Device-Free Sensing for Personnel Detection in a Foliage Environment

Zhong, Y; Yang, Y; Zhu, X; Dutkiewicz, E; Zhou, Z; Jiang, T

Device-Free Sensing for Personnel Detection in a Foliage Environment

Zhong, Y Yang, Y

Zhu, X

Dutkiewicz, E

Zhou, Z Jiang, T

Permalink

Publication Type:: Journal Article
Citation:: IEEE Geoscience and Remote Sensing Letters, 2017, 14 (6), pp. 921 - 925
Issue Date:: 2017-06-01

Closed Access

	Filename	Description	Size
	07911189.pdf	Published Version	1.18 MB	Adobe PDF	View/Open

Copyright Clearance Process

Recently Added
In Progress
Closed Access

This item is closed access and not available.

Full metadata record

Field	Value	Language
dc.contributor.author	Zhong, Y	en_US
dc.contributor.author	Yang, Y https://orcid.org/0000-0001-7439-2156	en_US
dc.contributor.author	Zhu, X https://orcid.org/0000-0002-5814-394X	en_US
dc.contributor.author	Dutkiewicz, E https://orcid.org/0000-0002-4268-9286	en_US
dc.contributor.author	Zhou, Z	en_US
dc.contributor.author	Jiang, T	en_US
dc.date.issued	2017-06-01	en_US
dc.identifier.citation	IEEE Geoscience and Remote Sensing Letters, 2017, 14 (6), pp. 921 - 925	en_US
dc.identifier.issn	1545-598X	en_US
dc.identifier.uri	http://hdl.handle.net/10453/123958
dc.description.abstract	© 2017 IEEE. In this letter, the possibility of using device-free sensing (DFS) technology for personnel detection in a foliage environment is investigated. Although the conventional algorithm that based on statistical properties of the received-signal strength (RSS) for target detection at indoor or open-field environment has come a long way in recent years, it is still questionable if this algorithm is fully functional at outdoor with the changing atmosphere and ground conditions, such as a foliage environment. To answer this question, a variety of the measured data have been taken using different targets in a foliage environment. Applying these data along with support vector machine, the impact on detection accuracy due to different classification algorithms is studied. An algorithm that based on the extraction of the high-order cumulant (HOC) of the signals is presented, while the conventional RSS-based one is used as a benchmark. The measurement results show that the classification accuracy of the HOC-based algorithm is better than the RSS-based one by at least 17%. Moreover, to ensure the reliability of the HOC-based approach, the impact on classification accuracy due to different numbers of training samples and different values of signal-to-noise ratio is extensively verified using experimentally recorded samples. To the best of our knowledge, this is the first time that a DFS-based sensing approach is demonstrated to have a potential to distinguish between human and small-animal targets in a foliage environment.	en_US
dc.relation	http://purl.org/au-research/grants/arc/DE160101032
dc.relation.ispartof	IEEE Geoscience and Remote Sensing Letters	en_US
dc.relation.isbasedon	10.1109/LGRS.2017.2687938	en_US
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject.classification	Geological & Geomatics Engineering	en_US
dc.title	Device-Free Sensing for Personnel Detection in a Foliage Environment	en_US
dc.type	Journal Article
utslib.citation.volume	6	en_US
utslib.citation.volume	14	en_US
utslib.for	0906 Electrical and Electronic Engineering	en_US
utslib.for	0909 Geomatic Engineering	en_US
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Electrical and Data Engineering
pubs.organisational-group	/University of Technology Sydney/Strength - GBDTC - Global Big Data Technologies
utslib.copyright.status	closed_access	*
pubs.issue	6	en_US
pubs.publication-status	Published	en_US
pubs.volume	14	en_US

Abstract:

© 2017 IEEE. In this letter, the possibility of using device-free sensing (DFS) technology for personnel detection in a foliage environment is investigated. Although the conventional algorithm that based on statistical properties of the received-signal strength (RSS) for target detection at indoor or open-field environment has come a long way in recent years, it is still questionable if this algorithm is fully functional at outdoor with the changing atmosphere and ground conditions, such as a foliage environment. To answer this question, a variety of the measured data have been taken using different targets in a foliage environment. Applying these data along with support vector machine, the impact on detection accuracy due to different classification algorithms is studied. An algorithm that based on the extraction of the high-order cumulant (HOC) of the signals is presented, while the conventional RSS-based one is used as a benchmark. The measurement results show that the classification accuracy of the HOC-based algorithm is better than the RSS-based one by at least 17%. Moreover, to ensure the reliability of the HOC-based approach, the impact on classification accuracy due to different numbers of training samples and different values of signal-to-noise ratio is extensively verified using experimentally recorded samples. To the best of our knowledge, this is the first time that a DFS-based sensing approach is demonstrated to have a potential to distinguish between human and small-animal targets in a foliage environment.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/123958