  The possible applications and benefits of autonomous Unmanned Aerial Vehicle
(UAV) use in urban areas are gaining considerable attention. Before these
possibilities can be realized, it is essential that UAVs be able to navigate
reliably and precisely in urban environments. The most common means of
determining the location of a UAV is to utilize position measurements from
Global Navigation Satellite Systems (GNSS). In urban environments, however,
GNSS measurements are significantly degraded due to occlusions and multipath.
This research analyzes the use of camera Line-of-Sight (LOS) measurements to
self-describing fiducials as a replacement for conventional GNSS measurements.
An extended Kalman filter (EKF) is developed and validated for the purpose of
combining continuous measurements from an Inertial Measurement Unit (IMU) with
the discrete LOS measurements to accurately estimate the states of a UAV. The
sensitivity of the estimation error covariance to various system parameters is
assessed, including IMU grade, fiducial placement, vehicle altitude, and image
processing frequency.
