Empirical Mode Decomposition Analysis Research Articles

Mid-to-long Term Wind Power Forecasting Based on ARIMA-BP Combined Model

Background: Increasing the accuracy of the output power forecasting for wind power is helpful to the improvement of the reliability of power dispatching. Objective: This study aimed to improve the forecasting accuracy of mid-to-long term wind power. Methods: A mid-to-long term wind power forecasting based on ARIMA-BP combined model was proposed. The Empirical Mode Decomposition (EMD) was used to decompose the historical wind power series and obtain the Intrinsic Mode Function (IMF) and residual components, thereby obtaining more regular components. Then, the optimum feature set was obtained based on the minimum Redundancy Maximum Relevance (mRAR) to improve the prediction accuracy for feature extraction. After that, the high-frequency components were predicted using the Back Propagation (BP) neural network model, while the low-frequency components were predicted using the Autoregressive Integrated Moving Average model (ARIMA). Finally, the predicted components obtained were superimposed to deduce the final mid- and long-term wind power prediction results. Results: An analysis was conducted according to the actual data from a typical wind farm. After comparison, it was found that, after empirical mode decomposition and feature extraction analysis, the error of the intelligent combination algorithm based on the ARIMA-BP combined model was smaller than that using only the BP neural network or only the ARIMA. Conclusion: By means of actual data analysis, the effectiveness of the method proposed by the study for mid- and long-term wind power prediction was verified.

The decomposition of guided waves overlapped in the time–frequency domain in composite materials

Compared with traditional bulk wave detection methods, guided wave detection has higher efficiency and accuracy, and therefore has a wider range of applications. However, the guided waves have two inherent properties, namely dispersion and multimodality. In propagation, the various modes of guided wave signals are usually overlapped in the time–frequency domain, which greatly limits the applicability of ultrasonic detection. In this paper, the generalized dispersive mode decomposition method is introduced to separate the modes overlapped in the time–frequency domain in the composite materials. In the method, the overlapped modes in the time–frequency domain can be separated by estimating the group delay of corresponding dispersive modes without any prior knowledge. When the estimated group delay is equal to the true group delay, the corresponding mode is completely separated from the overlapped modes. The dispersion curves of the carbon fiber composite laminate with a determined propagation direction were reconstructed by employing a combination of the generalized dispersive mode decomposition and zero-crossing techniques. And the accuracy and effectiveness were further validated through comparison with the results obtained from empirical mode decomposition and independent component analysis. The proposed method presents a useful guideline for the mode decomposition in the carbon composites and helps detect the mechanical properties or defects of the composite materials.

Non-stationary signal analysis utilizing VMD and EMD algorithm for multiple fault classification for industrial applications

The Hilbert spectrum images of intrinsic mode functions (IMF) of empirical mode decomposition (EMD) analysis and variational mode decomposition (VMD) analysis of faulty machine vibration signals are used in deep convolutional neural network (DCNN) for machine fault classification in which the DCNN automatically learns the features from spectral images using convolution layer. Though both EMD and VMD analysis suit well for non-stationary signal analysis, VMD has the merit of aliasing free IMFs. In this paper, the performance improvement of DCNN classification for a non-stationary vibration signal dataset using VMD is brought out. The numerical experiment uses the Hilbert spectrum images of 4 EMD-IMFs and 4 VMD-IMFs in DCNN to classify 10 different faults of the Case Western Reserve University (CWRU) bearing dataset. The confusion matrices are obtained and the plot of model accuracies in terms of epochs for the DCNN is analysed. It is shown that the spectrum images of one of the four EMD-IMFs, IMF0, give a validation accuracy of 100% and in the case of VMD the spectrum images of two of the four VMD-IMFs, IMF0, and IMF1 give a validation accuracy of 100%. This reveals that non-aliasing IMFs of VMD are better at classifying bearing faults. Further to bring out the merits of VMD analysis for non-stationary signals the numerical experiment is conducted using VMD analysis for binary fault classification of the milling dataset which is more non-stationary than the bearing dataset which is proved by plotting the statistical parameters of both datasets against time. It is found that the DCNN classification is 100% accurate for IMF3 of VMD analysis which is much better than the 81% accuracy provided by EMD analysis as per existing literature. The performance comparison highlights the merits of VMD analysis over EMD analysis and other state-of-the-art methods and ensemble learning methods.

