Principal Component Decomposition Research Articles

Relative Moment Tensor Inversion for Microseismicity: Application to Clustered Earthquakes in the Cascadia Forearc

The relative abundance of small earthquakes affords significant opportunities for improved understanding of regional seismotectonics; however, determining moment tensors for such events recorded on regional networks is complicated by low signal-to-noise ratios, sparse station sampling and complex wave propagation at short periods. We build upon previous work in designing a multiple-event, simultaneous moment tensor inversion scheme for small earthquakes that employs constraints from P-wave polarities, relative amplitudes of P- and S-waves recorded at common stations, and local magnitude estimates. Our method does not require a priori knowledge of a reference moment tensor. High-fidelity polarity and relative amplitude data are recovered using principal component decomposition of clustered-event waveforms. These data are employed within a multi-stage iterative framework to invert for moment tensors and incorporate local magnitude information. Synthetic examples employing as few as four high-quality and spatially-distributed stations yield accurate moment tensor estimates. We demonstrate our approach on a cluster of seismicity near San Juan Island, Washington, USA, within the Cascadia forearc. Our results are consistent with previous characterization of the local stress regime, and support an interpretation of swarm behaviour resulting from migration of fluids originating from dehydration of the subducting Juan de Fuca plate.

Machine learning of ECG waveforms and cardiac magnetic resonance for response and survival after cardiac resynchronization therapy

Cardiac resynchronization therapy (CRT) can lead to marked symptom reduction and improved survival in selected patients with heart failure with reduced ejection fraction (HFrEF); however, many candidates for CRT based on clinical guidelines do not have a favorable response. A better way to identify patients expected to benefit from CRT that applies machine learning to accessible and cost-effective diagnostic tools such as the 12-lead electrocardiogram (ECG) could have a major impact on clinical care in HFrEF by helping providers personalize treatment strategies and avoid delays in initiation of other potentially beneficial treatments. This study addresses this need by demonstrating that a novel approach to ECG waveform analysis using functional principal component decomposition (FPCD) performs better than measures that require manual ECG analysis with the human eye and also at least as well as a previously validated but more expensive approach based on cardiac magnetic resonance (CMR). Analyses are based on five-fold cross validation of areas under the curve (AUCs) for CRT response and survival time after the CRT implant using Cox proportional hazards regression with stratification of groups using a Gaussian mixture model approach. Furthermore, FPCD and CMR predictors are shown to be independent, which demonstrates that the FPCD electrical findings and the CMR mechanical findings together provide a synergistic model for response and survival after CRT. In summary, this study provides a highly effective approach to prognostication after CRT in HFrEF using an accessible and inexpensive diagnostic test with a major expected impact on personalization of therapies.

Data-Based In-Cylinder Pressure Model with Cyclic Variations for Combustion Control: An RCCI Engine Application

Cylinder-pressure-based control is a key enabler for advanced pre-mixed combustion concepts. In addition to guaranteeing robust and safe operation, it allows for cylinder pressure and heat release shaping. This requires fast control-oriented combustion models. Over the years, mean-value models have been proposed that can predict combustion metrics (e.g., gross indicated mean effective pressure (IMEPg), or the crank angle where 50% of the total heat is released (CA50)) or models that predict the full in-cylinder pressure. However, these models are not able to capture cycle-to-cycle variations. The inclusion of the cycle-to-cycle variations is important in the control design for combustion concepts, like reactivity-controlled compression ignition, that can suffer from large cycle-to-cycle variations. In this study, the in-cylinder pressure and cycle-to-cycle variations are modelled using a data-based approach. The in-cylinder conditions and fuel settings are the inputs to the model. The model combines principal component decomposition and Gaussian process regression. A detailed study is performed on the effects of the different hyperparameters and kernel choices. The approach is applicable to any combustion concept, but is most valuable for advance combustion concepts with large cycle-to-cycle variation. The potential of the proposed approach is successfully demonstrated for a reactivity-controlled compression ignition engine running on diesel and E85. The average prediction error of the mean in-cylinder pressure over a complete combustion cycle is 0.051 bar and of the corresponding mean cycle-to-cycle variation is 0.24 bar2. This principal-component-decomposition-based approach is an important step towards in-cylinder pressure shaping. The use of Gaussian process regression provides important information on cycle-to-cycle variation and provides next-cycle control information on safety and performance criteria.

