IMPROVED TRAJECTORY ESTIMATION OF REENTRY VEHICLES FROM RADAR MEASUREMENTS USING ONLINE ADAPTIVE INPUT ESTIMATOR

Modeling error is the major concerning issue in the trajectory estimat ion. This paper formulates the dynamic model of a reentry vehicle in r eentry phase for identification with an unmodeled acceleration input c overing possible model errors. Moreover, this work presents a novel on -line estimation approach, adaptive filter, to identify the trajectory of a reentry vehicle from a single radar measured data. This proposed approach combines the extended Kalman filter and the recursive least- squares estimator of input with the hypothetical testing scheme. The r ecursive least-squares estimator is provided not only to extract the m agnitude of the unmodeled input but to offer a testing criterion to de tect the onset and presence of the input. Numerical simulation demonst rates the superior capabilities in accuracy and robustness of the prop osed method. In real flight analysis, the adaptive filter also perform s an excellent estimation and prediction performances. The recommended trajectory estimation method can support defense and tactical operati ons for anti-tactical ballistic missile warfare.