Integrated vision/inertial navigation system design using nonlinear filtering

Addresses the problem of navigation system design for autonomous aircraft landing. New nonlinear filter structures are introduced to estimate the position of an aircraft with respect to a possibly moving landing site, such as a naval vessel, based on measurements provided by airborne vision and inertial sensors. By exploring the geometry of the navigation problem, the navigation filter dynamics are cast in the framework of linear parametrically varying systems (LPVs). Using this set-up, filter performance and stability are studied in an H_∞ setting by resorting to the theory of linear matrix inequalities (LMIs). The design of nonlinear, globally stable filters to meet adequate H_∞ performance measures is thus converted into that of determining the feasibility of a related set of LMIs and finding a solution to them, if it exists. This is done by resorting to widely available numerical tools that borrow from convex optimization techniques. The paper develops the mathematical framework that is required for integrated vision/inertial navigation system design and details a design example for an air vehicle landing on an aircraft carrier