## 1 Introduction
\begin{table}
\begin{tabular}{c c} \hline \hline
**MDP Elements** & **LEO SOP Positioning Task** \\ \hline Environment & Agent-weighted RLS Positioning Framework \\ Agent & Trained neural network \\ States & Epoch characteristics and geometry dilution of \\ precision & precision \\ Actions & Epoch weighting decisions \\ Rewards & Variation in positioning error \\ Policy & Epoch weighting policy \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The Markov Decision Process (MDP) elements of using Deep Reinforcement Learning (DRL) to train agents to optimize low Earth orbit (LEO) signal of opportunity (SOP) positioning using Recursive Least Squares (RLS). Building upon the DDQN, we developed the DDQN-based Epoch Weighting (DDQN-EW) algorithm by (1) employing a lightweight neural network to extract positioning-relevant features across satellite, epoch, signal, and positioning layers; (2) designing collaborative interaction between the agent and RLS for weight-based decision-making; and (3) formulating a reward mechanism sensitive to positioning error convergence dynamics. This specialized architecture enables intelligent epoch-wise weighting while maintaining computational efficiency for real-time navigation applications. We trained the agent using the DDQN-EW algorithm on a training set comprising 393,933 real Iridium epochs extracted from 107 h of continuous observation. The agent learns to perform real-time epoch weighting by integrating multiple epoch characteristics. Unlike traditional static epoch weighting and screening algorithms, the proposed approach learns policy directly from extensive empirical data to autonomously generate optimal weighting policy through DRL. During evaluation, the agent generates dynamic weights through real-time analysis of epoch characteristics (SNR/Doppler/elevation angle, etc. ), which serve as adaptive factors in RLS positioning, thereby establishing a closed-loop optimization framework. Due to the agent's comprehensive consideration of multi-level characteristics and temporal dependency-aware decision-making, the proposed AWR-PF demonstrates superior positioning accuracy throughout the entire recursive process compared to both standard RLS and randomly weighted RLS in experimental trials. The main contributions of this study are threefold:  
* Systematic analysis of multi-level characteristics affecting LEO SOP positioning accuracy based on numerous independent trials. * Proposal of a novel Agent-Weighted RLS Positioning Framework (AWR-PF), a pioneering application of artificial intelligence (AI) algorithms in LEO SOP positioning. By employing an agent trained on extensive real-world Iridium signal and comprehensively incorporating multiple positioning accuracy-influencing factors, our framework achieves superior positioning performance. * Definition of an innovative MDP model with precisely formulated states, actions, and rewards, effectively transforming practical opportunistic signal positioning problems into a model suitable for DRL algorithm training, thereby establishing a paradigm for intelligent algorithm applications in future LEO opportunistic signal positioning systems. The remainder of this paper is organized as follows: Section 2 characterizes the Iridium signal epoch features that influence positioning accuracy. Section 3 presents the proposed Agent-Weighted RLS Positioning Framework. Section 4 presents the proposed MDP model and details the agent training process using the DDQN-EW algorithm. Section 5 analyzes the training and evaluation results. Section 6 presents some valuable discussions. Finally, Section 7 concludes the paper.