Abstract: A new kind of sigma-point Kalman filter was proposed, quadrature Kalman filter(QKF), for the purpose of re-entry ballistic target tracking applications. The new filter linearized the nonlinear functions using statistical linear regression method through a set of Gaussian-Hermite quadrature points that parameterized the Gaussian density. The simulation experiment compared this new sigma point filter with EKF, DDF and UKF. Simulation results show that the estimation errors of all sigma point filters are all lower than that of EKF. The estimation error of QKF is lower than that of UKF, and its filtering credibility is almost same as that of UKF. The calculation complexity of QKF is a litter higher than that of UKF. The new sigma-point filter is an effective algorithm.

Key words: sigma point, Kalman filters, quadrature Kalman filter, tracking