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

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "AI Remote Sensing".

Deadline for manuscript submissions: 1 May 2025 | Viewed by 5817

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.

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. Remote Sensing 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 2700 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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Published Papers (5 papers)

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Research

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18 pages, 10318 KiB  
Article
FE-SKViT: A Feature-Enhanced ViT Model with Skip Attention for Automatic Modulation Recognition
by Guangyao Zheng, Bo Zang, Penghui Yang, Wenbo Zhang and Bin Li
Remote Sens. 2024, 16(22), 4204; https://doi.org/10.3390/rs16224204 - 11 Nov 2024
Viewed by 532
Abstract
Automatic modulation recognition (AMR) is widely employed in communication systems. However, under conditions of low signal-to-noise ratio (SNR), recent studies reveal limitations in achieving high AMR accuracy. In this work, we introduce a novel network architecture that leverages a transformer-inspired approach tailored for [...] Read more.
Automatic modulation recognition (AMR) is widely employed in communication systems. However, under conditions of low signal-to-noise ratio (SNR), recent studies reveal limitations in achieving high AMR accuracy. In this work, we introduce a novel network architecture that leverages a transformer-inspired approach tailored for AMR, called Feature-Enhanced Transformer with skip-attention (FE-SKViT). This innovative design adeptly harnesses the advantages of translation variant convolution and the Transformer framework, handling intra-signal variance and small cross-signal variance to achieve enhanced recognition accuracy. Experimental results on RadioML2016.10a, RadioML2016.10b, and RML22 datasets demonstrate that the Feature-Enhanced Transformer with skip-attention (FE-SKViT) excels over other methods, particularly under low SNR conditions ranging from −4 to 6 dB. 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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22 pages, 5859 KiB  
Article
A Multi-Active and Multi-Passive Sensor Fusion Algorithm for Multi-Target Tracking in Dense Group Clutter Environments
by Yongquan Zhang, Fan Yang, Wenbo Zhang, Aomen Shang and Zhibin Li
Remote Sens. 2024, 16(22), 4120; https://doi.org/10.3390/rs16224120 - 5 Nov 2024
Viewed by 672
Abstract
Multi-target tracking (MTT) of multi-active and multi-passive sensor (MAMPS) systems in dense group clutter environments is facing significant challenges in measurement fusion. Due to the difference in measurement information characteristics in MAMPS fusion, it is difficult to effectively correlate and fuse different types [...] Read more.
Multi-target tracking (MTT) of multi-active and multi-passive sensor (MAMPS) systems in dense group clutter environments is facing significant challenges in measurement fusion. Due to the difference in measurement information characteristics in MAMPS fusion, it is difficult to effectively correlate and fuse different types of sensors’ measurements, leading to difficulty in taking full advantage of various types of sensors to improve target tracking accuracy. To this end, we present a novel MAMPS fusion algorithm, which is based on centralized measurement association fusion (MAF) and distributed deep neural network (DNN) track fusion, named the MAMPS-MAF-DNN algorithm. Firstly, to reduce the impact of the dense group clutter, a clutter pre-processing algorithm is elaborated, which combines the advantages of the CFDP (cluster by finding density peaks) and double threshold screening algorithms. Then, for the single-active and multi-passive sensor (SAMPS) system, a centralized MAF algorithm based on angle information is developed, called the SAMPS-MAF algorithm. Finally, the SAMPS-MAF algorithm is extended to the MAMPS system within the DNN framework, and the complete MAMPS-MAF-DNN algorithm is proposed. Experimental results indicate that, compared to the existing MAF and covariance intersection (CI) fusion algorithms, the proposed MAMPS-MAF-DNN algorithm can fully combine the advantages of multi-active and multi-passive sensors, efficiently reduce the computational complexity, and obviously improve the tracking accuracy. 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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23 pages, 10938 KiB  
Article
GASSF-Net: Geometric Algebra Based Spectral-Spatial Hierarchical Fusion Network for Hyperspectral and LiDAR Image Classification
by Rui Wang, Xiaoxi Ye, Yao Huang, Ming Ju and Wei Xiang
Remote Sens. 2024, 16(20), 3825; https://doi.org/10.3390/rs16203825 - 14 Oct 2024
Viewed by 909
Abstract
The field of multi-source remote sensing observation is becoming increasingly dynamic through the integration of various remote sensing data sources. However, existing deep learning methods face challenges in differentiating between internal and external relationships and capturing fine spatial features. These models often struggle [...] Read more.
