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Radar Target Detection, Imaging and Recognition (2nd Edition)
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Radar Target Detection, Imaging and Recognition (2nd Edition)

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

Deadline for manuscript submissions: 1 July 2026 | Viewed by 1937

Special Issue Editors

Prof. 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: chaos theory and applications; mobile robot; radar-jamming game-evolution technology; multifunctional 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. Yuanhang Wang

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 Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Radar can sense the target and environment at any time and in any weather and is a kind of sensor that 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.

Therefore, this Special Issue is a continuation of the first successfully organized Special issue on recent advanced techniques in the fields of theory and application of radar detection, imaging and recognition for the Sensors journal.

Topics may include, but are not limited to, the following:

  • Radar detection, tracking, and 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.

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

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

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Related Special Issue

Published Papers (3 papers)

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Research

34 pages, 13840 KB  
Article
An Adaptive Detection Algorithm for Non-Uniform Sea Clutter Background Targets Based on Iterative Weighting and Sample Purification
by Hang Su, Liang Zhang, Cheng Zhao and Ke Li
Sensors 2026, 26(10), 3195; https://doi.org/10.3390/s26103195 (registering DOI) - 18 May 2026
Abstract
To address the severe performance degradation of radar weak target detection induced by dense cluster targets and sea-spike interference in nonhomogeneous sea clutter environments, this paper proposes an enhanced Adaptive Normalized Matched Filter algorithm based on iterative weighting and sample purification (IWP-ANMF). The [...] Read more.
To address the severe performance degradation of radar weak target detection induced by dense cluster targets and sea-spike interference in nonhomogeneous sea clutter environments, this paper proposes an enhanced Adaptive Normalized Matched Filter algorithm based on iterative weighting and sample purification (IWP-ANMF). The proposed algorithm establishes a closed-loop iterative detection framework capable of highly sensitive discrimination of anomalous data within the reference window—particularly cluster targets and strong discrete sea spikes that severely distort covariance matrix features—identifying them as “contaminated samples.” During each iteration, target-likelihood statistics are calculated for all reference samples based on the current covariance matrix estimate. Subsequently, an adaptive deep-notch suppression strategy is applied to contaminated samples, such as cluster targets, according to their statistical characteristics, thereby progressively purifying the sample covariance matrix (SCM) estimation. Theoretically, this iterative procedure is rigorously proven to converge to the optimal solution of a robust weighted covariance matrix estimation problem. Comprehensive validations using both Monte Carlo simulations and measured K-distributed sea clutter data demonstrate that, compared to classical ANMF and Generalized Inner Product (GIP) approaches, the proposed algorithm exhibits outstanding robustness and detection performance when confronted with heterogeneous contamination scenarios, especially high-density cluster targets. This method effectively eliminates the blind-zone expansion and performance deterioration caused by the wideband masking of cluster targets, significantly enhancing weak target detection capabilities under complex maritime conditions. Full article
(This article belongs to the Special Issue Radar Target Detection, Imaging and Recognition (2nd Edition))
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25 pages, 20212 KB  
Article
Radar Resolution Enhancement Based on Burg-Aided MIMO-DBS and Burg-Aided MIMO-SAR
by Muge Bekar, Ali Bekar, Anum Pirkani, Christopher John Baker and Marina Gashinova
Sensors 2026, 26(9), 2698; https://doi.org/10.3390/s26092698 - 27 Apr 2026
Viewed by 685
Abstract
Autonomous systems require sensors that provide high-resolution imagery in adverse lighting and weather conditions for advanced situational awareness. In this regard, radars are a mandatory component of autonomous systems. Although Multiple-Input Multiple-Output (MIMO) radars provide high angular resolution beyond that of their actual [...] Read more.
Autonomous systems require sensors that provide high-resolution imagery in adverse lighting and weather conditions for advanced situational awareness. In this regard, radars are a mandatory component of autonomous systems. Although Multiple-Input Multiple-Output (MIMO) radars provide high angular resolution beyond that of their actual physical dimension, much higher cross-range resolutions are required, especially in traffic congested areas, to differentiate and recognize closely positioned targets. The motion of the MIMO radar platform can be exploited to obtain higher cross-range resolution in the off-boresight direction, using Synthetic Aperture Radar (SAR) and Doppler Beam Sharpening (DBS) techniques, but improvements in the boresight direction, the most crucial direction for path planning, require the use of super-resolution techniques. This paper proposes a technique that combines the Burg algorithm with MIMO-SAR and MIMO-DBS radar data to enhance the cross-range resolution in the boresight direction and to achieve further enhanced cross-range resolution in off-boresight directions. The proposed technique is applied to both frequency domain and time domain data in back-projection (BP) and DBS image formation processing. A comprehensive comparison is made, with evaluation of corresponding performance and operational complexity. The performance of the technique is validated through simulation, lab-based and real-world experiments at a frequency of 77 GHz. Full article
(This article belongs to the Special Issue Radar Target Detection, Imaging and Recognition (2nd Edition))
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21 pages, 3416 KB  
Article
Multi-UAV-Borne Surveillance Radar Trajectory Planning Method Based on Imitation Learning
by Xuchao Gao, Mingqiang Li, Kai Guan and Jianjun Ge
Sensors 2026, 26(9), 2691; https://doi.org/10.3390/s26092691 - 26 Apr 2026
Viewed by 866
Abstract
To address the high computational complexity and insufficient real-time performance of traditional multi-radar trajectory planning methods in complex multi-platform sensing scenarios, this study proposes an imitation-learning-based trajectory planning method for multi-radar systems. The method features a trajectory policy neural network architecture based on [...] Read more.
To address the high computational complexity and insufficient real-time performance of traditional multi-radar trajectory planning methods in complex multi-platform sensing scenarios, this study proposes an imitation-learning-based trajectory planning method for multi-radar systems. The method features a trajectory policy neural network architecture based on multi-semantic information, and involves a training-data construction method with coverage rate as the optimization objective. The trajectory policy neural network is then trained via an imitation-learning algorithm with an auxiliary target. Simulation results show that the proposed method achieves an average coverage rate of 93.95%, and improves the single-step decision efficiency by a factor of 6.7 compared with heuristic-based trajectory optimization methods. Full article
(This article belongs to the Special Issue Radar Target Detection, Imaging and Recognition (2nd Edition))
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