36 results about "Singular matrix" patented technology

The invention relates to a matrix decomposition device and method based on a memristor cross array, which are suitable for efficient hardware implementation of singular value decomposition (SVD), the memristor cross array receives a conductance value mapped by a Grubrum matrix constructed by a to-be-decomposed matrix and a bit line input voltage converted by a bit line column vector, and outputs a corresponding current; converting the corresponding current into a voltage vector; performing normalization processing on the voltage vector to obtain a normalized vector; judging whether convergence occurs or not; performing normalization processing on the steady-state voltage vector to obtain a final output vector; and according to the final output vector, main characteristic values are extracted based on a first normalization circuit, and main singular values and corresponding singular matrixes are calculated. Compared with the prior art, the high parallel computing characteristic of the memristor cross array is utilized, efficient operation and hardware acceleration of matrix decomposition are achieved, the method has the advantages of being low in power consumption, high in speed and good in expansibility, and the method is suitable for application scenes such as artificial intelligence, signal processing and large-scale matrix operation.

The application discloses a pedestrian re-identification-based metric loss framework improvement method and related equipment, and the method comprises the following steps: dimension reduction processing is performed on a covariance matrix fitting overall distribution of different categories of samples by using a diagonalization method; diagonal elements of the covariance matrix of different categories are stored by using a proxy matrix, and the uncertainty range of the sample is mapped into a feature space by the proxy matrix; singular value decomposition is performed on an output layer parameter matrix of a feature extraction network by using an SVD decomposition network; a left singular matrix is used to replace the original parameter matrix, the correlation of the Euclidean distance of the feature vector is not changed, the feature vector is projected into an orthogonal feature solution space, and the correlation decoupling of the latitude of the feature vector is completed; and the improvement of the metric loss framework is completed according to the extracted dimension correlation decoupled feature vector. The application reduces the calculation complexity and model complexity of the loss function, and improves the training efficiency and model performance.

The invention relates to an underwater acoustic positioning method and device, equipment and a storage medium. Comprising the following steps: acquiring position coordinates of an underwater target at a first moment according to a four-buoy positioning method; setting the position coordinate as an iteration initial value of a Levenberg-Marquardt algorithm; constructing a residual equation set by adopting a three-buoy positioning method; and calculating the position coordinates of the underwater target at the second moment according to the iterative initial value and the residual equation set by adopting a Levenberg-Marquardt algorithm. Therefore, when the Levenberg-Marquardt algorithm is adopted to calculate the position coordinates of the underwater target at the second moment, the iterative initial value of the Levenberg-Marquardt algorithm is the position coordinates of the underwater target at the first moment, and the position coordinates are obtained through four-buoy positioning. Therefore, the accuracy of the position coordinates of the underwater target at the first moment is relatively high, so that the iterative initial value of the Levenberg-Marquardt algorithm is closer to a real solution, the occurrence of a singular matrix is avoided, and the positioning accuracy of the underwater target is effectively improved.

The invention provides a spatial domain-polarization domain multi-dimensional parameter estimation method for a bistatic polarization FDA-MIMO radar, and the method comprises the steps: constructing a dual-system radar system composed of a polarization MIMO radar and a polarization FDA-MIMO radar, and transmitting a signal and receiving an echo signal through the system; calculating a covariance matrix of the echo signal and then decomposing the covariance matrix to obtain a decomposition formula comprising a signal subspace and a noise subspace; constructing a selection matrix by using the signal subspace and the rotation invariant feature of the array element, and then estimating the space angle of the target signal; and constructing a non-singular matrix by using the signal subspace, estimating a polarization parameter of the target signal, and solving a distance parameter of the target signal by combining the angle parameter and the polarization parameter. According to the invention, a polarized MIMO and polarized FDA-MIMO dual-mode working system is utilized to realize joint sensing of target space position information and polarization information, and decoupling and high-precision estimation of multi-dimensional parameters can be effectively realized.