Robust inference of panel data models with interactive fixed effects under long memory: A frequency domain approach

This paper studies a linear panel data model with interactive fixed effects wherein regressors, factors and idiosyncratic error terms are all stationary but with potential long memory. The setup involves a new formulation of panel data models, where weakly dependent regressors, factors and idiosyncratic errors are embedded as a special case. Standard methods based on principal component decomposition and least squares estimation, as in Bai (2009), are found to be biased and distorted in inference. To cope with this failure and to provide a simple implementable estimation procedure, a frequency domain least squares estimation is proposed. The limit distribution of the frequency domain estimator is established and a self-normalized approach to inference without the need for plug-in estimation of memory parameters is developed. Simulations show that the frequency domain estimator performs robustly under short memory and outperforms the time domain estimator when long range dependence is present. An empirical illustration is provided, examining the long-run relationship between stock returns and realized volatility.

Robust function-on-function interaction regression

A function-on-function regression model with quadratic and interaction effects of the covariates provides a more flexible model. Despite several attempts to estimate the model’s parameters, almost all existing estimation strategies are non-robust against outliers. Outliers in the quadratic and interaction effects may deteriorate the model structure more severely than their effects in the main effect. We propose a robust estimation strategy based on the robust functional principal component decomposition of the function-valued variables and [Formula: see text]-estimator. The performance of the proposed method relies on the truncation parameters in the robust functional principal component decomposition of the function-valued variables. A robust Bayesian information criterion is used to determine the optimum truncation constants. A forward stepwise variable selection procedure is employed to determine relevant main, quadratic, and interaction effects to address a possible model misspecification. The finite-sample performance of the proposed method is investigated via a series of Monte-Carlo experiments. The proposed method’s asymptotic consistency and influence function are also studied in the supplement, and its empirical performance is further investigated using a U.S. COVID-19 dataset.

A Symmetrized Dot Pattern Extraction Method Based on Frobenius and Nuclear Hybrid Norm Penalized Robust Principal Component Analysis and Decomposition and Reconstruction.

Due to their symmetrized dot pattern, rolling bearings are more susceptible to noise than time-frequency characteristics. Therefore, this article proposes a symmetrized dot pattern extraction method based on the Frobenius and nuclear hybrid norm penalized robust principal component analysis (FNHN-RPCA) as well as decomposition and reconstruction. This method focuses on denoising the vibration signal before calculating the symmetric dot pattern. Firstly, the FNHN-RPCA is used to remove the non-correlation between variables to realize the separation of feature information and interference noise. After, the residual interference noise, irrelevant information, and fault features in the separated signal are clearly located in different frequency bands. Then, the ensemble empirical mode decomposition is applied to decompose this information into different intrinsic mode function components, and the improved DPR/KLdiv criterion is used to select components containing fault features for reconstruction. In addition, the symmetrized dot pattern is used to visualize the reconstructed signal. Finally, method validation and comparative analysis are conducted on the CWRU datasets and experimental bench data, respectively. The results show that the improved criteria can accurately complete the screening task, and the proposed method can effectively reduce the impact of strong noise interference on SDPs.

An unsupervised online anomaly detection method for metal additive manufacturing processes via a statistical time-frequency domain algorithm

Anomalies often occur in metal additive manufacturing from processing inconsistencies and uncertainty. A robust fault detection system that uses sensor measurements such as melt pool imaging has the potential to improve part quality and save production time by anticipating print failure. Toward this goal, we develop and validate a fault detection technique using melt pool geometry-related measurements from an in situ near-infrared optical camera. This method is unsupervised and is trained on a small dataset, mitigating human error in classifying fault types, and reducing lead times for preparing training datasets. Furthermore, this method uses learned geometry-informed nominal behavior of the melt pool signal to make informed decisions on the process health. There are spatial-temporal characteristics embedded in the melt pool images, caused by the periodicity in the geometry-dependent raster pattern. These characteristics can be captured in the frequency domain using the signal spectrogram, a representation of the frequency content over time. Defects will appear in the spectrogram, disrupting the healthy spectral response. To quantify healthy spectrograms, we use principal component (PC) decomposition to extract the features of these spectrograms as a set of nominal basis vectors. Anomaly detection is then performed by calculating the error between the original and reconstructed spectrogram vector by projection of the spectrogram PCs onto the nominal basis. The reconstruction error for anomalous signals is larger than that from healthy signals, which is then used for fault detection. A one-tailed statistical test is used to determine the fault detection threshold for the reconstruction error signal. This method is tested on three raster patterns and performs better than a comparative time-series thresholding method. We demonstrate that this time-frequency algorithm can detect both temporal faults (which occur at a single time instant) and spatial faults (such as those introduced by an improper sintering), differentiating them from nominal operation.

