Environment-Assisted Passive WiFi Tracking with Self-Localizing Asynchronous Sniffers

Suraweera, N; Li, S; Johnson, M; Collings, IB; Hanly, SV; Ni, W; Hedley, M

Environment-Assisted Passive WiFi Tracking with Self-Localizing Asynchronous Sniffers

Suraweera, N Li, S Johnson, M Collings, IB Hanly, SV Ni, W

Hedley, M

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Publisher:: Institute of Electrical and Electronics Engineers (IEEE)
Publication Type:: Journal Article
Citation:: IEEE Systems Journal, 2020, 14, (4), pp. 4798-4809
Issue Date:: 2020-12-01

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Field	Value	Language
dc.contributor.author	Suraweera, N
dc.contributor.author	Li, S
dc.contributor.author	Johnson, M
dc.contributor.author	Collings, IB
dc.contributor.author	Hanly, SV
dc.contributor.author	Ni, W https://orcid.org/0000-0002-4933-594X
dc.contributor.author	Hedley, M
dc.date.accessioned	2020-12-29T03:20:40Z
dc.date.available	2020-12-29T03:20:40Z
dc.date.issued	2020-12-01
dc.identifier.citation	IEEE Systems Journal, 2020, 14, (4), pp. 4798-4809
dc.identifier.issn	1932-8184
dc.identifier.issn	1937-9234
dc.identifier.uri	http://hdl.handle.net/10453/144988
dc.description.abstract	© 2007-2012 IEEE. This article considers the problem of passive tracking of a noncooperating target indoors. We develop a novel system to localize a moving target using asynchronous self-localizing sniffer nodes, which passively listen to WiFi signals transmitted by the target. The proposed system uses only the time-difference-of-arrival between multipath components (multipath TDoA) at each receiver. This does not require phase synchronization. We develop two novel localization algorithms; one uses batch processing and the other is online. We also design a novel multipath association algorithm. A custom-designed hardware platform is developed to prototype the proposed system. The accuracy of the proposed system is verified experimentally. For signal-to-noise ratio of 10 dB, both proposed algorithms achieve target localization accuracies better than 40 cm with probability 0.95 without needing any knowledge of target or sniffer locations. If the location of one sniffer node is known, the accuracy improves to 15 cm.
dc.language	en
dc.publisher	Institute of Electrical and Electronics Engineers (IEEE)
dc.relation.ispartof	IEEE Systems Journal
dc.relation.isbasedon	10.1109/JSYST.2019.2960510
dc.rights	© 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.	en_US
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	0906 Electrical and Electronic Engineering
dc.subject.classification	Operations Research
dc.title	Environment-Assisted Passive WiFi Tracking with Self-Localizing Asynchronous Sniffers
dc.type	Journal Article
utslib.citation.volume	14
utslib.for	0906 Electrical and Electronic Engineering
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
utslib.copyright.status	closed_access	*
dc.date.updated	2020-12-29T03:20:17Z
pubs.issue	4
pubs.publication-status	Published
pubs.volume	14
utslib.citation.issue	4

Abstract:

© 2007-2012 IEEE. This article considers the problem of passive tracking of a noncooperating target indoors. We develop a novel system to localize a moving target using asynchronous self-localizing sniffer nodes, which passively listen to WiFi signals transmitted by the target. The proposed system uses only the time-difference-of-arrival between multipath components (multipath TDoA) at each receiver. This does not require phase synchronization. We develop two novel localization algorithms; one uses batch processing and the other is online. We also design a novel multipath association algorithm. A custom-designed hardware platform is developed to prototype the proposed system. The accuracy of the proposed system is verified experimentally. For signal-to-noise ratio of 10 dB, both proposed algorithms achieve target localization accuracies better than 40 cm with probability 0.95 without needing any knowledge of target or sniffer locations. If the location of one sniffer node is known, the accuracy improves to 15 cm.

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http://hdl.handle.net/10453/144988