A compression device (101) is configured so as to comprise: a matrix acquisition unit (1) that sequentially acquires partial feature matrices for each of a plurality of partial images obtained by dividing an image; and a first global matrix calculation unit (3) that calculates a first global singular value matrix that is a first global singular value matrix in which the first partial feature matrix acquired by the matrix acquisition unit (1) is decomposed into singular values and which includes the singular value of the first partial feature matrix, and a first global right singular matrix that is a first global right singular matrix that includes a right singular vector corresponding to the singular value of the first partial feature matrix. In addition, the compression device (101) is provided with: a target matrix calculation unit (4) that calculates a b-th target matrix (b = integer 2 or greater) which is the b-th matrix subject to singular value decomposition, and which includes a b-th partial feature matrix which is the b-th partial feature matrix acquired by the matrix acquisition unit (1), a (b-1)th global singular value matrix, and a (b-1)th global right singular matrix; and a second global matrix calculation unit (5) that decomposes the b-th target matrix calculated by the target matrix calculation unit (4) into singular values, calculates the singular value matrix of the b-th target matrix including the singular values of the b-th target matrix as a b-th global singular value matrix, and calculates the right singular matrix of the b-th object matrix including a right singular vector corresponding to the singular value of the b-th target matrix as a b-th global right singular matrix.

The invention relates to a tubular reactor temperature control method under the perspective of a singular partial differential equation. The method comprises the following steps: establishing a tubular reactor temperature concentration reaction rate system model described by a singular partial differential equation system; simplifying the temperature concentration reaction rate system model of the tubular reactor into a singular partial differential equation system; carrying out approximate linearization on the singular partial differential equation system through a fuzzy method, and designing an integral sliding mode surface containing a singular matrix; designing a sliding mode controller; and respectively analyzing the sliding mode motion accessibility and the index tolerance of the singular partial differential equation system based on a Lyapunov method to obtain a sliding mode controller gain, and applying the sliding mode controller gain to the sliding mode controller to complete the control of the temperature of the tubular reactor. According to the method, the defect that a traditional bivariate model cannot explicitly represent dynamic coupling of the reaction rate can be thoroughly overcome, the inhibition capability on the temperature runaway phenomenon is improved from the source, and the safety accident risk is remarkably reduced.

The application belongs to the technical field of scientific and technological information processing, and provides a method and system for determining a cause-effect relationship based on a scientific and technological information analysis knowledge graph. The method comprises the following steps: determining a first verb, a second verb and a scientific and technological information text segment to be processed, performing vector conversion to obtain a first verb embedding vector, a second verb embedding vector and a first sentence embedding vector, performing vector merging and attention operation to obtain a first transformation matrix, performing first transformation processing on the first transformation matrix to obtain a second sentence embedding vector; after performing second transformation processing on the first sentence embedding vector and the second sentence embedding vector, performing SVD decomposition processing, inputting the obtained left singular matrix into a verb cause-effect relationship prediction model, and outputting a cause-effect relationship analysis result of the first verb and the second verb. The application can focus on learning relevant key information of the first verb and the second verb, reduce information redundancy, and improve the classification accuracy of the cause-effect relationship.

The present invention provides a target detection model optimization method, apparatus, medium, and equipment. The method includes: S1, selecting a target detection model suitable for deployment on an edge device; S2, decomposing the two-dimensional matrix corresponding to the weight tensor of each convolutional layer in the target detection model into a left singular matrix, a diagonal singular value matrix, and a right singular matrix; S3, selecting the first R singular values ​​from the diagonal singular value matrix, and retaining the corresponding left singular vectors and right singular vectors; S4, reconstructing the weight tensor of the convolutional layer; S5, evaluating the target detection model to determine whether the target detection model meets the requirements of the edge device; if so, taking the current target detection model as the optimized target detection model; otherwise, adjusting R and returning to S2. In this way, the number of parameters used in the target detection model can be reduced, and while taking into account the accuracy of the model, the scale of the target detection model can be reduced so that it can be deployed on resource-constrained devices.

