Algorithm of Impact Point Prediction for Intercepting Reentry Vehicles

Defence Science Journal

Cheng-Yu Liu¹  Pan-Chio Tuan²  Chiun-Chien Liu² 

[1] Lee-Ming Institute of Technology, Taipei;Chung Cheng Institute of Technology, Tao Yuan

关键词: Reentry vehicle;    trajectory estimation;    input estimation;    adaptive Kalman filter;    impact point prediction;    counterparallel guidance law;   

DOI  :  

学科分类：社会科学、人文和艺术（综合）

来源: Defence Scientific Information & Documentation Centre

【 摘 要 】

Intercepting reentry vehicles is difficult because these move nearly at hypersonic speeds that traditional interceptors cannot match. Counterparallel guidance law was developed for defending a high speed target that guides the interceptor to intercept the target at a 180° aspect angle. When applying the counterparallel guidance law, it is best to predict the impact point before launch. Estimation and prediction of a reentry vehicle path are the first steps in establishing the impact point prediction algorithm. Model validation is a major challenge within the overall trajectory estimation problem. The adaptive Kalman filter, consising of an extended Kalman filter and a recursive input estimator, accurately estimates reentry vehicle trajectory by means of an input estimator which processes the model validation problem. This investigation presents an algorithm of impact point prediction for a reentry vehicle and an interceptor at an optimal intercept altitude based on the adaptive Kalman filter. Numerical simulation using a set of data, generated from a complicated model, verifies the accuracy of the proposed algorithm. The algorithm also performs exceptionally well using a set of flight test data. The presented algorithm is effective in solving the intercept problems.

【 授权许可】

Unknown