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Recent Advances in Ultra Wideband Radar Sensing and Radar Target Recognition

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

Deadline for manuscript submissions: closed (20 September 2024) | Viewed by 1807

Special Issue Editors


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Guest Editor
Sustainable Mining Research Program, Commonwealth Scientific and Industrial Research Organisation, Pullenvale, QLD 4069, Australia
Interests: radar target characterization; laser sensing technologies; mining; space sensing

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Guest Editor
College of Sciences and Engineering, University of Tasmania, Sandy Bay TAS 7001, Australia
Interests: ultra wideband radar; radar target recognition; transient electromagnetics; radar signal processing; antenna arrays; mutual coupling; biomedical electromagnetics
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Recent advancements in semiconductor technologies and wideband microwave components have paved the way for significant developments in Ultra Wideband (UWB) radar sensing and radar target recognition. These advancements have accelerated the progress of UWB radar technologies in both the time and frequency domains, enabling the generation of sub-nanosecond pulses and broadening the capabilities of radar signal processing and imaging.

This special issue aims to provide a platform for researchers to share their latest findings and explore theoretical developments and application-based work in the field.

We welcome submissions covering a wide range of topics, including, but not limited to:

  • Radar Target Detection
  • Radar Target Recognition
  • Range Profiles
  • Impulse Radar Technologies
  • Wideband Radar Sensing
  • Polarimetric Radar

The applications of interest for this special issue include but not limited to:

  • Medical Diagnosis
  • Space Sensing
  • Smart Homes
  • Internet of Things (IoT)
  • Mining
  • Ground Penetrating Radar
  • Weather Radar

Additionally, we encourage submissions on related topics such as:

  • Radar Cross Section and Electromagnetic Scattering Analysis (Transient / Broadband / Polarimetric)
  • Antenna and Microwave Components designed for radar applications
  • Signal Processing and Artificial Intelligence for Target Detection, Classification and Recognition

We invite researchers from academia, industry, and research institutions to contribute original research papers. Submissions should present novel ideas, methodologies, experimental results, or applications related to UWB radar sensing and radar target recognition. All submitted papers will undergo a rigorous peer-review process to ensure the quality and relevance of accepted papers.

Dr. Chad Hargrave
Dr. Hoi-Shun Antony Lui
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.

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Published Papers (1 paper)

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Research

16 pages, 2033 KiB  
Article
Deciphering Optimal Radar Ensemble for Advancing Sleep Posture Prediction through Multiview Convolutional Neural Network (MVCNN) Approach Using Spatial Radio Echo Map (SREM)
by Derek Ka-Hei Lai, Andy Yiu-Chau Tam, Bryan Pak-Hei So, Andy Chi-Ho Chan, Li-Wen Zha, Duo Wai-Chi Wong and James Chung-Wai Cheung
Sensors 2024, 24(15), 5016; https://doi.org/10.3390/s24155016 - 2 Aug 2024
Cited by 3 | Viewed by 1238
Abstract
Assessing sleep posture, a critical component in sleep tests, is crucial for understanding an individual’s sleep quality and identifying potential sleep disorders. However, monitoring sleep posture has traditionally posed significant challenges due to factors such as low light conditions and obstructions like blankets. [...] Read more.
Assessing sleep posture, a critical component in sleep tests, is crucial for understanding an individual’s sleep quality and identifying potential sleep disorders. However, monitoring sleep posture has traditionally posed significant challenges due to factors such as low light conditions and obstructions like blankets. The use of radar technolsogy could be a potential solution. The objective of this study is to identify the optimal quantity and placement of radar sensors to achieve accurate sleep posture estimation. We invited 70 participants to assume nine different sleep postures under blankets of varying thicknesses. This was conducted in a setting equipped with a baseline of eight radars—three positioned at the headboard and five along the side. We proposed a novel technique for generating radar maps, Spatial Radio Echo Map (SREM), designed specifically for data fusion across multiple radars. Sleep posture estimation was conducted using a Multiview Convolutional Neural Network (MVCNN), which serves as the overarching framework for the comparative evaluation of various deep feature extractors, including ResNet-50, EfficientNet-50, DenseNet-121, PHResNet-50, Attention-50, and Swin Transformer. Among these, DenseNet-121 achieved the highest accuracy, scoring 0.534 and 0.804 for nine-class coarse- and four-class fine-grained classification, respectively. This led to further analysis on the optimal ensemble of radars. For the radars positioned at the head, a single left-located radar proved both essential and sufficient, achieving an accuracy of 0.809. When only one central head radar was used, omitting the central side radar and retaining only the three upper-body radars resulted in accuracies of 0.779 and 0.753, respectively. This study established the foundation for determining the optimal sensor configuration in this application, while also exploring the trade-offs between accuracy and the use of fewer sensors. Full article
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