Trajectory Sequence Prediction Algorithm for Hypersonic Gliding Target with Variable Maneuver

A trajectory prediction algorithm based on ARIMA-UKF is proposed to solve the accuracy problem of trajectory prediction of hypersonic gliding target with variable maneuver in near space. Firstly, the Unscented Kalman Filter (UKF) algorithm is used to track and estimate the state of the target under...

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Autor principal:	Chen Nanhua, Zhao Liangyu, Yong Enmi, Lou Taishan
Formato:	article
Lenguaje:	ZH
Publicado:	Editorial Office of Aero Weaponry 2021
Materias:	\|hypersonic gliding target\|trajectory prediction\|model identification\|arima\|ukf Motor vehicles. Aeronautics. Astronautics TL1-4050
Acceso en línea:	https://doaj.org/article/768f80074a8f4410afc91f691760ab4a
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id	oai:doaj.org-article:768f80074a8f4410afc91f691760ab4a
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spelling	oai:doaj.org-article:768f80074a8f4410afc91f691760ab4a2021-11-30T00:13:49ZTrajectory Sequence Prediction Algorithm for Hypersonic Gliding Target with Variable Maneuver1673-504810.12132/ISSN.1673-5048.2021.0007https://doaj.org/article/768f80074a8f4410afc91f691760ab4a2021-04-01T00:00:00Zhttps://www.aeroweaponry.avic.com/fileup/1673-5048/PDF/1623391927746-145523825.pdfhttps://doaj.org/toc/1673-5048A trajectory prediction algorithm based on ARIMA-UKF is proposed to solve the accuracy problem of trajectory prediction of hypersonic gliding target with variable maneuver in near space. Firstly, the Unscented Kalman Filter (UKF) algorithm is used to track and estimate the state of the target under the condition of target maneuver change, which provides basic data for trajectory prediction. Secondly, the Autoregressive In tegrated Moving Average (ARIMA) model is determined through stationarity analysis, model identification, parameters estimation and model diagnosis of the data, and predicting the acceleration information of the target. Finally, the trajectory of the target is predicted by combining the one-step prediction method in the UKF algorithm. The simulation results show that the UKF algorithm can provide tracking estimation data with position error less than 100 m and velocity error less than 1.2 m/s for trajectory prediction. In the case of target maneuverability, compared with the composite function fitting prediction method, the position accuracy of the ARIMA-UKF algorithm in 150 s, 100 s and 50 s is improved by 5 km, 4.7 km and 2.4 km respectively.Chen Nanhua, Zhao Liangyu, Yong Enmi, Lou TaishanEditorial Office of Aero Weaponryarticle\|hypersonic gliding target\|trajectory prediction\|model identification\|arima\|ukfMotor vehicles. Aeronautics. AstronauticsTL1-4050ZHHangkong bingqi, Vol 28, Iss 2, Pp 40-48 (2021)
institution	DOAJ
collection	DOAJ
language	ZH
topic	\|hypersonic gliding target\|trajectory prediction\|model identification\|arima\|ukf Motor vehicles. Aeronautics. Astronautics TL1-4050
spellingShingle	\|hypersonic gliding target\|trajectory prediction\|model identification\|arima\|ukf Motor vehicles. Aeronautics. Astronautics TL1-4050 Chen Nanhua, Zhao Liangyu, Yong Enmi, Lou Taishan Trajectory Sequence Prediction Algorithm for Hypersonic Gliding Target with Variable Maneuver
description	A trajectory prediction algorithm based on ARIMA-UKF is proposed to solve the accuracy problem of trajectory prediction of hypersonic gliding target with variable maneuver in near space. Firstly, the Unscented Kalman Filter (UKF) algorithm is used to track and estimate the state of the target under the condition of target maneuver change, which provides basic data for trajectory prediction. Secondly, the Autoregressive In tegrated Moving Average (ARIMA) model is determined through stationarity analysis, model identification, parameters estimation and model diagnosis of the data, and predicting the acceleration information of the target. Finally, the trajectory of the target is predicted by combining the one-step prediction method in the UKF algorithm. The simulation results show that the UKF algorithm can provide tracking estimation data with position error less than 100 m and velocity error less than 1.2 m/s for trajectory prediction. In the case of target maneuverability, compared with the composite function fitting prediction method, the position accuracy of the ARIMA-UKF algorithm in 150 s, 100 s and 50 s is improved by 5 km, 4.7 km and 2.4 km respectively.
format	article
author	Chen Nanhua, Zhao Liangyu, Yong Enmi, Lou Taishan
author_facet	Chen Nanhua, Zhao Liangyu, Yong Enmi, Lou Taishan
author_sort	Chen Nanhua, Zhao Liangyu, Yong Enmi, Lou Taishan
title	Trajectory Sequence Prediction Algorithm for Hypersonic Gliding Target with Variable Maneuver
title_short	Trajectory Sequence Prediction Algorithm for Hypersonic Gliding Target with Variable Maneuver
title_full	Trajectory Sequence Prediction Algorithm for Hypersonic Gliding Target with Variable Maneuver
title_fullStr	Trajectory Sequence Prediction Algorithm for Hypersonic Gliding Target with Variable Maneuver
title_full_unstemmed	Trajectory Sequence Prediction Algorithm for Hypersonic Gliding Target with Variable Maneuver
title_sort	trajectory sequence prediction algorithm for hypersonic gliding target with variable maneuver
publisher	Editorial Office of Aero Weaponry
publishDate	2021
url	https://doaj.org/article/768f80074a8f4410afc91f691760ab4a
work_keys_str_mv	AT chennanhuazhaoliangyuyongenmiloutaishan trajectorysequencepredictionalgorithmforhypersonicglidingtargetwithvariablemaneuver
_version_	1718406871181688832

Trajectory Sequence Prediction Algorithm for Hypersonic Gliding Target with Variable Maneuver

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