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

• URL: http://arxiv.org/abs/2605.12569v1
• Date: Tue, 12 May 2026 10:03:41 GMT
• Title: Active Sensing with Meta-Reinforcement Learning for Emitter Localization from RF Observations
• Abstract summary: Global navigation satellite system (GNSS) interference poses a serious threat to reliable positioning.<n>In this paper, we formulate interference localization as an active sensing problem.<n>We propose a reinforcement learning framework in which an agent sequentially explores the environment to infer the position of an emitter source.
• Score: 34.91678998140352
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

Related papers

• Physics-Informed Domain-Invariant Feature Learning with Autoencoder-Driven Gaussian Clustering for Robust Non-line-of-Sight Scenarios [0.786430477112975]
  Jamming and spoofing pose significant threats to wireless and satellite navigation.<n>We propose a hybrid learning framework that incorporates physics-informed constraints into deep neural networks.<n>We show that the proposed method reduces AoA estimation error by up to 6 in low-exemplar settings.
  arXiv  Detail & Related papers  (2026-06-21T10:42:12Z)
• RSGround-R1: Rethinking Remote Sensing Visual Grounding through Spatial Reasoning [61.84363374647606]
  Remote Sensing Visual Grounding (RSVG) aims to localize target objects in large-scale aerial imagery based on natural language descriptions.<n>These descriptions often rely heavily on positional cues, posing unique challenges for Multimodal Large Language Models (MLLMs) in spatial reasoning.<n>We propose a reasoning-guided, position-aware post-training framework, dubbed textbfRSGround-R1, to progressively enhance spatial understanding.
  arXiv  Detail & Related papers  (2026-01-29T12:35:57Z)
• Grounded Test-Time Adaptation for LLM Agents [75.62784644919803]
  Large language model (LLM)-based agents struggle to generalize to novel and complex environments.<n>We propose two strategies for adapting LLM agents by leveraging environment-specific information available during deployment.
  arXiv  Detail & Related papers  (2025-11-06T22:24:35Z)
• RL-Aided Cognitive ISAC: Robust Detection and Sensing-Communication Trade-offs [4.7199341867016456]
  This paper proposes a reinforcement learning-aided cognitive framework for integrated sensing and communication.<n>A Wald-type detector is employed for robust target detection under non-Gaussian clutter.<n>A SARSA-based RL algorithm enables adaptive estimation of target positions without prior environmental knowledge.
  arXiv  Detail & Related papers  (2025-11-04T15:49:51Z)
• RadioDiff-Loc: Diffusion Model Enhanced Scattering Congnition for NLoS Localization with Sparse Radio Map Estimation [24.71314779247058]
  This paper proposes a novel generative inference framework for NLoS localization based on conditional diffusion models.<n>By leveraging the physical insight that diffracted electromagnetic energy concentrates near building edges, we develop a sampling strategy.<n>We normalize all sampled RSS values relative to the maximum observed intensity, enabling the construction of a power-invariant radio map.
  arXiv  Detail & Related papers  (2025-09-02T01:43:23Z)
• Backscatter Device-aided Integrated Sensing and Communication: A Pareto Optimization Framework [59.30060797118097]
  Integrated sensing and communication (ISAC) systems potentially encounter significant performance degradation in densely obstructed urban non-line-of-sight scenarios.<n>This paper proposes a backscatter approximation (BD)-assisted ISAC system, which leverages passive BDs naturally distributed in environments of enhancement.
  arXiv  Detail & Related papers  (2025-07-12T17:11:06Z)
• EHGCN: Hierarchical Euclidean-Hyperbolic Fusion via Motion-Aware GCN for Hybrid Event Stream Perception [57.54227977352417]
  Event cameras emit high-speed event stream for perception tasks.<n>We propose a novel approach named EHGCN to perceive event stream in both Euclidean and hyperbolic spaces.
  arXiv  Detail & Related papers  (2025-04-23T11:01:03Z)
• Sensor Control for Information Gain in Dynamic, Sparse and Partially Observed Environments [1.5402666674186938]
  We present an approach for autonomous sensor control for information gathering under partially observable, dynamic and sparsely sampled environments. We extend the Deep Anticipatory Network (DAN) Reinforcement Learning framework by (1) improving exploration in sparse, non-stationary environments using a novel information gain reward. We also extend this problem to situations in which sampling from the intended RF spectrum/field is limited and propose a model-based version of the original RL algorithm that fine-tunes the controller via a model that is iteratively improved from the limited field sampling.
  arXiv  Detail & Related papers  (2022-11-03T00:03:14Z)
• Deep Reinforcement Learning for IRS Phase Shift Design in Spatiotemporally Correlated Environments [93.30657979626858]
  We propose a deep actor-critic algorithm that accounts for channel correlations and destination motion. We show that, when channels aretemporally correlated, the inclusion of the SNR in the state representation with function approximation in ways that inhibit convergence.
  arXiv  Detail & Related papers  (2022-11-02T22:07:36Z)
• Reinforcement Learning for Low-Thrust Trajectory Design of Interplanetary Missions [77.34726150561087]
  This paper investigates the use of reinforcement learning for the robust design of interplanetary trajectories in presence of severe disturbances. An open-source implementation of the state-of-the-art algorithm Proximal Policy Optimization is adopted. The resulting Guidance and Control Network provides both a robust nominal trajectory and the associated closed-loop guidance law.
  arXiv  Detail & Related papers  (2020-08-19T15:22:15Z)
• A Reinforcement Learning based approach for Multi-target Detection in Massive MIMO radar [12.982044791524494]
  This paper considers the problem of multi-target detection for massive multiple input multiple output (MMIMO) cognitive radar (CR) We propose a reinforcement learning (RL) based algorithm for cognitive multi-target detection in the presence of unknown disturbance statistics. Numerical simulations are performed to assess the performance of the proposed RL-based algorithm in both stationary and dynamic environments.
  arXiv  Detail & Related papers  (2020-05-10T16:29:06Z)
• Deep Reinforcement Learning with Robust and Smooth Policy [90.78795857181727]
  We propose to learn a smooth policy that behaves smoothly with respect to states. We develop a new framework -- textbfSmooth textbfRegularized textbfReinforcement textbfLearning ($textbfSR2textbfL$), where the policy is trained with smoothness-inducing regularization. Such regularization effectively constrains the search space, and enforces smoothness in the learned policy.
  arXiv  Detail & Related papers  (2020-03-21T00:10:29Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.