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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: 15 December 2025 | Viewed by 5339

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. 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 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. 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 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 (7 papers)

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Research

14 pages, 7668 KiB  
Article
A Machine Learning Method for the Fast Simulation of the Scattering Characteristics of a Target Under a Planar Layered Medium
by Zhaoyu Wang, Qinghe Zhang, Zhaoyang Shen, Lei Zhang and Han Liu
Sensors 2025, 25(8), 2481; https://doi.org/10.3390/s25082481 - 15 Apr 2025
Viewed by 199
Abstract
Numerical simulation of ground-penetrating radar (GPR) has been widely used to enhance the interpretation of GPR data and serves as a key component in Full Waveform Inversion (FWI). In response to the time-consuming numerical computation of layered medium and buried targets, which leads [...] Read more.
Numerical simulation of ground-penetrating radar (GPR) has been widely used to enhance the interpretation of GPR data and serves as a key component in Full Waveform Inversion (FWI). In response to the time-consuming numerical computation of layered medium and buried targets, which leads to inefficiency in full-wave inversion, this paper proposes a machine learning-based forward scattering rapid solution method. Using the detection of rebar buried in concrete under sand as the GPR application scenario, with scene parameters such as concrete moisture content, rebar radius, and burial depth, scattering echo signals are obtained via Finite Difference Time Domain (FDTD) simulation. Principal component analysis (PCA) is applied to reduce the dimensionality of the echo data, and the first 40 principal component weight coefficients are selected as the output of the deep learning network. An innovative cyclic nested deep learning network architecture is designed, which not only fully explores the intrinsic causal relationship between the scene parameters and the principal component weight coefficients, but also refines and corrects each predicted principal component. The numerical results demonstrate that, compared with traditional machine learning methods, the cyclic nested machine learning network architecture offers higher prediction accuracy and learning efficiency, validating the effectiveness of the proposed method. Full article
(This article belongs to the Special Issue Radar Target Detection, Imaging and Recognition)
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21 pages, 5113 KiB  
Article
An Active Radar Interferometer Utilizing a Heterodyne Principle-Based Target Modulator
by Simon Müller, Andreas R. Diewald and Georg Fischer
Sensors 2025, 25(6), 1711; https://doi.org/10.3390/s25061711 - 10 Mar 2025
Viewed by 403
Abstract
The Active Radar Interferometer (AcRaIn) represents a novel approach in secondary radar technology, aimed at environments with high reflective clutter, such as pipes and tunnels. This study introduces a compact design minimizing peripheral components and leveraging commercial semiconductor technologies operating in the 24 [...] Read more.
The Active Radar Interferometer (AcRaIn) represents a novel approach in secondary radar technology, aimed at environments with high reflective clutter, such as pipes and tunnels. This study introduces a compact design minimizing peripheral components and leveraging commercial semiconductor technologies operating in the 24 GHz ISM band. A heterodyne principle was adopted to enhance unambiguity and phase coherence without requiring synchronization or separate communication channels. Experimental validation involved free-space and pipe measurements, demonstrating functionality over distances up to 150 m. The radar system effectively reduced interference and achieved high precision in both straight and bent pipe scenarios, with deviations below 1.25% compared to manual measurements. By processing signals at intermediate frequencies, advantages such as improved efficiency, isolation, and system flexibility were achieved. Notably, the integration of amplitude modulation suppressed passive clutter, enabling clearer signal differentiation. Key challenges identified include optimizing signal processing and addressing logarithmic signal attenuation for better precision. These findings underscore AcRaIn’s potential for pipeline monitoring and similar applications. Full article
(This article belongs to the Special Issue Radar Target Detection, Imaging and Recognition)
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20 pages, 688 KiB  
Article
Adversarial Range Gate Pull-Off Jamming Against Tracking Radar
by Yuanhang Wang, Yi Han and Yi Jiang
Sensors 2025, 25(5), 1553; https://doi.org/10.3390/s25051553 - 3 Mar 2025
Viewed by 565
Abstract
Range gate pull-off (RGPO) jamming is an effective method for track deception aimed at radar systems. Nevertheless, enhancing the effectiveness of the jamming strategy continues to pose challenges, restricting the RGPO jamming method from achieving its maximum potential. This paper focuses on addressing [...] Read more.
Range gate pull-off (RGPO) jamming is an effective method for track deception aimed at radar systems. Nevertheless, enhancing the effectiveness of the jamming strategy continues to pose challenges, restricting the RGPO jamming method from achieving its maximum potential. This paper focuses on addressing the problem of optimizing the strategy for white-box RGPO jamming, serving as a foundational step toward quantitative optimization research on RGPO jamming strategies. In the white-box scenario, it is presumed that the jammer has full knowledge of the target radar’s tracking system, encompassing both the choice of tracking method and its parameter configurations. The intricate interactions between the jammer and the tracking radar introduce three primary challenges: (1) Formulating an algebraic expression for the objective function of the jamming strategy optimization is nontrivial; (2) Direct observation of jamming effects from the target radar is challenging; (3) Noise renders the jamming outcomes unpredictable. To tackle these challenges, this study formulates the optimization of the RGPO jamming strategy as an adversarial stochastic simulation optimization (ASSO) problem and introduces a novel solution for the white-box RGPO jamming strategy optimization: a local simulation-assisted particle swarm optimization algorithm with an equal resampling scheme (PSO-ER). The PSO-ER algorithm searches for optimal jamming strategies while utilizing a localized simulation of the tracking radar to evaluate the effectiveness of candidate jamming strategies. Experiments conducted on four benchmark cases confirm that the proposed approach is capable of generating well-tuned strategies for white-box RGPO jamming. Full article
