Landmark-based navigation for airborne sensor systems

The challenge of ownship navigation for an airborne platform in the absence of precise navigation information is an important problem. In this paper, the problem is solved using the assumed known GPS locations of landmarks by casting it in a Bayesian state-space framework. It is assumed that no information is available from the navigation sensors. The platform kinematic state is inferred by using a nonlinear filter, such as the extended Kalman filter. The performance is assessed as a function of the density of landmarks and platform manoeuvres in a simulation environment.