Global Navigation Satellite System Receiver Positioning in Harsh Environments via Clock Bias Prediction by Empirical Mode Decomposition and Back Propagation Neural Network Method.

This paper proposes a novel method to improve the clock bias short-term prediction accuracy of navigation receivers then solve the problem of low positioning accuracy when the satellite signal quality deteriorates. Considering that the clock bias of a navigation receiver is equivalent to a virtual satellite, the predicted value of clock bias is used to assist navigation receivers in positioning. Consequently, a combined prediction method for navigation receiver clock bias based on Empirical Mode Decomposition (EMD) and Back Propagation Neural Network (BPNN) analysis theory is demonstrated. In view of systematic errors and random errors in the clock bias data from navigation receivers, the EMD method is used to decompose the clock bias data; then, the BPNN prediction method is used to establish a high-precision clock bias prediction model; finally, based on the clock bias prediction value, the three-dimensional positioning of the navigation receiver is realized by expanding the observation equation. The experimental results show that the proposed model is suitable for clock bias time series prediction and providing three-dimensional positioning information meets the requirements of navigation application in the harsh environment of only three satellites.

Secular changes in the tropical stratospheric water vapour entry induced by the Indo-Pacific warm pool warming

A decreasing trend in the tropical (30°S ∼ 30°N) stratospheric water vapour (SWV) entry in recent four decades (from 1984 to 2020) is detected based on the Stratospheric Water and OzOne Satellite Homogenized (SWOOSH) measurements and the ERA5 reanalysis dataset using linear regression and Ensemble Empirical Mode Decomposition (EEMD) analysis. With the concurrent warming of the sea surface temperature (SST), the Indo-Pacific warm pool (IPWP) appears to be the most significant region among the tropical oceans based on correlation analysis. >43% of the decreasing tropical lower SWV trend is likely to be related to the IPWP SST warming. To validate this relationship, two groups of idealized runs are carried out with version 4 of the Whole Atmosphere Community Climate Model (WACCM4) and version 5 of the Community Atmosphere Model (CAM5). Both simulations agree with the observational-based linkage. The IPWP-SST-warming forced simulations show that the temperature in the tropical tropopause has decreased at the rate of around 0.318 K per decade in the coldest point region, as the tropical convection over the IPWP has become more vigorous and excited stronger equatorial waves to produce adiabatic cooling around tropopause. This cooling tropical tropopause leads to a dehydrating tropical lower stratosphere at the rate of 0.025 ppmv per decade, as expected by the freeze-drying mechanism. These results imply the substantial warming trend of IPWP is an important factor for the long-term trend of the tropical SWV entry under climate change, and a better representation of this relationship in the model is critical for the SWV projection under future climate scenarios.

EEG rhythm separation and time-frequency analysis of fast multivariate empirical mode decomposition for motor imagery BCI.

Motor imagery electroencephalogram (EEG) is widely employed in brain-computer interface (BCI) systems. As a time-frequency analysis method for nonlinear and non-stationary signals, multivariate empirical mode decomposition (MEMD) and its noise-assisted version (NA-MEMD) has been widely used in the preprocessing step of BCI systems for separating EEG rhythms corresponding to specific brain activities. However, when applied to multichannel EEG signals, MEMD or NA-MEMD often demonstrate low robustness to noise and high computational complexity. To address these issues, we have explored the advantages of our recently proposed fast multivariate empirical mode decomposition (FMEMD) and its noise-assisted version (NA-FMEMD) for analyzing motor imagery data. We emphasize that FMEMD enables a more accurate estimation of EEG frequency information and exhibits a more noise-robust decomposition performance with improved computational efficiency. Comparative analysis with MEMD on simulation data and real-world EEG validates the above assertions. The joint average frequency measure is employed to automatically select intrinsic mode functions that correspond to specific frequency bands. Thus, FMEMD-based classification architecture is proposed. Using FMEMD as a preprocessing algorithm instead of MEMD can improve the classification accuracy by 2.3% on the BCI Competition IV dataset. On the Physiobank Motor/Mental Imagery dataset and BCI Competition IV Dataset 2a, FMEMD-based architecture also attained a comparable performance to complex algorithms. The results indicate that FMEMD proficiently extracts feature information from small benchmark datasets while mitigating dimensionality constraints resulting from computational complexity. Hence, FMEMD or NA-FMEMD can be a powerful time-frequency preprocessing method for BCI.

