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 pr...

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Autores principales:	Jose Abreu, Roberto L. Oliveira, Joao V. Fonseca Neto
Formato:	article
Lenguaje:	EN
Publicado:	Wiley 2021
Materias:	Telecommunication TK5101-6720
Acceso en línea:	https://doaj.org/article/59dc88b4cb354a0f96ba140d9d64e226
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spelling	oai:doaj.org-article:59dc88b4cb354a0f96ba140d9d64e2262021-11-09T10:16:47ZA comprehensive approach to predict a rocket's impact with stochastic estimators and artificial neural networks1751-96831751-967510.1049/sil2.12067https://doaj.org/article/59dc88b4cb354a0f96ba140d9d64e2262021-12-01T00:00:00Zhttps://doi.org/10.1049/sil2.12067https://doaj.org/toc/1751-9675https://doaj.org/toc/1751-9683Abstract 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.Jose AbreuRoberto L. OliveiraJoao V. Fonseca NetoWileyarticleTelecommunicationTK5101-6720ENIET Signal Processing, Vol 15, Iss 9, Pp 649-665 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Telecommunication TK5101-6720
spellingShingle	Telecommunication TK5101-6720 Jose Abreu Roberto L. Oliveira Joao V. Fonseca Neto A comprehensive approach to predict a rocket's impact with stochastic estimators and artificial neural networks
description	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.
format	article
author	Jose Abreu Roberto L. Oliveira Joao V. Fonseca Neto
author_facet	Jose Abreu Roberto L. Oliveira Joao V. Fonseca Neto
author_sort	Jose Abreu
title	A comprehensive approach to predict a rocket's impact with stochastic estimators and artificial neural networks
title_short	A comprehensive approach to predict a rocket's impact with stochastic estimators and artificial neural networks
title_full	A comprehensive approach to predict a rocket's impact with stochastic estimators and artificial neural networks
title_fullStr	A comprehensive approach to predict a rocket's impact with stochastic estimators and artificial neural networks
title_full_unstemmed	A comprehensive approach to predict a rocket's impact with stochastic estimators and artificial neural networks
title_sort	comprehensive approach to predict a rocket's impact with stochastic estimators and artificial neural networks
publisher	Wiley
publishDate	2021
url	https://doaj.org/article/59dc88b4cb354a0f96ba140d9d64e226
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A comprehensive approach to predict a rocket's impact with stochastic estimators and artificial neural networks

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