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Electronics
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Radar Signal Processing Technology
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Radar Signal Processing Technology

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

Deadline for manuscript submissions: 15 December 2024 | Viewed by 6145

Special Issue Editors

Prof. Dr. Junfeng Wang

E-Mail Website
Guest Editor
Department of Electronic Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
Interests: signal processing; radar imaging; medical imaging; astronomical imaging
Dr. Zenghui Zhang

E-Mail Website
Guest Editor
School of Electronics, Information, and Electrical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
Interests: deep learning for SAR image analysis; multi-source image fusion; radar imaging techniques
Special Issues, Collections and Topics in MDPI journals
Dr. Hao Yan

E-Mail Website
Guest Editor
Department of Electronic Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
Interests: information optics; signal processing

Special Issue Information

Dear Colleagues,

Radar is used to detect, locate, and image targets on the ground, on the sea, in the air, in space, and even below the ground. It can attain a large detection range, a high location accuracy, and a fine imaging resolution. It can operate in all weather conditions at all times. These advantages have led to its wide application in military, civilian, and scientific fields. For years, it has experienced a steady growth, with advances in radio-frequency technology, antenna technology, and signal processing technology. Especially, in recent years, signal processing technology has played a more and more important role in the improvement in radar performance, the extension of radar application, and the development of new radar systems. The higher and higher requirements of radar have brought not only increasingly more opportunities but also growing challenges to radar signal processing.

This Special Issue aims to collect contributions reporting recent developments in the field of radar signal processing. It is a good platform for people to exchange their ideas and methods to promote the research in this field. We are pleased to invite you to present breakthrough, innovative, and high-level work about radar signal processing technology.

In this Special Issue, original research articles and reviews are welcome. The scope of this Special Issue includes but is not limited to the following topics:

  • Radar systems;
  • Radar applications;
  • SAR/ISAR;
  • Motion estimation and compensation;
  • Image enhancement;
  • Target detection and recognition;
  • Moving-target detection and tracking;
  • InSAR/InISAR;
  • Array signal processing;
  • Cognitive radar technology;
  • Waveform design and optimization;
  • Radar jamming/anti-jamming technology.

We look forward to receiving your contributions.

Prof. Dr. Junfeng Wang
Dr. Zenghui Zhang
Dr. Hao Yan
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

  • radar signal processing
  • SAR
  • ISAR
  • InSAR
  • InISAR
  • target detection
  • target recognition
  • cognitive radar
  • waveform design
  • radar jamming

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Published Papers (7 papers)

