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Radar and Sonar Imaging and Processing IV

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Remote Sensing Image Processing".

Deadline for manuscript submissions: 30 April 2024 | Viewed by 2787

Special Issue Editors


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Guest Editor
Department of Geodesy, Faculty of Civil and Environmental Engineering, Gdansk Technical University, Narutowicza St. 11/12, Gdansk, Poland
Interests: radar navigation; comparative (terrain-based) navigation; multi-sensor data fusion; radar and sonar target tracking; sonar imaging and understanding; MBES bathymetry; ASV; artificial neural networks; geoinformatics
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
Department of Geoinformatics and Hydrography, Faculty of Navigation, Maritime University of Szczecin, 70-500 Szczecin, Poland
Interests: target tracking; data fusion; maritime radars; spatial analysis; artificial neural networks; mobile cartography
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In recent years, radar and sonar technology has been at the center of several major developments in remote sensing, both in civilian and defense applications. It allows us to observe targets in space, on land, at sea, and underwater, providing increasingly precise information about them. Although radar technology has been around for more than a century, it is still evolving and is now implemented in a variety of maritime, air, satellite, and land applications. Novel technologies, such as sparse image reconstruction and multistatic active and passive SAR and ISAR imaging, are improving image quality and broadening the scope of application. The rapid development of three-dimensional automotive radars that can recognize different objects and assign the risk of collision is another example of this technology’s progress. Aside from classical pulse radars, the use of FMCW technology in maritime radars is becoming increasingly popular. Simultaneously, sonar technology has also been used for dozens of decades, initially only for military solutions, but today, using 3D versions, it is applied in many underwater tasks, such as underwater surface imaging, target detection, tracking, etc. The impact of sonar technologies has grown, particularly at the beginning of the autonomous vehicle era. Recently, the influence of artificial intelligence on radar and sonar image processing and understanding has emerged. Radar and sonar systems are mounted on smart and flexible platforms and on several types of unmanned vehicles. Both of these technologies focus on remote detection of targets, and both may encounter many common scientific challenges. Unfortunately, specialists from the radar and sonar fields do not interact with each other enough, slowing down progress in both areas.

This Special Issue will report on the most recent advances and trends in the field of remote sensing for radar and sonar image processing, addressing novel developments and applications, and providing practical solutions to open questions. Following the success of the previous two installments, this third installment aims to further improve data and knowledge exchange within the scientific community, while also allowing experts from other fields to understand radar and sonar problems. Topics in this Special Issue include, but are not limited to, the following:

  • 3D radar and 3D sonar imaging;
  • Artificial intelligence for radar and sonar data processing;
  • Automatic target detection and classification;
  • Automotive and maritime radar;
  • Ground penetrating radar application in civil engineering;
  • Interferometric methods;
  • Multi-sensor data fusion;
  • Passive and active radar imaging (SAR, ISAR);
  • Radar and sonar surveillance systems;
  • Radar and sonar target tracking and anti-collision algorithms and methods;
  • Radar and sonar technology for autonomous vehicles;
  • Radar sensors design and platform developments;
  • Side scan sonar, imaging sonar, chirp sonar, and forward-looking sonar;
  • Sonar image processing, data reduction, feature extraction, and image understanding;
  • Synergy between radar, sonar, and other sensors.

Prof. Dr. Andrzej Stateczny
Dr. Witold Kazimierski
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. Remote Sensing 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 2700 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.

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Published Papers (3 papers)