Retina images classification based on 2D empirical mode decomposition and multifractal analysis

Diabetic retinopathy is an ocular disease caused by long-term damage to the retina due to high blood sugar levels. Elevated blood sugar can impair the microvasculature in the retina, leading to vascular abnormalities and the formation of abnormal new blood vessels. These changes can manifest in the retina as hemorrhages, leaks, vessel dilation, retinal edema, and retinal detachment. The retinas of individuals with diabetes exhibit different morphologies compared to those without the condition. Most histological images cannot be accurately described using traditional geometric shapes or methods. Therefore, this study aims to evaluate and classify the morphology of retinas with varying degrees of severity using multifractal geometry. In the initial experiments, two-dimensional empirical mode decomposition was employed to extract high-frequency detailed features, and the classification process was based on the most relevant features in the multifractal spectrum associated with disease factors. To eliminate less significant features, the random forest algorithm was utilized. The proposed method achieved an accuracy of 96%, sensitivity of 96%, and specificity of 95%.

On the links between sea level and temperature variations in the Chesapeake Bay and the Atlantic Meridional Overturning Circulation (AMOC)

AbstractRecent studies found that on long time scales there are often unexplained opposite trends in sea level variability between the upper and lower Chesapeake Bay (CB). Therefore, daily sea level and temperature records were analyzed in two locations, Norfolk in the southern CB and Baltimore in the northern CB; surface currents from Coastal Ocean Dynamics Application Radar (CODAR) near the mouth of CB were also analyzed to examine connections between the CB and the Atlantic Ocean. The observations in the bay were compared with daily Atlantic Meridional Overturning Circulation (AMOC) observations during 2005–2021. Empirical Mode Decomposition (EMD) analysis was used to show that variations of sea level and temperature in the upper and lower CB are positively correlated with each other for short time scales of months to few years, but anticorrelated on low frequency modes representing decadal variability and long-term nonlinear trends. The long-term CB modes seem to be linked with AMOC variability through variations in the Gulf Stream and the wind-driven Ekman transports over the North Atlantic Ocean. AMOC variability correlates more strongly with variability in the southern CB near the mouth of the bay, where surface currents indicate potential links with AMOC variability. For example, when AMOC and the Gulf Stream were especially weak during 2009–2010, sea level in the southern bay was abnormally high, temperatures were colder than normal and outflow through the mouth of CB was especially high. Sea level in the upper bay responded to this change only 1–2 years later, which partly explains phase differences within the bay. A persistent trend of 0.22 cm/s per year of increased outflow from the CB, may be a sign of a climate-related trend associated with combination of weakening AMOC and increased precipitation and river discharge into the CB.

Time Delay Study of Ultrasonic Gas Flowmeters Based on VMD-Hilbert Spectrum and Cross-Correlation.

The accuracy of ultrasonic flowmeter time delay measurement is directly affected by the processing method of the ultrasonic echo signal. This paper proposes a method for estimating the time delay of the ultrasonic gas flowmeter based on the Variational Mode Decomposition (VMD)-Hilbert Spectrum and Cross-Correlation (CC). The method improves the accuracy of the ultrasonic gas flowmeter by enhancing the quality of the echo signal. To denoise forward and reverse ultrasonic echo signals collected at various wind speeds, a Butterworth filter is initially used. The ultrasonic echo signals are then analyzed by Empirical Mode De-composition (EMD) and VMD analysis to obtain the Intrinsic Mode Function (IMF) containing distinct center frequencies, respectively. The Hilbert spectrum time-frequency diagram is used to evaluate the results of the VMD and EMD decompositions. It is found that the IMF decomposed by VMD has a better filtering performance and better anti-interference performance. Therefore, the IMF with a better effect is selected for signal reconstruction. The ultrasonic time delay is then calculated using the Cross-Correlation algorithm. The self-developed ultrasonic gas flowmeter was tested on the experimental platform of the gas flow standard devices using this signal processing method. The results show a maximum indication error of 0.84% within the flow range of 60-606 m3/h, with a repeatability of no more than 0.29%. These results meet the 1-level accuracy requirements as outlined in the national ultrasonic flowmeters calibration regulation JJG1030-2007.