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Research

16 pages, 7515 KiB  
Article
Maneuvering Trajectory Synthetic Aperture Radar Processing Based on the Decomposition of Transfer Functions in the Frequency Domain Using Average Blurred Edge Width Assessment
by Chenguang Yang, Duo Wang, Fukun Sun and Kaizhi Wang
Electronics 2024, 13(20), 4100; https://doi.org/10.3390/electronics13204100 - 17 Oct 2024
Viewed by 236
Abstract
With the rapid development of synthetic aperture radar (SAR), delivery platforms are gradually becoming diversified and miniaturized. The SAR flight process is susceptible to external influences, resulting in unsatisfactory imaging results, so it is necessary to optimize imaging processing in combination with the [...] Read more.
With the rapid development of synthetic aperture radar (SAR), delivery platforms are gradually becoming diversified and miniaturized. The SAR flight process is susceptible to external influences, resulting in unsatisfactory imaging results, so it is necessary to optimize imaging processing in combination with the SAR imaging quality assessment (IQA) index. Based on the principle of SAR imaging, this paper analyzes the impact of defocusing on imaging results caused by mismatched filters and draws on the assessment algorithm of motion blur, proposing a SAR IQA index based on average blurred edge width (ABEW) in the salient area. In addition, the idea of decomposing the transfer function in the frequency domain and fitting the matched filter with a polynomial is also proposed. The estimation of the flight trajectory is changed to a correction of the matched filter, avoiding the precise estimation of Doppler parameters and complex calculations during the time–frequency conversion process. The effectiveness of ABEW was verified by using SAR images of real scenes, and the results were highly consistent with the actual image quality. The imaging processing was tested using the echo signals generated by the errors introduced during the flight process, and more satisfactory imaging results were obtained by using ABEW with the filter for correction. The imaging process was tested using the echo signal generated by introducing errors during the flight, and the filter was corrected using ABEW as an index, obtaining a comparatively ideal imaging result. Full article
(This article belongs to the Special Issue Radar Signal Processing Technology)
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12 pages, 1317 KiB  
Article
Efficient Sparse Bayesian Learning Model for Image Reconstruction Based on Laplacian Hierarchical Priors and GAMP
by Wenzhe Jin, Wentao Lyu, Yingrou Chen, Qing Guo, Zhijiang Deng and Weiqiang Xu
Electronics 2024, 13(15), 3038; https://doi.org/10.3390/electronics13153038 - 1 Aug 2024
Viewed by 549
Abstract
In this paper, we present a novel sparse Bayesian learning (SBL) method for image reconstruction. We integrate the generalized approximate message passing (GAMP) algorithm and Laplacian hierarchical priors (LHP) into a basic SBL model (called LHP-GAMP-SBL) to improve the reconstruction efficiency. In our [...] Read more.
In this paper, we present a novel sparse Bayesian learning (SBL) method for image reconstruction. We integrate the generalized approximate message passing (GAMP) algorithm and Laplacian hierarchical priors (LHP) into a basic SBL model (called LHP-GAMP-SBL) to improve the reconstruction efficiency. In our SBL model, the GAMP structure is used to estimate the mean and variance without matrix inversion in the E-step, while LHP is used to update the hyperparameters in the M-step.The combination of these two structures further deepens the hierarchical structures of the model. The representation ability of the model is enhanced so that the reconstruction accuracy can be improved. Moreover, the introduction of LHP accelerates the convergence of GAMP, which shortens the reconstruction time of the model. Experimental results verify the effectiveness of our method. Full article
(This article belongs to the Special Issue Radar Signal Processing Technology)
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16 pages, 19129 KiB  
Article
Ship Detection in SAR Images Based on Steady CFAR Detector and Knowledge-Oriented GBDT Classifier
by Shuqi Sun and Junfeng Wang
Electronics 2024, 13(14), 2692; https://doi.org/10.3390/electronics13142692 - 10 Jul 2024
Viewed by 723
Abstract
Ship detection is a significant issue in remote sensing based on Synthetic Aperture Radar (SAR). This paper combines the advantages of a steady constant false alarm rate (CFAR) detector and a knowledge-oriented Gradient Boosting Decision Tree (GBDT) classifier to achieve the location and [...] Read more.
Ship detection is a significant issue in remote sensing based on Synthetic Aperture Radar (SAR). This paper combines the advantages of a steady constant false alarm rate (CFAR) detector and a knowledge-oriented Gradient Boosting Decision Tree (GBDT) classifier to achieve the location and the classification of ship candidates. The steady CFAR detector smooths the image by a moving-average filter and models the probability distribution of the smoothed clutter as a Gaussian distribution. The mean and the standard deviation of the Gaussian distribution are estimated according to the left half of the histogram to remove the effect of land, ships, and other targets. From the Gaussian distribution and a preset constant false alarm rate, a threshold is obtained to segment land, ships, and other targets from the clutter. Then, a series of morphological operations are introduced to eliminate land and extract ships and other targets, and an active contour algorithm is utilized to refine ships and other targets. Finally, ships are recognized from other targets by a knowledge-oriented GBDT classifier. Based on the brain-like ship-recognition process, we change the way of the decision-tree generation and achieve a higher classification performance than the original GBDT. The results on the AIRSARShip-1.0 dataset demonstrate that this scheme has a competitive performance against deep learning, especially in the detection of offshore ships. Full article
(This article belongs to the Special Issue Radar Signal Processing Technology)
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17 pages, 5985 KiB  
Article
Advanced Method for Improving Marine Target Tracking Based on Multiple-Plot Processing of Radar Images
by Xung Ha Vo, Trung Kien Nguyen, Phung Bao Nguyen and Van Minh Duong
Electronics 2024, 13(13), 2548; https://doi.org/10.3390/electronics13132548 - 28 Jun 2024
Viewed by 560
Abstract
Advancements in technology have led to the development of high-resolution radars that provide highly detailed images of targets over a wide field of view. These radar images can significantly improve filtering and tracking accuracy, especially in marine environments. However, traditional methods like the [...] Read more.
