Active Sensing with Meta-Reinforcement Learning for Emitter Localization from RF Observations
arXiv:2605.12569v2 Announce Type: replace-cross 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
The increasing sophistication of AI and reinforcement learning, combined with the growing threat of GNSS interference, is driving innovation in active sensing for emitter localization.
Reliable positioning is critical for both civilian infrastructure and military operations, making advanced GNSS interference localization technology a strategic imperative for national security and economic stability.
This research outlines a pathway to more resilient and autonomous systems capable of pinpointing GNSS interference sources, reducing vulnerability to jamming and spoofing attacks.
- · Defense contractors
- · GNSS receiver manufacturers
- · AI/ML developers
- · National security agencies
- · Adversarial actors relying on jamming
- · Systems highly dependent on unhardened GNSS
Improved accuracy and speed in locating sources of GNSS interference, enhancing navigation system robustness.
Reduced effectiveness of electronic warfare tactics aimed at disrupting positioning, potentially influencing military doctrine.
Proliferation of active sensing AI in other critical infrastructure protection, leading to a new era of 'smart' defensive systems.
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Read at arXiv cs.AI