  In GNSS-denied environments, aiding a vehicle's inertial navigation system
(INS) is crucial to reducing the accumulated navigation drift caused by sensor
errors (e.g. bias and noise). One potential solution is to use measurements of
gravity as an aiding source. The measurements are matched to a geo-referenced
map of Earth's gravity in order to estimate the vehicle's position. In this
paper, we propose a novel formulation of the map matching problem using a
hidden Markov model (HMM). Specifically, we treat the spatial cells of the map
as the hidden states of the HMM and present a Viterbi style algorithm to
estimate the most likely sequence of states, i.e. most likely sequence of
vehicle positions, that results in the sequence of observed gravity
measurements. Using a realistic gravity map, we demonstrate the accuracy of our
Viterbi map matching algorithm in a navigation scenario and illustrate its
robustness compared to existing methods.
