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arXiv · 2504.11119

Integration of a high-fidelity model of quantum sensors with a map-matching filter for quantum-enhanced navigation

Samuel Lellouch·Michael Holynski
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Abstract

Harnessing the potential of quantum sensors to assist in navigation requires enabling their operation in complex, dynamic environments and integrating them within existing navigation systems. While cross-couplings from platform dynamics generally degrade quantum measurements in a complex manner, navigation filters would need to be designed to handle such complex quantum sensor data. In this work, we report on the realization of a high-fidelity model of an atom-interferometry-based gravity gradiometer and demonstrate its integration with a map-matching navigation filter. Relying on the ability of our model to simulate the sensor behaviour across various dynamic platform environments, we show that aiding navigation via map matching using quantum gravity gradiometry results in stable trajectories, and highlight the importance of non-Gaussian errors arising from platform dynamics as a key challenge to map-matching navigation. We derive requirements for mitigating these errors, such as maintaining sensor tilt below 3.3 degrees, to inform future sensor development priorities. This work demonstrates the value of an end-to-end approach that could support future optimization of the overall navigation system. Beyond navigation, our atom interferometer modelling framework could be relevant to current research and innovation endeavours with quantum gravimeters, gradiometers and inertial sensors.

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Samuel Lellouch, Michael Holynski. 2025-08-08. Integration of a high-fidelity model of quantum sensors with a map-matching filter for quantum-enhanced navigation. https://doi.org/10.1088/2058-9565%2Fadf2d9

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