###### Abstract
Compared to methods utilizing Kalman filters or particle filters for precise location estimation, their approach may suffer from reduced stability in high-mobility NTN environments. [PERSON] et al. investigated inter-satellite mobility management and network topology design and proposed an inter-satellite laser link planning framework to ensure reliable topology in optical satellite networks [27]. While their study makes significant contributions to NTN network architecture design, it does not directly address the handover challenges between LEO satellites and UE in satellite-to-terrestrial communication, nor does it tackle mobility management issues in ground-to-satellite connectivity. In [28], [PERSON] et al. provided a comprehensive survey on handover solutions for 5G LEO satellite networks, analyzing various handover mechanisms, including _RSRP_-based handover and location-based handover. They also proposed an antenna gain-based handover, which leverages the predictability of satellite trajectories and the antenna gain characteristics of satellite beams, allowing UE to operate without relying on traditional radio measurements. The particle filters, also known as sequential Monte Carlo (SMC) methods, are a powerful tool for estimating dynamic systems that evolve over time [29]. They are widely used in scenarios where the system state is not directly observable and measurements are noisy or uncertain. A particle filter represents the probability distribution of a system's state using a set of weighted particles, which are propagated and updated iteratively based on system dynamics and measurement data [30]. The RL is a branch of machine learning that enables an agent to learn and adapt its decision-making process through continuous interaction with its environment. By exploring different actions and observing their outcomes, the agent aims to develop a strategy that maximizes the total rewards accumulated over time [31]. Unlike supervised learning, RL does not require labeled data; instead, it relies on feedback from the environment in the form of rewards or penalties [32]. The combination of particle filters and reinforcement learning leverages the strengths of both methods. Particle filters provide accurate system state estimations, which serve as inputs for the RL framework. RL uses these estimations to optimize decision-making processes, such as handover parameter tuning in NTN. This integration enhances the adaptability and robustness of systems operating in dynamic and uncertain environments. Building on the AI-driven NTN mobility management research outlined in [16], this work integrates machine learning techniques, specifically reinforcement learning, into the handover decision-making process to enhance mobility performance in NTN. Our study leverages AI's adaptability to real-time mobility patterns, aiming to minimize unnecessary handovers while ensuring seamless service continuity in dynamic satellite environments. In existing research on NTN mobility management, RL and particle filtering (PF) have been applied separately in different scenarios. However, the LBH-PRL approach proposed in this study differs significantly in both methodology and application. Compared to conventional RL-driven NTN handover strategies, our approach not only employs RL for optimizing handover decisions but also introduces an adaptive parameter adjustment mechanism that dynamically adjusts hysteresis, _TTT_, and handover decisions based on environmental variations, making it more suitable for different NTN scenarios. Existing RL methods typically use fixed or offline-optimized parameters, which limits their adaptability in dynamic environments. By contrast, our approach enhances robustness and improves decision-making efficiency in highly dynamic NTN conditions. Additionally, while particle filtering is commonly used in navigation and tracking systems, its application in NTN handover decision-making has been limited.