Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
8.3
4.2
Journals
Remote Sensing
Special Issues
Recent Advances in Nonlinear Processing Technique for Radar Sensing
remotesensing-logo
Submit to Special Issue Submit Abstract to Special Issue Review for Remote Sensing Propose a Special Issue

Journal Menu

► Journal Menu

Journal Browser

► Journal Browser
share announcement

Recent Advances in Nonlinear Processing Technique for Radar Sensing

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

Deadline for manuscript submissions: 31 December 2025 | Viewed by 2326

Special Issue Editors

Dr. Xiaoqiang Hua

E-Mail Website
Guest Editor
College of Meteorology and Oceanography, National University of Defense Technology, Changsha, China
Interests: information geometry; Riemannian geometry; radar/sonar signal processing; underwater environmental modelling; image processing; machine learning; target detection
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Yongqiang Cheng

E-Mail Website
Guest Editor
College of Electronic Science, National University of Defense Technology, Changsha 410073, China
Interests: information geometry; statistical signal processing; target detection
Dr. Hao Wu

E-Mail Website
Guest Editor
College of Electronic Science, National University of Defense Technology, Changsha 410073, China
Interests: radar target detection; information geometry; statistical signal processing

Special Issue Information

Dear Colleagues,

The nonlinear processing technique has received great attention from different groups in information science. Many problems in information theory, and statistics, are non-Gaussian, and linear processing would lead to information loss, while nonlinear processing implies high dimensionality and contains enormous amounts of information. Consequently, the nonlinear processing technique has been applied to solve many problems related to information processing in high-dimensional space, and has particularly achieved important research developments in fields of radar signal processing, computer vision, biomedical engineering, and in an interdisciplinary way, laying a foundation for further engineering applications.

This Special Issue focuses on recent advances in the nonlinear processing technique for radar sensing, including a wide range of new processing techniques and experimental advances. Topics of interest include, but are not limited to, the following:

  • Nonlinear processing technique in radar target detection, tracking, and imaging;
  • Information geometric radar sensing;
  • Riemannian geometry and its applications to radar sensing;
  • Nonlinear processing technique in SAR image processing;
  • Deep learning for radar signal processing.

Dr. Xiaoqiang Hua
Prof. Dr. Yongqiang Cheng
Dr. Hao Wu
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.

Keywords

  • information geometry
  • Riemannian manifold
  • nonlinear processing technique
  • deep learning
  • radar sensing
  • SAR image processing

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.
  • e-Book format: Special Issues with more than 10 articles can be published as dedicated e-books, ensuring wide and rapid dissemination.

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

Published Papers (2 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

Jump to: Other

17 pages, 2716 KiB  
Article
Disentangled Representation Learning for Robust Radar Inter-Pulse Modulation Feature Extraction and Recognition
by Luyao Zhang, Mengtao Zhu, Ziwei Zhang and Yunjie Li
Remote Sens. 2024, 16(19), 3585; https://doi.org/10.3390/rs16193585 - 26 Sep 2024
Viewed by 1024
Abstract
Modern Multi-Function Radars (MFRs) are sophisticated sensors that are capable of flexibly adapting their control parameters in transmitted pulse sequences. In complex electromagnetic environments, efficiently and accurately recognizing the inter-pulse modulations of non-cooperative radar pulse sequences is a key step for modern Electronic [...] Read more.
Modern Multi-Function Radars (MFRs) are sophisticated sensors that are capable of flexibly adapting their control parameters in transmitted pulse sequences. In complex electromagnetic environments, efficiently and accurately recognizing the inter-pulse modulations of non-cooperative radar pulse sequences is a key step for modern Electronic Support (ES) systems. Existing recognition methods focus more on algorithmic designs, such as neural network structure designs, to improve recognition performance. However, in open electromagnetic environments with increased flexibility in radar transmission, these methods would suffer performance degradation due to domain shifts between training and testing datasets. To address this issue, this study proposes a robust radar inter-pulse modulation feature extraction and recognition method based on disentangled representation learning. At first, inspired by the Representation Learning Theory (RLT), the received radar pulse sequences can be disentangled into three explanatory factors related to (i) modulation types, (ii) modulation parameters, and (iii) measurement characteristics, such as measurement noise. Then, an explainable radar pulse sequence disentanglement network is proposed based on auto-encoding variational Bayes. The features extracted through the proposed method can effectively represent the key latent factors related to recognition tasks and maintain performance under domain shift conditions. Experiments on both ideal and non-ideal situations demonstrate the effectiveness, robustness, and superiority of the proposed method in comparison with other methods. Full article
(This article belongs to the Special Issue Recent Advances in Nonlinear Processing Technique for Radar Sensing)
►▼ Show Figures

Graphical abstract

Other

Jump to: Research

15 pages, 11951 KiB  
Technical Note
Axis Estimation of Spaceborne Targets via Inverse Synthetic Aperture Radar Image Sequence Based on Regression Network
by Wenjing Guo, Qi Yang, Hongqiang Wang and Chenggao Luo
Remote Sens. 2024, 16(22), 4148; https://doi.org/10.3390/rs16224148 - 7 Nov 2024
Viewed by 693
Abstract
Axial estimation is an important task for detecting non-cooperative space targets in orbit, with inverse synthetic aperture radar (ISAR) imaging serving as a fundamental approach to facilitate this process. However, most of the existing axial estimation methods usually rely on manually extracting and [...] Read more.
Axial estimation is an important task for detecting non-cooperative space targets in orbit, with inverse synthetic aperture radar (ISAR) imaging serving as a fundamental approach to facilitate this process. However, most of the existing axial estimation methods usually rely on manually extracting and matching features of key corner points or linear structures in the images, which may result in a degradation in estimation accuracy. To address these issues, this paper proposes an axial estimation method for spaceborne targets via ISAR image sequences based on a regression network. Firstly, taking the ALOS satellite as an example, its Computer-Aided Design (CAD) model is constructed through a prior analysis of its structural features. Subsequently, target echoes are generated using electromagnetic simulation software, followed by imaging processing, analysis of imaging characteristics, and the determination of axial labels. Finally, in contrast to traditional classification approaches, this study introduces a straightforward yet effective regression network specifically designed for ISAR image sequences. This network transforms the classification loss into a loss function constrained by the minimum mean square error, which can be utilized to adaptively perform the feature extraction and estimation of axial parameters. The effectiveness of the proposed method is validated through both electromagnetic simulations and experimental data. Full article
(This article belongs to the Special Issue Recent Advances in Nonlinear Processing Technique for Radar Sensing)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-2
Back to TopTop