用强化学习让设备主动探测干扰源位置，提升复杂环境定位成功率。
Active Sensing with Meta-Reinforcement Learning for Emitter Localization from RF Observations
- 设计智能代理通过逐步探索环境，从射频信号中推断干扰源位置。
- 在模拟环境中实现80.1%的定位成功率，优于传统方法。
- 适合研究无线干扰定位、智能感知系统的研究人员参考。
全球导航卫星系统（GNSS）干扰对可靠定位构成严重威胁，尤其在室内及多径丰富的环境中，干扰源定位极具挑战性。本文将GNSS干扰定位建模为一种主动感知问题，提出一种强化学习（RL）框架，其中智能体通过序列化探索环境，利用2×2贴片天线获取的射频（RF）观测数据推断干扰源位置。由于单次快照测量在多径传播和变化信道条件下常存在歧义，该任务被建模为部分可观测决策过程。为此，所提框架结合高维射频感知与深度强化学习及循环策略学习。研究了基于值函数和基于策略的方法，即深度Q网络（DQN）与近端策略优化（PPO），并分析其在领域偏移下的表现。实验基于Sionna射线追踪模块生成的仿真数据集进行，该数据集提供真实传播效应和多样化环境配置。结果表明，该方法在复杂环境中达到80.1%的定位成功率，验证了强化学习在自适应干扰定位中的潜力。总体而言，仿真辅助训练为应对恶劣传播环境下的鲁棒定位提供了可行路径。
原文摘要 · Abstract (English)
Global navigation satellite system (GNSS) interference poses a serious threat to reliable positioning, especially in indoor and multipath-rich environments where source localization is highly challenging. In this paper, we formulate GNSS interference localization as an active sensing problem and propose a reinforcement learning (RL) framework in which an agent sequentially explores the environment to infer the position of an emitter source from radio frequency (RF) observations acquired with a 2x2 patch antenna. The localization task is modeled as a partially observable decision process, since single-snapshot measurements are often ambiguous under multipath propagation and changing channel conditions. To address this, the proposed framework combines high-dimensional RF sensing with deep RL and recurrent policy learning. We investigate both value-based and policy-based approaches, namely Deep Q-Networks (DQN) and Proximal Policy Optimization (PPO), and study their behavior under domain shift. The approach is evaluated on a simulated dataset generated with the Sionna ray-tracing module, which provides realistic propagation effects and diverse environment configurations. Experimental results show that the proposed method achieves a localization success rate of 80.1, demonstrating the potential of RL for adaptive GNSS interference localization. Overall, the results highlight simulation-assisted training as a promising direction for robust interference localization in challenging propagation environments.
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