  Robust aiding of inertial navigation systems in GNSS-denied environments is
critical for the removal of accumulated navigation error caused by the drift
and bias inherent in inertial sensors. One way to perform such an aiding uses
matching of geophysical measurements, such as gravimetry, gravity gradiometry
or magnetometry, with a known geo-referenced map. Although simple in concept,
this map matching procedure is challenging: the measurements themselves are
noisy; their associated spatial location is uncertain; and the measurements may
match multiple points within the map (i.e. non-unique solution). In this paper,
we propose a probabilistic multiple hypotheses tracker to solve the map
matching problem and allow robust inertial navigation aiding. Our approach
addresses the problem both locally, via probabilistic data association, and
temporally by incorporating the underlying platform kinematic constraints into
the tracker. The map matching output is then integrated into the navigation
system using an unscented Kalman filter. Additionally, we present a statistical
measure of local map information density -- the map feature variability -- and
use it to weight the output covariance of the proposed algorithm. The
effectiveness and robustness of the proposed algorithm are demonstrated using a
navigation scenario involving gravitational map matching.
