Hypersonic vehicle trajectory tracking based on moving horizon estimation

Xudong Ji; Zhe Chen; Zichu Liu; Changsheng Gao; Wuxing Jing

doi:10.1016/j.ifacol.2025.11.477

Hypersonic vehicle trajectory tracking based on moving horizon estimation

Xudong Ji^*
, Zhe Chen
, Zichu Liu
, Changsheng Gao
, Wuxing Jing

^*Corresponding author for this work

School of Astronautics

Harbin Institute of Technology
Ltd.

Research output: Contribution to journal › Conference article › peer-review

Abstract

This study presents a maneuvering target tracking algorithm based on moving horizon estimation(MHE). MHE reformulates state and parameter estimation as a constrained optimization problem incorporating glide phase physical constraints, while CKF derives arrival costs via covariance updates without Jacobian computations. CKF mitigates linearization error accumulation and improves posterior probability approximation in nonlinear regimes. Numerical simulations show the algorithm can improve tracking accuracy and numerical stability, demonstrating its effectiveness.

Original language	English
Pages (from-to)	2148-2153
Number of pages	6
Journal	IFAC-PapersOnLine
Volume	59
Issue number	20
DOIs	https://doi.org/10.1016/j.ifacol.2025.11.477
State	Published - 1 Aug 2025
Event	23th IFAC Symposium on Automatic Control in Aerospace, ACA 2025 - Harbin, China Duration: 2 Aug 2025 → 6 Aug 2025

Keywords

hypersonic vehicle
maneuvering target tracking
moving horizon estimation
nonlinear filtering algorithm
state-parameter estimation

Access to Document

10.1016/j.ifacol.2025.11.477

Cite this

@article{69c13f362f6e44f2819350c680f35b6a,

title = "Hypersonic vehicle trajectory tracking based on moving horizon estimation",

abstract = "This study presents a maneuvering target tracking algorithm based on moving horizon estimation(MHE). MHE reformulates state and parameter estimation as a constrained optimization problem incorporating glide phase physical constraints, while CKF derives arrival costs via covariance updates without Jacobian computations. CKF mitigates linearization error accumulation and improves posterior probability approximation in nonlinear regimes. Numerical simulations show the algorithm can improve tracking accuracy and numerical stability, demonstrating its effectiveness.",

keywords = "hypersonic vehicle, maneuvering target tracking, moving horizon estimation, nonlinear filtering algorithm, state-parameter estimation",

author = "Xudong Ji and Zhe Chen and Zichu Liu and Changsheng Gao and Wuxing Jing",

note = "Publisher Copyright: Copyright {\textcopyright} 2025 The Authors.; 23th IFAC Symposium on Automatic Control in Aerospace, ACA 2025 ; Conference date: 02-08-2025 Through 06-08-2025",

year = "2025",

month = aug,

day = "1",

doi = "10.1016/j.ifacol.2025.11.477",

language = "英语",

volume = "59",

pages = "2148--2153",

journal = "IFAC-PapersOnLine",

issn = "2405-8971",

publisher = "Elsevier B.V.",

number = "20",

}

TY - JOUR

T1 - Hypersonic vehicle trajectory tracking based on moving horizon estimation

AU - Ji, Xudong

AU - Chen, Zhe

AU - Liu, Zichu

AU - Gao, Changsheng

AU - Jing, Wuxing

PY - 2025/8/1

Y1 - 2025/8/1

N2 - This study presents a maneuvering target tracking algorithm based on moving horizon estimation(MHE). MHE reformulates state and parameter estimation as a constrained optimization problem incorporating glide phase physical constraints, while CKF derives arrival costs via covariance updates without Jacobian computations. CKF mitigates linearization error accumulation and improves posterior probability approximation in nonlinear regimes. Numerical simulations show the algorithm can improve tracking accuracy and numerical stability, demonstrating its effectiveness.

AB - This study presents a maneuvering target tracking algorithm based on moving horizon estimation(MHE). MHE reformulates state and parameter estimation as a constrained optimization problem incorporating glide phase physical constraints, while CKF derives arrival costs via covariance updates without Jacobian computations. CKF mitigates linearization error accumulation and improves posterior probability approximation in nonlinear regimes. Numerical simulations show the algorithm can improve tracking accuracy and numerical stability, demonstrating its effectiveness.

KW - hypersonic vehicle

KW - maneuvering target tracking

KW - moving horizon estimation

KW - nonlinear filtering algorithm

KW - state-parameter estimation

UR - https://www.scopus.com/pages/publications/105025892143

U2 - 10.1016/j.ifacol.2025.11.477

DO - 10.1016/j.ifacol.2025.11.477

M3 - 会议文章

AN - SCOPUS:105025892143

SN - 2405-8971

VL - 59

SP - 2148

EP - 2153

JO - IFAC-PapersOnLine

JF - IFAC-PapersOnLine

IS - 20

T2 - 23th IFAC Symposium on Automatic Control in Aerospace, ACA 2025

Y2 - 2 August 2025 through 6 August 2025

ER -

Hypersonic vehicle trajectory tracking based on moving horizon estimation

Abstract

Keywords

Access to Document

Other files and links

Fingerprint

Cite this