mlsanityData to 14 Oct 2026

Active Sensing with Meta-Reinforcement Learning for Emitter Localization from RF Observations

arXiv 2605.12569 ↗First posted 12 May 2026eess.SP cs.AIcited by 0

On the map: Deep reinforcement learning methods → RL for energy, networks and operations

Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in RL for energy, networks and operations, one of 15 directions in Deep reinforcement learning methods. Builds on 8 of its 28 references that are in the map. First posted on arXiv 12 May 2026.

Builds on

8 of 28 references.

References · 8

  1. 2017Proximal Policy Optimization Algorithmscited by 15,215
  2. 2017Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networkscited by 4,897
  3. 2013Playing Atari with Deep Reinforcement Learningcited by 3,274
  4. 2022Sionna: An Open-Source Library for Next-Generation Physical Layer Researchcited by 145
  5. 2020Generalization in Reinforcement Learning by Soft Data Augmentationcited by 137
  6. 2019"Jam Me If You Can'': Defeating Jammer with Deep Dueling Neural Network Architecture and Ambient Backscattering Augmented Communicationscited by 7
  7. 2024Temporal Source Recovery for Time-Series Source-Free Unsupervised Domain Adaptationcited by 4
  8. 2025Attention-Based Fusion of IQ and FFT Spectrograms with AoA Features for GNSS Jammer Localizationcited by 3

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  2. 2025Bayesian-Driven Graph Reasoning for Active Radio Map Construction
  3. 2024Causality-Driven Reinforcement Learning for Joint Communication and Sensing
  4. 2024Movable Antenna-Equipped UAV for Data Collection in Backscatter Sensor Networks: A Deep Reinforcement Learning-based Approach
  5. 2025Anti-Jamming Sensing with Distributed Reconfigurable Intelligent Metasurface Antennas
  6. 2025Meta-Reinforcement Learning for Fast and Data-Efficient Spectrum Allocation in Dynamic Wireless Networks
  7. 2024A Memory-Based Reinforcement Learning Approach to Integrated Sensing and Communication
  8. 2025Beam Selection in ISAC using Contextual Bandit with Multi-modal Transformer and Transfer Learning