Real-Time Aircraft Configuration and State Estimation

We introduce algorithms that fully utilize all available sensor data from Navy aircraft to estimate flight critical variables such as the center of gravity, mass, air/ground, and engine health. Our generic approach treats estimation as a nonlinear optimization problem without the need for special handling of heterogeneous sensor data. This enables us to add and remove sensors dynamically at flight time, easing integration and maintaining robustness to asynchronous sensor updates. In terms of performance, our algorithm converges to the optimal estimate (the best we can get). We also evaluate the performance of each sensor’s contribution at run-time and remove them from the estimation process if it is faulty. Thus, malfunctioning sensors can be detected, removed, and reintroduced when they begin to produce usable data. Finally, before flight, during design time, when new variables are to be included for estimation purposes,  we provide an algorithm that allows us to check analytically if a given set of sensors can provide the information required for observability, i.e., the Navy can then evaluate the minimum sensor-set needed to estimate the variables.