  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.
