sensors-logo

Journal Browser

Journal Browser

Radar Remote Sensing and Applications—2nd Edition

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

Deadline for manuscript submissions: 30 September 2025 | Viewed by 3325

Special Issue Editor


E-Mail Website
Guest Editor
School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China
Interests: terahertz imaging and sensing; SAR imaging and application; object detection and identification
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Radar sensors are pervasive in remote sensing and related applications. Today, the available radar systems and the corresponding algorithms are still not perfect, but compared to twenty years ago, they are more robust, sophisticated, and user-friendly, and the radar data are of higher quality. Many radar platforms and algorithms are still for specialized tasks/applications only. Therefore, original and innovative methodological contributions to address the key challenge of radar system design and algorithmic data processing are required. As for radar remote sensing data, a key challenge is that systematic acquisitions are still insufficient, despite the availability of a few consolidated products. This hampers applications. Positive attempts to obtain application-oriented radar data have been carried out by many academies, research institutes, and space agencies, but the related research is still quite demanding. Otherwise, deep learning techniques bring the biggest breakthroughs in the machine learning field. In radar remote sensing, deep learning leverages the nature of huge remote sensing data and provides an end-to-end fashion where powerful adaptive feature representations can be automatically learned from the raw data. In response, the purpose of this Special Issue is to provide a platform for the discussion of the major challenges, latest developments, and recent advances in radar remote sensing and SAR-related fields.

Potential topics include, but are not limited to, the following points:

  • Advanced radar sensing systems, algorithms, and technologies;
  • Synthetic aperture radar (SAR) technologies and applications;
  • Millimetre wave radar, Terahertz radar, and LIDAR techniques;
  • Distributed radar, MIMO radar imaging, and applications;
  • Advanced data acquisition and signal processing for radar applications;
  • Novel designing of radar waveform, antenna, and systems;
  • Simultaneous localization and mapping (SLAM) with radar sensors;
  • Jamming and anti-jamming techniques for radar systems and applications;
  • Image processing and multi-sensor fusion in remote sensing scenarios;
  • Image interpretation and segmentation, object detection and recognition in radar remote sensing;
  • Artificial intelligence and machine learning-based approaches to radar sensing;
  • Sensing applications with radar in earth observation, ocean, agriculture, city, and so on;
  • Advanced data visualization techniques of radar sensing;
  • Object reconstruction from multidimensional radar point clouds.

Dr. Shunjun Wei
Guest Editor

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 sensing applications
  • remote sensing
  • radar imaging and detection
  • advanced radar systems
  • SLAM
  • jamming and anti-jamming
  • multi-sensor fusion
  • artificial intelligence

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • Reprint: MDPI Books provides the opportunity to republish successful Special Issues in book format, both online and in print.

Further information on MDPI's Special Issue policies can be found here.

Related Special Issue

Published Papers (2 papers)

Order results
Result details
Select all
Export citation of selected articles as:

Research

19 pages, 30716 KiB  
Article
A Novel Methodology for GB-SAR Estimating Parameters of the Atmospheric Phase Correction Model Based on Maximum Likelihood Estimation and the Gauss-Newton Algorithm
by Xiheng Li and Yu Liu
Sensors 2024, 24(17), 5699; https://doi.org/10.3390/s24175699 - 1 Sep 2024
Viewed by 1388
Abstract
Atmospheric phase error is the main factor affecting the accuracy of ground-based synthetic aperture radar (GB-SAR). The atmospheric phase screen (APS) may be very complicated, so the atmospheric phase correction (APC) model is very important; in particular, the parameters to be estimated in [...] Read more.
Atmospheric phase error is the main factor affecting the accuracy of ground-based synthetic aperture radar (GB-SAR). The atmospheric phase screen (APS) may be very complicated, so the atmospheric phase correction (APC) model is very important; in particular, the parameters to be estimated in the model are the key to improving the accuracy of APC. However, the conventional APC method first performs phase unwrapping and then removes the APS based on the least-squares method (LSM), and the general phase unwrapping method is prone to introducing unwrapping error. In particular, the LSM is difficult to apply directly due to the phase wrapping of permanent scatterers (PSs). Therefore, a novel methodology for estimating parameters of the APC model based on the maximum likelihood estimation (MLE) and the Gauss-Newton algorithm is proposed in this paper, which first introduces the MLE method to provide a suitable objective function for the parameter estimation of nonlinear far-end and near-end correction models. Then, based on the Gauss-Newton algorithm, the parameters of the objective function are iteratively estimated with suitable initial values, and the Matthews and Davies algorithm is used to optimize the Gauss-Newton algorithm to improve the accuracy of parameter estimation. Finally, the parameter estimation performance is evaluated based on Monte Carlo simulation experiments. The method proposed in this paper experimentally verifies the feasibility and superiority, which avoids phase unwrapping processing unlike the conventional method. Full article
(This article belongs to the Special Issue Radar Remote Sensing and Applications—2nd Edition)
Show Figures

Figure 1

20 pages, 6180 KiB  
Article
Flight Attitude Estimation with Radar for Remote Sensing Applications
by Christoph Weber, Marius Eggert and Thomas Udelhoven
Sensors 2024, 24(15), 4905; https://doi.org/10.3390/s24154905 - 29 Jul 2024
Viewed by 1327
Abstract
Unmanned aerial vehicles (UAVs) and radar technology have benefitted from breakthroughs in recent decades. Both technologies have found applications independently of each other, but together, they also unlock new possibilities, especially for remote sensing applications. One of the key factors for a remote [...] Read more.
Unmanned aerial vehicles (UAVs) and radar technology have benefitted from breakthroughs in recent decades. Both technologies have found applications independently of each other, but together, they also unlock new possibilities, especially for remote sensing applications. One of the key factors for a remote sensing system is the estimation of the flight attitude. Despite the advancements, accurate attitude estimation remains a significant challenge, particularly due to the limitations of a conventional Inertial Measurement Unit (IMU). Because these sensors may suffer from issues such as drifting, additional effort is required to obtain a stable attitude. Against that background, this study introduces a novel methodology for making an attitude estimation using radar data. Herein, we present a drone measurement system and detail its calculation process. We also demonstrate our results using three flight scenarios and outline the limitations of the approach. The results show that the roll and pitch angles can be calculated using the radar data, and we conclude that the findings of this research will help to improve the flight attitude estimation of remote sensing flights with a radar sensor. Full article
(This article belongs to the Special Issue Radar Remote Sensing and Applications—2nd Edition)
Show Figures

Figure 1

Back to TopTop