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Radar Signal Processing and System Design for Urban Health
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Radar Signal Processing and System Design for Urban Health

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

Deadline for manuscript submissions: closed (30 April 2022) | Viewed by 30549

Special Issue Editor

Dr. Ashish Kumar Singh

E-Mail Website
Guest Editor
University of Allahabad, Prayagraj, India
Interests: radar signal processing; micro-doppler analysis; deep learning; FPGA; beamforming; radar system design
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In the past few decades, radar technology has gained significant attention in different fields, such as automobile, monitoring of infrastructure, healthcare, gesture recognition, and target classification. Radar systems using sophisticated signal processing can be used as contactless monitoring of vital signs (such as heartrate, respiration, and blood pressure). In fact, these radar systems are crucial for vital sign monitoring of infants, patients with severe burns, and those with sleep apnea syndrome.

New emerging applications of radar technology also include classification of human activities and gait analysis for activity of daily living (ADL) and in-house healthcare. Radar signal processing based on 3D radar data and micro-Doppler signatures has also been adapted to characterize the pattern of human movement and tracking (such as fall detection). Machine learning has been applied to remote sensing for the classification and characterization of target using radar imaging.

The aim of this Special Issue is to provide an overview of the latest advances and trends of radar technology for urban healthcare applications. In addition, we expect the implementation of radar signal processing and radar system design with an interdisciplinary approach, such as a multisensory approach. In this Special Issue, we also welcome the application of deep learning, 5G radar, and communication protocols to the radar system for the biomedical domain. We invite you to submit your recent research which focuses on radar signal processing and system design for solving healthcare challenges.

Dr. Ashish Kumar Singh
Guest Editor

Manuscript Submission Information

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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.

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Keywords

  • Radar sensors
  • Radar signal processing
  • Noncontact vital signs
  • 3D radar imaging
  • Radar tracking
  • Biomedical monitoring
  • Feature extraction
  • Human activity classification
  • Multisensory data fusion

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Published Papers (7 papers)

