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Electronics
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New Insights in Radar Imaging
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New Insights in Radar Imaging

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Microwave and Wireless Communications".

Deadline for manuscript submissions: 15 May 2024 | Viewed by 3126

Special Issue Editors

Dr. Gang Xu

E-Mail Website
Guest Editor
State Key Laboratory of Millimeter Waves, Southeast University, Nanjing 210096, China
Interests: SAR/ISAR imaging; sparse radar imaging
Special Issues, Collections and Topics in MDPI journals
Dr. Jia Duan

E-Mail Website
Guest Editor
The School of Electronics and Communication, Sun Yat-sen University, Shenzhen 528406, China
Interests: ISAR imaging; attributed scattering center; non-cooperative target recognition
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Imaging radars, including synthetic aperture radars (SARs) and inverse synthetic aperture radars (ISARs), are a class of significantly important remote sensors which can effectively work in all weather conditions, all day and over long distances. Currently, radar imaging can provide very high-resolution images and multi-dimensional measurements (such as multi-channel, multi-aspect, multi-frequency, multi-polarization, multi-temporal, etc.) in a short period of time. Furthermore, with the recent developments in system design, advanced signal processing and artificial intelligence theory, radar imaging technology has been demonstrating significant innovations with intelligent features. For example, machine learning and deep learning methods have been applied to radar imaging, yielding new paradigms (models, concepts and architecture designs) of intelligent radar systems and processing architectures. Meanwhile, information extraction and target interpretation using intelligent technology from multi-dimensional big data is also a very important issue.

In order to support the development of increasingly sophisticated and intelligent radar imaging technologies, this Special Issue focuses on the most recent radar imaging systems, efficient multi-dimensional radar imaging strategies, the most recent radar imaging theories based on intelligent technology, and their innovative applications in a variety of fields. It primarily entails the investigation of the most recent radar imaging mechanisms and imaging theories, as well as the detection, classification and recognition of targets of interest in radar images, and the capture and mining of image target information.

Dr. Gang Xu
Dr. Jia Duan
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. Electronics 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 2400 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

  • advanced and intelligent imaging radar system design
  • multi-mode radar imaging theory and architecture
  • multi-frequency (microwave, mmw-wave, THz) radar imaging theory and architecture
  • multi-dimensional radar imaging theory and architecture
  • machine learning and deep-learning-based radar imaging
  • three-dimensional SAR/ISAR imaging and parameter inversion
  • sparse imaging technology of SAR, ISAR and TomoSAR
  • radar image enhancement and feature extraction
  • radar image target recognition and surface classification

Published Papers (3 papers)

