Attitude Estimation of Falcon 9 Rocket Body Based on Photometric Characteristics
Abstract
The proliferation of space debris, driven by increasingly frequent space activities, poses a severe risk to operational satellites and future missions. Consequently, the surveillance and characterization of space objects have become critical. Current methods for attitude estimation of space objects often rely on target-specific models, such as those with a closed set of facets, and typically employ nonlinear filtering or machine learning techniques. However, these approaches often lack general applicability. This study proposes a more general approach to attitude estimation based on photometric light curves. We define a loss function that quantifies the discrepancy between the measured light curve and a theoretical model parameterized by the attitude parameters. By leveraging automatic differentiation to compute the gradients of this function, we use a gradient descent algorithm to achieve rapid and accurate attitude estimation. The proposed method provides a versatile and efficient framework for attitude estimation, overcoming the target-specificity limitations of existing techniques, with the parameters estimation time reduced to 15 seconds and the mean square error lowered to three decimal places. Validation on the third-party database demonstrates that the method exhibits strong generalization capability.
Versions
Related
- Photometric Redshift Estimation with Hybrid Quantum Neural Network
- Combining Rubin and Roman photometry and its implications for photometric redshift estimation
- Multi-sensor fusion for fine-guidance and milliarcsecond-level attitude estimation of balloon-borne telescope
- Dynamics of Biased Domain Walls: The Rocket Effect
- A Student Sounding Rocket Experiment to Probe Electron Density in the Lower Ionosphere
BibTeX
@misc{li2026attitude,
title = {{Attitude Estimation of Falcon 9 Rocket Body Based on Photometric Characteristics}},
author = {Hui Li and Rong-Wang Li and Ru-Feng Tang and Yu-Qiang Li},
howpublished = {arXiv},
year = {2026},
month = oct,
doi = {10.48550/arxiv.2610.24596},
eprint = {2610.24596},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2610.24596},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - Attitude Estimation of Falcon 9 Rocket Body Based on Photometric Characteristics
AU - Hui Li
AU - Rong-Wang Li
AU - Ru-Feng Tang
AU - Yu-Qiang Li
PY - 2026
DA - 2026/10/17
PB - arXiv
DO - 10.48550/arxiv.2610.24596
UR - https://arxiv.org/abs/2610.24596
AB - The proliferation of space debris, driven by increasingly frequent space activities, poses a severe risk to operational satellites and future missions. Consequently, the surveillance and characterization of space objects have become critical. Current methods for attitude estimation of space objects often rely on target-specific models, such as those with a closed set of facets, and typically employ nonlinear filtering or machine learning techniques. However, these approaches often lack general applicability. This study proposes a more general approach to attitude estimation based on photometric light curves. We define a loss function that quantifies the discrepancy between the measured light curve and a theoretical model parameterized by the attitude parameters. By leveraging automatic differentiation to compute the gradients of this function, we use a gradient descent algorithm to achieve rapid and accurate attitude estimation. The proposed method provides a versatile and efficient framework for attitude estimation, overcoming the target-specificity limitations of existing techniques, with the parameters estimation time reduced to 15 seconds and the mean square error lowered to three decimal places. Validation on the third-party database demonstrates that the method exhibits strong generalization capability.
ER -