Quantifying the Advantage of Vector over Scalar Magnetic Sensor Networks for Undersea Surveillance
Wenchao Li, Xuezhi Wang, Qiang Sun, Allison N. Kealy, Andrew D. Greentree
TL;DR
This paper compares scalar and vector magnetometer networks for undersea surveillance, showing that vector networks offer better tracking accuracy and resilience.
Contribution
The study introduces a novel comparison of scalar and vector magnetometer networks using an unscented Kalman filter for target tracking.
Findings
Vector magnetometer networks significantly outperform scalar networks in tracking accuracy.
Vector networks demonstrate greater resilience in target tracking under challenging conditions.
Abstract
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.
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Taxonomy
TopicsAtomic and Subatomic Physics Research · Magnetic Field Sensors Techniques · Inertial Sensor and Navigation