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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 2548

Special Issue Editor

Prof. Dr. Emanuel Puschita

E-Mail Website
Guest Editor
1. Communications Department, Technical University of Cluj-Napoca, 28 Memorandumului Street, 400114 Cluj-Napoca, Romania
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 Issues, Collections and Topics in MDPI journals

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

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

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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Published Papers (2 papers)

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Research

17 pages, 10273 KB  
Article
Deep Learning-Based Approach for Automatic Defect Detection in Complex Structures Using PAUT Data
by Kseniia Barshok, Jung-In Choi and Jaesun Lee
Sensors 2025, 25(19), 6128; https://doi.org/10.3390/s25196128 - 3 Oct 2025
Viewed by 889
Abstract
This paper presents a comprehensive study on automated defect detection in complex structures using phased array ultrasonic testing data, focusing on both traditional signal processing and advanced deep learning methods. As a non-AI baseline, the well-known signal-to-noise ratio algorithm was improved by introducing [...] Read more.
This paper presents a comprehensive study on automated defect detection in complex structures using phased array ultrasonic testing data, focusing on both traditional signal processing and advanced deep learning methods. As a non-AI baseline, the well-known signal-to-noise ratio algorithm was improved by introducing automatic depth gate calculation using derivative analysis and eliminated the need for manual parameter tuning. Even though this method demonstrates robust flaw indication, it faces difficulties for automatic defect detection in highly noisy data or in cases with large pore zones. Considering this, multiple DL architectures—including fully connected networks, convolutional neural networks, and a novel Convolutional Attention Temporal Transformer for Sequences—are developed and trained on diverse datasets comprising simulated CIVA data and real-world data files from welded and composite specimens. Experimental results show that while the FCN architecture is limited in its ability to model dependencies, the CNN achieves a strong performance with a test accuracy of 94.9%, effectively capturing local features from PAUT signals. The CATT-S model, which integrates a convolutional feature extractor with a self-attention mechanism, consistently outperforms the other baselines by effectively modeling both fine-grained signal morphology and long-range inter-beam dependencies. Achieving a remarkable accuracy of 99.4% and a strong F1-score of 0.905 on experimental data, this integrated approach demonstrates significant practical potential for improving the reliability and efficiency of NDT in complex, heterogeneous materials. Full article
(This article belongs to the Special Issue AI-Powered RF Sensing and Signal Intelligence: Advances in Detection and Classification Techniques)
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18 pages, 2702 KB  
Article
How to Talk to Your Classifier: Conditional Text Generation with Radar–Visual Latent Space
by Julius Ott, Huawei Sun, Lorenzo Servadei and Robert Wille
Sensors 2025, 25(14), 4467; https://doi.org/10.3390/s25144467 - 17 Jul 2025
Viewed by 1097
Abstract
Many radar applications rely primarily on visual classification for their evaluations. However, new research is integrating textual descriptions alongside visual input and showing that such multimodal fusion improves contextual understanding. A critical issue in this area is the effective alignment of coded text [...] Read more.
Many radar applications rely primarily on visual classification for their evaluations. However, new research is integrating textual descriptions alongside visual input and showing that such multimodal fusion improves contextual understanding. A critical issue in this area is the effective alignment of coded text with corresponding images. To this end, our paper presents an adversarial training framework that generates descriptive text from the latent space of a visual radar classifier. Our quantitative evaluations show that this dual-task approach maintains a robust classification accuracy of 98.3% despite the inclusion of Gaussian-distributed latent spaces. Beyond these numerical validations, we conduct a qualitative study of the text output in relation to the classifier’s predictions. This analysis highlights the correlation between the generated descriptions and the assigned categories and provides insight into the classifier’s visual interpretation processes, particularly in the context of normally uninterpretable radar data. Full article
(This article belongs to the Special Issue AI-Powered RF Sensing and Signal Intelligence: Advances in Detection and Classification Techniques)
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