1
|
Liebetruth M, Kehe K, Steinritz D, Sammito S. Systematic Literature Review Regarding Heart Rate and Respiratory Rate Measurement by Means of Radar Technology. SENSORS (BASEL, SWITZERLAND) 2024; 24:1003. [PMID: 38339721 PMCID: PMC10857015 DOI: 10.3390/s24031003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/18/2023] [Revised: 01/23/2024] [Accepted: 01/30/2024] [Indexed: 02/12/2024]
Abstract
The use of radar technology for non-contact measurement of vital parameters is increasingly being examined in scientific studies. Based on a systematic literature search in the PubMed, German National Library, Austrian Library Network (Union Catalog), Swiss National Library and Common Library Network databases, the accuracy of heart rate and/or respiratory rate measurements by means of radar technology was analyzed. In 37% of the included studies on the measurement of the respiratory rate and in 48% of those on the measurement of the heart rate, the maximum deviation was 5%. For a tolerated deviation of 10%, the corresponding percentages were 85% and 87%, respectively. However, the quantitative comparability of the results available in the current literature is very limited due to a variety of variables. The elimination of the problem of confounding variables and the continuation of the tendency to focus on the algorithm applied will continue to constitute a central topic of radar-based vital parameter measurement. Promising fields of application of research can be found in particular in areas that require non-contact measurements. This includes infection events, emergency medicine, disaster situations and major catastrophic incidents.
Collapse
Affiliation(s)
- Magdalena Liebetruth
- German Air Force Centre of Aerospace Medicine, 51147 Cologne, Germany
- Department of Occupational Medicine, Faculty of Medicine, Otto von Guericke University of Magdeburg, 39120 Magdeburg, Germany
| | - Kai Kehe
- Bundeswehr Medical Service Headquarter, Department A-VI Public Health, 56072 Koblenz, Germany
| | - Dirk Steinritz
- Bundeswehr Institute of Pharmacology and Toxicology, 80937 Munich, Germany
| | - Stefan Sammito
- German Air Force Centre of Aerospace Medicine, 51147 Cologne, Germany
- Department of Occupational Medicine, Faculty of Medicine, Otto von Guericke University of Magdeburg, 39120 Magdeburg, Germany
| |
Collapse
|
2
|
El Abbaoui A, Sodoyer D, Elbahhar F. Contactless Heart and Respiration Rates Estimation and Classification of Driver Physiological States Using CW Radar and Temporal Neural Networks. SENSORS (BASEL, SWITZERLAND) 2023; 23:9457. [PMID: 38067830 PMCID: PMC10708560 DOI: 10.3390/s23239457] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/03/2023] [Revised: 10/27/2023] [Accepted: 10/30/2023] [Indexed: 12/18/2023]
Abstract
The measurement and analysis of vital signs are a subject of significant research interest, particularly for monitoring the driver's physiological state, which is of crucial importance for road safety. Various approaches have been proposed using contact techniques to measure vital signs. However, all of these methods are invasive and cumbersome for the driver. This paper proposes using a non-contact sensor based on continuous wave (CW) radar at 24 GHz to measure vital signs. We associate these measurements with distinct temporal neural networks to analyze the signals to detect and extract heart and respiration rates as well as classify the physiological state of the driver. This approach offers robust performance in estimating the exact values of heart and respiration rates and in classifying the driver's physiological state. It is non-invasive and requires no physical contact with the driver, making it particularly practical and safe. The results presented in this paper, derived from the use of a 1D Convolutional Neural Network (1D-CNN), a Temporal Convolutional Network (TCN), a Recurrent Neural Network particularly the Bidirectional Long Short-Term Memory (Bi-LSTM), and a Convolutional Recurrent Neural Network (CRNN). Among these, the CRNN emerged as the most effective Deep Learning approach for vital signal analysis.