The strengthened impact of water availability at interannual and decadal time scales on vegetation GPP

AbstractWater availability (WA) is a key factor influencing the carbon cycle of terrestrial ecosystems under climate warming, but its effects on gross primary production (EWA‐GPP) at multiple time scales are poorly understood. We used ensemble empirical mode decomposition (EEMD) and partial correlation analysis to assess the WA‐GPP relationship (RWA‐GPP) at different time scales, and geographically weighted regression (GWR) to analyze their temporal dynamics from 1982 to 2018 with multiple GPP datasets, including near‐infrared radiance of vegetation GPP, FLUXCOM GPP, and eddy covariance–light‐use efficiency GPP. We found that the 3‐ and 7‐year time scales dominated global WA variability (61.18% and 11.95%), followed by the 17‐ and 40‐year time scales (7.28% and 8.23%). The long‐term trend also influenced 10.83% of the regions, mainly in humid areas. We found consistent spatiotemporal patterns of the EWA‐GPP and RWA‐GPP with different source products: In high‐latitude regions, RWA‐GPP changed from negative to positive as the time scale increased, while the opposite occurred in mid‐low latitudes. Forests had weak RWA‐GPP at all time scales, shrublands showed negative RWA‐GPP at long time scales, and grassland (GL) showed a positive RWA‐GPP at short time scales. Globally, the EWA‐GPP, whether positive or negative, enhanced significantly at 3‐, 7‐, and 17‐year time scales. For arid and humid zones, the semi‐arid and sub‐humid zones experienced a faster increase in the positive EWA‐GPP, whereas the humid zones experienced a faster increase in the negative EWA‐GPP. At the ecosystem types, the positive EWA‐GPP at a 3‐year time scale increased faster in GL, deciduous broadleaf forest, and savanna (SA), whereas the negative EWA‐GPP at other time scales increased faster in evergreen needleleaf forest, woody savannas, and SA. Our study reveals the complex and dynamic EWA‐GPP at multiple time scales, which provides a new perspective for understanding the responses of terrestrial ecosystems to climate change.

Study on failure/fracture mechanisms of 3D printed objects by FDM with AE method (Evaluation by using Empirical Mode Decomposition and Chaos analysis)

Study on failure/fracture mechanisms of 3D printed objects by FDM with AE method (Evaluation by using Empirical Mode Decomposition and Chaos analysis)

Changes in Qinghai Lake Area and Their Interactions with Climatic Factors

Lakes play a crucial role in the global water cycle and significantly contribute to enhancing regional ecological environments and simulating economic growth. In this study, based on the data from the Landsat TM 4-5, Landsat 7 ETM SLC-off, and Landsat 8-9 OLI/TIRS C2 L2 satellites, the surface area of Qinghai Lake is obtained by using the Normalized Difference Water Index (NDWI) method. Additionally, leveraging the European Centre for Medium-Range Weather Forecasts (ECMWF) fifth-generation land surface reanalysis dataset (ERA5-Land), we analyzed the interplay between lake area and related climate factors by using the Noise Assisted–Multivariate Empirical Mode Decomposition (NA-MEMD) and wavelet coherence analysis method. The surface area of Qinghai Lake showed an overall expansion trend from 1986 to 2022, with an expansion rate of 2.89 km2/a. Precipitation, temperature, and evapotranspiration (ET) also showed an increasing trend, with the largest increasing trend in autumn, summer, and summer, respectively. The area of Qinghai Lake did not demonstrate distinct periodic patterns from 1986 to 2022, in contrast to the marked 8–16 month oscillations observed in precipitation, temperature, and ET. In the phase of lake area expansion between 2008 and 2016, changes in the lake’s surface area were observed to trail behind variations in precipitation and temperature by approximately three months. Furthermore, the shift in ET was found to lag behind alterations in the lake area, displaying a delay of 3–6 months.

Multifractal analysis of the Southern Oscillation Index

In this paper, multifractal properties of the Southern Oscillation Index (SOI) have been analyzed using Empirical Mode Decomposition (EMD) and Multifractal Detrended Fluctuation Analysis (MFDFA) for the time interval between 1991 to 2020. The joint application of the EMD and detrended fluctuation analysis methods allowed the signal to be separated into three subsets, each with different scaling properties. Then each of these subsequences was analyzed using the MFDFA.Results show that a different multifractality structure characterizes each subset. We observe the strongest multifractality in the macroscale component followed by the medium-scale subsequence. The microscale has the lowest multifractality of the three. The removal of temporal correlations by shuffling the sequences showed that the leading cause of multifractality is the different long-range correlations for small and large fluctuations.