The compression device (101) is configured to include: a matrix acquisition unit (1) that sequentially acquires a partial feature matrix for each of a plurality of partial images into which an image is segmented; and a first global matrix calculation unit (3) that performs singular value decomposition on the first partial feature matrix acquired by the matrix acquisition unit (1), i.e., the first partial feature matrix, and calculates a first global singular value matrix containing the singular values ​​of the first partial feature matrix as the first global singular value matrix, and a first global right singular matrix containing the right singular vectors corresponding to the singular values ​​of the first partial feature matrix as the first global right singular matrix. In addition, the compression device (101) includes: an object matrix calculation unit (4) that calculates a matrix that is the object matrix of the bth singular value decomposition, which includes the bth partial feature matrix obtained by the matrix acquisition unit (1) (b is an integer of 2 or more), the (b-1)th global singular value matrix, and the (b-1)th global right singular matrix; and a second global matrix calculation unit (5) that performs singular value decomposition on the object matrix of the bth object matrix calculated by the object matrix calculation unit (4), calculates the singular value matrix of the object matrix of the bth object matrix that includes the singular values ​​of the object matrix of the bth object matrix as the global singular value matrix of the bth object matrix, and calculates the right singular matrix of the object matrix of the bth object matrix that includes the right singular vector corresponding to the singular value of the object matrix of the bth object matrix as the global right singular matrix of the bth object matrix.

The present invention discloses a method and device for dividing a practical stability domain of a wind power grid-connected system considering probability characteristics, which relates to the field of data processing technology. By performing probability modeling analysis on the uncertainties of the active power and load power of a wind farm, a first probability parameter of the active power change and a second probability parameter of the load power change are obtained. The probability parameter item is updated based on the first probability parameter and the second probability parameter. When it is determined that the value of the probability parameter item reaches a specific critical value, it is judged whether the Hessian matrix of a differential equation group at an equilibrium point in an equilibrium point set is a singular matrix. If it is a singular matrix, a bifurcation analysis is performed on the wind power active power and the load reactive power of a target node to obtain a bifurcation curve and each bifurcation point. The practical stability domain is divided based on each bifurcation point and the bifurcation curve, so that the influence of the uncertainty of the wind power injected active power and the load reactive power on the practical stability domain is taken into account when dividing the practical stability domain.

The application relates to the technical field of data processing, and discloses a multi-dimensional data intelligent management method for regional safety management software, which comprises the following steps: obtaining a left singular matrix, a right singular matrix, a diagonal line matrix and eigenvectors of the left singular matrix and the right singular matrix of a two-dimensional driving data matrix; obtaining a first kind of reservation degree, which is divided into time periods; obtaining an adjusted voting value and marking important time periods; obtaining an influence weight value, determining a first eigenvector, obtaining a reconstructed two-dimensional driving data matrix of the first eigenvector, obtaining a reconstructed influence weight value according to the reconstructed two-dimensional driving data matrix, and then obtaining a feature weight of the first eigenvector; and determining data that needs to be reserved and stored according to the feature weight, so as to realize multi-dimensional data intelligent management for the regional safety management software. The application aims to solve the problem of low multi-dimensional data intelligent management efficiency caused by excessive redundant data.

The present application discloses a method for calculating the equivalent resistance between nodes in a resistor network, which includes: determining the admittance matrix G of the resistor network; converting the admittance matrix into a non-singular matrix #imgabs0# based on the permutation matrix P; performing LDL on the non-singular matrix #imgabs1# T Decompose and obtain the lower triangular matrix L with diagonal 1 and its elimination tree, diagonal matrix D; determine the equivalent resistance R to be calculated in the resistor network ij Node i, node j, and the standard unit vector e corresponding to node i i , the standard unit vector e corresponding to node j j With the permutation matrix P and the lower triangular matrix L, establish the equation group to be solved LXi=P T e i , LX j =P T e j , X i 、X j is the solution of the system of equations to be solved; based on the elimination tree, determine the solution X of the system of equations to be solved i 、X j The non-zero position of ; based on the solution of the equation system X i 、X j The non-zero position of the equation to be solved is calculated i 、X j ; Based on the solution X of the system of equations to be solved i 、X j And the diagonal matrix D, calculate the equivalent resistance R between node i and node j ij .

