A comprehensive approach to predict a rocket's impact with stochastic estimators and artificial neural networks

Abstract

One of the current ways to continue space research is to launch ballistic rockets that carry scientific payloads. To improve the accuracy of the instantaneous evolution of the payload impact on the Earths surface, it is necessary to estimate indirect measures more efficiently. This study proposes a comprehensive approach to determine the impact point prediction of ballistic rocket payloads. This approach combines tracking algorithms that are based on stochastic estimators with artificial neural network (ANN) models. Four existing stochastic estimators, namely a recursive Kalman filter (RKF), an extended Kalman filter (EKF), an unscented Kalman filter (UKF), and a particle filter (PF) are compared with four proposed stochastic estimators. These include a recursive Kalman filter aided by an ANN (RKFN), an extended Kalman filter aided by an ANN (EKFN), an unscented Kalman filter aided by an ANN (UKFN), and a particle filter aided by an ANN (PFN). This study shows that the results that are obtained through the proposed tracking algorithms RKFN, EKFN, UKFN, and PFN are better than those of the existing tracking algorithms RKF, EKF, UKF, and PF. The proposed estimators can be efficient low-cost tools to mitigate inaccuracies during tracking up to the payload's impact.