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Advances in Remote Sensing and Electromagnetic Spectrum Sensing: Data Acquisition and Signal Processing

A special issue of Sensors (ISSN 1424-8220).

Deadline for manuscript submissions: 30 November 2025 | Viewed by 1843

Special Issue Editors

Prof. Dr. Hongbing Ji

E-Mail Website
Guest Editor
School of Electronic Engineering, Xidian University, Xi’an 710071, China
Interests: radar signal processing; target tracking; information fusion; intelligent information processing
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Lin Li

E-Mail Website
Guest Editor
School of Electronic Engineering, Xidian University, Xi’an 710071, China
Interests: non-stationary signal processing; intelligent electromagnetic spectrum sensing
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Tiancheng Li

E-Mail Website
Guest Editor
Key Laboratory of Information Fusion Technology, School of Automation, Northwestern Polytechnical University, Xi’an 710072, China
Interests: information fusion; sensor network; positioning; target tracking
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The electromagnetic spectrum has gradually become a cornerstone of economic and social development. As integrated radar systems, communications, navigation, and other sensor systems have advanced, remote sensing and electromagnetic spectrum sensing are transforming from a process of detection to recognition, from classical models to deep learning, from single sensor to multi-sensor information fusion, and from single function to composite sensing. In response to challenges related to the complex electromagnetic environment ranging from several kHz to over 100 GHz, future remote sensing and electromagnetic spectrum sensing frameworks should possess self-learning and environmental adaptability, leading to the creation of systematic and comprehensive intelligent systems. This Special Issue will highlight recent progress related to these topics.

This Special Issue will address issues related to state-of-the-art remote sensing and electromagnetic spectrum sensing approaches applicable to data acquisition and signal processing for radar, communication and navigation, providing cross-disciplinary ideas to address present and future challenges. Topics of interest include, but are not limited to, the following:

  • Remote sensing;
  • Cognitive radar systems;
  • Collaborative radar network;
  • Intelligent spectrum sensing;
  • Spectrum sharing and cooperation;
  • Electromagnetic space security;
  • Spectrum perception and cognition;
  • Distributed collaborative sensing;
  • Ubiquitous intelligent sensing;
  • Resilient PNT (positioning, navigation and timing);
  • Signal processing;
  • Target tracking;
  • Multi-sensor information fusion;
  • Automatic target recognition;
  • Automatic modulation classification.

Original research articles and reviews are both welcome in this Special Issue.

You may choose our Joint Special Issue in Remote Sensing. 

Prof. Dr. Hongbing Ji
Prof. Dr. Lin Li
Prof. Dr. Tiancheng Li
Guest Editors

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

  • remote sensing
  • spectrum sensing
  • signal processing
  • target recognition
  • target tracking
  • signal classification
  • information fusion
  • deep learning

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (2 papers)

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Research

22 pages, 3666 KiB  
Article
Resource-Constrained Specific Emitter Identification Based on Efficient Design and Network Compression
by Mengtao Wang, Shengliang Fang, Youchen Fan and Shunhu Hou
Sensors 2025, 25(7), 2293; https://doi.org/10.3390/s25072293 - 4 Apr 2025
Viewed by 361
Abstract
Specific emitter identification (SEI) methods based on deep learning (DL) have effectively addressed complex, multi-dimensional signal recognition tasks by leveraging deep neural networks. However, this advancement introduces challenges such as model parameter redundancy and high feature dimensionality, which pose limitations for resource-constrained (RC) [...] Read more.
Specific emitter identification (SEI) methods based on deep learning (DL) have effectively addressed complex, multi-dimensional signal recognition tasks by leveraging deep neural networks. However, this advancement introduces challenges such as model parameter redundancy and high feature dimensionality, which pose limitations for resource-constrained (RC) edge devices, especially in Internet of Things (IoT) applications. To tackle these problems, we propose an RC-SEI method based on efficient design and model compression. Specifically, for efficient design, we have developed a lightweight convolution network (LCNet) that aims to balance performance and complexity. Regarding model compression, we introduce sparse regularization techniques in the fully connected (FC) layer, achieving over 99% feature dimensionality reduction. Furthermore, we have comprehensively evaluated the proposed method on public automatic-dependent surveillance-broadcast (ADS-B) and Wi-Fi datasets. Simulation results demonstrate that our proposed method exhibits superior performance in terms of both recognition accuracy and model complexity. Specifically, LCNet achieved accuracies of 99.40% and 99.90% on the ADS-B and Wi-Fi datasets, respectively, with only 33,510 and 33,544 parameters. These results highlight the feasibility and potential of our proposed RC-SEI method for RC scenarios. Full article
(This article belongs to the Special Issue Advances in Remote Sensing and Electromagnetic Spectrum Sensing: Data Acquisition and Signal Processing)
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14 pages, 658 KiB  
Communication
Signal Separation Operator Based on Wavelet Transform for Non-Stationary Signal Decomposition
by Ningning Han, Yongzhen Pei and Zhanjie Song
Sensors 2024, 24(18), 6026; https://doi.org/10.3390/s24186026 - 18 Sep 2024
Viewed by 1142
Abstract
This paper develops a new frame for non-stationary signal separation, which is a combination of wavelet transform, clustering strategy and local maximum approximation. We provide a rigorous mathematical theoretical analysis and prove that the proposed algorithm can estimate instantaneous frequencies and sub-signal modes [...] Read more.
This paper develops a new frame for non-stationary signal separation, which is a combination of wavelet transform, clustering strategy and local maximum approximation. We provide a rigorous mathematical theoretical analysis and prove that the proposed algorithm can estimate instantaneous frequencies and sub-signal modes from a blind source signal. The error bounds for instantaneous frequency estimation and sub-signal recovery are provided. Numerical experiments on synthetic and real data demonstrate the effectiveness and efficiency of the proposed algorithm. Our method based on wavelet transform can be extended to other time–frequency transforms, which provides a new perspective of time–frequency analysis tools in solving the non-stationary signal separation problem. Full article
(This article belongs to the Special Issue Advances in Remote Sensing and Electromagnetic Spectrum Sensing: Data Acquisition and Signal Processing)
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