According to an aspect, there is provided an apparatus configured to perform the following. The apparatus obtains an approximate effective channel matrix for a radio channel between the apparatus, acting as a transmitter, and a receiver. The apparatus calculates an eigenvalue decomposition, EVD, of a matrix product of the approximate effective channel matrix and a conjugate transpose of the approximate effective channel matrix and determines, based on the EVD, a left singular matrix of a singular value decomposition, SVD, of the approximate effective channel matrix and a diagonal matrix of singular values of the SVD of the approximate effective channel matrix. The apparatus calculates eigenvectors of the approximate effective channel matrix based on the approximate effective channel matrix and the left singular matrix of the SVD.

The invention provides a distributed power model updating method and device based on model incremental training and electronic equipment. According to the implementation scheme, when local power incremental data of distributed power nodes are insufficient, singular value decomposition is carried out on a local model parameter matrix of a local power model in the distributed power nodes, and a left singular matrix, a diagonal matrix and a right singular matrix are obtained; respectively extracting corresponding column vectors from the left singular matrix and the right singular matrix based on main singular values in the diagonal matrix to obtain an important parameter matrix; and determining a non-important parameter matrix based on a difference matrix between the local model parameter matrix and the important parameter matrix, and performing low-rank decomposition on the non-important parameter matrix to obtain a local first low-rank parameter matrix and a local second low-rank parameter matrix, thereby performing global model updating and updating the local power model of each power node. By adopting the method, the model training speed and precision can be improved, and the model is prevented from drifting.

The invention belongs to the field of wireless communication, and discloses a pre-coding method for a multi-user MIMO-OTFS system. The method comprises the following steps: establishing an optimization problem according to a pre-coding matrix, a complete channel matrix, a system noise variance, a user weight and a total transmitting power constraint in a multi-user MIMO-OTFS communication model; a complete channel matrix in the optimization problem is approximated to be an LoS path approximation channel matrix, a matrix SVD is obtained, precoding matrix design is carried out based on a right singular matrix in an SVD decomposition result, and a directional component and a power component are included; substituting the designed precoding matrix into an optimization problem, constructing a sub-optimization problem about the power component and the complex gain of each sub-channel, optimizing the transmitting power of each sub-channel, and outputting the optimal power component; and constructing a final precoding matrix according to the direction component and the optimal power component. According to the method, efficient utilization of system resources can be realized, and the calculation complexity can be greatly reduced while the WSR performance is ensured.

The invention discloses an imaging satellite task synthesis method and system based on maximum weight group search, a product, equipment and a storage medium, belongs to the technical field of imaging satellite task planning, and solves the problem that target points as many as possible can not be synthesized in each observation in the prior art. And the technical problem of influence of different orbits, breadths and data delivery requirements on task synthesis in a satellite constellation can be flexibly solved. Extracting parameter information of the visible imaging window; constructing a composite matrix; and judging whether the composite matrixes are singular matrixes or not, if so, outputting all the established composite strips, and ending the process. And if not, establishing a synthesis strip of the maximum weight group, deleting the tasks in the synthesis strip of the maximum weight group and the tasks conflicting with the tasks, updating the synthesis matrix, and then re-judging whether the synthesis matrix is a singular matrix or not. The imaging satellite task synthesis method is used for synthesizing target points as many as possible in each observation, and can flexibly cope with different synthesis targets.