Semi-supervised diagnosis method for coupling faults of key rotating components based on EEMD-KPCA under cross working conditions

To address the challenges of extracting coupled fault features from key rotating components and classifying them under changeable operating conditions, a semi-supervised fault diagnosis method is proposed. First, Ensemble Empirical Mode Decomposition and Kernel Principal Component Analysis are employed to decompose the original coupled fault signals and reduce feature dimensionality. Experiments are conducted on labeled datasets, yielding an average classification accuracy of 92.43%. To further classify unlabeled datasets under various working conditions, a probability distribution estimation function is incorporated and a confidence threshold is set. For unlabeled data with probabilities greater than the confidence threshold, a pseudo-label is added to increase the labeled data quantity. Thus, it makes learning from these unlabeled data possible. A comparison with the other three methods under cross working conditions showcases the superiority of the proposed approach.

Development of control strategy for community battery energy storage system in grid-connected microgrid of high photovoltaic penetration level

The focus of this paper is to develop a control strategy for a community battery bank in a grid-connected microgrid in which a significant level of photovoltaic generation is embedded. In order to minimize the capacity of the community battery, the power transfer capacity of the interconnection link between the microgrid and the external grid system is utilized to safe maximum levels. Through Empirical Mode Decomposition analysis of the net power flows of the microgrid, the daily and seasonal modes of the net power oscillations are identified as the two dominant low-frequency components. Whence a rule-based operational strategy is developed to control the power flows of the community battery via a novel dynamic referencing scheme for the state-of-charge of the battery bank. The battery control scheme counteracts the dominant daily and seasonal modes of oscillations of the net power. The numerical calculations performed for a case study shows that the proposed scheme leads to an approximately 16% decrease in the required battery capacity for particular growth rate of solid-electrolyte interphase film in the battery bank. The scheme does not require the forecasting of the net power, and thus, it has an increased degree of robustness when the community battery undertakes the power buffering task.

On application of empirical mode decomposition for turbulence analysis in open-channel flows

Large-scale coherent structures are key elements of open-channel flow turbulence, quantification of which remains elusive. In this work, we use empirical mode decomposition (EMD) to break down a velocity time series into different modes, denoted as “intrinsic mode functions” (IMFs). Analysis of velocity auto- and co-spectra indicates that large-scale (LSMs) and very large-scale (VLSMs) fluid motions are sufficiently represented by particular groups of IMFs. A correlation between LSMs and VLSMs, identified by the EMD analysis, was found to generate 7% of the Reynolds shear stresses. However, the EMD analysis of surrogate velocity signals with randomized spectral phases demonstrated that the revealed correlation is actually an artefact of the EMD approach and should not be interpreted physically.

New Marginal Spectrum Feature Information Views of Humpback Whale Vocalization Signals Using the EMD Analysis Methods.

Marginal spectrum (MS) feature information of humpback whale vocalization (HWV) signals is an interesting and significant research topic. Empirical mode decomposition (EMD) is a powerful time-frequency analysis tool for marine mammal vocalizations. In this paper, new MS feature innovation information of HWV signals was extracted using the EMD analysis method. Thirty-six HWV samples with a time duration of 17.2 ms were classified into Classes I, II, and III, which consisted of 15, 5, and 16 samples, respectively. The following ratios were evaluated: the average energy ratios of the 1 first intrinsic mode function (IMF1) and residual function (RF) to the referred total energy for the Class I samples; the average energy ratios of the IMF1, 2nd IMF (IMF2), and RF to the referred total energy for the Class II samples; the average energy ratios of the IMF1, 6th IMF (IMF6), and RF to the referred total energy for the Class III samples. These average energy ratios were all more than 10%. The average energy ratios of IMF1 to the referred total energy were 9.825%, 13.790%, 4.938%, 3.977%, and 3.32% in the 2980-3725, 3725-4470, 4470-5215, 10,430-11,175, and 11,175-11,920 Hz bands, respectively, in the Class I samples; 14.675% and 4.910% in the 745-1490 and 1490-2235 Hz bands, respectively, in the Class II samples; 12.0640%, 6.8850%, and 4.1040% in the 2980-3725, 3725-4470, and 11,175-11,920 Hz bands, respectively, in the Class III samples. The results of this study provide a better understanding, high resolution, and new innovative views on the information obtained from the MS features of the HWV signals.

