While quantum accelerometers sense with extremely low drift and low bias, their practical sensing capabilities face at least 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 recovered measurement from the quantum accelerometer is used to estimate bias and drift of the classical accelerometer which is then removed from the system output. We demonstrate the enhanced error performance achieved by the proposed fusion method using a simulated 1D accelerometer precision test scenario. We conclude with a discussion on fusion error and potential solutions.

虽然量子加速度计具有极低的漂移和低偏差，但与经典加速度计相比，它们的实际传感能力至少面临两个限制：由于冷原子询问时间导致的较低采样率；以及由于信号相位缠绕而减小的动态范围。在本文中，我们提出了一种最大似然概率数据融合方法，在该方法下，可以通过将量子加速度计的实际相位与平台上的经典加速度计的输出融合来解缠。因此，从量子加速度计恢复的测量值用于估计经典加速度计的偏差和漂移，然后从系统输出中去除。我们使用模拟的 1D 加速度计精度测试场景展示了所提出的融合方法所实现的增强错误性能。