The invention provides a sea clutter suppression method based on K-means clustering, and the method comprises the steps: carrying out the preprocessing of a radar echo signal, and carrying out the singular value decomposition of a fast and slow time dimension pulse pressure data matrix; performing singular value spectrum distribution analysis on the singular value matrix to obtain a singular value, performing spatial correlation analysis on the right singular matrix to obtain a spatial correlation coefficient, and performing average Doppler estimation on the left singular matrix to obtain an autocorrelation estimation value; constructing a three-dimensional space of {a singular value, a spatial correlation coefficient and an autocorrelation estimation value}; three clustering centers are randomly set, and K-means clustering is carried out to complete classification formed by clutter, target and noise signals; and eliminating clutter subspaces, and reconstructing a fast and slow time dimension pulse pressure data matrix to complete clutter suppression.

The present invention discloses a patch clamp electrophysiological data processing method and system, which obtains ion channel current data by collecting patch clamp microelectrodes; removes baseline drift from the ion channel current data by an adaptive quadratic linear fitting function to obtain baseline-corrected current data; discretizes and performs high-frequency filtering on the baseline-corrected current data based on a preset sampling frequency to obtain a filtered current signal sequence; calculates an optimal embedding dimension by a false nearest neighbor method, calculates an optimal delay time based on a mean shift method, and performs second phase space reconstruction using the filtered current signal sequence, the optimal embedding dimension, and the optimal delay time to obtain an optimal vector set and a phase space reconstruction matrix; calculates a principal component vector set and the information entropy corresponding to each vector by a singular value decomposition algorithm and the Shannon formula, obtains a principal vector sequence by discrimination using a preset information entropy threshold, and obtains electrophysiological characteristic data by singular matrix reconstruction.

The present invention discloses a generalized Schur decomposition delay estimation method for GNSS array tensor modeling. Specifically, the method comprises the following steps: first, calculating the HOSVD of the signal tensor, compressing the core tensor and singular matrix, performing a generalized Schur decomposition on the two frontal slices of the core tensor, and then upper triangulating the matrix of the frontal slice of the core tensor; using LSKRF to decompose the matrix and solve the factor matrix, and then using the factor matrix to find the corresponding Loss of Sight (LOS) signal components; then, calculating the signal amplitude based on the structure of the signal tensor; interpolating to obtain the post-correlation interpolation function of the correlator and the LOS signal, and estimating the LOS delay based on the interpolation function peak search. The present invention achieves high-precision and low-computational-complexity delay estimation of GNSS signals, with higher robustness and lower computational complexity.

The invention discloses a matrix dimension reduction circuit and system based on singular value decomposition, and relates to the technical field of singular value decomposition. The circuit comprises a first on-chip cache unit used for obtaining and caching a matrix to be subjected to dimension reduction; the matrix decomposition calculation unit is connected with the first on-chip cache unit and is used for performing singular value decomposition on the matrix to be subjected to dimension reduction to obtain a first singular value matrix and a first right singular matrix; the matrix dimension reduction unit is connected with the matrix decomposition calculation unit, and is used for performing dimension reduction processing on the first singular value matrix and the first right singular matrix to obtain a second singular value matrix and a second right singular matrix, and obtaining a left singular matrix according to the matrix to be subjected to dimension reduction and the second right singular matrix; obtaining a dimension reduction matrix according to the second singular value matrix, the left singular matrix and the second right singular matrix; and the second on-chip cache unit is connected with the matrix dimension reduction unit and is used for caching and outputting the dimension reduction matrix.

The embodiments of the present specification disclose a kind of based on differential privacy's data to be shared processing method, device, equipment and medium. Scheme can include: obtaining original data matrix;The original data matrix contains data to be shared;The data to be shared contains privacy information;The singular value decomposition is carried out to the original data matrix, and left singular matrix, diagonal matrix and right singular matrix are obtained;Based on the preset perturbation value, the diagonal matrix is perturbed, and the perturbed diagonal matrix is obtained;Based on the left singular matrix, the perturbed diagonal matrix and the right singular matrix, perturbed data matrix is obtained;Using the preset Gaussian random matrix, the perturbed data matrix is randomly transformed, and covariance matrix is obtained;The covariance matrix is used to publish the data to be shared.

