  This paper proposes a novel convex optimization framework for designing
robust Kalman filters that guarantee a user-specified steady-state error while
maximizing process and sensor noise. The proposed framework simultaneously
determines the Kalman gain and the robustness margin in terms of the process
and sensor noise. This is the first paper to present such a joint formulation
for Kalman filtering. The proposed methodology is validated through two
distinct examples: the Clohessy-Wiltshire-Hill equations for a chaser
spacecraft in an elliptical orbit and the longitudinal motion model of an F-16
aircraft.
