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Sensing and Signal Analysis in Synthetic Aperture Radar Systems
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Sensing and Signal Analysis in Synthetic Aperture Radar Systems

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

Deadline for manuscript submissions: 15 May 2024 | Viewed by 4888

Special Issue Editors

Dr. Zenghui Zhang

E-Mail Website
Guest Editor
School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
Interests: radar signal processing; SAR image interpretation; target detection and recognition
Special Issues, Collections and Topics in MDPI journals
Dr. Weiwei Guo

E-Mail Website
Guest Editor
Center of Digital Innovation, Tongji University, Shanghai 100092, China
Interests: intelligent sensing and recognition; machine learning and pattern recognition for visual and SAR image interpretation
Prof. Dr. Wenxian Yu

E-Mail Website
Guest Editor
Shanghai Key Lab. of Intelligent Sensing and Recognition, School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
Interests: radar target recognition; remote sensing data processing; multimodal navigation technology

Special Issue Information

Dear Colleagues, 

SAR, a type of active imaging sensor that works in the microwave band, has developed rapidly in recent decades. Various high-resolution, multi-mode spaceborne and airborne SAR sensors have driven the technological progress in many application areas. In recent years, SAR systems and applications have been continuously broadened, such as small UAV SAR, millimeter-wave vehicle-mounted SAR, distributed/passive SAR, etc., as well as new signal- and image-processing methods developed with artificial intelligence. Therefore, the current Special Issue aims to bring together the latest relevant research including new architectures of SAR systems and corresponding signal processing and image interpretation methods.

The Guest Editors invite contributions to this Special Issue of Sensors in relation to topics including, but not limited to:

(1) New SAR system architectures;

(2) Millimeter-wave vehicle-mounted SAR;

(3) Distributed/passive SAR;

(4) SAR signal processing theory and methods;

(5) SAR image interpretation methods;

(6) SAR target detection and recognition;

(7) SAR processing with multimodal data.

Dr. Zenghui Zhang
Dr. Weiwei Guo
Prof. Dr. Wenxian Yu
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.

Published Papers (4 papers)

