Spatially Distributed Task-Oriented Compression for Multi-Emitter Localization and Characterization with Spectral Overlap
arXiv:2606.01446v1 Announce Type: cross Abstract: Radio frequency spectrum awareness requires the ability to detect, localize, and characterize emitters in dense and contested wireless environments. In this work, we propose a task-oriented distributed compression framework for joint multi-emitter localization and characterization using spatially distributed receivers. Each receiver observes a short window of complex IQ samples, converts the observation to a time--frequency representation, and encodes it into a compact latent vector. A central fusion decoder combines the receiver latents to est
The increasing density and complexity of wireless environments necessitate advanced AI-driven solutions for spectrum awareness and security.
This development is crucial for national security, battlefield management, and intelligence gathering, enabling superior situational awareness in contested electromagnetic environments.
The ability to accurately detect, localize, and characterize multiple emitters with spectral overlap will enhance electronic warfare capabilities and reduce the overhead of traditional signal processing.
- · Defence contractors
- · Intelligence agencies
- · AI/ML research labs
- · Signal processing engineers
- · Adversaries relying on stealth communication
- · Legacy signal processing systems
Improved military and intelligence capabilities for spectrum control and reconnaissance.
Accelerated development of counter-technologies for stealth and anti-detection in electronic warfare.
Potential for a new arms race in AI-driven electronic warfare capabilities and counter-measures.
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