106 results about "Singular value decomposition method" patented technology

The invention provides a data processing method for seabed-based monitoring equipment, which belongs to the technical field of electric digital data processing, and comprises the following steps: normalizing acquired data, removing abnormal values, extracting key features by using a singular value decomposition method, and segmenting by using a self-adaptive sliding time domain window to form time sequence data blocks. According to the method, a CUDA parallel processing flow is creatively used to construct a disorder matrix and a drift matrix, and a disorder mode feature vector, a drift principal component and an abnormal feature matrix are fused through a tensor decomposition technology to construct a seabed environment state tensor model. Meanwhile, a drift rule in a long-time sequence is learned by utilizing a deep sea environment perception attention network model, real-time monitoring data is corrected and compensated, and finally a seabed state evaluation standard is established by adopting a hierarchical clustering algorithm, so that the key technical problem that environment change and equipment drift in seabed monitoring data are difficult to distinguish is effectively solved.

The invention discloses a water vapor chromatography modeling method and device based on GNSS-MET, and belongs to the technical field of atmospheric water vapor information inversion. According to the method, satellite signals and meteorological parameters are obtained through a GNSS-MET receiver, and the oblique path water vapor content (SWV) is calculated to serve as chromatography input; densely and uniformly distributed observation stations are selected to construct a chromatography area, and voxel grids are divided; a horizontal constraint is constructed by using Gaussian distance weighting, a vertical constraint is constructed in combination with a water vapor vertical index distribution characteristic, and ERA5 reanalysis data is introduced as a prior constraint, so that the limitation of a traditional sounding data constraint is solved; and resolving the tomographic equation through a singular value decomposition (SVD) method to obtain three-dimensional water vapor density distribution. According to the method, the high temporal-spatial resolution characteristic of ERA5 data is utilized, the precision and reliability of water vapor chromatography are remarkably improved, and the method is suitable for the fields of extreme weather prediction, meteorological monitoring and the like.

The invention discloses a method for extracting while-digging seismic signal features based on local frequency, and the method comprises the steps: obtaining while-digging seismic data, and carrying out the preprocessing of the obtained while-digging seismic data; performing time-frequency analysis according to the preprocessed data to generate a time-frequency spectrum, and constructing a local frequency weight matrix according to the time-frequency spectrum; performing weighted singular value decomposition on the seismic data while digging according to the local frequency weight matrix to obtain an optimized decomposition matrix; and performing low-rank reconstruction on the optimized decomposition matrix, and extracting the characteristics of the seismic signal while digging. On the basis of a local weighted singular value decomposition method, local frequency energy weights of reflected signals are extracted through time-frequency analysis, and singular value decomposition is guided to strengthen effective components and suppress noise in low-rank approximation. According to the method, self-adaptive enhancement and noise separation of signals are achieved, the signal-to-noise ratio and fidelity of coal-rock interface reflection characteristics are remarkably improved while the calculation efficiency is guaranteed, and reliable technical support is provided for excavation-following detection under complex geological conditions.

The invention provides an image restoration method based on adaptive weighted tensor completion, and relates to the technical field of image processing and application, and the method comprises the steps: obtaining to-be-restored image data, and carrying out the tensor of the to-be-restored image data, and obtaining input tensor data; constructing a tensor completion model based on an adaptive weighted tensor nuclear norm; wherein a weight matrix in the tensor completion model can be adaptively updated along with input tensor data; and based on an alternating direction multiplier method or an approximate singular value decomposition method based on tensor QR decomposition, solving the tensor completion model, and outputting restored tensor data to realize image restoration. According to the scheme, the image restoration quality can be improved.

