TY - GEN
T1 - A Fast Space-Based Angles-Only Initial Orbit Determination Algorithm for Very Short Arcs
AU - Guan, Yi
AU - He, Ying
AU - Nie, Tao
AU - Zhu, Shengying
N1 - Publisher Copyright:
© 2025 Technical Committee on Control Theory, Chinese Association of Automation.
PY - 2025
Y1 - 2025
N2 - Space-based initial orbit determination (IOD) is crucial for space situational awareness. However, classical algorithms often fail to converge or become trapped in trivial solutions when applied to very short arc scenarios with angles-only measurements. To overcome this challenge, this paper proposes a simple and fast algorithm based on the online sequential extreme learning machine (OSELM). First, the dynamic model and angles-only measurement model of the space target were constructed. Then, an OSELM-based orbit determination algorithm is designed, including the generation of the training dataset, data preprocessing, and network structure design, which focuses on establishing a nonlinear mapping model from angles-only measurements to the orbital state of space targets using the OSELM. Finally, numerical simulations for low Earth orbit (LEO) scenarios demonstrate the algorithm's high accuracy in estimating the target initial state, along with strong noise resistance and generalization capability.
AB - Space-based initial orbit determination (IOD) is crucial for space situational awareness. However, classical algorithms often fail to converge or become trapped in trivial solutions when applied to very short arc scenarios with angles-only measurements. To overcome this challenge, this paper proposes a simple and fast algorithm based on the online sequential extreme learning machine (OSELM). First, the dynamic model and angles-only measurement model of the space target were constructed. Then, an OSELM-based orbit determination algorithm is designed, including the generation of the training dataset, data preprocessing, and network structure design, which focuses on establishing a nonlinear mapping model from angles-only measurements to the orbital state of space targets using the OSELM. Finally, numerical simulations for low Earth orbit (LEO) scenarios demonstrate the algorithm's high accuracy in estimating the target initial state, along with strong noise resistance and generalization capability.
KW - Angles-only
KW - Initial orbit determination
KW - Online sequential extreme learning machine
KW - Very short arcs
UR - https://www.scopus.com/pages/publications/105020279706
U2 - 10.23919/CCC64809.2025.11179688
DO - 10.23919/CCC64809.2025.11179688
M3 - Conference contribution
AN - SCOPUS:105020279706
T3 - Chinese Control Conference, CCC
SP - 4233
EP - 4238
BT - Proceedings of the 44th Chinese Control Conference, CCC 2025
A2 - Sun, Jian
A2 - Yin, Hongpeng
PB - IEEE Computer Society
T2 - 44th Chinese Control Conference, CCC 2025
Y2 - 28 July 2025 through 30 July 2025
ER -