融合经典加速度计和量子加速度计提高惯性导航性能
Enhancing Inertial Navigation Performance via Fusion of Classical and Quantum Accelerometers
摘要：
虽然量子加速度计以极低的漂移和低偏置进行感测，但与经典加速度计相比，它们的实际感测能力面临两个限制：由于冷原子询问时间而导致的较低采样率，以及由于信号相位缠绕而导致的动态范围减小。本文提出了一种极大似然概率数据融合方法，通过将量子加速度计的实际相位与平台上经典加速度计的输出进行融合，即可解包裹量子加速度计的实际相位。因此，所提出的方法使量子加速度计应用于实际的惯性导航方案，具有增强的性能。从量子加速度计恢复的测量也被用来重新校准经典加速度计。我们证明了所提出的融合方法，使用模拟的一维惯性导航的情况下实现的增强的误差性能。最后，我们讨论融合误差和潜在的解决方案。
英文摘要：
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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