Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
4.7
2.9
Journals
Electronics
Special Issues
Radar System and Radar Signal Processing
share announcement

Radar System and Radar Signal Processing

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Circuit and Signal Processing".

Deadline for manuscript submissions: 16 December 2024 | Viewed by 1280

Special Issue Editor

Dr. Fei Wang

E-Mail Website
Guest Editor
School of Software Engineering, Xi'an Jiaotong University, Xi'an 710049, China
Interests: machine learning; deep learning; sensors; signal processing

Special Issue Information

Dear Colleagues,

At present, radars play a crucial role in various sectors including defense, meteorology, aviation, space exploration, automobile safety, and human–computer interaction. The aim of this Special Issue is to present the latest research results in the area of radar systems and radar signal processing techniques as a response to the growing demand for radars.

In this Special Issue, original research articles and reviews are welcome. Research areas may include (but are not limited to) the following:

  • Advances in novel radar systems including Cognitive Radar, MIMO Radar, Quantum Radar, Synthetic Aperture Radar, etc.;
  • Radar signal processing;
  • Algorithms for real-time radar processing;
  • Machine Learning and AI in Radar;
  • Radar applications;
  • Toolboxes for radar signal processing;
  • Novel radar datasets;
  • Literature reviews, benchmarks, and empirical study on radar systems and radar signals.

We look forward to receiving your contributions.

Dr. Fei Wang
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. Electronics 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 2400 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 systems
  • radar signal processing
  • radar applications
  • radar algorithms
  • radar dataset
  • radar benchmarks
  • radar toolboxes

Published Papers (1 paper)

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

Research

20 pages, 12030 KiB  
Article
S2S-Sim: A Benchmark Dataset for Ship Cooperative 3D Object Detection
by Wenbin Yang, Xinzhi Wang, Xiangfeng Luo, Shaorong Xie and Junxi Chen
Electronics 2024, 13(5), 885; https://doi.org/10.3390/electronics13050885 - 26 Feb 2024
Cited by 2 | Viewed by 775
Abstract
The rapid development of vehicle cooperative 3D object-detection technology has significantly improved the perception capabilities of autonomous driving systems. However, ship cooperative perception technology has received limited research attention compared to autonomous driving, primarily due to the lack of appropriate ship cooperative perception [...] Read more.
The rapid development of vehicle cooperative 3D object-detection technology has significantly improved the perception capabilities of autonomous driving systems. However, ship cooperative perception technology has received limited research attention compared to autonomous driving, primarily due to the lack of appropriate ship cooperative perception datasets. To address this gap, this paper proposes S2S-sim, a novel ship cooperative perception dataset. Ship navigation scenarios were constructed using Unity3D, and accurate ship models were incorporated while simulating sensor parameters of real LiDAR sensors to collect data. The dataset comprises three typical ship navigation scenarios, including ports, islands, and open waters, featuring common ship classes such as container ships, bulk carriers, and cruise ships. It consists of 7000 frames with 96,881 annotated ship bounding boxes. Leveraging this dataset, we assess the performance of mainstream vehicle cooperative perception models when transferred to ship cooperative perception scenes. Furthermore, considering the characteristics of ship navigation data, we propose a regional clustering fusion-based ship cooperative 3D object-detection method. Experimental results demonstrate that our approach achieves state-of-the-art performance in 3D ship object detection, indicating its suitability for ship cooperative perception. Full article
(This article belongs to the Special Issue Radar System and Radar Signal Processing)
►▼ Show Figures

Figure 1

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