State Estimation of Spiraling Target using Divided Difference Kalman Filter
Abhijit Bhattacharyya · Proceedings of International Conference on Intelligent Unmanned Systems · 2013
Interception of modern Ballistic missiles performing spiralling motion due to the resonance between pitch and roll modes require accurate state estimation of relative kinematic parameters describing spiralling motion. Recently few nonlinear estimation techniques such as Unscented Kalman filter (UKF), Divided Difference Filter (DDF) and other techniques promise to be performing better than Extended Kalman Filters (EKF), although the claim depends on particular nonlinear problem. In this paper, Divided Difference Kalman Filter (DDF) is used to capture the spiralling motion of re-entry ballistic missile by representing target acceleration through sinusoidal function in inertial frame. A nine state estimator formulation for Divided Difference Filter is presented here which includes three relative positions, three relative velocities, spiralling frequency of missile, axial acceleration and maneuvering coefficient.