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Advanced Robust Processing Techniques and Sensor Technologies for Complex Radio Frequency Signals

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Sensor Networks".

Deadline for manuscript submissions: 31 October 2026 | Viewed by 1507

Special Issue Editor

Dr. Yanwu Ding

E-Mail Website
Guest Editor
Department of Electrical Engineering and Computer Science, Wichita State University, 1845 N. Fairmount, Wichita, KS 67260, USA
Interests: ignal processing and communication systems; cognitive wireless networks; cooperative systems; WCDMA, WiMAX, MIMO, and OFDM system

Special Issue Information

Dear Colleagues,

This Special Issue emphasizes advanced methodologies and cutting-edge technologies aimed at enhancing the processing, detection, and analysis of complex radio frequency (RF) signals in various dynamic environments. It offers valuable insights into the latest advancements in RF signal processing to improve performance and efficiency in wireless communication, radar, IoT, and sensor-based applications.

The highlights include passive RF sensing, enabling non-intrusive spectrum monitoring and real-time sensing of the RF environment, and multispectral sensing and detection for enhanced signal detection across various frequency bands. AI and machine learning algorithms are explored for automating signal classification, detection, and prediction while addressing challenges such as the mismatch between training and testing data. Additionally, the journal highlights the use of high-fidelity RF digital twins, virtual models that simulate RF systems for testing and optimization before deployment, and their integration with advanced sensing technologies to enable more precise RF system performance and real-time monitoring.

Dr. Yanwu Ding
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 250 words) can be sent to the Editorial Office for assessment.

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

  • passive RF sensing
  • multispectral sensing and detection
  • AI and ML algorithms for RF applications
  • processing the mismatch between training and testing data in ML algorithms
  • high-fidelity RF digital twins

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Published Papers (2 papers)

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Research

23 pages, 11748 KB  
Article
Polarization-Regularized Adversarial Pruning for Efficient Radio Frequency Fingerprint Identification on IoT Devices
by Caidan Zhao, Haoliang Jiang, Jinhui Yu, Zepeng Meng and Xuhao He
Sensors 2026, 26(6), 2005; https://doi.org/10.3390/s26062005 - 23 Mar 2026
Viewed by 503
Abstract
Radio frequency fingerprint identification (RFFI) based on physical-layer characteristics provides a reliable solution for secure authentication of Internet of Things (IoT) devices. Deep neural networks have demonstrated strong capability in improving RFFI performance; however, their high computational complexity and large parameter size pose [...] Read more.
Radio frequency fingerprint identification (RFFI) based on physical-layer characteristics provides a reliable solution for secure authentication of Internet of Things (IoT) devices. Deep neural networks have demonstrated strong capability in improving RFFI performance; however, their high computational complexity and large parameter size pose significant challenges for deployment on resource-constrained edge devices. In RFFI tasks, existing pruning methods often lack effective performance recovery strategies, which leads to noticeable degradation in identification accuracy after pruning. To address this issue, this paper proposes a pruning method based on adversarial learning and polarization regularization. Polarization regularization is applied to learnable soft masks to effectively distinguish channels to be pruned from those to be retained. In addition, an adversarial learning-based performance recovery strategy is introduced to align the output feature distributions between the baseline network and the pruning network, thereby improving identification accuracy after pruning. Experimental results on multiple RFFI datasets demonstrate that the proposed method can effectively prune ResNet18 and VGG16, achieving substantial reductions in model complexity with only minor losses in identification performance. Full article
(This article belongs to the Special Issue Advanced Robust Processing Techniques and Sensor Technologies for Complex Radio Frequency Signals)
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19 pages, 2968 KB  
Article
CBAM-Enhanced CNN-LSTM with Improved DBSCAN for High-Precision Radar-Based Gesture Recognition
by Shiwei Yi, Zhenyu Zhao and Tongning Wu
Sensors 2026, 26(6), 1835; https://doi.org/10.3390/s26061835 - 14 Mar 2026
Viewed by 558
Abstract
In recent years, radar-based gesture recognition technology has been widely applied in industrial and daily life scenarios. However, increasingly complex application scenarios have imposed higher demands on the accuracy and robustness of gesture recognition algorithms, and challenges such as clutter interference, inter-gesture similarity, [...] Read more.
In recent years, radar-based gesture recognition technology has been widely applied in industrial and daily life scenarios. However, increasingly complex application scenarios have imposed higher demands on the accuracy and robustness of gesture recognition algorithms, and challenges such as clutter interference, inter-gesture similarity, and spatial–temporal feature ambiguity limit recognition performance. To address these challenges, a novel framework named CECL, which incorporates the Convolutional Block Attention Module (CBAM) into a Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) architecture, is proposed for high-accuracy radar-based gesture recognition. The CBAM adaptively highlights discriminative spatial regions and suppresses irrelevant background, and the CNN-LSTM network captures temporal dynamics across gesture sequences. During gesture signal processing, the Blackman window is applied to suppress spectral leakage. Additionally, a combination of wavelet thresholding and dynamic energy nulling is employed to effectively suppress clutter and enhance feature representation. Furthermore, an improved Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm further eliminates isolated sparse noise while preserving dense and valid target signal regions. Experimental results demonstrate that the proposed algorithm achieves 98.33% average accuracy in gesture classification, outperforming other baseline models. It exhibits excellent recognition performance across various distances and angles, demonstrating significantly enhanced robustness. Full article
(This article belongs to the Special Issue Advanced Robust Processing Techniques and Sensor Technologies for Complex Radio Frequency Signals)
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