用轻量强化学习实现低轨卫星厘米级定位，省算力易部署。
DRL-Based Beam Positioning for LEO Satellite Constellations with Weighted Least Squares
- 用DQN直接学卫星权重，结合几何信息和导频信号。
- 2维场景下定位误差仅0.395米，计算开销小。
- 适合资源受限的低轨卫星载荷，兼顾精度与速度。
本文研究一种轻量级深度强化学习（DRL）辅助加权框架，用于无信道状态信息的低轨卫星星座多星定位，每颗可见卫星每周期提供一个服务波束（一个导频响应）。采用离散动作的深度Q网络（DQN）直接从接收导频测量值和几何特征中学习卫星权重，同时通过增强型加权最小二乘（WLS）估计器实现物理一致的定位，并联合估计接收机时钟偏差。所提混合设计侧重于精度与运行效率的平衡，而非绝对监督最优性。在包含10颗可见卫星的典型二维场景下，该方法实现了亚米级精度（0.395米均方根误差），且计算开销低，支持资源受限的低轨卫星载荷的实际部署。
原文摘要 · Abstract (English)
This paper investigates a lightweight deep reinforcement learning (DRL)-assisted weighting framework for CSI-free multi-satellite positioning in LEO constellations, where each visible satellite provides one serving beam (one pilot response) per epoch. A discrete-action Deep Q-Network (DQN) learns satellite weights directly from received pilot measurements and geometric features, while an augmented weighted least squares (WLS) estimator provides physics-consistent localization and jointly estimates the receiver clock bias. The proposed hybrid design targets an accuracy-runtime trade-off rather than absolute supervised optimality. In a representative 2-D setting with 10 visible satellites, the proposed approach achieves sub-meter accuracy (0.395m RMSE) with low computational overhead, supporting practical deployment for resource-constrained LEO payloads.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。