The invention discloses an anchoring crack rock deformation displacement lightweight prediction method and equipment, and belongs to the technical field of intelligent early warning of geotechnical engineering disasters. Constructing a prediction model by adopting a random configuration network method; partial hidden nodes with low contribution degree in the prediction model are eliminated by using a singular value decomposition method, the number of the hidden nodes in the prediction model is reduced, and the prediction model is lightened; excellent new hidden nodes are searched through a random hidden node increasing and replacing technology to replace original hidden nodes in the prediction model, a lightweight prediction model is reconstructed, and the prediction precision is improved; the lightweight prediction model is utilized to predict the deformation displacement of the anchoring crack rock; the method has simple steps, and can accurately predict the deformation displacement of the anchoring crack rock based on the position of the anchor rod and the load level of the anchoring crack rock, thereby ensuring the safety of geotechnical engineering.

To provide a method, a system, a device, and a medium for measuring temperature and for reducing noise of a distributed optical fiber, which relate to the technical field of noise reduction of a distributed sensing system.SOLUTION: A method includes the steps of: obtaining an anti-Stokes signal and a Stokes signal of a photovoltaic assembly collected by temperature measurement of a distributed optical fiber and a noise reduction system, calculating a temperature ratio signal; dividing the temperature ratio signal into a plurality of temperature ratio signal sub-blocks; performing initial iterative noise reduction processing on original sub-matrices corresponding to different temperature ratio signal sub-blocks for different iteration times by using a singular value decomposition method combining fusion functions, to obtain noise-reduced temperature data sub-blocks corresponding to each temperature ratio signal sub-block; determining an iteration time with a good noise reduction processing effect and a reference sub-block, and performing iterative noise reduction processing on a non-reference sub-block based on the iteration time with the good noise reduction processing effect, splicing the non-reference sub-block into noise-reduced temperature fusion data, and calculating a final temperature demodulation result.SELECTED DRAWING: Figure 1

The invention provides a Prony algorithm-based cable segment wave velocity acquisition method, device and system, and a medium, and the method comprises the steps: testing a to-be-tested cable, and obtaining a cable signal transfer function; on the basis of the cable signal transfer function, in combination with cable joint distribution, constructing a cable signal approximation function based on a Prony method; constructing a linear prediction model of the cable signal based on the cable signal approximation function; performing denoising processing on the linear prediction model based on a singular value decomposition method to obtain a denoised linear prediction model; solving the denoised linear prediction model to obtain an attenuation coefficient; and calculating the segmented wave velocity of the cable based on the attenuation coefficient. According to the method, the Prony estimation method is combined with the singular value decomposition noise reduction algorithm, high-precision extraction of attenuation constants and segmented wave velocity decoupling are achieved, and then the electrical distance positioning precision is improved.

The invention discloses a truss type load-bearing structure time domain dynamics topological optimization method and system based on an intrinsic orthogonal decomposition method, and relates to the technical field of truss type load-bearing structure topological optimization. The method is provided for solving the problems of high calculation power and the like caused by large-scale space-time response iteration of original transient problems and intensive calculation of dual problem sensitivity analysis in the prior art. According to the method, a local geometric control strategy for the cross interference problem of rod piece units is introduced, and a transient dynamics full-order topological optimization model of the truss structure is established through an equal geometric stiffness diffusion method. Based on an intrinsic orthogonal decomposition method, a transient dynamics reduced-order model of a truss structure is constructed, a reduced-order model base is updated in real time through an incremental singular value decomposition method, and then efficient solving of a transient dynamics equation and an adjoint equation in sensitivity analysis is achieved. And updating an iterative design variable by adopting a penalty function method, and seeking the optimal layout design of the truss structure. The invention provides a time-domain dynamics topological optimization method of the truss type load-bearing structure based on an intrinsic orthogonal decomposition method, and the method can be used for efficiently solving the dynamics layout optimization problem of the truss structure under the action of a transient load.

