Trajectory estimation for ballistic missile in boost stage using robust filtering

This study proposes a new estimation method, referred as the high-degree cubature Huber-based filtering (HCHF). The novel algorithm makes use of the fifth-degree cubature rule to numerically compute Gaussian-weighted integrals, which, with a set of non-linear state equations, are used to compute weighted means and covariance required for measurement update. The measurement update is designed based on the Huber technique which is a combined minimum of l(1) and l(2) norm estimation. Therefore, the HCHF could exhibit robustness when the measurement noise is highly non-Gaussian. The method is applied for tracking ballistic missile in presence of non-Gaussian noise in satellite line-of-sight measurements. The tracking performance is compared with that of extended Kalman filtering and conventional cubature Kalman filtering. The simulation results verified the effectiveness of the proposed non-linear filtering.