  Accurate tracking in urban environments necessitates target birth, survival,
and detection models that quantify the impact of terrain and building geometry
on the sequential estimation procedure. Current efforts assume that target
trajectories are limited to fixed paths, such as road networks. In these
settings a single airborne platform with a downward-facing camera is capable of
fully observing a target, outside of a few obstructed regions that can be
determined a priori (e.g. tunnels). However, many practical target types are
not necessarily restricted to road networks and thus require knowledge of
azimuthal shadowed regions to the sensor. In this paper, we propose the
integration of geospatial data for an urban environment into a particle filter
realization of a random finite set target tracking algorithm. Specifically, we
use 3D building polygons to compute the azimuthal shadowed regions with respect
to deployed sensor location. The particle filter predict and update steps are
modified such that (1) target births are assumed to occur in line-of-sight
(LOS) regions, (2) targets do not move into obstructions, (3) true target
detections only occur in LOS regions. The localization error performance
improvement for a single target Bernoulli filter under these modifications is
presented using freely available building vector data of New York City.