Collapse
Affiliation(s)
- Amal El Abbaoui
- COSYS-LEOST, University Gustave Eiffel, F-59650 Villeneuve d’Ascq, France;
| | | | - Fouzia Elbahhar
- COSYS-LEOST, University Gustave Eiffel, F-59650 Villeneuve d’Ascq, France;
| |
Collapse
|
3
|
Xu L, Lien J, Li H, Gillian N, Nongpiur R, Li J, Zhang Q, Cui J, Jorgensen D, Bernstein A, Bedal L, Hayashi E, Yamanaka J, Lee A, Wang J, Shin D, Poupyrev I, Thormundsson T, Pathak A, Patel S. Soli-enabled noncontact heart rate detection for sleep and meditation tracking. Sci Rep 2023; 13:18008. [PMID: 37865634 PMCID: PMC10590449 DOI: 10.1038/s41598-023-44714-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2023] [Accepted: 10/11/2023] [Indexed: 10/23/2023] Open
Abstract
Heart rate (HR) is a crucial physiological signal that can be used to monitor health and fitness. Traditional methods for measuring HR require wearable devices, which can be inconvenient or uncomfortable, especially during sleep and meditation. Noncontact HR detection methods employing microwave radar can be a promising alternative. However, the existing approaches in the literature usually use high-gain antennas and require the sensor to face the user's chest or back, making them difficult to integrate into a portable device and unsuitable for sleep and meditation tracking applications. This study presents a novel approach for noncontact HR detection using a miniaturized Soli radar chip embedded in a portable device (Google Nest Hub). The chip has a [Formula: see text] dimension and can be easily integrated into various devices. The proposed approach utilizes advanced signal processing and machine learning techniques to extract HRs from radar signals. The approach is validated on a sleep dataset (62 users, 498 h) and a meditation dataset (114 users, 1131 min). The approach achieves a mean absolute error (MAE) of 1.69 bpm and a mean absolute percentage error (MAPE) of [Formula: see text] on the sleep dataset. On the meditation dataset, the approach achieves an MAE of 1.05 bpm and a MAPE of [Formula: see text]. The recall rates for the two datasets are [Formula: see text] and [Formula: see text], respectively. This study represents the first application of the noncontact HR detection technology to sleep and meditation tracking, offering a promising alternative to wearable devices for HR monitoring during sleep and meditation.
Collapse
Affiliation(s)
- Luzhou Xu
- Google LLC, 6420 Sequence Drive, San Diego, CA, 92121, USA.
| | - Jaime Lien
- Google LLC, 1600 Amphitheatre Parkway, Mountain View, CA, 94043, USA
| | - Haiguang Li
- Google LLC, 1600 Amphitheatre Parkway, Mountain View, CA, 94043, USA
| | - Nicholas Gillian
- Google LLC, 1600 Amphitheatre Parkway, Mountain View, CA, 94043, USA
| | - Rajeev Nongpiur
- Google LLC, 1600 Amphitheatre Parkway, Mountain View, CA, 94043, USA
| | - Jihan Li
- Google LLC, 1600 Amphitheatre Parkway, Mountain View, CA, 94043, USA
| | - Qian Zhang
- Google LLC, 1600 Amphitheatre Parkway, Mountain View, CA, 94043, USA
| | - Jian Cui
- Google LLC, 1600 Amphitheatre Parkway, Mountain View, CA, 94043, USA
| | - David Jorgensen
- Google LLC, 1600 Amphitheatre Parkway, Mountain View, CA, 94043, USA
| | - Adam Bernstein
- Google LLC, 1600 Amphitheatre Parkway, Mountain View, CA, 94043, USA
| | - Lauren Bedal
- Google LLC, 1600 Amphitheatre Parkway, Mountain View, CA, 94043, USA
| | - Eiji Hayashi
- Google LLC, 1600 Amphitheatre Parkway, Mountain View, CA, 94043, USA
| | - Jin Yamanaka
- Google LLC, 1600 Amphitheatre Parkway, Mountain View, CA, 94043, USA
| | - Alex Lee
- Google LLC, 1600 Amphitheatre Parkway, Mountain View, CA, 94043, USA
| | - Jian Wang
- Google LLC, 1600 Amphitheatre Parkway, Mountain View, CA, 94043, USA