Familial aggregation of multimorbidity in Sweden: national explorative family study

ObjectivesTo examine whether multimorbidity aggregates in families in Sweden.DesignNational explorative family study.SettingSwedish Multigeneration Register linked to the National Patient Register, 1997-2015. Multimorbidity was assessed with a modified counting method of...

COVID-19 knowledge deconstruction and retrieval: an intelligent bibliometric solution.

COVID-19 has been an unprecedented challenge that disruptively reshaped societies and brought a massive amount of novel knowledge to the scientific community. However, as this knowledge flood continues surging, researchers have been disadvantaged by not having access to a platform that can quickly synthesize emerging information and link the new knowledge to the latent knowledge foundation. Aiming to fill this gap, we propose a research framework and develop a dashboard that can assist scientists in identifying, retrieving, and understanding COVID-19 knowledge from the ocean of scholarly articles. Incorporating principal component decomposition (PCD), a knowledge mode-based search approach, and hierarchical topic tree (HTT) analysis, the proposed framework profiles the COVID-19 research landscape, retrieves topic-specific latent knowledge foundation, and visualizes knowledge structures. The regularly updated dashboard presents our research results. Addressing 127,971 COVID-19 research papers from PubMed, the PCD topic analysis identifies 35 research hotspots, along with their inner correlations and fluctuating trends. The HTT result segments the global knowledge landscape of COVID-19 into clinical and public health branches and reveals the deeper exploration of those studies. To supplement this analysis, we additionally built a knowledge model from research papers on the topic of vaccination and fetched 92,286 pre-Covid publications as the latent knowledge foundation for reference. The HTT analysis results on the retrieved papers show multiple relevant biomedical disciplines and four future research topics: monoclonal antibody treatments, vaccinations in diabetic patients, vaccine immunity effectiveness and durability, and vaccination-related allergic sensitization.

Mitigation of Calibration Ringing in the Context of the MTG-S IRS Instrument

EUMETSAT is currently developing the on-ground processing chain of the infrared sounders (IRS) on-board the Meteosat third-generation sounding satellites (MTG-S). In this context, the authors investigated the impact of a particular type of radiometric error, called hereafter calibration ringing. It arises in Fourier transform spectrometers when the instrument’s radiometric transfer function (RTF) varies within the domain of the instrument’s spectral response function (SRF). The expected radiometric errors were simulated in the context of the MTG-S IRS instrument in the long-wave infrared (LWIR) band. Making use of a principal components (PCs) decomposition, a software correction, called RTF uniformisation, was designed and its performance was assessed in the context of MTG-S IRS.

Tensor Principal Component Analysis in High Dimensional CP Models

The CP decomposition for high dimensional non-orthogonal spiked tensors is an important problem with broad applications across many disciplines. However, previous works with theoretical guarantee typically assume restrictive incoherence conditions on the basis vectors for the CP components. In this paper, we propose new computationally efficient composite PCA and concurrent orthogonalization algorithms for tensor CP decomposition with theoretical guarantees under mild incoherence conditions. The composite PCA applies the principal component or singular value decompositions twice, first to a matrix unfolding of the tensor data to obtain singular vectors and then to the matrix folding of the singular vectors obtained in the first step. It can be used as an initialization for any iterative optimization schemes for the tensor CP decomposition. The concurrent orthogonalization algorithm iteratively estimates the basis vector in each mode of the tensor by simultaneously applying projections to the orthogonal complements of the spaces generated by other CP components in other modes. It is designed to improve the alternating least squares estimator and other forms of the high order orthogonal iteration for tensors with low or moderately high CP ranks, and it is guaranteed to have second or higher order convergence when the error of any given initial estimator is bounded by a small constant. Our theoretical investigation provides estimation accuracy and convergence rates for the two proposed algorithms. Both proposed algorithms are applicable to deterministic tensor, its noisy version, and the order- <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$2K$ </tex-math></inline-formula> covariance tensor of order- <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$K$ </tex-math></inline-formula> tensor data in a factor model with uncorrelated factors. Simulation experiments demonstrate significant practical superiority of our approach over existing methods.

