Improved GMTI-tracking using road maps and topographic information

Tracking around targets with airborne GMTI sensor measurements proves to be a challenging task due to high tar-et density, high clutter, and low visibility. The exploitation of non-standard background information such as road maps and terrain information is therefore highly desirable for the enhancement of track quality and track continuity. The present paper presents a Bayesian approach to incorporate such information consistently. It is particularly suited to deal with winding roads and networks of roads. Key issues are: modeling the target dynamics in quasi one-dimensional road coordinates and mapping onto ground coordinates using linear road segments. The case of several, intersecting roads with different characteristics. such as mean curvature. slope. or visibility. is treated within all Interacting Multiple Model scheme. The iterative filter equations are formulated within a framework of Gaussian sum approximations on the one hand and a numerically exact Particle Filter approach on the other hand. Simulation results for single targets taken from a realistic ground scenario show strongly reduced tar-et location errors compared to the case of neglecting road-map information. By using a realistic GMTI sensor model, early detection of stopping targets is demonstrated.