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Radar Target Detection, Imaging and Recognition
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Radar Target Detection, Imaging and Recognition

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

Deadline for manuscript submissions: 31 March 2025 | Viewed by 352

Special Issue Editors

Dr. Tianxian Zhang

E-Mail Website
Guest Editor
School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu 610056, China
Interests: radar jamming game evolution technology; detection and communication integrated resource management and control technology; weak target detection technology; multi-functional waveform design
Special Issues, Collections and Topics in MDPI journals
Dr. Xueting Li

E-Mail Website
Guest Editor
School of Aeronautics and Astronautics, Sichuan University, Chengdu 610065, China
Interests: signal detection; multi-sensor resource management; multi-function integrated system resource optimization
Special Issues, Collections and Topics in MDPI journals
Dr. Wang Yuanhang

E-Mail Website
Guest Editor
School of Information and Communication Engineering, Dalian University of Technology, Dalian 116024, China
Interests: Radar jamming game evolution technology; multi-functional radar system

Special Issue Information

Dear Colleagues,

Radar can sense the target and environment at any time and any weather, is a kind of sensor which plays an important role in a wide range of applications, such as target detection, imaging and recognition. With the advances in radar hardware and software technologies, more flexible radar working modes with more potential have been exploited, together with new theories and methods for advanced radar detection, imaging and recognition. Nowadays, radar detection, imaging and recognition have become an international front and hotspot in the field of sensor research.

The present Special Issue aims to exhibit a number of recent advanced techniques in the fields of theory and application of radar detection, imaging and recognition. Topic may include but not limited to the following topics:

  • Radar detection, tracking, parameter estimation
  • Clutter or jamming suppression
  • Beamforming
  • SAR/ISAR/ultra-wideband radar
  • Radar imaging technology
  • Radar target recognition technology
  • Synthetic aperture techniques
  • Signal and data processing
  • Advanced RF and antenna technologies
  • Waveform diversity
  • Radar design and simulation
  • Radar jamming

Dr. Tianxian Zhang
Dr. Xueting Li
Dr. Wang Yuanhang
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

  • radar imaging technology
  • radar design and simulation
  • radar detection, tracking, parameter estimation

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Published Papers (1 paper)

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Research

17 pages, 3646 KiB  
Article
Motion Clutter Suppression for Non-Cooperative Target Identification Based on Frequency Correlation Dual-SVD Reconstruction
by Weikun He, Yichuan Luo and Xiaoxiao Shang
Sensors 2024, 24(16), 5298; https://doi.org/10.3390/s24165298 (registering DOI) - 15 Aug 2024
Abstract
Non-cooperative targets, such as birds and unmanned aerial vehicles (UAVs), are typical low-altitude, slow, and small (LSS) targets with low observability. Radar observations in such scenarios are often complicated by strong motion clutter originating from sources like airplanes and cars. Hence, distinguishing between [...] Read more.
Non-cooperative targets, such as birds and unmanned aerial vehicles (UAVs), are typical low-altitude, slow, and small (LSS) targets with low observability. Radar observations in such scenarios are often complicated by strong motion clutter originating from sources like airplanes and cars. Hence, distinguishing between birds and UAVs in environments with strong motion clutter is crucial for improving target monitoring performance and ensuring flight safety. To address the impact of strong motion clutter on discriminating between UAVs and birds, we propose a frequency correlation dual-SVD (singular value decomposition) reconstruction method. This method exploits the strong power and spectral correlation characteristics of motion clutter, contrasted with the weak scattering characteristics of bird and UAV targets, to effectively suppress clutter. Unlike traditional clutter suppression methods based on SVD, our method avoids residual clutter or target loss while preserving the micro-motion characteristics of the targets. Based on the distinct micro-motion characteristics of birds and UAVs, we extract two key features: the sum of normalized large eigenvalues of the target’s micro-motion component and the energy entropy of the time–frequency spectrum of the radar echoes. Subsequently, the kernel fuzzy c-means algorithm is applied to classify bird and UAV targets. The effectiveness of our proposed method is validated through results using both simulation and experimental data. Full article
(This article belongs to the Special Issue Radar Target Detection, Imaging and Recognition)
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