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Quantifying the Advantage of Vector over Scalar Magnetic Sensor Networks for Undersea Surveillance
by
Kealy, Allison N.
, Sun, Qiang
, Li, Wenchao
, Greentree, Andrew D.
, Wang, Xuezhi
in
Comparative analysis
/ Innovations
/ Magnetic fields
/ magnetic sensing
/ National security
/ sensor networks
/ Sensors
/ Surveillance
/ target tracking
/ unscented Kalman filter
2026
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Quantifying the Advantage of Vector over Scalar Magnetic Sensor Networks for Undersea Surveillance
by
Kealy, Allison N.
, Sun, Qiang
, Li, Wenchao
, Greentree, Andrew D.
, Wang, Xuezhi
in
Comparative analysis
/ Innovations
/ Magnetic fields
/ magnetic sensing
/ National security
/ sensor networks
/ Sensors
/ Surveillance
/ target tracking
/ unscented Kalman filter
2026
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Do you wish to request the book?
Quantifying the Advantage of Vector over Scalar Magnetic Sensor Networks for Undersea Surveillance
by
Kealy, Allison N.
, Sun, Qiang
, Li, Wenchao
, Greentree, Andrew D.
, Wang, Xuezhi
in
Comparative analysis
/ Innovations
/ Magnetic fields
/ magnetic sensing
/ National security
/ sensor networks
/ Sensors
/ Surveillance
/ target tracking
/ unscented Kalman filter
2026
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Quantifying the Advantage of Vector over Scalar Magnetic Sensor Networks for Undersea Surveillance
Journal Article
Quantifying the Advantage of Vector over Scalar Magnetic Sensor Networks for Undersea Surveillance
2026
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Overview
Magnetic monitoring of maritime environments is an important problem for monitoring and optimising shipping, as well as national security. New developments in compact, fibre-coupled quantum magnetometers have led to the opportunity to critically evaluate how best to create such a sensor network. Here we explore various magnetic sensor network architectures for target identification. Our modelling compares networks of scalar vs. vector magnetometers. We implement an unscented Kalman filter approach to perform target tracking, and we find that vector networks provide a significant improvement in target tracking, specifically tracking accuracy and resilience compared with scalar networks.
Publisher
MDPI AG,Multidisciplinary Digital Publishing Institute (MDPI)
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