The application discloses a kind of amplitude and phase high-precision estimation method and device suitable for fast dynamic change signal, the method of the application includes the discrete signal sequence of continuous signal sampling, constructs Hankel matrix and decomposes left singular matrix U, diagonal matrix D, right singular matrix V;The estimated value of the number of frequency components p is estimated by D, combined with extracting the subspace of right singular matrix V and using least square method to obtain diagonal matrix Q;The damping coefficient of frequency component is calculated by Q and constructs system matrix A;The dynamic phasor coefficient vector S is calculated by A and discrete signal sequence using least square method;The amplitude and phase of continuous signal are calculated by S and damping coefficient.The application aims at solving the problem that the main lobe overlap of dense harmonic / interharmonic with small frequency interval in power system causes spectral leakage interference, extremely serious even can submerge part of main spectrum line, and then lead to the accuracy of spectral leakage affecting the amplitude and phase estimation of dynamic change signal.

The invention relates to a micro-grid information processing method and device, computer equipment, a computer readable storage medium and a computer program product. The method comprises the following steps: acquiring operation information of a micro-grid system and a state space model of the micro-grid system under the influence of network attack and external interference; performing matrix change on the state space model by using a preset nonsingular matrix to obtain a first subsystem model of the micro-grid system under the influence of the network attack and a second subsystem model of the micro-grid system under the external interference; performing sliding mode processing on the first subsystem model and the second subsystem model based on a sliding mode observer to obtain a first observer dynamic equation and a second observer dynamic equation; and determining a network attack estimation value of the micro-grid system based on the operation information and the first observer dynamic equation, and determining an external interference estimation value based on the operation information and the second observer dynamic equation. By adopting the method, the system information processing accuracy can be improved.

The present invention provides a distributed power model update method, device and electronic device based on model incremental training. The implementation scheme is as follows: when the local power incremental data of the distributed power node is insufficient, the local model parameter matrix of the local power model in the distributed power node is subjected to singular value decomposition to obtain a left singular matrix, a diagonal matrix and a right singular matrix; based on the main singular values ​​in the diagonal matrix, the corresponding column vectors are extracted from the left singular matrix and the right singular matrix respectively to obtain an important parameter matrix; based on the difference matrix between the local model parameter matrix and the important parameter matrix, the non-important parameter matrix is ​​determined, and the non-important parameter matrix is ​​subjected to low-rank decomposition to obtain a local first low-rank parameter matrix and a local second low-rank parameter matrix, so as to perform a global model update and then update the local power model of each power node. The present invention can improve the speed and accuracy of model training and avoid model drift.

The application discloses a composite signal denoising method for biological cell adhesion force extraction. Firstly, a preliminary treatment is performed on a living cell mechanics parameter collected under a physiological state to obtain a cell force-displacement curve. A Hankle matrix is used to construct the force-displacement data to a two-dimensional matrix space. Secondly, a matrix H constructed by the force-displacement data is decomposed according to a singular value decomposition theory to obtain corresponding left and right singular matrices and singular values arranged in descending order. A slope of singular value change is calculated k Then, several larger singular value points are reserved, and the singular value points are used to approximate the original matrix H to complete matrix reconstruction. A Symlets7 wavelet is selected as a base function to perform four-layer wavelet decomposition on the force-displacement data after singular value decomposition denoising. A hard threshold function is used to perform wavelet reconstruction on the force-displacement data, so that the denoised force-displacement curve is obtained.

