Input Estimation Algorithms for Reentry Vehicle Trajectory Estimation

Abstract

Fast and accurate estimation of trajectory is important in tracking and intercepting reentry vehicles. Validating model is a real challenge associated with the qverall trajectory estimation problem. Input estimation technique provides a’solution to this challenge. Two input estimation algorithms were introduced based on different assumptions about the input applied to the model. This investigation presents approaches consisting of an extended Kahnan filter and two input estimation algorithms to identify the reentry vehicle trajectory in its terminal phase using data from a single radar source. Numerical simulations with data generated from two models demonstrate superior capabilities as measured by accuracy compared to the extended Kalman filter. Evaluation using real flight data provides the consistent results. The comparison between two input estimation algorithms is also presented. The trajectory estimation approaches based on two algorithms are effective in solving the reentry vehicle tracking problem.