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Related Experiment Video

Updated: Jul 16, 2026

Three-dimensional Super Resolution Microscopy of F-actin Filaments by Interferometric PhotoActivated Localization Microscopy (iPALM)

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Dual-Layer Factor-Graph Optimization for Delayed Star-Tracker/IMU Fusion in Highly Dynamic Spacecraft Attitude

Chao Zhang¹, Yanjun Yu¹, Huayi Li¹

¹School of Astronautics, Harbin Institute of Technology, Harbin 150001, China.

Sensors (Basel, Switzerland)

|July 15, 2026

Summary

This study introduces a dual-layer framework for spacecraft attitude estimation, fusing star tracker and inertial measurements. The method enhances accuracy and robustness, even with asynchronous and delayed sensor data.

Keywords:

asynchronous sensor fusion delayed measurement factor graph optimization spacecraft attitude estimation

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Area of Science:

Aerospace Engineering
Robotics
Control Systems

Background:

Accurate spacecraft attitude estimation is critical for dynamic missions.
Challenges include asynchronous sensing, motion blur, and delayed data from star trackers and inertial measurement units (IMUs).
Existing fusion methods struggle with highly dynamic conditions and sensor data inconsistencies.

Purpose of the Study:

To develop a robust attitude estimation framework for highly dynamic spacecraft.
To address challenges posed by asynchronous sensing, motion blur, and delayed outputs.
To improve temporal consistency and accuracy in attitude estimation.

Main Methods:

A dual-layer factor graph optimization framework is proposed.
Lower layer: Fuses high-rate IMU data with motion-blurred star streak observations.
Upper layer: Integrates delayed attitude constraints, propagated star vectors, and inertial constraints.

Main Results:

The framework demonstrates higher estimation accuracy and robustness compared to existing methods.
It shows improved tolerance to delayed or intermittent star-tracker observations.
Computational efficiency is maintained for near-real-time onboard implementation.

Conclusions:

The proposed dual-layer framework effectively handles asynchronous and blurred sensor data for spacecraft attitude estimation.
It offers a significant improvement in accuracy, robustness, and temporal consistency.
The method is suitable for near-real-time onboard applications in highly dynamic scenarios.