State estimation using UKF and predictive guidance for engaging barrel roll aircrafts

A generalized state estimation approach is presented in this paper for accurately estimating the states of an barrel roll maneuver aircraft. A nine state Unscented Kalman filter (UKF) formulation is presented here with three relative positions, three relative velocities, barrel frequency of aircraft, axial acceleration and maneuvering coefficient. A new nonlinear predictive zero-effort-miss based guidance algorithm is also presented in this paper. Extensive six degree-of-freedom simulation experiments, which includes noisy seeker measurements, a nonlinear dynamic inversion based autopilot for the interceptor along with appropriate actuator and sensor models, magnitude and rate saturation limits for n deflections etc., show that near-zero miss distance (i.e. hit-to-kill level performance) can be obtained when these two new techniques are applied together.