Precise analysis of gravitational waves from binary neutron star coalescence using Hilbert–Huang transform based on Akima spline interpolation

Abstract The equation of state (EOS) information of neutron stars (NSs) can be obtained by analyzing the post-merger phases of gravitational waves resulting from the coalescence of NS binaries. In a previous study, we proposed a method to discriminate the EOS using the Hilbert–Huang transform (HHT). The HHT comprises empirical mode decomposition (EMD) and Hilbert spectrum analysis. An essential aspect of the EMD involves the generation of envelopes through interpolating extrema values. The original EMD (used in the previous study) utilizes cubic spline (CS) interpolation. However, the CS occasionally produces pseudo oscillations and overshoots that may decrease the performance of the EMD. In this study, we propose an extended version of the HHT by substituting Akima spline interpolation for the CS. We compared the ability of the original HHT (based on the CS) and the proposed HHT (based on the Akima spline) to discriminate the EOS. The results reveal that the proposed HHT yields a more precise analysis than the original HHT. With the proposed HHT, the number of events for discriminating the EOS is enhanced by a factor of 11.4.

The impacts of temperature averages, variabilities and extremes on China’s winter wheat yield and its changing rate

China is the world’s largest producer and consumer of wheat. The impact of temperature averages, variabilities, and extremes on winter wheat yield changes is still not very clear. The annual production data for winter wheat in China’s provinces and municipalities and NCEP-NCAR Reanalysis-1 data were used from November to April in the period 1949–2018, to investigate the impact of temperature-related variables, such as the winter average temperature (), winter variance of temperature (), extreme hot days (EHD), and extreme cold days (ECD), on China’s winter wheat yield. Ensemble Empirical Mode Decomposition (EEMD) analysis showed that winter wheat yield has an in-phase relationship with average temperature but an out-of-phase relationship with variance of temperature, extreme hot days, and extreme cold days on timescales greater than 20 years. The changing rates of winter wheat yield and temperature-related variables were well measured by their sliding trends. At the overwintering growth stage, the increasing rate of average temperature and extreme hot days (temperature variance and extreme cold days) exhibit negative (positive) correlations with the rate of winter wheat yield change, with the strongest correlation observed in southeast China. During the tillering growth stage, the changing rates of average temperature exhibited a positive correlation with the rate of winter wheat yield change, whereas negative associations were observed with temperature variance, extreme hot days, and extreme cold days. Among the regions, Central China showed the weakest correlations. At the reviving growth stage, however, the relationship of changing rates of temperature-related variables with that of winter wheat yield was much weaker. These observational results are important and can be used as a reference in climate models for improving the climatic impacts on the winter wheat yield.

A Multi-Target Localization and Vital Sign Detection Method Using Ultra-Wide Band Radar.

Life detection technology using ultra-wideband (UWB) radar is a non-contact, active detection technology, which can be used to search for survivors in disaster rescues. The existing multi-target detection method based on UWB radar echo signals has low accuracy and has difficulty extracting breathing and heartbeat information at the same time. Therefore, this paper proposes a new multi-target localization and vital sign detection method using ultra-wide band radar. A target recognition and localization method based on permutation entropy (PE) and K means++ clustering is proposed to determine the number and position of targets in the environment. An adaptive denoising method for vital sign extraction based on ensemble empirical mode decomposition (EEMD) and wavelet analysis (WA) is proposed to reconstruct the breathing and heartbeat signals of human targets. A heartbeat frequency extraction method based on particle swarm optimization (PSO) and stochastic resonance (SR) is proposed to detect the heartbeat frequency of human targets. Experimental results show that the PE-K means++ method can successfully recognize and locate multiple human targets in the environment, and its average relative error is 1.83%. Using the EEMD-WA method can effectively filter the clutter signal, and the average relative error of the reconstructed respiratory signal frequency is 4.27%. The average relative error of heartbeat frequency detected by the PSO-SR method was 6.23%. The multi-target localization and vital sign detection method proposed in this paper can effectively recognize all human targets in the multi-target scene and provide their accurate location and vital signs information. This provides a theoretical basis for the technical system of emergency rescue and technical support for post-disaster rescue.