Infrared Target Detection Based on Joint Spatio-Temporal Filtering and L1 Norm Regularization.

Infrared target detection is often disrupted by a complex background, resulting in a high false alarm and low target recognition. This paper proposes a robust principal component decomposition model with joint spatial and temporal filtering and L1 norm regularization to effectively suppress the complex backgrounds. The model establishes a new anisotropic Gaussian kernel diffusion function, which exploits the difference between the target and the background in the spatial domain to suppress the edge contours. Furthermore, in order to suppress the dynamically changing background, we construct an inversion model that combines temporal domain information and L1 norm regularization to globally constrain the low rank characteristics of the background, and characterize the target sparse component with L1 norm. Finally, the overlapping multiplier method is used for decomposition and reconstruction to complete the target detection.Through relevant experiments, the proposed background modeling method in this paper has a better background suppression effect in different scenes. The average values of the three evaluation indexes, SSIM, BSF and IC, are 0.986, 88.357 and 18.967, respectively. Meanwhile, the proposed detection method obtains a higher detection rate compared with other algorithms under the same false alarm rate.

Optimization of diffuse reflectance spectroscopy measurements for direct and rapid screening of pesticides: A case study of spinach

Although diffuse reflectance spectroscopy (DRS) measurements can be collected rapidly and simultaneously, the resulting datasets are imbalanced and redundant due to the highly correlated spectral features collected on relatively few samples. Consequently, modelling these datasets using machine learning (ML) techniques is challenging and necessitates longer training times and more computational resources. Furthermore, models developed with such data are frequently prone to overfitting, resulting in promising but often non-reproducible results. We demonstrate the advantage of using an eigenvector decomposition principal component analysis (PCA) in reducing the dimensionality and data mining of DRS measurements in the short near-infrared region (750–900 nm). A total of 547 DRS measurements consisting of 151 wavelengths were acquired from spinach samples sprayed with two different pesticides and control samples. The measurements were later preprocessed with a Savitzky-Golay filter and multiplicative scatter analysis. After performing PCA on the preprocessed data, two principal components (PCs) that explained 77% of the cumulative variance and maximized the interclass variation were extracted and used as inputs to three ML models namely; artificial neural networks, support vector machine and random forest, to classify the samples. Re-sampling was used to tune the models and avoid overfitting. The performance of the models was compared using raw DRS data, pre-processed (PP) DRS data, and PCs data. The results show that pesticide classification using PCs data requires the least amount of training time (average 2.4 s) for all the models, and achieves 100% classification accuracy. In addition, it was observed that spectral data pre-processing improves accuracy and training time when compared to using raw spectral data. These findings are particularly encouraging since they demonstrate the possibility of developing rapid and accurate classification models for screening pesticide residues in fresh produce based on DRS measurements with minimal computational resources.

Bayesian tomography with prior-knowledge-based parametrization and surrogate modelling

SUMMARYWe present a Bayesian tomography framework operating with prior-knowledge-based parametrization that is accelerated by surrogate models. Standard high-fidelity forward solvers (e.g. finite-difference time-domain schemes) solve wave equations with natural spatial parametrizations based on fine discretization. Similar parametrizations, typically involving tens of thousand of variables, are usually employed to parametrize the subsurface in tomography applications. When the data do not allow to resolve details at such finely parametrized scales, it is often beneficial to instead rely on a prior-knowledge-based parametrization defined on a lower dimension domain (or manifold). Due to the increased identifiability in the reduced domain, the concomitant inversion is better constrained and generally faster. We illustrate the potential of a prior-knowledge-based approach by considering ground penetrating radar (GPR) traveltime tomography in a crosshole configuration with synthetic data. An effective parametrization of the input (i.e. the permittivity distributions determining the slowness field) and compression in the output (i.e. the traveltime gathers) spaces are achieved via data-driven principal component decomposition based on random realizations of the prior Gaussian-process model with a truncation determined by the performances of the standard solver on the full and reduced model domains. To accelerate the inversion process, we employ a high-fidelity polynomial chaos expansion (PCE) surrogate model. We investigate the impact of the size of the training set on the performance of the PCE and show that a few hundreds design data sets is sufficient to provide reliable Markov chain Monte Carlo inversion at a fraction of the cost associated with a standard approach involving a fine discretization and physics-based forward solvers. Appropriate uncertainty quantification is achieved by reintroducing the truncated higher order principle components in the original model space after inversion on the manifold and by adapting a likelihood function that accounts for the fact that the truncated higher order components are not completely located in the null space.

