Title: Enhancing Inertial Navigation Performance via Fusion of Classical and Quantum Accelerometers Show Chinese title

Title: 通过经典加速度计和量子加速度计融合提升惯性导航性能

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. Show Chinese abstract

Abstract: 尽管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面临两个限制：由于冷原子探测时间导致的较低采样率，以及由于信号相位包裹导致的动态范围减小。 在本文中，我们提出了一种最大似然概率数据融合方法，在这种方法下，通过融合平台上的经典加速度计输出，可以解开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能够在实际惯性导航场景中应用并提高性能。 从量子加速度计恢复的测量值还用于重新校准经典加速度计。 我们使用模拟的一维惯性导航场景演示了所提出的融合方法实现的增强误差性能。 最后，我们讨论了融合误差及其潜在解决方案。