Enhancing Inertial Navigation Performance via Fusion of Classical and Quantum Accelerometers

• URL: http://arxiv.org/abs/2103.09378v1
• Date: Wed, 17 Mar 2021 00:38:28 GMT
• Title: Enhancing Inertial Navigation Performance via Fusion of Classical and Quantum Accelerometers
• Authors: Xuezhi Wang, Allison Kealy, Christopher Gilliam, Simon Haine, John Close, Bill Moran, Kyle Talbot, Simon Williams, Kyle Hardman, Chris Freier, Paul Wigley, Angela White, Stuart Szigeti and Sam Legge
• Abstract summary: We propose a maximum likelihood probabilistic data fusion method for quantum accelerometers. The proposed method enables quantum accelerometers to be applied in practical inertial navigation scenarios. We demonstrate the enhanced error performance achieved by the proposed fusion method using a simulated 1D inertial navigation scenario.
• Score: 6.230800551280429
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: While quantum accelerometers sense with extremely low drift and low bias, their practical sensing capabilities face two limitations compared with classical accelerometers: a lower sample rate due to cold atom interrogation time, and a reduced dynamic range due to signal phase wrapping. In this paper, we propose a maximum likelihood probabilistic data fusion method, under which the actual phase of the quantum accelerometer can be unwrapped by fusing it with the output of a classical accelerometer on the platform. Consequently, the proposed method enables quantum accelerometers to be applied in practical inertial navigation scenarios with enhanced performance. The recovered measurement from the quantum accelerometer is also used to re-calibrate the classical accelerometer. We demonstrate the enhanced error performance achieved by the proposed fusion method using a simulated 1D inertial navigation scenario. We conclude with a discussion on fusion error and potential solutions.

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