The field of multi-source remote sensing observation is becoming increasingly dynamic through the integration of various remote sensing data sources. However, existing deep learning methods face challenges in differentiating between internal and external relationships and capturing fine spatial features. These models often struggle to effectively capture comprehensive information across remote sensing data bands, and they have inherent differences in the size, structure, and physical properties of different remote sensing datasets. To address these challenges, this paper proposes a novel geometric-algebra-based spectral–spatial hierarchical fusion network (GASSF-Net), which uses geometric algebra for the first time to process multi-source remote sensing images, enabling a more holistic approach to handling these images by simultaneously leveraging the real and imaginary components of geometric algebra to express structural information. This method captures the internal and external relationships between remote sensing image features and spatial information, effectively fusing the features of different remote sensing data to improve classification accuracy. GASSF-Net uses geometric algebra (GA) to represent pixels from different bands as multivectors, thus capturing the intrinsic relationships between spectral bands while preserving spatial information. The network begins by deeply mining the spectral–spatial features of a hyperspectral image (HSI) using pairwise covariance operators. These features are then extracted through two branches: a geometric-algebra-based branch and a real-valued network branch. Additionally, the geometric-algebra-based network extracts spatial information from light detection and ranging (LiDAR) to complement the elevation data lacking in the HSI. Finally, a genetic-algorithm-based cross-fusion module is introduced to fuse the HSI and LiDAR data for improved classification. Experiments conducted on three well-known datasets, Trento, MUUFL, and Houston, demonstrate that GASSF-Net significantly outperforms traditional methods in terms of classification accuracy and model efficiency. 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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25 pages, 13951 KiB  
Article
1D-CNN-Transformer for Radar Emitter Identification and Implemented on FPGA
by Xiangang Gao, Bin Wu, Peng Li and Zehuan Jing
Remote Sens. 2024, 16(16), 2962; https://doi.org/10.3390/rs16162962 - 12 Aug 2024
Viewed by 1896
Abstract
Deep learning has brought great development to radar emitter identification technology. In addition, specific emitter identification (SEI), as a branch of radar emitter identification, has also benefited from it. However, the complexity of most deep learning algorithms makes it difficult to adapt to [...] Read more.
Deep learning has brought great development to radar emitter identification technology. In addition, specific emitter identification (SEI), as a branch of radar emitter identification, has also benefited from it. However, the complexity of most deep learning algorithms makes it difficult to adapt to the requirements of the low power consumption and high-performance processing of SEI on embedded devices, so this article proposes solutions from the aspects of software and hardware. From the software side, we design a Transformer variant network, lightweight convolutional Transformer (LW-CT) that supports parameter sharing. Then, we cascade convolutional neural networks (CNNs) and the LW-CT to construct a one-dimensional-CNN-Transformer(1D-CNN-Transformer) lightweight neural network model that can capture the long-range dependencies of radar emitter signals and extract signal spatial domain features meanwhile. In terms of hardware, we design a low-power neural network accelerator based on an FPGA to complete the real-time recognition of radar emitter signals. The accelerator not only designs high-efficiency computing engines for the network, but also devises a reconfigurable buffer called “Ping-pong CBUF” and two-level pipeline architecture for the convolution layer for alleviating the bottleneck caused by the off-chip storage access bandwidth. Experimental results show that the algorithm can achieve a high recognition performance of SEI with a low calculation overhead. In addition, the hardware acceleration platform not only perfectly meets the requirements of the radar emitter recognition system for low power consumption and high-performance processing, but also outperforms the accelerators in other papers in terms of the energy efficiency ratio of Transformer layer processing. 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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16 pages, 2967 KiB  
Technical Note
Field Programmable Gate Array (FPGA) Implementation of Parallel Jacobi for Eigen-Decomposition in Direction of Arrival (DOA) Estimation Algorithm
by Shuang Zhou and Li Zhou
Remote Sens. 2024, 16(20), 3892; https://doi.org/10.3390/rs16203892 - 19 Oct 2024
Viewed by 722
Abstract
The eigen-decomposition of a covariance matrix is a key step in the Direction of Arrival (DOA) estimation algorithms such as subspace classes. Eigen-decomposition using the parallel Jacobi algorithm implemented on FPGA offers excellent parallelism and real-time performance. Addressing the high complexity and resource [...] Read more.
The eigen-decomposition of a covariance matrix is a key step in the Direction of Arrival (DOA) estimation algorithms such as subspace classes. Eigen-decomposition using the parallel Jacobi algorithm implemented on FPGA offers excellent parallelism and real-time performance. Addressing the high complexity and resource consumption of the traditional parallel Jacobi algorithm implemented on FPGA, this study proposes an improved FPGA-based parallel Jacobi algorithm for eigen-decomposition. By analyzing the relationship between angle calculation and rotation during the Jacobi algorithm decomposition process, leveraging parallelism in the data processing, and based on the concepts of time-division multiplexing and parallel partition processing, this approach effectively reduces FPGA resource consumption. The improved parallel Jacobi algorithm is then applied to the classic DOA estimation algorithm, the MUSIC algorithm, and implemented on Xilinx’s Zynq FPGA. Experimental results demonstrate that this parallel approach can reduce resource consumption by approximately 75% compared to the traditional method but introduces little additional time consumption. The proposed method in this paper will solve the problem of great hardware consumption of eigen-decomposition based on FPGA in DOA applications. 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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