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Research

23 pages, 5902 KiB  
Article
Improved Lv’s Distribution for Noisy Multicomponent LFM Signals Analysis
by Kai Yang, Xueshi Li, Yang Li and Jibin Zheng
Electronics 2024, 13(2), 244; https://doi.org/10.3390/electronics13020244 - 05 Jan 2024
Viewed by 527
Abstract
This paper presents the improved Lv’s distribution (ImLVD) for noisy multicomponent linear frequency-modulated (LFM) signals analysis, which is of significant importance in radar signal processing. Two goals of this paper are (i) to overcome drawbacks of the Lv’s distribution (LVD), and (ii) to [...] Read more.
This paper presents the improved Lv’s distribution (ImLVD) for noisy multicomponent linear frequency-modulated (LFM) signals analysis, which is of significant importance in radar signal processing. Two goals of this paper are (i) to overcome drawbacks of the Lv’s distribution (LVD), and (ii) to study mechanisms of the constant delay introduction. Theoretical comparisons in cross-term suppression, resolution, peak-to-sidelobe level, anti-noise performance and implementation are performed for the maximum likelihood (ML) method, Wigner–Hough transform (WHT), LVD, parameterized centroid frequency–chirp rate distribution (PCFCRD) and ImLVD. Based on theoretical comparisons and illustrative examples, superiorities of the ImLVD are demonstrated and several unclear mechanisms of the introduced constant delay are interpreted. Finally, three numerical examples are given to illustrate that, because of the high cross-term suppression, resolution, peak-to-sidelobe level and anti-noise performance without the non-uniform integration variable, the ImLVD is more suitable for noisy multicomponent LFM signals analysis. Full article
(This article belongs to the Special Issue New Insights in Radar Imaging)
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17 pages, 6167 KiB  
Article
Using the Displaced Phase Center Azimuth Multiple Beams Technique with Spaceborne Synthetic Aperture Radar Systems for Multichannel Reconstruction of Accelerated Moving Targets
by Wei Xu, Yu Chen, Pingping Huang, Weixian Tan and Yaolong Qi
Electronics 2023, 12(24), 4954; https://doi.org/10.3390/electronics12244954 - 10 Dec 2023
Viewed by 754
Abstract
The displaced phase center multiple azimuth beams (DPCMAB) technique can help spaceborne synthetic aperture radar (SAR) systems obtain the high-resolution wide-swath (HRWS) imaging capacity, and azimuth multichannel reconstruction is usually required due to azimuth non-uniform sampling. Compared with stationary and moving targets, the [...] Read more.
The displaced phase center multiple azimuth beams (DPCMAB) technique can help spaceborne synthetic aperture radar (SAR) systems obtain the high-resolution wide-swath (HRWS) imaging capacity, and azimuth multichannel reconstruction is usually required due to azimuth non-uniform sampling. Compared with stationary and moving targets, the range history and azimuth signal model of the moving target with an acceleration are obviously different. The azimuth multichannel signal model of an accelerated moving target is established, and the relationship between acceleration and Doppler parameters is analyzed. Furthermore, the impact of the acceleration on azimuth multichannel reconstruction and imaging results is simulated and analyzed. According to the azimuth multichannel signal model, an azimuth multichannel reconstruction approach for accelerated moving targets is proposed. The key point of the proposed reconstruction approach is the modified azimuth multichannel matrix, which is related not only to azimuth and slant velocities but also accelerations. The target’s velocities and accelerations are obtained using multiple Doppler parameter estimations. Compared with the conventional method of processing the raw data of accelerated moving targets, this proposed method could distinctly suppress image defocusing and pairs of false targets. Simulation results on point targets validate the proposed azimuth multichannel reconstruction approach. Full article
(This article belongs to the Special Issue New Insights in Radar Imaging)
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22 pages, 6624 KiB  
Article
CCDS-YOLO: Multi-Category Synthetic Aperture Radar Image Object Detection Model Based on YOLOv5s
by Min Huang, Zexu Liu, Tianen Liu and Jingyang Wang
Electronics 2023, 12(16), 3497; https://doi.org/10.3390/electronics12163497 - 18 Aug 2023
Cited by 4 | Viewed by 1242
Abstract
Synthetic Aperture Radar (SAR) is an active microwave sensor that has attracted widespread attention due to its ability to observe the ground around the clock. Research on multi-scale and multi-category target detection methods holds great significance in the fields of maritime resource management [...] Read more.
Synthetic Aperture Radar (SAR) is an active microwave sensor that has attracted widespread attention due to its ability to observe the ground around the clock. Research on multi-scale and multi-category target detection methods holds great significance in the fields of maritime resource management and wartime reconnaissance. However, complex scenes often influence SAR object detection, and the diversity of target scales also brings challenges to research. This paper proposes a multi-category SAR image object detection model, CCDS-YOLO, based on YOLOv5s, to address these issues. Embedding the Convolutional Block Attention Module (CBAM) in the feature extraction part of the backbone network enables the model’s ability to extract and fuse spatial information and channel information. The 1 × 1 convolution in the feature pyramid network and the first layer convolution of the detection head are replaced with the expanded convolution, Coordinate Conventional (CoordConv), forming a CRD-FPN module. This module more accurately perceives the spatial details of the feature map, enhancing the model’s ability to handle regression tasks compared to traditional convolution. In the detector segment, a decoupled head is utilized for feature extraction, offering optimal and effective feature information for the classification and regression branches separately. The traditional Non-Maximum Suppression (NMS) is substituted with the Soft Non-Maximum Suppression (Soft-NMS), successfully reducing the model’s duplicate detection rate for compact objects. Based on the experimental findings, the approach presented in this paper demonstrates excellent results in multi-category target recognition for SAR images. Empirical comparisons are conducted on the filtered MSAR dataset. Compared with YOLOv5s, the performance of CCDS-YOLO has been significantly improved. The mAP@0.5 value increases by 3.3% to 92.3%, the precision increases by 3.4%, and the mAP@0.5:0.95 increases by 6.7%. Furthermore, in comparison with other mainstream detection models, CCDS-YOLO stands out in overall performance and anti-interference ability. Full article
(This article belongs to the Special Issue New Insights in Radar Imaging)
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