UC Santa Barbara

State estimation is a crucial part of navigation and control methods, however mostwell-known state estimation techniques assume some combination of linearity, Gaussian noise, as well as uniform sampling and synchrony of the process and measurements. This is a limitation for problems like integrated aircraft navigation, switched circuit moni- toring, and maneuvering vehicle tracking where these simplifying assumptions may not hold. In this thesis, state estimation methods that accommodate these facets of real-world problems are explored. Methods discussed include finite-horizon nonlinear real-time op- timization methods and Switched Kalman filtering for asynchronously switching systems. Some theoretical convergence results are presented along with results in simulations based on the aforementioned real-world systems.