## 1 Introduction
In 2024, a research team from Curtin University achieved positioning accuracy of approximately 10 m by performing Doppler-based joint positioning using SOPs from the Starlink, OneWeb, and Iridium constellations [24]. In 2024, a study employing a differential architecture analyzed and compensated for LEO satellite ephemeris errors [25]. In the integrated positioning experiment combining LEO opportunistic signals with an Inertial Navigation System (INS), the system achieved an average Root Mean Square Error (RMSE) of 11.74 m and a final positioning error of 12.01 m. At the signal level, some research achievements have been made to address the limitations of LEO SOPs, including the short availability of signal duration, irregular acquisition times, and large SNR fluctuations. In 2020, a research team from Tsinghua University developed an algorithm utilizing implicit pilot signals in Iridium SOP to enhance Doppler frequency estimation accuracy, achieving a 70% improvement in Doppler estimation accuracy [26]. That same year, a novel Quadratic Square Accumulating Instantaneous Doppler Estimation (QSA-IDE) algorithm was proposed to improve Doppler estimation accuracy under weak signal conditions [27]. Experimental results demonstrated that this algorithm achieved a 2D average positioning error of 163 m, representing a 17 m improvement over conventional methods. In 2022, the same research team further refined their approach by proposing a \"phase-time\" algorithm that leveraged entire Iridium signal frames rather than just pilot segments for Doppler estimation [28]. Through 800 positioning experiments, this enhanced algorithm was shown to improve positioning accuracy by 31.57-42.81% compared to traditional methods. However, existing studies remain confined to single-factor optimization (satellite/epoch/signal level) and lack joint modeling capability for fusing-level error coupling mechanisms. More fundamentally, conventional methods cannot perceive single-epoch-level signal degradation in real time, leading to severely compromised positioning robustness in complex dynamic scenarios. This paper aims to resolve this core challenge. This study systematically analyzes multi-level factors affecting LEO SOP positioning accuracy and proposes an innovative Agent-Weighted Recursive Least Squares (RLS) Positioning Framework (AWR-PF). The proposed framework employs an agent to evaluate the credibility of multi-level characteristics and decides epoch weights for real-time RLS iteration. Leveraging the inherent temporal dependency of RLS algorithms, our approach incorporates previously overlooked critical dimensions--epoch arrival sequence and inter-epoch dependencies. Unlike conventional \"snapshot\" processing, our framework treats positioning as a continuous decision-making process where each epoch generates a positioning state and the agent's weighting actions directly influence future positioning states. Our agent learns to predict how current epoch weighting affects future positioning accuracy based on historical epochs. This epoch weighting problem involves complex environmental conditions and strong inter-epoch decision dependencies. Since Deep Reinforcement Learning (DRL) excels at handling sequential decision-making under uncertainty [29], we adopt DRL to address this challenge. To enable DRL-based training, we formulate the positioning problem as a Markov Decision Process (MDP) with multi-dimensional states and discrete actions, effectively transforming it into a model suitable for deep reinforcement learning. As illustrated in Table 1, this MDP model trains an agent to optimize LEO SOP positioning performance through sequential decision-making. As an advanced DRL method, Double Deep Q-Network (DDQN) effectively mitigates overestimation bias by decoupling action selection from value estimation [30]. This method enables dynamic and precise epoch weight allocation, making it particularly suitable for multi-factor, noise-corrupted weighting scenarios in LEO SOP positioning.