Impact of Seasonal Variations on Foliage Penetration Experiment: A WSN-Based Device-Free Sensing Approach

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

Impact of Seasonal Variations on Foliage Penetration Experiment: A WSN-Based Device-Free Sensing Approach

Zhong, Y

Yang, Y

Zhu, X

Huang, Y

Dutkiewicz, E

Zhou, Z Jiang, T

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Publication Type:: Journal Article
Citation:: IEEE Transactions on Geoscience and Remote Sensing, 2018, 56 (9), pp. 5035 - 5045
Issue Date:: 2018-09-01

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Field	Value	Language
dc.contributor.author	Zhong, Y https://orcid.org/0000-0002-9309-3407	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	Huang, Y https://orcid.org/0000-0002-1363-5318	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	2018-09-01	en_US
dc.identifier.citation	IEEE Transactions on Geoscience and Remote Sensing, 2018, 56 (9), pp. 5035 - 5045	en_US
dc.identifier.issn	0196-2892	en_US
dc.identifier.uri	http://hdl.handle.net/10453/132042
dc.description.abstract	© 2018 IEEE. Foliage penetration (FOPEN) has been found to be a critical mission for a variety of applications, ranging from surveillance to military. Recently, an emerging technology, namely wireless sensor network (WSN)-based device-free sensing (DFS), has been introduced to the domain of FOPEN. This technology only utilizes radio-frequency signals for target detection and classification; thus, no additional hardware is required, just a wireless transceiver. Although the feasibility of using this technology for human detection indoors has been explored to some extent, it is questionable if the same technology can be transferred to outdoors. As far as FOPEN is concerned, the impact of seasonal variations on detection accuracy can be severe. To address this concern, in this paper, an experiment is conducted in four seasons, and how to ensure reasonable detection accuracy with seasonal variations is intensively investigated. To fully evaluate the potential of using the WSN-based DFS for FOPEN, an impulse-radio ultrawideband technology-based prototype is used to collect data samples in different seasons. Unlike the conventional approach based on a combination of statistical properties of received-signal strength and a support vector machine, this approach adopts two special measures for performance enhancement. One measure is to use a higher order cumulant (HOC) algorithm for feature extraction, so that the impact on detection accuracy due to unwanted clutters can be minimized. The other one is to determine the optimal parameters of the classifier by means of a flower pollination algorithm. Consequently, the adverse effects on detection accuracy due to variations of weather conditions in four seasons can be accommodated. According to the experimental result, it is shown that the average classification accuracy of the presented approach can be improved by at least 20% under all seasons with an ensured robustness.	en_US
dc.relation	http://purl.org/au-research/grants/arc/DE160101032
dc.relation.ispartof	IEEE Transactions on Geoscience and Remote Sensing	en_US
dc.relation.isbasedon	10.1109/TGRS.2018.2804346	en_US
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject.classification	Geological & Geomatics Engineering	en_US
dc.title	Impact of Seasonal Variations on Foliage Penetration Experiment: A WSN-Based Device-Free Sensing Approach	en_US
dc.type	Journal Article
utslib.citation.volume	9	en_US
utslib.citation.volume	56	en_US
utslib.for	0906 Electrical and Electronic Engineering	en_US
utslib.for	0909 Geomatic Engineering	en_US
utslib.for	0404 Geophysics	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
pubs.organisational-group	/University of Technology Sydney/Students
utslib.copyright.status	closed_access	*
pubs.issue	9	en_US
pubs.publication-status	Published	en_US
pubs.volume	56	en_US

Abstract:

© 2018 IEEE. Foliage penetration (FOPEN) has been found to be a critical mission for a variety of applications, ranging from surveillance to military. Recently, an emerging technology, namely wireless sensor network (WSN)-based device-free sensing (DFS), has been introduced to the domain of FOPEN. This technology only utilizes radio-frequency signals for target detection and classification; thus, no additional hardware is required, just a wireless transceiver. Although the feasibility of using this technology for human detection indoors has been explored to some extent, it is questionable if the same technology can be transferred to outdoors. As far as FOPEN is concerned, the impact of seasonal variations on detection accuracy can be severe. To address this concern, in this paper, an experiment is conducted in four seasons, and how to ensure reasonable detection accuracy with seasonal variations is intensively investigated. To fully evaluate the potential of using the WSN-based DFS for FOPEN, an impulse-radio ultrawideband technology-based prototype is used to collect data samples in different seasons. Unlike the conventional approach based on a combination of statistical properties of received-signal strength and a support vector machine, this approach adopts two special measures for performance enhancement. One measure is to use a higher order cumulant (HOC) algorithm for feature extraction, so that the impact on detection accuracy due to unwanted clutters can be minimized. The other one is to determine the optimal parameters of the classifier by means of a flower pollination algorithm. Consequently, the adverse effects on detection accuracy due to variations of weather conditions in four seasons can be accommodated. According to the experimental result, it is shown that the average classification accuracy of the presented approach can be improved by at least 20% under all seasons with an ensured robustness.

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http://hdl.handle.net/10453/132042