Supervised classification of curves via a combined use of functional data analysis and tree-based methods

Technological advancement led to the development of tools to collect vast amounts of data usually recorded at temporal stamps or arriving over time, e.g. data from sensors. Common ways of analysing this kind of data also involve supervised classification techniques; however, despite constant improvements in the literature, learning from high-dimensional data is always a challenging task due to many issues such as, for example, dealing with the curse of dimensionality and looking for a trade-off between complexity and accuracy. Nowadays, research in functional data analysis (FDA) and statistical learning is very lively to address these drawbacks adequately. This study offers a supervised classification strategy that combines FDA and tree-based procedures. Specifically, we introduce functional classification trees, functional bagging, and functional random forest exploiting the functional principal components decomposition as a tool to extract new features and build functional classifiers. In addition, we introduce new tools to support the understanding of the classification rules, such as the functional empirical separation prototype, functional predicted separation prototype, and the leaves’ functional deviance. Furthermore, we suggest some possible solutions for choosing the number of functional principal components and functional classification trees to be implemented in the supervised classification procedure. This research aims to provide an approach to improve the accuracy of the functional classifier, serve the interpretation of the functional classification rules, and overcome the classical drawbacks due to the high-dimensionality of the data. An application on a real dataset regarding daily electrical power demand shows the functioning of the supervised classification proposal. A simulation study with nine scenarios highlights the performance of this approach and compares it with other functional classification methods. The results demonstrate that this line of research is exciting and promising; indeed, in addition to the benefits of the suggested interpretative tools, we exceed the previously established accuracy records on a dataset available online.

FUNCTION-ON-FUNCTION LINEAR QUANTILE REGRESSION

In this study, we propose a function-on-function linear quantile regression model that allows for more than one functional predictor to establish a more flexible and robust approach. The proposed model is first transformed into a finitedimensional space via the functional principal component analysis paradigm in the estimation phase. It is then approximated using the estimated functional principal component functions, and the estimated parameter of the quantile regression model is constructed based on the principal component scores. In addition, we propose a Bayesian information criterion to determine the optimum number of truncation constants used in the functional principal component decomposition. Moreover, a stepwise forward procedure and the Bayesian information criterion are used to determine the significant predictors for including in the model. We employ a nonparametric bootstrap procedure to construct prediction intervals for the response functions. The finite sample performance of the proposed method is evaluated via several Monte Carlo experiments and an empirical data example, and the results produced by the proposed method are compared with the ones from existing models.

Principal component decomposition of acoustic and neural representations of time-varying pitch reveals adaptive efficient coding of speech covariation patterns

Understanding the effects of statistical regularities on speech processing is a central issue in auditory neuroscience. To investigate the effects of distributional covariance on the neural processing of speech features, we introduce and validate a novel approach: decomposition of time-varying signals into patterns of covariation extracted with Principal Component Analysis. We used this decomposition to assay the sensory representation of pitch covariation patterns in native Chinese listeners and non-native learners of Mandarin Chinese tones. Sensory representations were examined using the frequency-following response, a far-field potential that reflects phase-locked activity from neural ensembles along the auditory pathway. We found a more efficient representation of the covariation patterns that accounted for more redundancy in the form of distributional covariance. Notably, long-term language and short-term training experiences enhanced the sensory representation of these covariation patterns.

