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

TL;DR

This paper introduces a reinforcement learning framework for active emitter localization using RF observations, addressing challenges of multipath and changing environments with deep RL and recurrent policies.

Contribution

It formulates GNSS interference localization as an active sensing problem and applies deep RL with recurrent policies to improve robustness under domain shifts.

Findings

Achieves 80.1% localization success rate in simulated environments.

Combines high-dimensional RF sensing with deep RL and recurrent policy learning.

Demonstrates potential of RL for adaptive interference localization in complex environments.

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.