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Research

23 pages, 15291 KiB  
Article
Anti-Interference Bottom Detection Method of Multibeam Echosounders Based on Deep Learning Models
by Junxia Meng, Jun Yan and Qinghe Zhang
Remote Sens. 2024, 16(3), 530; https://doi.org/10.3390/rs16030530 - 30 Jan 2024
Viewed by 537
Abstract
Multibeam echosounders, as the most commonly used bathymetric equipment, have been widely applied in acquiring seabed topography and underwater sonar images. However, when interference occurs in the water column, traditional bottom detection methods may fail, resulting in discontinuities in the bathymetry and distortion [...] Read more.
Multibeam echosounders, as the most commonly used bathymetric equipment, have been widely applied in acquiring seabed topography and underwater sonar images. However, when interference occurs in the water column, traditional bottom detection methods may fail, resulting in discontinuities in the bathymetry and distortion in the sonar images. To solve this problem, we propose an anti-interference bottom detection method based on deep learning models. First, the variation differences of backscatter strengths at different incidence angles and the failure conditions of traditional methods were analyzed. Second, the details of our deep learning models are explained. And these models were trained using samples in the specular reflection, scatter reflection, and high-incidence angle regions, respectively. Third, the bottom detection procedures of the along-track and across-track water column data using the trained models are provided. In the experiments, multibeam data with strong interferences in the water column were selected. The bottom detection results of the along-track water column data at incidence angles of 0°, 35°, and 60° and the across-track ping data validated the effectiveness of our method. By comparison, our method acquired the correct bottom position when the traditional methods had inaccurate or even no detection results. Our method can be used to supplement existing methods and effectively improve bathymetry robustness under interference conditions. Full article
(This article belongs to the Special Issue Radar and Sonar Imaging and Processing IV)
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18 pages, 9781 KiB  
Article
A Novel Intrapulse Beamsteering SAR Imaging Mode Based on OFDM-Chirp Signals
by Shenjing Wang, Feng He and Zhen Dong
Remote Sens. 2024, 16(1), 126; https://doi.org/10.3390/rs16010126 - 28 Dec 2023
Viewed by 614
Abstract
The multiple-input multiple-output synthetic aperture radar (MIMO SAR) system has developed rapidly since its discovery. At the same time, the low-disturbance and high-gain requirements of the MIMO system are continuing to increase. Through the application of digital beamforming (DBF) techniques, the multidimensional waveform [...] Read more.
The multiple-input multiple-output synthetic aperture radar (MIMO SAR) system has developed rapidly since its discovery. At the same time, the low-disturbance and high-gain requirements of the MIMO system are continuing to increase. Through the application of digital beamforming (DBF) techniques, the multidimensional waveform encoding (MWE) technique can play a key role in MIMO systems, which can greatly improve the system’s performance, especially the multi-mission capability of radar. Intrapulse beamsteering in elevation is a typical form of multi-dimensional waveform encoding which can greatly improve the transmitting efficiency and multi-mission performance of radar. However, because of the high sensitivity of the DBF technique to height, there is significant deterioration in performance in the presence of terrain undulations. The OFDM (Orthogonal Frequency Division Multiplexing) technique is widely used in communication. Due to the similarity of radar and communication systems and the great waveform diversity of OFDM signals, the OFDM radar has recently begun to emerge as a new radar system, simultaneously, the orthogonality of OFDM signals is in the time and frequency domains, and is not affected by terrain undulation. So, this paper proposes a novel radar mode combining intrapulse beamsteering in elevation and OFDM-Chirp signals, that is, the combination of “beam orthogonality” and “waveform orthogonality”, which can greatly improve the performance and fault tolerance to interference signals. In this manuscript, the system working mode and signal processing flow are introduced in detail, and simulations for both point targets and distributed targets are carried out to verify the feasibility of the proposed mode. Simultaneously, a comparison experiment is carried out, which shows the high level of fault tolerance to terrain undulation and the high potential of the proposed radar mode in Earth observation. Full article
(This article belongs to the Special Issue Radar and Sonar Imaging and Processing IV)
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19 pages, 11209 KiB  
Article
A Novel Imaging Algorithm for Wide-Beam Multiple-Receiver Synthetic Aperture Sonar Systems
by Jiafeng Zhang, Guangli Cheng, Jinsong Tang, Zhimin Xie and Haoran Wu
Remote Sens. 2023, 15(15), 3745; https://doi.org/10.3390/rs15153745 - 27 Jul 2023
Viewed by 1131
Abstract
In existing imaging algorithms for wide-beam multiple-receiver synthetic aperture sonar (SAS) systems, the double-square-root (DSR) range history of each receiver is generally converted into the sum of a single-square-root (SSR) range history and an error term using displaced phase center aperture (DPCA) approximation. [...] Read more.
In existing imaging algorithms for wide-beam multiple-receiver synthetic aperture sonar (SAS) systems, the double-square-root (DSR) range history of each receiver is generally converted into the sum of a single-square-root (SSR) range history and an error term using displaced phase center aperture (DPCA) approximation. Therefore, before imaging, each receiver’s error term needs to be individually compensated in the azimuth frequency domain, which is computationally expensive. As a result, a novel wide-beam multiple-receiver SAS system algorithm with low complexity and high precision is suggested. First, the translation relationship between the range histories of the reference receiver and other receivers is used to derive an SSR approximation range history that takes into account the azimuth variance of the non-stop-hop-stop time while ignoring differential range curvature (DRC) between the range histories from different receivers. Then, using the principle of stationary phase (POSP), the two-dimensional (2-D) spectrum of the point target is obtained. Finally, the multiple-receiver data are transformed into monostatic SAS-equivalent data for imaging after phase correction, time delay correction, and azimuth reconstruction. The range-Doppler (RD) algorithm is used as an example to explain the specific steps of the proposed approach. Simulation data and ChinSAS data experiments verify that the proposed algorithm achieves an imaging performance that is comparable to that of the existing wide-beam algorithm, but with much higher computational efficiency, making it suitable for real-time imaging. Full article
(This article belongs to the Special Issue Radar and Sonar Imaging and Processing IV)
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