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.

翻译：本文研究了一种轻量级深度强化学习辅助的加权框架，用于低轨星座中无需信道状态信息的多卫星定位，其中每颗可见卫星每历元提供一个服务波束（一个导频响应）。离散动作深度Q网络直接从接收的导频测量值和几何特征中学习卫星权重，而增强的加权最小二乘估计器则提供物理一致性的定位并联合估计接收机时钟偏差。所提出的混合设计旨在实现精度与运行时间的权衡，而非追求绝对监督最优性。在具有10颗可见卫星的代表性二维场景中，该方法以较低的计算开销实现了亚米级精度（均方根误差0.395米），为资源受限的低轨卫星有效载荷的实际部署提供了支持。