Detecting the Presence of Intrusive Drilling in Secure Transport Containers Using Non-Contact Millimeter-Wave Radar.

millimeter-wave radar non-destructive sensing security

Journal

Sensors (Basel, Switzerland)
ISSN: 1424-8220
Titre abrégé: Sensors (Basel)
Pays: Switzerland
ID NLM: 101204366

Informations de publication

Date de publication:
01 Apr 2022
Historique:
received: 14 02 2022
revised: 25 03 2022
accepted: 30 03 2022
entrez: 12 4 2022
pubmed: 13 4 2022
medline: 13 4 2022
Statut: epublish

Résumé

We employ a 77-81 GHz frequency-modulated continuous-wave (FMCW) millimeter-wave radar to sense anomalous vibrations during vehicle transport at highway speeds for the first time. Secure metallic containers can be breached during transport by means of drilling into their sidewalls but detecting a drilling signature is difficult because the large vibrations of transport drown out the small vibrations of drilling. For the first time, we demonstrate that it is possible to use a non-contact millimeter-wave radar sensor to detect this micron-scale intrusive drilling while highway-speed vehicle movement shakes the container. With the millimeter-wave radar monitoring the microdoppler signature of the container's vibrating walls, we create a novel signal-processing pipeline consisting of range-angle tracking, time-frequency analysis, horizontal stripe image convolution, and principal component analysis to create a robust and powerful detection statistic to alarm if drilling is present. To support this pipeline, we develop a statistical model combining the vibrating container and the random vibrations induced by vehicle movement to explore the robustness of the sensor's detection capabilities. The presented results strongly support the inclusion of a millimeter-wave radar vibration sensor into a transport security system.

Identifiants

pubmed: 35408331
pii: s22072718
doi: 10.3390/s22072718
pmc: PMC9002560
pii:
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Subventions

Organisme : Lawrence Livermore National Laboratory
ID : B638423

Références

IEEE Trans Image Process. 2012 Apr;21(4):1663-75
pubmed: 22020688
Sensors (Basel). 2015 Mar 26;15(4):7412-33
pubmed: 25822139
Nat Commun. 2021 Jan 12;12(1):337
pubmed: 33436585

Auteurs

Samuel Wagner (S)

Department of Electrical and Computer Engineering, University of California, Davis, Davis, CA 95616, USA.

Ahmad Alkasimi (A)

Department of Electrical and Computer Engineering, University of California, Davis, Davis, CA 95616, USA.

Anh-Vu Pham (AV)

Department of Electrical and Computer Engineering, University of California, Davis, Davis, CA 95616, USA.

Classifications MeSH