Special Issue "Radar Remote Sensing on Life Activities"

A special issue of Remote Sensing (ISSN 2072-4292).

Deadline for manuscript submissions: 15 December 2019.

Special Issue Editors

Dr. Zhengyu Peng
E-Mail Website
Guest Editor
Aptiv Corporation, 2152 E Lincoln Rd, Kokomo, IN 46902
Interests: automotive radar; mm-wave radar; radio frequency and microwave systems; antenna array; beamforming
Special Issues and Collections in MDPI journals
Dr. Changzhi Li
E-Mail Website
Guest Editor
Department of Electrical & Computer Engineering, Texas Tech University, Box 43102, Lubbock, TX 79409-3102
Interests: radio frequency and microwave; wireless localization; non-contact motion sensing; healthcare monitoring; structural monitoring; biomedical radar
Special Issues and Collections in MDPI journals

Special Issue Information

Dear Colleagues,

Non-contact remote sensing of life activities, such as respiration, heartbeat, hand gestures, sleep, and walking based on radar sensors has attracted a lot of interest from both academia and industry in recent years. Using radar sensors, researchers have been exploring novel applications including indoor tracking, monitoring of vital signs, security surveillance, gesture recognition, and occupancy detection. Various radar sensors from bench-top systems to silicon on-chip integration have been widely reported. The operation frequency of these radar sensors ranges from a few MHz to sub-THz. Advanced algorithms such as machine learning and blind signal separation have also been adapted for radar-based life activity sensing. While the rapid advancements in radar remote sensing technologies have shown great promise in improving life quality, there still exist significant challenges to be solved.

We invite manuscripts for this forthcoming Special Issue in all aspects regarding radar remote sensing on life activities. Both reviews and original research articles on systems, hardware, or algorithms are welcome. Reviews should provide an up-to-date overview for the state-of-the-art technologies such as remote and accurate vital signs monitoring, life activity tracking, non-contact human-computer interface based on remote sensing of gesture commands, or any other radar based remote life activity sensing topics that have experienced significant advancements in the past decade. Original research papers should focus on a new approach or solve an important problem in radar-based life activity remote sensing. If you have ideas to discuss before submission, please feel free to contact us. We look forward to receiving your manuscript submitted to this Special Issue.

Dr. Zhengyu Peng
Dr. Changzhi Li
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 papers will be 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 1800 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

  • non-contact sensing
  • radar
  • vital signs
  • life activity tracking
  • biomedical applications
  • human-computer interface
  • security monitoring
  • microwave
  • radio frequency

Published Papers (3 papers)

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Research

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Open AccessArticle
Detection and Localization for Multiple Stationary Human Targets Based on Cross-Correlation of Dual-Station SFCW Radars
Remote Sens. 2019, 11(12), 1428; https://doi.org/10.3390/rs11121428 - 15 Jun 2019
Abstract
This paper demonstrates the feasibility of detection and localization of multiple stationary human targets based on cross-correlation of the dual-station stepped-frequency continuous-wave (SFCW) radars. Firstly, a cross-correlation operation is performed on the preprocessed pulse signals of two SFCW radars at different locations to [...] Read more.
This paper demonstrates the feasibility of detection and localization of multiple stationary human targets based on cross-correlation of the dual-station stepped-frequency continuous-wave (SFCW) radars. Firstly, a cross-correlation operation is performed on the preprocessed pulse signals of two SFCW radars at different locations to obtain the correlation coefficient matrix. Then, the constant false alarm rate (CFAR) detection is applied to extract the ranges between each target and the two radars, respectively, from the correlation matrix. Finally, the locations of human targets is calculated with the triangulation localization algorithm. This cross-correlation operation mainly brings about two advantages. On the one hand, the cross-correlation explores the correlation feature of target respiratory signals, which can effectively detect all targets with different signal intensities, avoiding the missed detection of weak targets. On the other hand, the pairing of two ranges between each target and two radars is implemented simultaneously with the cross-correlation. Experimental results verify the effectiveness of this algorithm. Full article
(This article belongs to the Special Issue Radar Remote Sensing on Life Activities)
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Open AccessArticle
A Novel Vital-Sign Sensing Algorithm for Multiple Subjects Based on 24-GHz FMCW Doppler Radar
Remote Sens. 2019, 11(10), 1237; https://doi.org/10.3390/rs11101237 - 24 May 2019
Abstract
A novel non-contact vital-sign sensing algorithm for use in cases of multiple subjects is proposed. The approach uses a 24 GHz frequency-modulated continuous-wave Doppler radar with the parametric spectral estimation method. Doppler processing and spectral estimation are concurrently implemented to detect vital signs [...] Read more.
A novel non-contact vital-sign sensing algorithm for use in cases of multiple subjects is proposed. The approach uses a 24 GHz frequency-modulated continuous-wave Doppler radar with the parametric spectral estimation method. Doppler processing and spectral estimation are concurrently implemented to detect vital signs from more than one subject, revealing excellent results. The parametric spectral estimation method is utilized to clearly identify multiple targets, making it possible to distinguish multiple targets located less than 40 cm apart, which is beyond the limit of the theoretical range resolution. Fourier transformation is used to extract phase information, and the result is combined with the spectral estimation result. To eliminate mutual interference, the range integration is performed when combining the range and phase information. By considering breathing and heartbeat periodicity, the proposed algorithm can accurately extract vital signs in real time by applying an auto-regressive algorithm. The capability of a contactless and unobtrusive vital sign measurement with a millimeter wave radar system has innumerable applications, such as remote patient monitoring, emergency surveillance, and personal health care. Full article
(This article belongs to the Special Issue Radar Remote Sensing on Life Activities)
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Review

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Open AccessReview
A Survey of Deep Learning-Based Human Activity Recognition in Radar
Remote Sens. 2019, 11(9), 1068; https://doi.org/10.3390/rs11091068 - 06 May 2019
Abstract
Radar, as one of the sensors for human activity recognition (HAR), has unique characteristics such as privacy protection and contactless sensing. Radar-based HAR has been applied in many fields such as human–computer interaction, smart surveillance and health assessment. Conventional machine learning approaches rely [...] Read more.
Radar, as one of the sensors for human activity recognition (HAR), has unique characteristics such as privacy protection and contactless sensing. Radar-based HAR has been applied in many fields such as human–computer interaction, smart surveillance and health assessment. Conventional machine learning approaches rely on heuristic hand-crafted feature extraction, and their generalization capability is limited. Additionally, extracting features manually is time–consuming and inefficient. Deep learning acts as a hierarchical approach to learn high-level features automatically and has achieved superior performance for HAR. This paper surveys deep learning based HAR in radar from three aspects: deep learning techniques, radar systems, and deep learning for radar-based HAR. Especially, we elaborate deep learning approaches designed for activity recognition in radar according to the dimension of radar returns (i.e., 1D, 2D and 3D echoes). Due to the difference of echo forms, corresponding deep learning approaches are different to fully exploit motion information. Experimental results have demonstrated the feasibility of applying deep learning for radar-based HAR in 1D, 2D and 3D echoes. Finally, we address some current research considerations and future opportunities. Full article
(This article belongs to the Special Issue Radar Remote Sensing on Life Activities)
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