| | - D Shin
- Google LLC, 1600 Amphitheatre Parkway, Mountain View, CA, 94043, USA
| | - Ivan Poupyrev
- Google LLC, 1600 Amphitheatre Parkway, Mountain View, CA, 94043, USA
| | | | - Anupam Pathak
- Google LLC, 19510 Jamboree Rd, Irvine, CA, 92612, USA
| | - Shwetak Patel
- Google LLC, 601 North 34st Street, Seattle, WA, 98103, USA
| |
Collapse
|
4
|
Tovar-Lopez FJ. Recent Progress in Micro- and Nanotechnology-Enabled Sensors for Biomedical and Environmental Challenges. SENSORS (BASEL, SWITZERLAND) 2023; 23:5406. [PMID: 37420577 DOI: 10.3390/s23125406] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/10/2023] [Revised: 06/01/2023] [Accepted: 06/05/2023] [Indexed: 07/09/2023]
Abstract
Micro- and nanotechnology-enabled sensors have made remarkable advancements in the fields of biomedicine and the environment, enabling the sensitive and selective detection and quantification of diverse analytes. In biomedicine, these sensors have facilitated disease diagnosis, drug discovery, and point-of-care devices. In environmental monitoring, they have played a crucial role in assessing air, water, and soil quality, as well as ensured food safety. Despite notable progress, numerous challenges persist. This review article addresses recent developments in micro- and nanotechnology-enabled sensors for biomedical and environmental challenges, focusing on enhancing basic sensing techniques through micro/nanotechnology. Additionally, it explores the applications of these sensors in addressing current challenges in both biomedical and environmental domains. The article concludes by emphasizing the need for further research to expand the detection capabilities of sensors/devices, enhance sensitivity and selectivity, integrate wireless communication and energy-harvesting technologies, and optimize sample preparation, material selection, and automated components for sensor design, fabrication, and characterization.
Collapse
|
5
|
Mauro G, De Carlos Diez M, Ott J, Servadei L, Cuellar MP, Morales-Santos DP. Few-Shot User-Adaptable Radar-Based Breath Signal Sensing. SENSORS (BASEL, SWITZERLAND) 2023; 23:804. [PMID: 36679598 PMCID: PMC9865656 DOI: 10.3390/s23020804] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/07/2022] [Revised: 01/04/2023] [Accepted: 01/07/2023] [Indexed: 06/17/2023]
Abstract
Vital signs estimation provides valuable information about an individual's overall health status. Gathering such information usually requires wearable devices or privacy-invasive settings. In this work, we propose a radar-based user-adaptable solution for respiratory signal prediction while sitting at an office desk. Such an approach leads to a contact-free, privacy-friendly, and easily adaptable system with little reference training data. Data from 24 subjects are preprocessed to extract respiration information using a 60 GHz frequency-modulated continuous wave radar. With few training examples, episodic optimization-based learning allows for generalization to new individuals. Episodically, a convolutional variational autoencoder learns how to map the processed radar data to a reference signal, generating a constrained latent space to the central respiration frequency. Moreover, autocorrelation over recorded radar data time assesses the information corruption due to subject motions. The model learning procedure and breathing prediction are adjusted by exploiting the motion corruption level. Thanks to the episodic acquired knowledge, the model requires an adaptation time of less than one and two seconds for one to five training examples, respectively. The suggested approach represents a novel, quickly adaptable, non-contact alternative for office settings with little user motion.
Collapse
Affiliation(s)
- Gianfranco Mauro
- Infineon Technologies AG, Am Campeon 1-15, 85579 Neubiberg, Germany
- Department of Electronic and Computer Technology, University of Granada, Avenida de Fuente Nueva s/n, 18071 Granada, Spain
| | | | - Julius Ott
- Infineon Technologies AG, Am Campeon 1-15, 85579 Neubiberg, Germany
- Department of Electrical and Computer Engineering, Technical University of Munich, Arcisstrasse 21, 80333 Munich, Germany
| | - Lorenzo Servadei
- Infineon Technologies AG, Am Campeon 1-15, 85579 Neubiberg, Germany
- Department of Electrical and Computer Engineering, Technical University of Munich, Arcisstrasse 21, 80333 Munich, Germany
| | - Manuel P. Cuellar
- Department of Computer Science and Artificial Intelligence, University of Granada, C/. Pdta. Daniel Saucedo Aranda s/n, 18015 Granada, Spain
| | - Diego P. Morales-Santos
- Department of Electronic and Computer Technology, University of Granada, Avenida de Fuente Nueva s/n, 18071 Granada, Spain
| |
Collapse
|
6
|
Ishrak MS, Cai F, Islam SMM, Borić-Lubecke O, Wu T, Lubecke VM. Doppler radar remote sensing of respiratory function. Front Physiol 2023; 14:1130478. [PMID: 37179837 PMCID: PMC10172641 DOI: 10.3389/fphys.2023.1130478] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2023] [Accepted: 04/05/2023] [Indexed: 05/15/2023] Open
Abstract
Doppler radar remote sensing of torso kinematics can provide an indirect measure of cardiopulmonary function. Motion at the human body surface due to heart and lung activity has been successfully used to characterize such measures as respiratory rate and depth, obstructive sleep apnea, and even the identity of an individual subject. For a sedentary subject, Doppler radar can track the periodic motion of the portion of the body moving as a result of the respiratory cycle as distinct from other extraneous motions that may occur, to provide a spatial temporal displacement pattern that can be combined with a mathematical model to indirectly assess quantities such as tidal volume, and paradoxical breathing. Furthermore, it has been demonstrated that even healthy respiratory function results in distinct motion patterns between individuals that vary as a function of relative time and depth measures over the body surface during the inhalation/exhalation cycle. Potentially, the biomechanics that results in different measurements between individuals can be further exploited to recognize pathology related to lung ventilation heterogeneity and other respiratory diagnostics.
Collapse
Affiliation(s)
- Mohammad Shadman Ishrak
- Department of Electrical and Computer Engineering, University of Hawaii at Manoa, Honolulu, HI, United States
- *Correspondence: Mohammad Shadman Ishrak,
| | - Fulin Cai
- School of Computing and Augmented Intelligence, Arizona State University, Tempe, AZ, United States
- ASU-Mayo Center for Innovative Imaging, Arizona State University, Tempe, AZ, United States
| | | | - Olga Borić-Lubecke
- Department of Electrical and Computer Engineering, University of Hawaii at Manoa, Honolulu, HI, United States
| | - Teresa Wu
- School of Computing and Augmented Intelligence, Arizona State University, Tempe, AZ, United States
- ASU-Mayo Center for Innovative Imaging, Arizona State University, Tempe, AZ, United States
| | - Victor M. Lubecke
- Department of Electrical and Computer Engineering, University of Hawaii at Manoa, Honolulu, HI, United States
| |
Collapse
|
7
|
Gao Z, Ali L, Wang C, Liu R, Wang C, Qian C, Sung H, Meng F. Real-Time Non-Contact Millimeter Wave Radar-Based Vital Sign Detection. SENSORS (BASEL, SWITZERLAND) 2022; 22:7560. [PMID: 36236659 PMCID: PMC9573470 DOI: 10.3390/s22197560] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/07/2022] [Revised: 09/21/2022] [Accepted: 09/26/2022] [Indexed: 06/16/2023]
Abstract
In this paper, the extraction of the life activity spectrum based on the millimeter (mm) wave radar is designed to realize the detection of target objects and the threshold trigger module. The maximum likelihood estimation method is selected to complete the design of the average early warning probability trigger function. The threshold trigger module is designed for the echo signal of static objects in the echo signal. It will interfere with the extraction of Doppler frequency shift results. The moving target detection method is selected, and the filter is designed. The static clutter interference is filtered without affecting the phase difference between the detection sequences, and the highlight target signal is improved. The frequency and displacement of thoracic movement are used as the detection data. Through the Fourier transform calculation of the sequence, the spectrum value is extracted within the estimated range of the heartbeat and respiration spectrum, and the heartbeat and respiration signals are picked up. The proposed design uses Modelsim and Quartus for CO-simulation to complete the simulation verification of the function, extract the number of logical units occupied by computing resources, and verify the algorithm with the vital signs experiment. The heartbeat and respiration were detected using the sports bracelet; the relative errors of heartbeat detection were 0-6.3%, the respiration detection was 0-9.5%, and the relative errors of heartbeat detection were overwhelmingly less than 5%.
