Improved Trajectory Estimation of Reentry Vehicles from Radar Measurements Using On-Line Adaptive Input Estimator (Special Section on Nonlinear Theory and Its Applications)

概要

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Modeling error is the major concerning issue in the trajectory estimation. This paper formulates the dynamic model of a reentry vehicle in reentry phase for identification with an unmodeled acceleration input covering 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 recursive least-squares estimator is provided not only to extract the magnitude of the unmodeled input but to offer a testing criterion to detect the onset and presence of the input. Numerical simulation demonstrates the superior capabilities in accuracy and robustness of the proposed method. In real flight analysis, the adaptive filter also performs an excellent estimation and prediction performances. The recommended trajectory estimation method can support defense and tactical operations for anti-tactical ballistic missile warfare.
社団法人電子情報通信学会の論文
1998-09-25