Out-of-surveillance target state estimation: a combined hospitability and synthetic inclination approach

This paper presents a novel approach for estimating out-of-surveillance mobile ground target states. The proposed algorithm introduces the concepts of hospitability and synthetic inclination maps and embeds them into a nonlinear Bayesian estimation filter, which in turn produces optimal target state estimates for a given out-of-surveillance mobile ground target of interest The optimality criterion here is defined in the minimum mean square error sense. Loosely speaking, the hospitability map can be viewed as a terrain-based map defining likelihood or a "weight" for each point on the earth's surface proportional to the ability of the target to move and maneuver at that location. On the other hand, the synthetic inclination map describes how the target favors certain regions within the search area, hence being "synthetically" inclined to move towards them.