Long-term inertial navigation (to keep a satellite on track, for example) is currently limited by accelerometer and gyrometer biases, which cause large position errors. Bias-free sensors based on atom interferometers have been proposed, but they generally lack sufficient bandwidth for navigation. To solve this problem, the authors hybridize an atom interferometer with a classical accelerometer, using an approach based on Kalman filtering that provides optimal, robust estimation of the classical accelerometer's bias, even in a harsh environment. This approach can readily be extended to other types of atom interferometers, such as gyrometers or gradiometers.
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Cheiney et al. (2018) studied this question.
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