DRL-Based LEO Constellation Design for Regional Navigation Enhancement
Zixuan Rui, Fangling Zeng, Xiaofeng Ouyang, Lichao Chen · 2026
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Description
Zixuan Rui and co-authors let a learning agent tune the search for better satellite constellations. Low orbit fleets for navigation pose a knotted design puzzle with many goals at once. Classic swarm search stalls early because its settings stay fixed. Their hybrid hands those settings to a deep reinforcement learning driver.
The driver watches each trial fleet's scores and adjusts the swarm's push and pull on the fly. In a regional design case over China, the tuned fleet beat a standard one clearly: sharper positioning geometry, better accuracy, and more overlapping coverage. The authors offer the frame as a general tool for aerospace design.
Catalog record
| CREATOR | Zixuan Rui, Fangling Zeng, Xiaofeng Ouyang, Lichao Chen |
|---|---|
| YEAR | 2026 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 50 KB |
| VERSION | 1.0 |
| LICENSE | CC BY 4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |