Quantifying the advantage of vector over scalar magnetic sensor networks for undersea surveillance

TL;DR

This paper compares scalar and vector magnetic sensor networks for undersea surveillance, demonstrating that vector networks significantly improve target tracking accuracy and resilience using quantum magnetometers and Kalman filtering.

Contribution

It provides a quantitative evaluation showing the superiority of vector magnetometer networks over scalar ones for maritime target tracking.

Findings

Vector networks improve tracking accuracy

Vector networks enhance resilience against noise

Quantum magnetometers enable effective undersea monitoring

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.