Algorithm of Impact Point Prediction for Intercepting Reentry Vehicles

Abstract

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.