(This article belongs to the Special Issue Radar Target Detection, Imaging and Recognition)
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26 pages, 1270 KiB  
Article
Node Selection and Path Optimization for Passive Target Localization via UAVs
by Xiaoyou Xing, Zhiwen Zhong, Xueting Li and Yiyang Yue
Sensors 2025, 25(3), 780; https://doi.org/10.3390/s25030780 - 28 Jan 2025
Viewed by 550
Abstract
The performance of passive target localization is affected by the positions of unmanned aerial vehicles (UAVs) at a large scale. In this paper, to improve resource utilization efficiency and localization accuracy, the node selection problem and the path optimization problem are jointly investigated. [...] Read more.
The performance of passive target localization is affected by the positions of unmanned aerial vehicles (UAVs) at a large scale. In this paper, to improve resource utilization efficiency and localization accuracy, the node selection problem and the path optimization problem are jointly investigated. Firstly, the target passive localization model is established and the Chan-based time difference of arrival (TDOA) localization method is introduced. Then, the Cramer–Rao lower bound (CRLB) for Chan-TDOA localization is derived, and the problems of node selection and path optimization are formulated. Secondly, a CRLB-based node selection method is proposed to properly divide the UAVs into several groups, localizing different targets, and a CRLB-based path optimization method is proposed to search for the optimal UAV position configuration at each time step. The proposed path optimization method also effectively handles no-fly-zone (NFZ) constraints, ensuring operational safety while maintaining optimal target tracking performance. Also, to improve the efficiency of path optimization, particle swarm algorithm (PSO) is applied to accelerate the searching process. Finally, numerical simulations are performed to verify the validity and effectiveness of the proposed methods in this paper. Full article
(This article belongs to the Special Issue Radar Target Detection, Imaging and Recognition)
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30 pages, 30400 KiB  
Article
Classification of Flying Drones Using Millimeter-Wave Radar: Comparative Analysis of Algorithms Under Noisy Conditions
by Mauro Larrat and Claudomiro Sales
Sensors 2025, 25(3), 721; https://doi.org/10.3390/s25030721 - 24 Jan 2025
Viewed by 1173
Abstract
This study evaluates different machine learning algorithms in detecting and identifying drones using radar data from a 60 GHz millimeter-wave sensor. These signals were collected from a bionic bird and two drones, namely DJI Mavic and DJI Phantom 3 Pro, which were represented [...] Read more.
This study evaluates different machine learning algorithms in detecting and identifying drones using radar data from a 60 GHz millimeter-wave sensor. These signals were collected from a bionic bird and two drones, namely DJI Mavic and DJI Phantom 3 Pro, which were represented in complex form to preserve amplitude and phase information. The first benchmarks used four algorithms, namely long short-term memory (LSTM), gated recurrent unit (GRU), one-dimensional convolutional neural network (Conv1D), and Transformer, and they were benchmarked for robustness under noisy conditions, including artificial noise types like white noise, Pareto noise, impulsive noise, and multipath interference. As expected, Transformer outperformed other algorithms in terms of accuracy, even on noisy data; however, in certain noise contexts, particularly Pareto noise, it showed weaknesses. For this purpose, we propose Multimodal Transformer, which incorporates more statistical features—skewness and kurtosis—in addition to amplitude and phase data. This resulted in a improvement in detection accuracy, even under difficult noise conditions. Our results demonstrate the importance of noise in processing radar signals and the benefits afforded by a multimodal presentation of data in detecting unmanned aerial vehicle and birds. This study sets up a benchmark for state-of-the-art machine learning methodologies for radar-based detection systems, providing valuable insight into methods of increasing the robustness of algorithms to environmental noise. Full article
(This article belongs to the Special Issue Radar Target Detection, Imaging and Recognition)
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19 pages, 8569 KiB  
Article
Two-Dimensional Scattering Center Estimation for Radar Target Recognition Based on Multiple High-Resolution Range Profiles
by Kang-In Lee, Jin-Hyeok Kim and Young-Seek Chung
Sensors 2024, 24(21), 6997; https://doi.org/10.3390/s24216997 - 30 Oct 2024
Cited by 1 | Viewed by 942
Abstract
A new estimation strategy on locations of two-dimensional target scattering centers for radar target recognition is developed by using multiple high-resolution range profiles (HRRPs). Based on the range information contained in multiple HRRPs obtained from various observation angles, the estimated target scattering centers [...] Read more.
A new estimation strategy on locations of two-dimensional target scattering centers for radar target recognition is developed by using multiple high-resolution range profiles (HRRPs). Based on the range information contained in multiple HRRPs obtained from various observation angles, the estimated target scattering centers can be successfully located at the intersection points of the lines passing through the multiple HRRP points. This geometry-based algorithm can significantly reduce the computational complexity while ensuring the ability to estimate the two-dimensional target scattering centers. The computational complexity is formulated and compared to that of the conventional methods based on the synthetic aperture radar (SAR) images and HRRP sequences. In order to verify the performance of the proposed algorithm, the numerical and experimental results for three different types of aircraft were compared to those from SAR images. At the end of this article, the estimated radar scattering centers are used as the target features for the conventional classifier machine to confirm its target classification performance. Full article
(This article belongs to the Special Issue Radar Target Detection, Imaging and Recognition)
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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 - 15 Aug 2024
Viewed by 941
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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