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Research

21 pages, 5940 KiB  
Article
Sub-Nyquist SAR Imaging and Error Correction Via an Optimization-Based Algorithm
by Wenjiao Chen, Li Zhang, Xiaocen Xing, Xin Wen and Qiuxuan Zhang
Sensors 2024, 24(9), 2840; https://doi.org/10.3390/s24092840 - 29 Apr 2024
Viewed by 389
Abstract
Sub-Nyquist synthetic aperture radar (SAR) based on pseudo-random time–space modulation has been proposed to increase the swath width while preserving the azimuthal resolution. Due to the sub-Nyquist sampling, the scene can be recovered by an optimization-based algorithm. However, these methods suffer from some [...] Read more.
Sub-Nyquist synthetic aperture radar (SAR) based on pseudo-random time–space modulation has been proposed to increase the swath width while preserving the azimuthal resolution. Due to the sub-Nyquist sampling, the scene can be recovered by an optimization-based algorithm. However, these methods suffer from some issues, e.g., manually tuning difficulty and the pre-definition of optimization parameters, and a low signal–noise ratio (SNR) resistance. To address these issues, a reweighted optimization algorithm, named pseudo-ℒ0-norm optimization algorithm, is proposed for the sub-Nyquist SAR system in this paper. A modified regularization model is first built by applying the scene prior information to nearly acquire the number of nonzero elements based on Bayesian estimation, and then this model is solved by the Cauchy–Newton method. Additionally, an error correction method combined with our proposed pseudo-ℒ0-norm optimization algorithm is also present to eliminate defocusing in the motion-induced model. Finally, experiments with simulated signals and strip-map TerraSAR-X images are carried out to demonstrate the effectiveness and superiority of our proposed algorithm. Full article
(This article belongs to the Special Issue Sensing and Signal Analysis in Synthetic Aperture Radar Systems)
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17 pages, 4782 KiB  
Article
Utilizing Polarization Diversity in GBSAR Data-Based Object Classification
by Filip Turčinović, Marin Kačan, Dario Bojanjac, Marko Bosiljevac and Zvonimir Šipuš
Sensors 2024, 24(7), 2305; https://doi.org/10.3390/s24072305 - 5 Apr 2024
Viewed by 475
Abstract
In recent years, the development of intelligent sensor systems has experienced remarkable growth, particularly in the domain of microwave and millimeter wave sensing, thanks to the increased availability of affordable hardware components. With the development of smart Ground-Based Synthetic Aperture Radar (GBSAR) system [...] Read more.
In recent years, the development of intelligent sensor systems has experienced remarkable growth, particularly in the domain of microwave and millimeter wave sensing, thanks to the increased availability of affordable hardware components. With the development of smart Ground-Based Synthetic Aperture Radar (GBSAR) system called GBSAR-Pi, we previously explored object classification applications based on raw radar data. Building upon this foundation, in this study, we analyze the potential of utilizing polarization information to improve the performance of deep learning models based on raw GBSAR data. The data are obtained with a GBSAR operating at 24 GHz with both vertical (VV) and horizontal (HH) polarization, resulting in two matrices (VV and HH) per observed scene. We present several approaches demonstrating the integration of such data into classification models based on a modified ResNet18 architecture. We also introduce a novel Siamese architecture tailored to accommodate the dual input radar data. The results indicate that a simple concatenation method is the most promising approach and underscore the importance of considering antenna polarization and merging strategies in deep learning applications based on radar data. Full article
(This article belongs to the Special Issue Sensing and Signal Analysis in Synthetic Aperture Radar Systems)
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21 pages, 5293 KiB  
Article
A Radar Echo Simulator for the Synthesis of Randomized Training Data Sets in the Context of AI-Based Applications
by Jonas Schorlemer, Jochen Altholz, Jan Barowski, Christoph Baer, Ilona Rolfes and Christian Schulz
Sensors 2024, 24(3), 836; https://doi.org/10.3390/s24030836 - 27 Jan 2024
Viewed by 726
Abstract
Supervised machine learning algorithms usually require huge labeled data sets to produce sufficiently good results. For many applications, these data sets are still not available today, and the reasons for this can be manifold. As a solution, the missing training data can be [...] Read more.
Supervised machine learning algorithms usually require huge labeled data sets to produce sufficiently good results. For many applications, these data sets are still not available today, and the reasons for this can be manifold. As a solution, the missing training data can be generated by fast simulators. This procedure is well studied and allows filling possible gaps in the training data, which can further improve the results of a machine learning model. For this reason, this article deals with the development of a two-dimensional electromagnetic field simulator for modeling the response of a radar sensor in an imaging system based on the synthetic aperture radar principle. The creation of completely random scenes is essential to achieve data sets with large variance. Therefore, special emphasis is placed on the development of methods that allow creating random objects, which can then be assembled into an entire scene. In the context of this contribution, we focus on humanitarian demining with regard to improvised explosive devices using a ground-penetrating radar system. This is an area where the use of trained classifiers is of great importance, but in practice, there are little to no labeled datasets for the training process. The simulation results show good agreement with the measurement results obtained in a previous contribution, demonstrating the possibility of enhancing sparse training data sets with synthetic data. Full article
(This article belongs to the Special Issue Sensing and Signal Analysis in Synthetic Aperture Radar Systems)
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18 pages, 164375 KiB  
Article
Evaluating Urban Building Damage of 2023 Kahramanmaras, Turkey Earthquake Sequence Using SAR Change Detection
by Xiuhua Wang, Guangcai Feng, Lijia He, Qi An, Zhiqiang Xiong, Hao Lu, Wenxin Wang, Ning Li, Yinggang Zhao, Yuedong Wang and Yuexin Wang
Sensors 2023, 23(14), 6342; https://doi.org/10.3390/s23146342 - 12 Jul 2023
Cited by 5 | Viewed by 2848
Abstract
On February 6, 2023 (local time), two earthquakes (Mw7.8 and Mw7.7) struck central and southern Turkey, causing extensive damage to several cities and claiming a toll of 40,000 lives. In this study, we propose a method for seismic building damage assessment and analysis [...] Read more.
On February 6, 2023 (local time), two earthquakes (Mw7.8 and Mw7.7) struck central and southern Turkey, causing extensive damage to several cities and claiming a toll of 40,000 lives. In this study, we propose a method for seismic building damage assessment and analysis by combining SAR amplitude and phase coherence change detection. We determined building damage in five severely impacted urban areas and calculated the damage ratio by measuring the urban area and the damaged area. The largest damage ratio of 18.93% is observed in Nurdagi, and the smallest ratio of 7.59% is found in Islahiye. We verified the results by comparing them with high-resolution optical images and AI recognition results from the Microsoft team. We also used pixel offset tracking (POT) technology and D-InSAR technology to obtain surface deformation using Sentinel-1A images and analyzed the relationship between surface deformation and post-earthquake urban building damage. The results show that Nurdagi has the largest urban average surface deformation of 0.48 m and Antakya has the smallest deformation of 0.09 m. We found that buildings in the areas with steeper slopes or closer to earthquake faults have higher risk of collapse. We also discussed the influence of SAR image parameters on building change recognition. Image resolution and observation geometry have a great influence on the change detection results, and the resolution can be improved by various means to raise the recognition accuracy. Our research findings can guide earthquake disaster assessment and analysis and identify influential factors of earthquake damage. Full article
(This article belongs to the Special Issue Sensing and Signal Analysis in Synthetic Aperture Radar Systems)
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