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Research

12 pages, 4283 KiB  
Communication
MIMO FMCW Radar with Doppler-Insensitive Polyphase Codes
by EunHee Kim
Remote Sens. 2022, 14(11), 2595; https://doi.org/10.3390/rs14112595 - 28 May 2022
Cited by 2 | Viewed by 2223
Abstract
Co-located MIMO is used to enlarge the antenna aperture virtually and increase the angular resolution. This paper shows FMCW radar using MIMO VAA. Polyphase codes are designed for modulating the successive chirps of transmitting signals. The codes are optimized to have low cross-correlations [...] Read more.
Co-located MIMO is used to enlarge the antenna aperture virtually and increase the angular resolution. This paper shows FMCW radar using MIMO VAA. Polyphase codes are designed for modulating the successive chirps of transmitting signals. The codes are optimized to have low cross-correlations regardless of the Doppler filter mismatch. Compared with orthogonal codes, the designed codes show robust performance for Doppler mismatch and lower angle estimation errors. The entire procedure is explained, and the simulation results are provided. Full article
(This article belongs to the Special Issue Radar Signal Processing and System Design for Urban Health)
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20 pages, 1986 KiB  
Article
Continuous Human Activity Recognition through Parallelism LSTM with Multi-Frequency Spectrograms
by Congzhang Ding, Yong Jia, Guolong Cui, Chuan Chen, Xiaoling Zhong and Yong Guo
Remote Sens. 2021, 13(21), 4264; https://doi.org/10.3390/rs13214264 - 23 Oct 2021
Cited by 11 | Viewed by 2965
Abstract
According to the real-living environment, radar-based human activity recognition (HAR) is dedicated to recognizing and classifying a sequence of activities rather than individual activities, thereby drawing more attention in practical applications of security surveillance, health care and human–computer interactions. This paper proposes a [...] Read more.
According to the real-living environment, radar-based human activity recognition (HAR) is dedicated to recognizing and classifying a sequence of activities rather than individual activities, thereby drawing more attention in practical applications of security surveillance, health care and human–computer interactions. This paper proposes a parallelism long short-term memory (LSTM) framework with the input of multi-frequency spectrograms to implement continuous HAR. Specifically, frequency-division short-time Fourier transformation (STFT) is performed on the data stream of continuous activities collected by a stepped-frequency continuous-wave (SFCW) radar, generating spectrograms of multiple frequencies which introduce different scattering properties and frequency resolutions. In the designed parallelism LSTM framework, multiple parallel LSTM sub-networks are trained separately to extract different temporal features from the spectrogram of each frequency and produce corresponding classification probabilities. At the decision level, the probabilities of activity classification from these sub-networks are fused by addition as the recognition output. To validate the proposed method, an experimental data set is collected by using an SFCW radar to monitor 11 participants who continuously perform six activities in sequence with three different transitions and random durations. The validation results demonstrate that the average accuracies of the designed parallelism unidirectional LSTM (Uni-LSTM) and bidirectional LSTM (Bi-LSTM) based on five frequency spectrograms are 85.41% and 96.15%, respectively, outperforming traditional Uni-LSTM and Bi-LSTM networks with only a single-frequency spectrogram by 5.35% and 6.33% at least. Additionally, the recognition accuracy of the parallelism LSTM network reveals an upward trend as the number of multi-frequency spectrograms (namely the number of LSTM subnetworks) increases, and tends to be stable when the number reaches 4. Full article
(This article belongs to the Special Issue Radar Signal Processing and System Design for Urban Health)
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23 pages, 4543 KiB  
Article
Dynamic Digital Signal Processing Algorithm for Vital Signs Extraction in Continuous-Wave Radars
by Carolina Gouveia, Daniel Albuquerque, José Vieira and Pedro Pinho
Remote Sens. 2021, 13(20), 4079; https://doi.org/10.3390/rs13204079 - 13 Oct 2021
Cited by 17 | Viewed by 3702
Abstract
Radar systems have been widely explored as a monitoring tool able to assess the subject’s vital signs remotely. However, their implementation in real application scenarios is not straightforward. Received signals encompass parasitic reflections that occur in the monitoring environment. Generally, those parasitic components, [...] Read more.
Radar systems have been widely explored as a monitoring tool able to assess the subject’s vital signs remotely. However, their implementation in real application scenarios is not straightforward. Received signals encompass parasitic reflections that occur in the monitoring environment. Generally, those parasitic components, often treated as a complex DC (CDC) offsets, must be removed in order to correctly extract the bio-signals information. Fitting methods can be used, but their implementation were revealed to be challenging when bio-signals are weak or when these parasitic reflections arise from non-static targets, changing the CDC offset properties over time. In this work, we propose a dynamic digital signal processing algorithm to extract the vital signs from radar systems. This algorithm includes a novel arc fitting method to estimate the CDC offsets on the received signal. The method revealed being robust to weaker signals, presenting a success rate of 95%, irrespective of the considered monitoring conditions. Furthermore, the proposed algorithm is able to adapt to slow changes in the propagation environment. Full article
(This article belongs to the Special Issue Radar Signal Processing and System Design for Urban Health)
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21 pages, 3036 KiB  
Article
Indoor Activity and Vital Sign Monitoring for Moving People with Multiple Radar Data Fusion
by Xiuzhu Yang, Xinyue Zhang, Yi Ding and Lin Zhang
Remote Sens. 2021, 13(18), 3791; https://doi.org/10.3390/rs13183791 - 21 Sep 2021
Cited by 23 | Viewed by 5283
Abstract
The monitoring of human activity and vital signs plays a significant role in remote health-care. Radar provides a non-contact monitoring approach without privacy and illumination concerns. However, multiple people in a narrow indoor environment bring dense multipaths for activity monitoring, and the received [...] Read more.
The monitoring of human activity and vital signs plays a significant role in remote health-care. Radar provides a non-contact monitoring approach without privacy and illumination concerns. However, multiple people in a narrow indoor environment bring dense multipaths for activity monitoring, and the received vital sign signals are heavily distorted with body movements. This paper proposes a framework based on Frequency Modulated Continuous Wave (FMCW) and Impulse Radio Ultra-Wideband (IR-UWB) radars to address these challenges, designing intelligent spatial-temporal information fusion for activity and vital sign monitoring. First, a local binary pattern (LBP) and energy features are extracted from FMCW radar, combined with the wavelet packet transform (WPT) features on IR-UWB radar for activity monitoring. Then the additional information guided fusing network (A-FuseNet) is proposed with a modified generative and adversarial structure for vital sign monitoring. A Cascaded Convolutional Neural Network (CCNN) module and a Long Short Term Memory (LSTM) module are designed as the fusion sub-network for vital sign information extraction and multisensory data fusion, while a discrimination sub-network is constructed to optimize the fused heartbeat signal. In addition, the activity and movement characteristics are introduced as additional information to guide the fusion and optimization. A multi-radar dataset with an FMCW and two IR-UWB radars in a cotton tent, a small room and a wide lobby is constructed, and the accuracies of activity and vital sign monitoring achieve 99.9% and 92.3% respectively. Experimental results demonstrate the superiority and robustness of the proposed framework. Full article
(This article belongs to the Special Issue Radar Signal Processing and System Design for Urban Health)
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21 pages, 9059 KiB  
Article
A Method for Reducing Timing Jitter’s Impact in Through-Wall Human Detection by Ultra-Wideband Impulse Radar
by Cheng Shi, Zhi-Kang Ni, Jun Pan, Zhijie Zheng, Shengbo Ye and Guangyou Fang
Remote Sens. 2021, 13(18), 3577; https://doi.org/10.3390/rs13183577 - 8 Sep 2021
Cited by 4 | Viewed by 2454
Abstract
Ultra-wideband (UWB) impulse radar is widely used for through-wall human respiration detection due to its high range resolution and high penetration capability. UWB impulse radar emits very narrow time pulses, which can directly obtain the impulse response of the target. However, the time [...] Read more.
Ultra-wideband (UWB) impulse radar is widely used for through-wall human respiration detection due to its high range resolution and high penetration capability. UWB impulse radar emits very narrow time pulses, which can directly obtain the impulse response of the target. However, the time interval between successive pulses emitted is not ideally fixed because of timing jitter. This results in the impulse response position of the same target not being fixed, but it is related to slow-time. The clutter scattered by the stationary target becomes non-stationary clutter, which affects the accurate extraction of the human respiration signal. In this paper, we propose a method for reducing timing jitter’s impact in through-wall human detection by UWB impulse radar. After the received signal is processed by the Fast Fourier transform (FFT) in slow-time, we model the range-frequency matrix in the frequency domain as a superposition of the low-rank representation of jitter-induced clutter data and the sparse representation of human respiratory data. By only extracting the sparse component, the impact of timing jitter in human respiration detection can be reduced. Both numerical simulated data and experimental data demonstrate that our proposed method can effectively remove non-stationary clutter induced by timing jitter and improve the accuracy of the human target signal extraction. Full article
(This article belongs to the Special Issue Radar Signal Processing and System Design for Urban Health)
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21 pages, 3706 KiB  
Article
Through-Wall Multi-Subject Localization and Vital Signs Monitoring Using UWB MIMO Imaging Radar
by Zhi Li, Tian Jin, Yongpeng Dai and Yongkun Song
Remote Sens. 2021, 13(15), 2905; https://doi.org/10.3390/rs13152905 - 23 Jul 2021
Cited by 43 | Viewed by 5968
Abstract
Radar-based non-contact vital signs monitoring has great value in through-wall detection applications. This paper presents the theoretical and experimental study of through-wall respiration and heartbeat pattern extraction from multiple subjects. To detect the vital signs of multiple subjects, we employ a low-frequency ultra-wideband [...] Read more.
Radar-based non-contact vital signs monitoring has great value in through-wall detection applications. This paper presents the theoretical and experimental study of through-wall respiration and heartbeat pattern extraction from multiple subjects. To detect the vital signs of multiple subjects, we employ a low-frequency ultra-wideband (UWB) multiple-input multiple-output (MIMO) imaging radar and derive the relationship between radar images and vibrations caused by human cardiopulmonary movements. The derivation indicates that MIMO radar imaging with the stepped-frequency continuous-wave (SFCW) improves the signal-to-noise ratio (SNR) critically by the factor of radar channel number times frequency number compared with continuous-wave (CW) Doppler radars. We also apply the three-dimensional (3-D) higher-order cumulant (HOC) to locate multiple subjects and extract the phase sequence of the radar images as the vital signs signal. To monitor the cardiopulmonary activities, we further exploit the VMD algorithm with a proposed grouping criterion to adaptively separate the respiration and heartbeat patterns. A series of experiments have validated the localization and detection of multiple subjects behind a wall. The VMD algorithm is suitable for separating the weaker heartbeat pattern from the stronger respiration pattern by the grouping criterion. Moreover, the continuous monitoring of heart rate (HR) by the MIMO radar in real scenarios shows a strong consistency with the reference electrocardiogram (ECG). Full article
(This article belongs to the Special Issue Radar Signal Processing and System Design for Urban Health)
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Graphical abstract

