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Recent Advances in Information Geometric Signal Processing
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Recent Advances in Information Geometric Signal Processing

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

Deadline for manuscript submissions: closed (15 November 2023) | Viewed by 3000

Special Issue Editors

Dr. Xiaoqiang Hua

E-Mail Website
Guest Editor
College of Meteorology and Oceanography, National University of Defence Technology, Changsha 410073, China
Interests: information geometry; Riemannian geometry; radar/sonar signal processing; underwater environmental modelling; SAR image processing; machine learning; target detection
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. Linyu Peng

E-Mail Website
Guest Editor
Department of Mechanical Engineering, Keio University, Hiyoshi 3-14-1, Yokohama 223-8522, Japan
Interests: mathematical optimization; algebraic and geometric theories of differential equations and finite difference equations; information geometry

Special Issue Information

Dear Colleagues,

The theory of information geometry arises from the mathematical study of information science, but has received great attention from different societies. Many problems in probability theory, information theory, and statistics, in the light of information geometry, can be transformed into Riemannian- or dual-geometric problems on Riemannian manifolds. Consequently, information geometry has been applied to solve many problems related to information processing from a geometric viewpoint, and has particularly achieved important research developments in fields of radar/sonar/communication signal processing, computer vision, biomedical engineering and interdisciplinary, laying a foundation for further engineering applications.

This Special Issue focuses on recent advances in information geometric signal processing, including a wide range of new geometric conceptions, new processing techniques and experimental advances. Topics of interest include but are not limited to:

(1) Information geometry and radar/sonar/communications signal processing;

(2) Information geometry and image processing;

(3) Information geometry and biomedical engineering;

(4) Manifold projection;

(5) Subspace signal processing;

(6) Geometric deep learning.

Dr. Xiaoqiang Hua
Prof. Dr. Yongqiang Cheng
Dr. Linyu Peng
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.

Keywords

  • information geometry
  • Riemannian manifold
  • geometric signal processing
  • deep learning
  • radar/sonar/communications signal processing
  • image processing

Published Papers (2 papers)

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Research

16 pages, 2842 KiB  
Article
Application of Data Particle Geometrical Divide Algorithms in the Process of Radar Signal Recognition
by Janusz Dudczyk and Łukasz Rybak
Sensors 2023, 23(19), 8183; https://doi.org/10.3390/s23198183 - 30 Sep 2023
Cited by 2 | Viewed by 628
Abstract
The process of recognising and classifying radar signals and their radiation sources is currently a key element of operational activities in the electromagnetic environment. Systems of this type, called ELINT class systems, are passive solutions that detect, process, and analyse radio-electronic signals, providing [...] Read more.
The process of recognising and classifying radar signals and their radiation sources is currently a key element of operational activities in the electromagnetic environment. Systems of this type, called ELINT class systems, are passive solutions that detect, process, and analyse radio-electronic signals, providing distinctive information on the identified emission source in the final stage of data processing. The data processing in the mentioned types of systems is a very sophisticated issue and is based on advanced machine learning algorithms, artificial neural networks, fractal analysis, intra-pulse analysis, unintentional out-of-band emission analysis, and hybrids of these methods. Currently, there is no optimal method that would allow for the unambiguous identification of particular copies of the same type of radar emission source. This article constitutes an attempt to analyse radar signals generated by six radars of the same type under comparable measurement conditions for all six cases. The concept of the SEI module for the ELINT system was proposed in this paper. The main aim was to perform an advanced analysis, the purpose of which was to identify particular copies of those radars. Pioneering in this research is the application of the author’s algorithm for the data particle geometrical divide, which at the moment has no reference in international publication reports. The research revealed that applying the data particle geometrical divide algorithms to the SEI process concerning six copies of the same radar type allows for almost three times better accuracy than a random labelling strategy within approximately one second. Full article
(This article belongs to the Special Issue Recent Advances in Information Geometric Signal Processing)
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15 pages, 2389 KiB  
Article
Improved RANSAC Point Cloud Spherical Target Detection and Parameter Estimation Method Based on Principal Curvature Constraint
by Qinghua Wu, Jiacheng Liu, Can Gao, Biao Wang, Gaojian Shen and Zhiang Li
Sensors 2022, 22(15), 5850; https://doi.org/10.3390/s22155850 - 05 Aug 2022
Cited by 9 | Viewed by 1799
Abstract
Spherical targets are widely used in coordinate unification of large-scale combined measurements. Through its central coordinates, scanned point cloud data from different locations can be converted into a unified coordinate reference system. However, point cloud sphere detection has the disadvantages of errors and [...] Read more.
Spherical targets are widely used in coordinate unification of large-scale combined measurements. Through its central coordinates, scanned point cloud data from different locations can be converted into a unified coordinate reference system. However, point cloud sphere detection has the disadvantages of errors and slow detection time. For this reason, a novel method of spherical object detection and parameter estimation based on an improved random sample consensus (RANSAC) algorithm is proposed. The method is based on the RANSAC algorithm. Firstly, the principal curvature of point cloud data is calculated. Combined with the k-d nearest neighbor search algorithm, the principal curvature constraint of random sampling points is implemented to improve the quality of sample points selected by RANSAC and increase the detection speed. Secondly, the RANSAC method is combined with the total least squares method. The total least squares method is used to estimate the inner point set of spherical objects obtained by the RANSAC algorithm. The experimental results demonstrate that the method outperforms the conventional RANSAC algorithm in terms of accuracy and detection speed in estimating sphere parameters. Full article
(This article belongs to the Special Issue Recent Advances in Information Geometric Signal Processing)
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