensor networks, Vehicle detection",

author = "Jason Hite and Kenneth Dayman and Nageswara Rao and Christopher Greulich and Satyabrata Sen and David Chichester and Andrew Nicholson and Dan Archer and Michael Willis and Irakli Garishvili and Andrew Rowe and James Ghawaly and Jared Johnson",

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year = "2020",

month = jul,

doi = "10.23919/FUSION45008.2020.9190452",

language = "English",

series = "Proceedings of 2020 23rd International Conference on Information Fusion, FUSION 2020",

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Hite, J, Dayman, K, Rao, N, Greulich, C, Sen, S, Chichester, D, Nicholson, A , Archer, D , Willis, M , Garishvili, I, Rowe, A, Ghawaly, J & Johnson, J 2020, Automated vehicle detection in a nuclear facility using low-frequency acoustic sensors. in Proceedings of 2020 23rd International Conference on Information Fusion, FUSION 2020., 9190452, Proceedings of 2020 23rd International Conference on Information Fusion, FUSION 2020, Institute of Electrical and Electronics Engineers Inc., 23rd International Conference on Information Fusion, FUSION 2020, Virtual, Pretoria, South Africa, 07/6/20. https://doi.org/10.23919/FUSION45008.2020.9190452

Automated vehicle detection in a nuclear facility using low-frequency acoustic sensors. / Hite, Jason; Dayman, Kenneth; Rao, Nageswara et al.
Proceedings of 2020 23rd International Conference on Information Fusion, FUSION 2020. Institute of Electrical and Electronics Engineers Inc., 2020. 9190452 (Proceedings of 2020 23rd International Conference on Information Fusion, FUSION 2020).

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

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AU - Greulich, Christopher

AU - Sen, Satyabrata

AU - Chichester, David

AU - Nicholson, Andrew

AU - Archer, Dan

AU - Willis, Michael

AU - Garishvili, Irakli

AU - Rowe, Andrew

AU - Ghawaly, James

AU - Johnson, Jared

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N2 - This article presents an analysis of the method of construction and results for a classifier intended to identify vehicles using low-frequency acoustic data collected by a distributed sensor network. This data is collected as part of a venture intended to explore data analytics and multisensor fusion techniques for the monitoring of activities at a test bed nuclear facility located at Oak Ridge National Laboratory in Oak Ridge, Tennessee. We describe the associated target signature and design a classifier based on a multilayer perceptron, followed by an analysis of its results. We discuss how overall accuracy is not the only consideration in constructing this classifier, and how for this application, it is actually desirable to operate at a lower level of accuracy in exchange for a reduction in the false alarm rate, as well as how this relates to the actual deployment of the classifier in practical use.

AB - This article presents an analysis of the method of construction and results for a classifier intended to identify vehicles using low-frequency acoustic data collected by a distributed sensor network. This data is collected as part of a venture intended to explore data analytics and multisensor fusion techniques for the monitoring of activities at a test bed nuclear facility located at Oak Ridge National Laboratory in Oak Ridge, Tennessee. We describe the associated target signature and design a classifier based on a multilayer perceptron, followed by an analysis of its results. We discuss how overall accuracy is not the only consideration in constructing this classifier, and how for this application, it is actually desirable to operate at a lower level of accuracy in exchange for a reduction in the false alarm rate, as well as how this relates to the actual deployment of the classifier in practical use.

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Hite J, Dayman K, Rao N, Greulich C, Sen S, Chichester D et al. Automated vehicle detection in a nuclear facility using low-frequency acoustic sensors. In Proceedings of 2020 23rd International Conference on Information Fusion, FUSION 2020. Institute of Electrical and Electronics Engineers Inc. 2020. 9190452. (Proceedings of 2020 23rd International Conference on Information Fusion, FUSION 2020). doi: 10.23919/FUSION45008.2020.9190452

Automated vehicle detection in a nuclear facility using low-frequency acoustic sensors

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