EP477: A classifier prioritizing exome and genome sequencing cases for re-analysis

It remains difficult to diagnose rare diseases by means of Genome or Exome Sequencing (GS or ES), with diagnostic yield of these tests estimated to range from 25% to 40%. Initially unsolved cases can become more solvable over time as knowledgebase annotations become more complete or as an individual patient’s phenotypic profile develops. However, it is not feasible to allocate human effort to re-analyzing the growing backlog of unsolved cases at a regular interval. The goal of this work is to identify cases which are most likely to be solvable based on a combination of demographic information and quantitative markers of the likely causality of genetic variation present in each patient. In a cohort of 611 patients who received ES, 187 (30.6%) were diagnosed and 79 had no medically meaningful findings. These groups were designated “solvable” and “not solvable”, respectively. The remaining 345 cases were designated as “potentially solvable”. For each patient, 28 features were recorded: sex, age, time since previous analysis, and the Likelihood Ratio (LR) scores of the top 25 variants as calculated by a modified version of LIRICAL, a likelihood ratio-driven algorithm which computes the post-test probability that each variant found in a patient’s Variant Call File (VCF) is causative of their phenotype. With an 80:20 train-test split of the 266 cases marked as either “solvable” or “not solvable”, we evaluated logistic regression and random forest models from a parameter grid search by 5-fold cross validation, resolving the LR scores into their principal components decomposition and using SMOTE to correct for class imbalances. This grid search selected a random forest model on 100 trees with maximum depth 10 and no minimum impurity split requirement. The selected model classified the test set with recall = 0.868 and precision = 0.767. Of the 345 potentially solvable cases, the model labeled 234 as “solvable”; based on the model’s precision, we estimate 179 of these cases are likely to be solvable with the remainder being false positives. Based on the model’s false negative rate, we estimate the model misses 23 solvable cases, implying a total of 202 solvable cases among the 345 potentially solvable cases. Thus, the model recovers 179/202 (88.6%) of the remaining solvable cases, while requiring inspection of only 234 (67.8%) of the 345 potentially solvable cases. By contrast, we would expect 234 randomly chosen potentially solvable cases to include approximately 118/202 (58.4%) of the remaining solvable cases. A machine learning approach has potential to prioritize re-analysis effort and obtain higher diagnostic yield at a lower human effort cost. This approach does not altogether depart from the default approach of re-analysis in order of previous analysis, as the time since previous analysis was a highly-weighted feature in many of the parameter configurations searched. Using a framework which includes both biological data and case-level demographics at regular intervals should give all cases a chance to eventually be considered for re-analysis.

Assessment of Regional Spatiotemporal Variations in Drought from the Perspective of Soil Moisture in Guangxi, China

Understanding the changes in regional droughts is important for promoting overall sustainable development. However, the spatiotemporal dynamics of soil droughts in Guangxi under the background of global warming and regional vegetation restoration have not been studied extensively, and the potential causes are scarcely understood. Here, using TerraClimate soil moisture data, we constructed a monthly standardized soil moisture index (SSMI), analyzed the seasonal and annual spatiotemporal distribution of droughts from the perspective of soil moisture, and studied past soil drought events in Guangxi. Migration methods of drought centroid, trend analysis, and principal component decomposition were used. In the interannual dynamics, the overall SSMI increased, indicating that the soil drought situation was gradually alleviated in Guangxi. Further, the frequency of extreme and severe droughts decreased with time, mainly in autumn and winter. During early drought stages, the migration path was short, which extended as the droughts progressed. Ocean temperature and soil moisture were strongly correlated, indicating that abnormal ocean surface temperature may drive soil moisture. This study provides scientific guidance for the early warning, prevention, and mitigation of losses associated with soil droughts in Guangxi and serves as valuable reference for understanding the impacts of large-scale climate anomalies on soil moisture.

Infrared Dim and Small Target Detection Algorithm Combining Multiway Gradient Regularized Principal Component Decomposition Model

In complex non-smooth backgrounds, infrared dim and small target targets generally have lower energy and occupy fewer pixels, and are easily swamped by clutter. To improve the detection capability of dim and small targets in non-smooth scenes, this paper proposes a new dim and small target detection method combining multidirectional gradient difference regularization principal component decomposition model. The method first establishes a new gradient difference regularization to constrain the low-rank subspaces of different image components, then construct a gradient difference regularization-based principal component decomposition model (GDR-PCD), and finally decomposes the model using the overlapping directional multiplier method to obtain the background impedance. The experimental results show that the method performs better in all six sequential image scenes than the traditional algorithm. Furthermore, the detection results verify the algorithm’s effectiveness in this paper.