An electric vehicle charging station recommendation method and system, applied to the related field of charging station recommendation. The method comprises: acquiring user charging features and influence degree values of user charging evaluation features on user charging preferences, wherein the user charging evaluation features are constructed on the basis of the user's evaluation information on charging stations (S1); calculating the user's comprehensive scores for different charging stations on the basis of the influence degree values, and constructing a user preference matrix by using the user's comprehensive scores for different charging stations as elements (S2); and decomposing the user preference matrix by means of a singular value decomposition method, acquiring potential matching relationships between the user and charging station features, and recommending a charging station to the user on the basis of the potential matching relationships (S3). The method solves the problem in the prior art that charging stations cannot be personalizedly recommended for different users, thereby improving user experience.

The invention discloses a heterogeneous air quality data fusion method based on sparse matrix decomposition. The method comprises the following steps: S1, collecting air quality data; s2, preprocessing the collected air quality data, and constructing a preliminary fusion matrix; s3, marking missing data in the preliminary fusion matrix, and constructing a sparse matrix; s4, carrying out matrix decomposition by adopting a mode of combining an improved singular value decomposition method and a matrix completion method based on low-rank representation, filling missing data, and carrying out feature weighted optimization on a matrix decomposition result by utilizing an attention mechanism; and S5, error measurement and consistency check are carried out. According to the method, sparse matrix decomposition and low-rank completion technologies are adopted, the fusion process of the air quality data is optimized in combination with an attention mechanism, and high data precision, high consistency and improvement of prediction accuracy are achieved.

The invention relates to a singular value decomposition-based personalized federated learning method, which is used for solving the problem of uncertainty of a system caused by equipment isomerism and the problem that a client falls behind due to extra burden caused by a storage and transmission mechanism in the prior art. Firstly, a customer selection strategy based on resource awareness is determined, and a server side establishes a quantitative scoring system by comprehensively analyzing computing resources and communication conditions of clients. And screening a plurality of clients with optimal comprehensive performance from all online clients to participate in local training. During local training of the client, historical weight parameters are reserved by adopting self-knowledge distillation (SKDSVD) combined with a singular value decomposition method, residual errors after decomposition are personalized model parameters and are stored in the local client, and universal parameters are transmitted to the server for aggregation. The SKDSVD algorithm reduces the training time delay and the fall-behind rate of the local client while optimizing the global model of the server to obtain the optimal convergence precision.

The invention relates to a bolt looseness diagnosis method, system and device and a medium. The method comprises the following steps: acquiring an original sound signal for knocking a bolt; decomposing the original sound signal by using a CEEMDAN method to obtain a plurality of IMF components; calculating a plurality of preset types of quantitative indexes for each IMF component, performing dynamic weighting, and screening out a plurality of effective IMF components from the plurality of IMF components based on a weighting result of the IMF components as effective components; determining a dynamic function window and a frequency spectrum gravity center according to the plurality of effective components, decomposing the effective components in combination with a singular value decomposition method, and performing screening and reconstruction to obtain de-noised signals; performing feature extraction on the de-noised signal to obtain a diagnosis feature; and inputting the diagnosis characteristics into a bolt looseness diagnosis model to obtain a bolt looseness diagnosis result. According to the method, the noise reduction effect of the sound signals collected under the working condition can be effectively improved, the obvious bolt looseness diagnosis accuracy and superiority are achieved, and the bolt looseness diagnosis precision and efficiency can be improved.

The invention provides a damping and noise-reducing installation method for electromechanical equipment, and belongs to the technical field of electromechanical equipment installing.The damping and noise-reducing installation method comprises the steps that firstly, a multi-dimensional vibration noise collection system is constructed, and comprehensive vibration noise characteristic data is collected; then extracting vibration noise features through wavelet transform and non-negative matrix factorization; constructing a small-scale model, and analyzing the influence of the installation parameters by adopting an orthogonal test method; obtaining a vibration noise variation matrix by using a singular value decomposition method; constructing a multi-objective optimization model based on the optimal installation parameters, and solving the optimal installation parameters by adopting a particle swarm algorithm; the parameter rationality is verified by applying an elastic damping dynamics equation set; matched damper materials and installation positions are selected; actual installation is carried out according to the optimal parameters, and the effect is verified; and finally, a parameter correction model is established, a complete knowledge base is formed, and accurate optimization of damping and noise reduction installation of the electromechanical equipment is realized.

The present invention provides a method for evaluating the efficiency of wide-area distributed photovoltaic power generation, which relates to the technical field of photovoltaic power generation efficiency evaluation, and includes: obtaining the reanalysis time-series data of solar radiation in the area where the distributed photovoltaic power station is located, calculating the eigenvectors by using the empirical orthogonal function decomposition method, and screening out the set of meteorological resource representative points for the final site selection in combination with the site selection conditions; collecting the measured data of the total solar radiation on the surface of the set of meteorological resource representative points, and performing correction processing on the reanalysis time-series data by using the singular value decomposition method; matching the solar radiation reanalysis grid for the distributed photovoltaic power station, and evaluating the efficiency of distributed photovoltaic power generation by using the physical method and the statistical method based on the corrected reanalysis time-series data to obtain the evaluation value. The present invention has wide applicability, and only needs to use the publicly available global meteorological reanalysis data set to realize the resource distribution analysis.

The invention discloses a mechanical arm 3D printing normal calibration method based on singular value decomposition. The mechanical arm 3D printing normal calibration method comprises the steps that S1, a dial indicator is installed at the tail end of a mechanical arm; s2, respectively establishing coordinate systems by taking the center point of the flange at the tail end of the mechanical arm, the actual contact point of the dial indicator and the non-deformation tail end point of the dial indicator as original points; s3, taking the central point of the flange at the tail end of the mechanical arm as a movement track of the point in any set space curve, and sampling to obtain a space point set of sampling points; s4, fitting a plane in the space by using a singular value decomposition method, so that the sum of squares of distances from all sampling points in the space point set to the plane is minimum, and further obtaining a three-dimensional space plane equation and a normal vector of the fitted plane; and S5, coordinate system posture correction is conducted on the mechanical arm tail end flange center point coordinate system, so that the axis of the center of the mechanical arm tail end flange is parallel to the normal vector, and the parallelism between the printing track surface and the forming platform is improved. The forming quality of the first layer of 3D printing can be effectively improved.

The invention belongs to the technical field of health state assessment, and discloses a health state assessment method and device, electronic equipment and a storage medium, and the method comprises the steps: obtaining the physiological feature data of a user in a preset period, constructing the physiological feature data into tensor data containing an individual dimension, a feature dimension and a time dimension, complementing missing data in the tensor data by using a frequency domain transformation method and a singular value decomposition method to obtain complemented physiological feature data, inputting the complemented physiological feature data into a preset health state evaluation model, and calculating to obtain a health state evaluation result of the user in a preset period; by inputting the complemented physiological feature data obtained by complementing with the frequency domain transformation method and the singular value decomposition method into the preset health state assessment model, the health state assessment result of the user in the preset period is calculated, and the accuracy of health state assessment is improved.

The invention provides a spectral data cube reconstruction method, device and equipment based on compressed physical prior, a storage medium and a program product, and relates to the technical field of spectral reconstruction, and the method comprises the steps: obtaining a two-dimensional measurement image formed after a to-be-observed target is coded and modulated by a spectral imaging chip; obtaining an original transmission spectrum matrix corresponding to each pixel in the spectral imaging chip; performing principal component analysis on the original transmission spectrum matrix in a spectrum dimension, and extracting a principal component feature vector through a singular value decomposition method; projecting the original transmission spectrum matrix into an orthogonal subspace formed by the principal component feature vectors to obtain a compressed physical prior vector; and inputting the two-dimensional measurement image and the compressed physical prior vector into a deep expansion neural network, and outputting a high-dimensional spectral data cube of the to-be-observed target. The reconstruction efficiency of the spectral data cube can be remarkably improved.

The invention provides an optical guiding method for recycling a deep-sea unmanned underwater vehicle, and belongs to the technical field of underwater vehicle recycling. Four blue-green LED guiding light sources are non-uniformly and annularly distributed and installed on the front end face of a recycling cage, the three-dimensional coordinates of the guiding light sources are calibrated in advance, and guiding light source images are collected in real time through a binocular camera; an adaptive exposure control technology is adopted to adjust image quality, statistical information calculation is performed on an identified light source contour, an effective guide light source target is determined through convex constraint configuration judgment and ellipse fitting screening, four guide light sources are constructed as a traveling salesman problem, and a nearest neighbor heuristic algorithm is adopted to complete number matching. A rotation matrix and a translation vector are solved through a singular value decomposition method according to the pre-measured coordinates and the real-time calculated coordinates, and collaborative optimization is carried out at the same time, so that the technical problems of insufficient optical guidance positioning precision and poor real-time performance in the recovery process of the unmanned underwater vehicle in the deep sea environment are solved.

The invention discloses an MZI structure not influenced by the polarization state of input light and an adjusting method thereof, and the method comprises the steps: carrying out the vibration reduction packaging of an MZI, and integrating an optical fiber polarization controller on one arm; stokes vectors of incident light and emergent light are measured by using a polarization analyzer, a rotation matrix of each part of a light path is fitted through a singular value decomposition method, and an optical fiber polarization controller is adjusted according to a result, so that polarization rotation transformation of two arms is consistent; polarization conversion of two arms of the interferometer is equivalent to rotation of a Stokes vector on a Poincare sphere around an axis. According to the method, MZI adjustment is carried out based on the polarization rotation transformation characteristic of the light path in the Stokes space, the problem of interference signal amplitude fluctuation caused by incident light polarization state change is solved through one-time calibration, complex electric feedback control is avoided, the system stability and reliability are remarkably improved, and the method is suitable for optical fiber sensing and related fields.

This invention discloses a high-precision FMCW laser ranging method based on the ESPRIT algorithm, belonging to the field of laser ranging technology. First, the laser beam is split into two beams by an optical beam splitter. One beam serves as the local oscillator beam, and the other as the signal beam. The signal beam hits the target object and returns, carrying the distance information of the target. The local oscillator beam and the signal beam are coupled in an optical coupler to obtain a difference frequency signal, which is then acquired by a photodetector and a digital acquisition card. A Hankel matrix is ​​constructed from the difference frequency signal. The Hankel matrix is ​​decomposed using singular value decomposition (SVD) to extract the signal subspace. Based on the decomposition of the signal subspace, an invertible matrix rotation operator is obtained and optimized to obtain the signal frequency, thereby calculating the distance to the target object. This invention is not only simple to implement and easy to operate, but also highly practical and suitable for widespread use.

The invention provides a robust binaural beam forming method. The robust binaural beam forming method comprises the steps of 1, calculating noisy signals received by all microphones in a binaural hearing aid; 2, calculating a covariance matrix of noisy signals and total noise; 3, calculating the binaural cue loss of the expected voice source and the jth interference source; step 4, calculating the worst binaural cue retention condition of the expected voice source; 5, the formula (13) in the step 4 is simplified by making the two norm of e smaller than or equal to a constant eta and utilizing a Cauchy-Schwarz inequality; 6, listing a cost function according to the total noise covariance matrix Rnn obtained in the step 2 and the Pa obtained in the step 5; and step 7, decomposing the matrix # imgabs0 # through a singular value decomposition method, and solving filters wL and wR by using a CVX toolbox in Matlab. According to the invention, under the condition of HRTF mismatch, the spatial cues are reserved as much as possible, and balance between spatial cue reservation and noise reduction effect is realized through parameter adjustment.

The application belongs to the technical field of three-dimensional point cloud registration, and specifically provides a point cloud registration method based on second-order spatial compatibility measurement, comprising the following steps: extracting corner points of source point cloud and target point cloud as key points by using Harris-3D algorithm; describing and preliminarily matching the extracted key points by using FPFH algorithm to obtain matching pairs; calculating a second-order spatial compatibility matrix between the matching pairs, and removing false matching pairs to obtain correct matching pairs; calculating a coarse registration result of two point clouds by using singular value decomposition method in combination with the correct matching pairs to obtain an initial pose; and calculating a fine registration result of point clouds according to the initial pose of two point clouds in combination with K-dimensional tree accelerated ICP algorithm to finally complete the point cloud registration work. The application effectively suppresses the influence of outliers on registration accuracy, and improves the accuracy and speed of point cloud registration.

The present application relates to aero-engine test bench structure health monitoring and performance evaluation technology, provide a kind of test bench performance attenuation evaluation method, system and equipment based on defect detection, the method comprises: based on at least two types of non-destructive testing method to construct the finite element numerical model of test bench;By singular value decomposition method to finite element numerical model order reduction and solution obtain order reduction model;The predicted structure displacement of test bench is obtained in order reduction model, the actual structure displacement of test bench under test load is collected, and the structure performance attenuation evaluation of test bench is carried out by predicted structure displacement and actual structure displacement.This application method can carry out online structure health identification and performance early warning to test bench by the degradation model constructed, with good accuracy, scalability and engineering practical value, can be widely applied in the state evaluation and fault prediction of aero-engine test bench.

The invention relates to an aero-engine test bed structure health monitoring and performance evaluation technology, and provides a test bed performance attenuation evaluation method, system and equipment based on defect detection, and the method comprises the steps: building a finite element numerical model of a test bed based on at least two types of non-destructive detection methods; performing order reduction and solution on the finite element numerical model through a singular value decomposition method to obtain an order reduction model; and acquiring the predicted structural displacement of the test bench in the reduced-order model, acquiring the actual structural displacement of the test bench under the test load, and evaluating the structural performance attenuation of the test bench through the predicted structural displacement and the actual structural displacement. According to the method, on-line structure health identification and performance early warning can be carried out on the test bed through the constructed degradation model in combination with real-time monitoring data, and the method has good accuracy, expandability and engineering practical value and can be widely applied to state evaluation and fault prediction of the aero-engine test bed.

The invention relates to the technical field of radars, and provides a multi-target parameter dimensionality reduction estimation method and device for a frequency-controlled array radar, and the method comprises the steps: determining a receiving signal matrix of the frequency-controlled array radar based on a transmitting steering vector and a receiving steering vector of the frequency-controlled array radar for each target, and a transmitting signal of the frequency-controlled array radar at each snapshot moment; solving the received signal matrix by adopting a high-order singular value decomposition method to obtain a noise subspace matrix of the received signal matrix; constructing a multi-target parameter dimensionality reduction estimation model based on the noise subspace matrix and the transmitting steering vector and the receiving steering vector of the frequency control array radar to each target; solving the multi-target parameter dimension reduction estimation model by adopting a polynomial rooting method so as to estimate an angle estimation value of each target; on the basis of the angle estimation value for each target, the distance estimation value for each target corresponding to the angle estimation value for each target is estimated. According to the method, under the condition of few snapshots, the calculation complexity is reduced, and the parameter estimation precision is also improved.

The invention discloses a multi-scale space target relative pose estimation method and system using parallel double encoders, and belongs to the technical field of space target pose estimation. The invention aims to solve the problems caused by distance change, illumination difference and insufficient multi-scale feature expression of a space target in a dynamic environment. According to the technical key points, a parallel double-encoder architecture is adopted, and local and global features of a space target are extracted; constructing a feature fusion module to fuse features extracted by the CNN and the Transform; multi-scale features are fused through FPN, and angular points are predicted; training and optimizing the network; and solving the pose by using a random consistency perspective n-point method. A plurality of non-coplanar control points are selected, other points are expressed as a linear combination of the control points, a linear equation set is constructed, and a rotation matrix and a translation vector between a target and a camera coordinate system are solved by using a singular value decomposition method. The deep learning network algorithm provides an efficient and accurate solution for space target pose estimation, the algorithm can deeply mine rich information of image data by adopting an encoder-decoder structure, and finally high-precision calculation of the six-degree-of-freedom pose of the target is achieved.

The utility model provides a kind of power station parameter matrix analysis method, belongs to power station fault diagnosis technical field, solve how to design a kind of power station parameter matrix analysis method, solve the numerous power station parameters, data effective information density is low, lead to the problem that fault cause analysis is difficult, and the massive data stream generated occupies a large amount of storage space, by power station parameter data matrixization representation, constructs parameter data matrix, by matrix operation, the rank of matrix is as quantization index, effectively distinguishes the information density of parameter, the correlation between different parameters can be analyzed by the multi-parameter matrix constructed, not limited by parameter number and time sequence length, and by matrix similarity operation, judge the similarity of unit state, provide quick judgment basis for similar fault in later period;Dimensionality reduction is carried out to operation data matrix using matrix singular value decomposition method, retain the part of large singular value proportion, can extract fault feature, realize compressed storage, save storage space.

The present invention provides a bearing degradation trend prediction method based on multi-resolution feature extraction and Bi-LSTM network. It aims to solve the problem that the bearing vibration signal contains a large amount of random noise in complex working conditions and environments, which affects the bearing degradation trend prediction accuracy. It fully considers the impact of historical vibration state on current vibration. The main steps are as follows: applying the full life cycle vibration signal of the accelerated bearing degradation test, performing time domain analysis on it to extract multi-angle eigenvalues ​​to form a feature set, screening sensitive features according to the trend and correlation of the features, using the multi-resolution singular value decomposition method to decompose and reconstruct the sensitive features, inputting the normalized sensitive features into the constructed Bi-LSTM network for training, and predicting the bearing degradation trend. Its purpose is to provide a prediction method that fully considers the impact of the historical vibration state of the bearing on the current moment, eliminates random interference during operation, retains important fault information in the bearing degradation process, and improves the accuracy of bearing degradation trend prediction.

The application relates to the technical field of radars, and provides a multi-target parameter dimension reduction estimation method and device for a frequency control array radar, which comprises the following steps: determining a receiving signal matrix of the frequency control array radar based on a transmitting steering vector, a receiving steering vector of each target and a transmitting signal of the frequency control array radar at each snapshot moment; solving the receiving signal matrix by using a high-order singular value decomposition method to obtain a noise subspace matrix of the receiving signal matrix; constructing a multi-target parameter dimension reduction estimation model based on the noise subspace matrix and the transmitting steering vector and the receiving steering vector of each target; solving the multi-target parameter dimension reduction estimation model by using a polynomial root-finding method to estimate an angle estimation value of each target; and estimating a distance estimation value of each target corresponding to the angle estimation value of each target based on the angle estimation value of each target. The application not only reduces the calculation complexity under the condition of few snapshots, but also improves the precision of parameter estimation.

This application discloses an adaptive singular value decomposition method for underwater acoustic signal denoising and its application. The method includes: Step 1) acquiring the noisy underwater acoustic signal to be analyzed; Step 2) constructing an m×n dimensional Hankel matrix based on the underwater acoustic signal, performing singular value decomposition on the Hankel matrix to obtain a singular value diagonal matrix; Step 3) obtaining the curvature of each singular value in the singular value diagonal matrix, determining a threshold based on the curvature, and acquiring the singular values ​​within the threshold as valid singular values; Step 4) retaining the valid singular values ​​in the singular value diagonal matrix, setting the other singular values ​​to zero, and updating the singular value diagonal matrix; Step 5) reconstructing the denoised Hankel matrix using the updated singular value diagonal matrix, selecting all elements of the first row of the denoised Hankel matrix and the m-1 elements from the nth column of the second row to the nth column of the mth row for restoration, to obtain the denoised underwater acoustic signal.