Work

Quantifying the Advantage of Vector over Scalar Magnetic Sensor Networks for Undersea Surveillance

Year: 2026

Type: article

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.

Source: Sensors

DOI: 10.3390/s26041290

Authors: Wenchao Li, Xuezhi Wang, Qiang Sun, Allison N. Kealy, Andrew D. Greentree

Institutions: RMIT University, Swinburne University of Technology

Language: English

Topic: Target Tracking and Data Fusion in Sensor Networks

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $3,137

Funder: RMIT University