Estimation of Spacecraft Angular Velocity Based on the Optical Flow of Star Images Using an Optimized Kalman Filter
Jiaqian Si, Yanxiong Niu, Haisha Niu, Zixuan Liu, Danni Liu · 2024
Read Estimation of Spacecraft Angular Velocity Based on the Optical Flow of Star Images Using an Optimized Kalman Filter by Jiaqian Si, Yanxiong Niu, Haisha Niu, Zixuan Liu, Danni Liu (2024) in the Tower of Babel, a free online library of public-domain works preserved in clean, searchable text. No sign-up. The full text is served as a plain-text file at a predictable URL, where text is present: https://antarctic-labs.com/tower-of-babel/pmc-11673832.txt.
Description
Jiaqian Si, Yanxiong Niu, Haisha Niu, Zixuan Liu, and Danni Liu find a better way for spacecraft to sense how fast they spin, in a 2024 study. Star sensors snap pictures of stars, and the drift of star images between shots reveals how the craft turns. Getting that spin rate right keeps a craft pointed as it should be.
The team pair the image drift with a Kalman filter, a method that blends noisy measures into a smooth estimate. Their version adds an adaptive factor that tunes the filter when motion changes fast. Tests show the tuned filter tracks spin more closely under shifting conditions. Better spin estimates mean steadier control for craft working far from any repair crew.
Catalog record
| CREATOR | Jiaqian Si, Yanxiong Niu, Haisha Niu, Zixuan Liu, Danni Liu |
|---|---|
| YEAR | 2024 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 29 KB |
| VERSION | 1.0 |
| LICENSE | CC BY 4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |