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[Good Article Recommendation] Ultra-wideband Radar Breathing and Heartbeat Detection System Combined with VMD and MUSIC [Good Article Recommendation] Ultra-wideband Radar Breathing and Heartbeat Detection System Combined with VMD and MUSIC Li Chunshuai Modern Radar Modern Radar Nanjing Research Institute of Electronic Technology (14th Research Institute of China Electronics Technology Group Corporation) ‘Modern Radar’ is an academic journal sponsored by the 14th Research Institute of China Electronics Technology Group Corporation. It is a monthly publication and a core journal in Chinese and China’s core science and technology journal. 20 original contents January 1, 2025, 09:05 Jiangsu [Citation Format] Li Chunshuai, Zhang Zhaoxia, Shi Bijun, et al. Ultra-wideband radar breathing and heartbeat detection system com- bined with VMD and MUSIC [J]. Modern Radar, 2024, 46(10): 86-94. Li Chunshuai, Zhang Zhaoxia, Shi Bijun, et al. Ultra-wideband radar breathing and heartbeat detection system combined with VMD and MUSIC [I]. Modern Radar, 2024, 46(10): 86-94. Ultra-wideband radar is an important tool for life detection remote sensing. In this article, using the strong penetration capability and high resolution of ultra-wideband radar, one can obtain information on human vital signs, and by processing the radar echo signal, one can obtain information on breathing and heartbeat, thus realizing non-contact monitoring of life signals. The article addresses issues such as echo signals being easily affected by environmental noise and heart signals being weak and easily influenced by respiratory harmonics, constructing a vital sign model to simulate human breathing and heartbeat frequencies, and proposing a method combining variational mode decomposition (VMD) and multiple signal classification algorithm (MUSIC). Experiments were conducted using the PulsON440 ultra-wideband radar at a distance of 1m, and compared to traditional methods like fast Fourier transform and singular value decomposition, this method provides more accurate extraction of breathing and heartbeat signals. The applicability of the method was validated under different distances and shielding conditions. The results show that the proposed method combining MUSIC and VMD can effectively separate small heartbeat signals from large breathing signals, accurately detecting breathing and heartbeat frequencies. 0. Introduction 1. Vital Sign Signal Modeling 2. Algorithm and Implementation 3. Simulation and Experimental Verification of Ultra-wideband Radar Life Monitoring Method 4. Conclusion References [1] Huang X M, Sun L, Tian T, et al. Real-time non-contact infant respiratory monitoring using UWB radar [C] // IEEE International Conference on Communication Technology. Hangzhou: IEEE, 2015: 493-496. [2] Duan Z Z, Liang Jing. Non-contact detection of vital signs using a UWB radar sensor [J]. IEEE Access, 2019, 71(7): 36888-36895. [3] Lim Y, Lin J S. Wavelet-transform-based data-length-variation technique for fast heart rate detection using 5.8 GHz CW Doppler radar [J]. IEEE Transactions on Microwave Theory and Techniques, 2017, 66(1): 568-576. [4] Janczarek I, Kedzierski W, Wilk I, et al. Comparison of daily heart rate variability in old and young horses: A preliminary study-science direct [J]. Journal of Veterinary Behavior, 2020, 38: 1-7. [5] Wang F H, Liang Q Q, Sun M Q, et al. The relationship between exposure to PM2.5 and heart rate variability in older adults: A systematic review and meta-analysis [J]. Chemosphere, 2020, 261: 127635. [6] Xie Kai, Wang Ying, Li Xiaoping, et al. Non-contact digital signal testing method based on near-field radiation [J]. Systems Engineering and Electronics Technology, 2010, 32(8): 1604-1607. [7] Chen Haifeng, Feng Yuan. Research on radar target classification and recognition technology based on CNN [J]. Modern Radar, 2022, 44(4): 38-43. [8] Li Fengmin, Yang Degui, Shen Mingxu, et al. An ultra-wideband radar moving target delay estimation method [J]. Modern Radar, 2022, 44(10): 48-53. [9] Rivera N V, Venkatesh S, Anderson C, et al. Multi-target estimation of heart and respiration rates using ultra-wideband sensors [C] // 2006 14th European Signal Processing Conferences. Florence, Italy: IEEE, 2006: 1-6. [10] Zhang Feng, Liang Buge, Rong Ruizhi, et al. Design and experiment of UWB radar life detector system [J]. Fire Science and Technology, 2016, 35(7): 967-969. [11] Anto Bennett M, Narmatha J, Pavithra B, et al. Hardware implementation of UWB radar for detection of trapped victims in a complex environment [J].al. Multi-target estimation of heart and respiration rates using ultrawideband sensors[C]// 2006 14th European Signal Processing Conferences. Florence, Italy: IEEE, 2006:1-6. [10] Zhang Feng, Liang Buge, Rong Ruizhi, et al. UWB radar life detection instrument system design and test[J]. Fire Science and Technology, 2016, 35(7):967-969. [11] ANT BENNET M, NARMATHA J, PAVITHRA B, et al. Hardware implementation of uwb radar for detection of trapped victims in complex environment[J]. International Journal on Smart Sensing and Intelligent Systems, 2022, 10(5):236-258. [12] MICHLER F, SHI K, SCHELLENBERGER S, et al. A clinically evaluated interferometric continuous-wave radar system for the contactless measurement of human vital parameters[J]. Sensors, 2019, 19(11):2492. [13] YAROVOY A G, LIGTHART L P, MATUZAS J et al. UWB radar for human being detection[J].IEEE Aerospace & Electronic Systems Magazine, 2006, 21(3):22-26. [14] LEE J E, LIM H S, JEONG S H.Method and apparatus for detecting surrounding environment based on sensing signals of frequency-modulated continuous wave radar and continuous wave radar[P].Seoul. US10036805B2.2018-07-31. [15] DRAGOMIRETSKIY K, ZOSSO D. Variational mode decomposition[J]. IEEE Transactions on Signal Processing, 2014, 62(3):531-544. [16] SHEN H M, XU C, YANG Y J, et al. Respiration and heartbeat rates measurement based on autocorrelation using IR-UWB radar[J]. IEEE Transactions on Circuits and Systems II: Express Briefs, 2018, 65(10):1470-1474. [17] NEGISHI T, SUN G, SATO S, et al. Infection screening system using thermography and CCD camera with good stability and swiftness for non-contact vital-signs measurement by feature matching and MUSIC algorithm[C]// Annual International Conference of the IEEE Engineering in Medicine and Biology Society IEEE Engineering in Medicine and Biology Society Annual International Conference. Berlin: IEEE, 2019:3183-3186. [18] MIX P, CHEN X H, LIU Q, et al. Radar signals modulation recognition based on bispectrum feature processing[C]// 2021 3rd International Conference on Electronic Engineering and Informatics. Dali: IOP Publishing, 2021:1-11. [19] Wang Guoqiang. Respiration and heartbeat monitoring method combined with ultra-wideband radar and multiple classification algorithms[D]. Taiyuan: Taiyuan University of Technology, 2021. [20] SACHS J, HELBIG M, HERRMANN R, et al. Remote vital sign detection for rescue, security, and medical care by ultra-wideband pseudo-noise radar[J]. Ad Hoc Networks, 2014, 13(FEB.PT.A):42-53. [21] BUGAEV A S, CHAPURSKY V V, IVASHOV S I. Mathematical simulation of remote detection of human breathing and heaatics. Dali:IOP Publishing, 2021:1-11. [19] Wang Guoqiang. Breathing and Heartbeat Monitoring Method Combining Ultra-Wideband Radar and Multiple Classification Algorithms [D]. Taiyuan: Taiyuan University of Technology, 2021. [20] SACHS J, HELBIG M, HERRMANN R, et al. Remote vital sign detection for rescue, security, and medical care by ultra-wideband pseudo-noise radar [J]. Ad Hoc Networks, 2014, 13 (FEB.PT.A): 42-53. [21] BUGAEV A S, CHAPURSKY V V, IVASHOVS I. Mathematical simulation of remote detection of human breathing and heartbeat by multifrequency radar on the background of local objects reflections [C] // IEEE International Radar Conference. Arlington, VA: IEEE, 2005:359-364. [22] WUS Y, TAN K, XIA Z H, et al. Improved human respiration detection method via ultra-wideband radar in through-wall or other similar conditions [J]. Iet Radar Sonar & Navigation, 2016, 10(3): 468-476. [23] UPADHYAY A, PACHORI R B. Speech enhancement based on mEMD-VMD method [J]. Electronics Letters, 2017, 53(7): 502-504. Author’s biography: Li Chunshuai, male, born in 1997, master’s degree, research direction is ultra-wideband radar and signal processing; Zhang Chaoxia, female, born in 1977, professor, research direction is radar signal detection and application, cognitive radar principles, environmental detection and optimization of deep learning algorithms; Shi Bijun, male, born in 1996, master’s degree, research direction is ultra-wideband radar fall detection; Wang Qian, female, born in 1976, master’s degree, research direction is signal detection and processing. Preview tags are not clickable Close More Name cleared Scan QR code to appreciate the author Like the Author Other Amount Articles No articles Like the Author Other Amount ¥ Minimum appreciation ¥0 OK Back Other Amount More Appreciation Amount ¥ Minimum appreciation ¥0 1 2 3 4 5 6 7 8 9 0 . Close More Mini Program Ad Search ‘undefined’ network results No messages No more data Send Message Write a message: Close Comment Submit More Emoji Scan to Follow Continue sliding for the next Touch to read the original Modern Radar Slide up for the next The current content may contain third-party commercial marketing information that has not been reviewed, please confirm whether to continue to visit. Continue to visit Cancel WeChat Public Platform Advertising Code Guide Got It Scan with Weixin to use this Mini Program Cancel Allow Cancel Allow × Analysis: , , , , , , , , , , , , . Video Mini Program Like , double tap to cancel like Wow , double tap to cancel watching Share Comment Favorite Modern Radar [Highly Recommended] Ultra-Wideband Radar Respiration and Heartbeat Detection System Combining VMD and MUSIC, Select the message identity ‘Radar in medical monitoring’, ‘Gradually establish non-contact radar monitoring’, ‘System 1-5, use digital circuits for non-contact testing 6. Ultra-wideband’, ‘Radar is applied in this field due to its strong ability to penetrate obstacles, achieving centimeter-level ranging’, ‘Accuracy, researchers use ultra-wideband radar for target classification and recognition [7 moving target estimation [rescue [1-10] respiration feature monitoring [1]. In 2003, the research team from Stanford University in the United States’, ‘Used the continuous wave radar system to monitor the vital signs of patients with cardiopulmonary diseases within 2m [12], but continuous wave radar is prone to attenuation during transmission, and its anti-interference ability is poor [13]; in 2006, Virginia, USA’, ‘Polytechnic Institute used impulse radio radar for multi-self-labeled human vital sign monitoring and achieved multi-target vital sign signals [14], but it is easy to cause Doppler shift during the monitoring process; in 2014, the literature [15] first proposed’, ‘VMD; in 2018, Shen Hongming’s team used VMD and autocorrelation to separate the respiration’, ‘Heartbeat signal from clutter and noise [16]. In 2019, NegishiToshiaki used matched filtering and MUSIC algorithm to measure breathing’, ‘And improved the evaluation of heartbeat signals [17]. In 2021, Mi Xinpeng’s team’, ‘Used VMD for preprocessing, extracted bispectrum features, and reduced the interference’, ‘The impact on the bispectrum features [ ]. In the same year, Wang Guoqiang used the MUSIC algorithm’, ‘In signal processing, traditional processing methods are suitable for processing stationary’, ‘Signals, while the echo signals of the target human body are non-stationary signals, and traditional Fourier’, ‘Transform, SVD, etc. cannot suppress the influence of respiratory harmonics on heartbeat signals’, ‘[20-22], which easily causes misjudgment of heartbeat frequency on the spectrum.’, ‘Generally speaking, the amplitude of the heartbeat signal is very small and difficult to detect. MUSIO’, ‘As a spatial spectral estimation algorithm, divides the signal into noise subspace’, ‘And effective component subspace, using its orthogonality, to perform spectral peak search’, ‘And determine the frequency of the desired signal (3, VMD is a newVMD；In 2018, the Shen Hongming team used VMD and autocorrelation to separate the respiratory and heartbeat signals from clutter and noise. In 2019, NEGISHIToshiaki used matched filtering and the MUSIC algorithm to measure respiration and improve the assessment of heartbeat signals. In 2021, the Mi Xinping team used VMD for preprocessing, extracted bispectral features, and reduced the impact of interference items on bispectral features. In the same year, Wang Guoqiang used the MUSIC algorithm. In signal processing, traditional processing methods are suitable for processing stationary signals, while the echo signals of the target human body are non-stationary signals. Traditional Fourier transform, SVD, etc. cannot suppress the impact of respiratory harmonics on the heartbeat signal, thus easily causing misjudgment of the heartbeat frequency on the spectrogram. Generally speaking, the amplitude of the heartbeat signal is very small and difficult to detect. MUSIC, as a spatial spectral estimation algorithm, divides the signal into noise subspace and effective component subspace and uses its orthogonality to perform spectral peak search to determine the frequency of the required signal. VMD is a new data analysis algorithm. By decomposing the input signal into a series of narrowband components centered around its corresponding center frequency, it can more accurately capture the characteristics of the signal. Addressing the deficiencies of traditional signal processing algorithms, we propose a method based on the MUSIC algorithm and the VMD algorithm to process echo signals. This method can separate and extract non-stationary echo signals, with advantages such as minimal impact from respiratory harmonics and clean and clear spectral peaks. To verify the effectiveness of the experimental method, simulate the amplitude changes in the chest cavity caused by human respiration and heartbeat, and generate periodic sinusoidal signals as the respiratory and heartbeat signals of the self-measured individual. The distance r between the receiver and the weak organism can be expressed as r = r0 + Δr(t), where r is the average distance between the radar and the weak organism, and Δr(t) is the distance change caused by the organism’s respiration. The respiratory modeling signal is yb(t) = A1sin(2πf1t), and the heartbeat modeling signal is y(t) = A2sin(2πf2t). Therefore, Δr(t) can be expressed as Δr(t) = A1sin(2πf1t) + A2sin(2πf2t), where A1 is the peak amplitude of the respiratory signal; f1 is the respiration frequency; A2 is the peak amplitude of the heartbeat signal; f2 is the heartbeat frequency. Principle of the 2.1 VMD algorithm Using VMD requires constructing a variational problem, repeatedly seeking the optimal solution of the variational model to determine the center frequency and bandwidth. The preprocessed signal is decomposed into k components, ensuring that the decomposed sequence is a modal component with a center frequency, its bandwidth is limited, and the sum of the estimated bandwidths of each modality is minimal. The constraint of this algorithm is that the summation of the modals matches the original signal. The expression is as follows: min λ{at |u w k|} s. t. ∑ u_k = f, where u = u1, u2,…, uk represents the k-th modal component after decomposition; ∞1, ∞2, …, ∞k represents the corresponding center frequencies; δ(t) is the Dirac distribution function, * is the convolution operator. Introducing the Lagrange multiplier λ, the constrained variational problem is transformed into an unconstrained variational problem: x = argmin {A(u, λ, t)}, where α is the quadratic penalty factor, which can reduce Gaussian noise interference. Then, using the Alternating Direction Multiplier Method (ADMM) iteration, each modal component u and center frequency ∞ can be obtained: ∞_k+1 = (f(∞))/(1+2α(∞-∞0)^2), where f(∞) is the Fourier transform corresponding to f(∞); u(∞) is the Fourier transform corresponding to the i-th sub-signal of u_0. The Lagrange multiplier λ is updated as follows: λ_n+1(∞) = f (λ_n/u), where ρ is the update parameter of the Lagrange multiplier. Once equation (11) is satisfied, the process will terminate and output K IMF components. | u_i+1 – u_i |_2/| u_i |_2