Advancements in technology have led to the development of high-resolution radars that provide highly detailed images of targets over a wide field of view. These radar images can significantly improve filtering and tracking accuracy, especially in marine environments. However, traditional methods like the binary and barycentric methods are inadequate, as they do not capture critical information for tracking targets, such as direction. Therefore, in this article, a new method for improving the estimation of target characteristics to improve tracking accuracy based on the processing of high-resolution radar images is proposed. The proposed method consists of three modules. Firstly, the radar images of the target are decomposed into layers to determine local maximum regions and to estimate target characteristics such as reflected energy and area and the centroids of plots. In the second module, the plots are grouped using a fuzzy logic system. The inputs of the fuzzy logic system include the above-estimated parameters of the targets. The output is the chance that the plot is at the center of the target. The plots with the highest chances are considered target centers, and the other plots are grouped into their respective target. At the end, the true target center is recalculated. This process is called modified fuzzy C-means (FCM-M). In the last stage, the estimated target center coordinates are fed into a Kalman filter (KF) to solve filtering and tracking problems. The performance is evaluated using a measured radar dataset. The experimental results show that the proposed method performs better than traditional methods based on binary image processing. Additionally, the proposed method offers extra information about the targets, such as their direction, the energy of each reflected part, and the area, which traditional methods does not provide. Full article
(This article belongs to the Special Issue Radar Signal Processing Technology)
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20 pages, 1181 KiB  
Article
Multivariate Time Series Feature Extraction and Clustering Framework for Multi-Function Radar Work Mode Recognition
by Ruozhou Fan, Mengtao Zhu and Xiongkui Zhang
Electronics 2024, 13(8), 1412; https://doi.org/10.3390/electronics13081412 - 9 Apr 2024
Viewed by 908
Abstract
Multi-Function Radars (MFRs) are sophisticated sensors with great agility and flexibility in adapting their transmitted waveform and control parameters. The recognition of MFR work modes based on the intercepted pulse sequences plays an important role in interpreting the functional purpose and threats of [...] Read more.
Multi-Function Radars (MFRs) are sophisticated sensors with great agility and flexibility in adapting their transmitted waveform and control parameters. The recognition of MFR work modes based on the intercepted pulse sequences plays an important role in interpreting the functional purpose and threats of a non-cooperative MFRs. However, due to the increased flexibility of MFRs, radar work modes with emerging new modulations and control parameters always appear, and the supervised classification method suffers performance degradation or even failure. Unsupervised learning and clustering of MFR pulse sequences becomes urgent and important. This paper establishes a unified multivariate MFR time series feature extraction and clustering framework for MFR work mode recognition. At first, various features are collected to form the feature set. The feature set includes features extracted through deep learning based on recurrent auto-encoders, multidimensional time series toolkit features, and manually crafted features for radar inter-pulse modulations. Subsequently, several feature selection algorithms, combined with different clustering and classification methods, are used for the selection of an “optimal” feature subset. Finally, the effectiveness and superiority of the proposed framework and selected features are validated through simulated and measured datasets. In the simulated dataset containing 20 classes of work modes, under the most severe non-ideal conditions, we achieve a clustering purity of 73.46% and an NMI of 84.28%. In the measured dataset with seven classes of work modes, we achieve a clustering purity of 86.96% and an NMI of 90.10%. Full article
(This article belongs to the Special Issue Radar Signal Processing Technology)
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21 pages, 397 KiB  
Article
Moving-Target Detection for FDA-MIMO Radar in Partially Homogeneous Environments
by Changshan He, Running Zhang, Bang Huang, Mingming Xu, Zhibin Wang, Lei Liu, Zheng Lu and Ye Jin
Electronics 2024, 13(5), 851; https://doi.org/10.3390/electronics13050851 - 23 Feb 2024
Cited by 3 | Viewed by 1046
Abstract
This paper delves into the problem of moving-target detection in partially homogeneous environments (PHE) with unknown Gaussian disturbance using a frequency diverse array multiple-input multiple-output (FDA-MIMO) radar. Using training data, we have derived expressions for four adaptive detectors, including the one-step and two-step [...] Read more.
This paper delves into the problem of moving-target detection in partially homogeneous environments (PHE) with unknown Gaussian disturbance using a frequency diverse array multiple-input multiple-output (FDA-MIMO) radar. Using training data, we have derived expressions for four adaptive detectors, including the one-step and two-step generalized likelihood ratio test (GLRT), two-step Rao (TRao) test, and two-step Wald (TWald) test criteria, respectively. All the proposed detectors are characterized by the constant false-alarm rate (CFAR). The theoretical analysis and simulation results validate the effectiveness of the proposed detectors. Full article
(This article belongs to the Special Issue Radar Signal Processing Technology)
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17 pages, 7346 KiB  
Article
W-Band FMCW MIMO System for 3-D Imaging Based on Sparse Array
by Wenyuan Shao, Jianmin Hu, Yicai Ji, Wenrui Zhang and Guangyou Fang
Electronics 2024, 13(2), 369; https://doi.org/10.3390/electronics13020369 - 16 Jan 2024
Cited by 1 | Viewed by 1190
Abstract
Multiple-input multiple-output (MIMO) technology is widely used in the field of security imaging. However, existing imaging systems have shortcomings such as numerous array units, high hardware costs, and low imaging resolutions. In this paper, a sparse array-based frequency modulated continuous wave (FMCW) millimeter [...] Read more.
Multiple-input multiple-output (MIMO) technology is widely used in the field of security imaging. However, existing imaging systems have shortcomings such as numerous array units, high hardware costs, and low imaging resolutions. In this paper, a sparse array-based frequency modulated continuous wave (FMCW) millimeter wave imaging system, operating in the W-band, is presented. In order to reduce the number of transceiver units of the system and lower the hardware cost, a linear sparse array with a periodic structure was designed using the MIMO technique. The system operates at 70~80 GHz, and the high operating frequency band and 10 GHz bandwidth provide good imaging resolution. The system consists of a one-dimensional linear array, a motion control system, and hardware for signal generation and image reconstruction. The channel calibration technique was used to eliminate inherent errors. The system combines mechanical and electrical scanning, and uses FMCW signals to extract distance information. The three-dimensional (3-D) fast imaging algorithm in the wave number domain was utilized to quickly process the detection data. The 3-D imaging of the target in the near-field was obtained, with an imaging resolution of 2 mm. The imaging ability of the system was verified through simulations and experiments. Full article
(This article belongs to the Special Issue Radar Signal Processing Technology)
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