The invention discloses a power distribution network data compression and reconstruction method based on high-order singular value decomposition, and belongs to the technical field of power distribution network data compression. Comprising the following steps: determining expectation and reconstruction error boundaries of same-type power distribution network data; acquiring the same type of power distribution network data, and judging whether the same type of power distribution network data is structured data; constructing a third-order data tensor, and performing standardization processing to obtain a standardized data tensor; performing high-order singular value decomposition on the standardized data tensor to obtain a left singular matrix, and calculating an initial truncation value; generating a core tensor and a factor matrix, multiplying the core tensor and the factor matrix to obtain a standardized reconstructed data tensor, and de-standardizing the standardized reconstructed data tensor to obtain a reconstructed data tensor; calculating a compression ratio and a reconstruction error; and judging whether an error requirement is met or not, and sending the core tensor and the factor matrix. According to the method, the characteristics of each mode in the multi-dimensional data can be captured, the problem that the intrinsic structure of a data space is damaged in the compression process is solved, and the operation efficiency and accuracy are improved.

The present invention discloses a method for implementing cross-domain physical layer security. First, pre-processing and post-processing matrices are designed for the MIMO-OTFS system. Specifically, the equivalent channel matrix of the legitimate channel (DD) domain is subjected to singular value decomposition (SVD). The right singular matrix is ​​selected to design the pre-processing matrix, and the left singular matrix is ​​selected to design the post-processing matrix. Secondly, the null space matrix of the time domain AN is calculated according to the MIMO-OTFS system processing flow and a zero-mean circularly symmetric complex Gaussian vector is generated, finally generating the time domain AN. The AN design of this method can achieve self-cancellation in the DD domain of the legitimate receiver, while forming interference at the eavesdropper, thereby widening the quality gap between the legitimate channel and the eavesdropped channel, and achieving signal security for the MIMO-OTFS system.

The present invention relates to an apparatus, a computer-readable storage medium, a method, and a system for a cryptosystem based on singular matrices. In particular, an encryption scheme of the cryptosystem comprises a generation of singular matrices representing the whole or a part of an information block to obtain a ciphertext and a private key to be sent to an intended receiver. In similar manner, a decryption scheme of the cryptosystem comprises an inversion of singular matrices representing the whole or a part of said ciphertext to obtain the information block from a sender. Certain embodiments comprising of an at least one processor, an at least one memory, an at least one storage medium, an at least one communication implement, and an at least one display implement is used to implement the present invention.

A method for beamforming is disclosed. The method performed by a beamformee may include receiving, from a beamformer, a sounding packet, computing estimated channel matrices based on the sounding packet, computing singular value decomposition (SVD) based on the estimated channel matrices, computing normalized right singular matrices based on SVD, computing precoding matrices based on a decompression scheme and the normalized right singular matrices, computing diagonal unitary matrices based on a product of the channel matrices and the precoding matrices, computing, during a beamforming phase, modified beamformed channel matrices based on the diagonal unitary matrices, and receiving, from the beamformer, a beamformed data transmission based on the modified beamformed channel matrices.

The application discloses a kind of underwater target positioning methods based on underwater acoustic particle velocity polarization processing.The method includes: arranging vector hydrophone array and carrying out underwater acoustic signal acquisition, and write the vector received signal of vector hydrophone array into tensor form;The tensor received signal of vector hydrophone array is carried out high-order singular value decomposition and the noise subspace of each dimension information matrix is solved by truncation to n module expansion left singular matrix;Array manifold matrix of vector hydrophone array is constructed and is written into polarization array manifold tensor;Azimuth-polarization two-dimensional spectrum search is carried out using array manifold tensor and noise subspace, and the azimuth and polarization parameters of the detected target are obtained from two-dimensional spectrum peak value.The underwater target positioning method provided by the application can accurately estimate the polarization parameters and the angle of arrival of incident signal, which helps to complete the underwater acoustic signal information based on frequency, wave number, mode and other characteristics by means of polarization parameters, and significantly improves the underwater target detection capability.