  This paper describes a novel tracking filter, designed primarily for use in
collision avoidance systems on autonomous surface vehicles (ASVs). The proposed
methodology leverages real-time kinematic information broadcast via the
Automatic Information System (AIS) messaging protocol, in order to estimate the
position, speed, and heading of nearby cooperative targets. The state of each
target is recursively estimated in geodetic coordinates using an unscented
Kalman filter (UKF) with kinematic equations derived from the spherical law of
cosines. This improves upon previous approaches, many of which employ the
extended Kalman filter (EKF), and thus require the specification of a local
planar coordinate frame, in order to describe the state kinematics in an easily
differentiable form. The proposed geodetic UKF obviates the need for this local
plane. This feature is particularly advantageous for long-range ASVs, which
must otherwise periodically redefine a new local plane to curtail linearization
error. In real-world operations, this recurring redefinition can introduce
error and complicate mission planning. It is shown through both simulation and
field testing that the proposed geodetic UKF performs as well as, or better
than, the traditional plane-Cartesian EKF, both in terms of estimation error
and stability.