Collapse
Affiliation(s)
- Zhiqiang Gao
- School of Astronautics, Harbin Institute of Technology, Harbin 150001, China
| | - Luqman Ali
- School of Information and Communication, Harbin Institute of Technology, Harbin 150001, China
| | - Cong Wang
- School of Information and Communication, Harbin Institute of Technology, Harbin 150001, China
| | - Ruizhi Liu
- School of Astronautics, Harbin Institute of Technology, Harbin 150001, China
| | - Chunwei Wang
- School of Astronautics, Harbin Institute of Technology, Harbin 150001, China
| | - Cheng Qian
- Ocean College, Zhejiang University, Hangzhou 310027, China
| | - Hokun Sung
- Korea Advanced Nano Fab Center (KANC), Suwon-si 443270, Korea
| | - Fanyi Meng
- School of Information and Communication, Harbin Institute of Technology, Harbin 150001, China
| |
Collapse
|
8
|
Ahmed S, Park J, Cho SH. Effects of Receiver Beamforming for Vital Sign Measurements Using FMCW Radar at Various Distances and Angles. SENSORS (BASEL, SWITZERLAND) 2022; 22:6877. [PMID: 36146226 PMCID: PMC9503483 DOI: 10.3390/s22186877] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/20/2022] [Revised: 09/04/2022] [Accepted: 09/08/2022] [Indexed: 06/16/2023]
Abstract
Short-range millimeter wave radar sensors provide a reliable, continuous and non-contact solution for vital sign extraction. Off-The-Shelf (OTS) radars often have a directional antenna (beam) pattern. The transmitted wave has a conical main lobe, and power of the received target echoes deteriorate as we move away from the center point of the lobe. While measuring vital signs, the human subject is often located at the center of the antenna lobe. Since beamforming can increase signal quality at the side (azimuth) angles, this paper aims to provide an experimental comparison of vital sign extraction with and without beamforming. The experimental confirmation that beamforming can decrease the error in the vital sign extraction through radar has so far not been performed by researchers. A simple, yet effective receiver beamformer was designed and a concurrent measurement with and without beamforming was made for the comparative analysis. Measurements were made at three different distances and five different arrival angles, and the preliminary results suggest that as the observation angle increases, the effectiveness of beamforming increases. At an extreme angle of 40 degrees, the beamforming showed above 20% improvement in heart rate estimation. Heart rate measurement error was reduced significantly in comparison with the breathing rate.
Collapse
|
9
|
Salem M, Elkaseer A, El-Maddah IAM, Youssef KY, Scholz SG, Mohamed HK. Non-Invasive Data Acquisition and IoT Solution for Human Vital Signs Monitoring: Applications, Limitations and Future Prospects. SENSORS (BASEL, SWITZERLAND) 2022; 22:s22176625. [PMID: 36081081 PMCID: PMC9460364 DOI: 10.3390/s22176625] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/26/2022] [Revised: 08/22/2022] [Accepted: 08/30/2022] [Indexed: 05/06/2023]
Abstract
The rapid development of technology has brought about a revolution in healthcare stimulating a wide range of smart and autonomous applications in homes, clinics, surgeries and hospitals. Smart healthcare opens the opportunity for a qualitative advance in the relations between healthcare providers and end-users for the provision of healthcare such as enabling doctors to diagnose remotely while optimizing the accuracy of the diagnosis and maximizing the benefits of treatment by enabling close patient monitoring. This paper presents a comprehensive review of non-invasive vital data acquisition and the Internet of Things in healthcare informatics and thus reports the challenges in healthcare informatics and suggests future work that would lead to solutions to address the open challenges in IoT and non-invasive vital data acquisition. In particular, the conducted review has revealed that there has been a daunting challenge in the development of multi-frequency vital IoT systems, and addressing this issue will help enable the vital IoT node to be reachable by the broker in multiple area ranges. Furthermore, the utilization of multi-camera systems has proven its high potential to increase the accuracy of vital data acquisition, but the implementation of such systems has not been fully developed with unfilled gaps to be bridged. Moreover, the application of deep learning to the real-time analysis of vital data on the node/edge side will enable optimal, instant offline decision making. Finally, the synergistic integration of reliable power management and energy harvesting systems into non-invasive data acquisition has been omitted so far, and the successful implementation of such systems will lead to a smart, robust, sustainable and self-powered healthcare system.
