AI-Powered RF Sensing and Signal Intelligence: Advances in Detection and Classification Techniques
A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Sensor Networks".
Deadline for manuscript submissions: 1 March 2026 | Viewed by 74
Special Issue Editor
2. National Institute for Research and Development of Isotopic and Molecular Technologies, 67-103 Donat Street, 400293 Cluj-Napoca, Romania
Interests: AI-powered RF signal analysis; real-time signal intelligence systems; security and surveillance applications; cognitive radio and spectrum monitoring
Special Issue Information
Dear Colleagues,
Recent advances in artificial intelligence (AI), machine learning (ML), and signal processing have opened new frontiers in the field of radio frequency (RF) sensing and signal intelligence. This Special Issue aims to bring together cutting-edge research focused on the development of intelligent systems capable of detecting, classifying, localizing, and interpreting RF signals in complex and dynamic environments.
We welcome original research and review articles exploring AI-powered approaches to signal detection, spectral analysis, source identification, and RF-based threat monitoring. Topics of interest include, but are not limited to, deep learning for RF classification, spectrogram-based methods, real-time signal intelligence systems, and the use of AI in non-cooperative sensing scenarios, such as drone detection or electronic surveillance.
The goal of this Special Issue is to provide a platform for interdisciplinary contributions that push the boundaries of autonomous signal intelligence and spectrum awareness, with applications ranging from wireless communications and cognitive radio to defense, security, and IoT monitoring.
Prof. Dr. Emanuel Puschita
Guest Editor
Manuscript Submission Information
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Keywords
- RF sensing
- signal intelligence (SIGINT)
- spectrum monitoring
- RF signal detection and classification
- I/Q time series analysis
- spectrogram-based methods
- artificial intelligence (AI)
- machine learning (ML)
- deep learning for RF
- real-time signal intelligence
- source localization
- security and surveillance applications
- non-cooperative sensing
- cognitive radio
- drone detection via RF
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