Maximum correntropy sparse Gauss–Hermite quadrature filter and its application in tracking ballistic missile
Wutao Qin, Xiaogang Wang, Naigang Cui · IET Radar Sonar & Navigation · 2017
A novel robust filter referred as maximum correntropy sparse Gauss–Hermite quadrature filter (MCSGHQF) is proposed. The novel algorithm makes use of the sparse Gauss–Hermite quadrature (SGHQ) rule to numerically compute Gaussian‐weighted integrals, which are propagated through non‐linear state equation, and then a weighted means and covariance is taken. As the sensor measurements are always corrupted by non‐Gaussian noise which is typically glint noise or mixed Gaussian‐impulsive noise, the measurement update is redesigned based on the maximum correntropy criterion instead of minimum mean square error. Therefore, the MCSGHQF could exhibit robustness to the non‐Gaussian measurement noise, especially impulsive noise. In addition, the study proposes the use of MCSGHQF for ballistic missile tracking during midcourse phase to deal with the non‐Gaussian noise. The tracking performance is compared with that of SGHQ filter (SGHQF), Huber‐based filter and extended Kalman filter by Monte‐Carlo simulations. The simulation results demonstrate that the MCSGHQF is effective in glint noise case and exhibits superior to Huber‐based filter in mixed Gaussian‐impulsive noise case.