Collapse
Affiliation(s)
- Mahmoud Salem
- Institute for Automation and Applied Informatics, Karlsruhe Institute of Technology, 76344 Eggenstein-Leopoldshafen, Germany
- Correspondence: ; Tel.: +49-0-721-608-25632
| | - Ahmed Elkaseer
- Institute for Automation and Applied Informatics, Karlsruhe Institute of Technology, 76344 Eggenstein-Leopoldshafen, Germany
- Karlsruhe Nano Micro Facility, Karlsruhe Institute of Technology, 76344 Eggenstein-Leopoldshafen, Germany
- Faculty of Engineering, Port Said University, Port Said 42526, Egypt
| | | | - Khaled Y. Youssef
- Faculty of Navigation Science and Space Technology, Beni-Suef University, Beni-Suef 2731070, Egypt
| | - Steffen G. Scholz
- Institute for Automation and Applied Informatics, Karlsruhe Institute of Technology, 76344 Eggenstein-Leopoldshafen, Germany
- Karlsruhe Nano Micro Facility, Karlsruhe Institute of Technology, 76344 Eggenstein-Leopoldshafen, Germany
- College of Engineering, Swansea University, Swansea SA2 8PP, UK
| | - Hoda K. Mohamed
- Faculty of Engineering, Ain Shams University, Cairo 11535, Egypt
| |
Collapse
|
10
|
Li Y, Long Q, Wu Z, Zhou Z. Low-Complexity Joint 3D Super-Resolution Estimation of Range Velocity and Angle of Multi-Targets Based on FMCW Radar. SENSORS (BASEL, SWITZERLAND) 2022; 22:6474. [PMID: 36080932 PMCID: PMC9460604 DOI: 10.3390/s22176474] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/11/2022] [Revised: 08/22/2022] [Accepted: 08/25/2022] [Indexed: 06/15/2023]
Abstract
Multi-dimensional parameters joint estimation of multi-targets is introduced to implement super-resolution sensing in range, velocity, azimuth angle, and elevation angle for frequency-modulated continuous waveform (FMCW) radar systems. In this paper, a low complexity joint 3D super-resolution estimation of range, velocity, and angle of multi-targets is proposed for an FMCW radar with a uniform linear array. The proposed method firstly constructs the size-reduced 3D matrix in the frequency domain for the system model of an FMCW radar system. Secondly, the size-reduced 3D matrix is established, and low complexity three-level cascaded 1D spectrum estimation implemented by applying the Lagrange multiplier method is developed. Finally, the low complexity joint 3D super-resolution algorithms are validated by numerical experiments and with a 77 GHz FMCW radar built by Texas Instruments, with the proposed algorithm achieving significant estimation performance compared to conventional algorithms.
Collapse
Affiliation(s)
- Yingchun Li
- School of Information Science and Engineering, Harbin Institute of Technology, Weihai 264209, China
| | - Qi Long
- School of Information Science and Engineering, Harbin Institute of Technology, Weihai 264209, China
| | - Zhongjie Wu
- School of Electronic Science, National University of Defense Technology, Changsha 410073, China
| | - Zhiquan Zhou
- School of Information Science and Engineering, Harbin Institute of Technology, Weihai 264209, China
| |
Collapse
|