Trajectory estimation of manoeuvring re-entry vehicles and non-linear filtering

This paper presents a modified Sridhar filter which is applied to the problem of the real-time estimation of the trajectory of a maneuvering re-entry vehicle (MARV) from its radar observations. The Sridhar filter is based on optimal control concepts, specifically the Pontryagin minimum principle and the method of invariant imbedding. In this approach the unknown forces on the MARV are treated as controls that drive the MARV dynamics to track the noisy observed trajectory. This treatment differs from that used with the extended Kalman filter (EKF), where a new state vector of the unknown forces acting on the MARV considered as Wiener processes is augmented to the MARV state vector. The Sridhar filter does not increase the MARV state vector so its computational time, for the cases studied, is about 20 percent that of the EKF. The performance, as measured by the point error, of the proposed approach is even better than that of the conventional EKF.