22 pages, 6619 KiB  
Article
Through-Wall Human Pose Reconstruction via UWB MIMO Radar and 3D CNN
by Yongkun Song, Tian Jin, Yongpeng Dai, Yongping Song and Xiaolong Zhou
Remote Sens. 2021, 13(2), 241; https://doi.org/10.3390/rs13020241 - 12 Jan 2021
Cited by 43 | Viewed by 6414
Abstract
Human pose reconstruction has been a fundamental research in computer vision. However, existing pose reconstruction methods suffer from the problem of wall occlusion that cannot be solved by a traditional optical sensor. This article studies a novel human target pose reconstruction framework using [...] Read more.
Human pose reconstruction has been a fundamental research in computer vision. However, existing pose reconstruction methods suffer from the problem of wall occlusion that cannot be solved by a traditional optical sensor. This article studies a novel human target pose reconstruction framework using low-frequency ultra-wideband (UWB) multiple-input multiple-output (MIMO) radar and a convolutional neural network (CNN), which is used to detect targets behind the wall. In the proposed framework, first, we use UWB MIMO radar to capture the human body information. Then, target detection and tracking are used to lock the target position, and the back-projection algorithm is adopted to construct three-dimensional (3D) images. Finally, we take the processed 3D image as input to reconstruct the 3D pose of the human target via the designed 3D CNN model. Field detection experiments and comparison results show that the proposed framework can achieve pose reconstruction of human targets behind a wall, which indicates that our research can make up for the shortcomings of optical sensors and significantly expands the application of the UWB MIMO radar system. Full article
(This article belongs to the Special Issue Radar Signal Processing and System Design for Urban Health)
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