Unscented Kalman filter for long-distance vessel tracking in geodetic coordinates

This paper describes a unique instantiation of the unscented Kalman filter (UKF) that is particularly well-suited to long-distance surface vessel tracking. The proposed leverages nonlinear process model predict position, speed, and heading targets directly in geodetic coordinates, obviating need for intermediate coordinate frame transformations, enabling use simple linear observer. These features differentiate from prior architectures, most which require definition planar system order describe linearized state kinematics an easily differentiable form. Reliance upon local Earth-tangent frames acceptable confined operating regions, but incurs substantial estimation error when target far origin. It shown this grows with distance-cubed By tracking able achieve more consistent performance, three-fold reduction computational cost. A series numerical simulations field exercises are performed ground these claims, verify efficacy filter.