63 results about "Toeplitz matrix" patented technology

An azimuth ambiguity suppression method for a spaceborne SAR in spatially non-uniform sampling relates to the technical field of radars. The method includes the following steps: step 1: compensating for a phase difference between the channels according to the locations of transmitting and receiving stations for channel echoes; step 2: calculating an equivalent azimuth sampling location of each channel at the moment of each pulse transmission; step 3: sorting the equivalent azimuth sampling locations of all channels and their corresponding echoes after the phase difference between the channels is compensated in ascending order; and step 4: calculating reconstructed echoes from the sorted equivalent azimuth sampling locations and corresponding echoes after the phase difference between the channels is compensated, by using an adaptive weight-conjugate gradient-Toeplitz matrix (ACT) algorithm. According to the present disclosure, a relative distance between the channels is not required to remain unchanged, which greatly expands the scope of application of azimuth multi-channel SAR echo reconstruction.

The invention discloses an unmanned aerial vehicle group detection and resolution method and device based on KT and DeANM. The method comprises the following steps: carrying out spatial-dimension digital beam forming conversion on echo signals to obtain three-dimensional echo signals; searching the acceleration and the velocity fuzzy number of the target according to the three-dimensional echo signal, and compensating the preliminarily filtered three-dimensional echo signal based on a keystone transformation algorithm; performing coarse estimation on the distance, the speed and the angle of the target according to the compensated three-dimensional echo signal; extracting a distance dimension and an angle dimension of the compensated three-dimensional echo signal according to the distance coarse estimation value, the speed coarse estimation value and the angle coarse estimation value, and performing inverse Fourier transform to obtain a two-dimensional echo signal; and performing decoupling atom norm minimization processing on the two-dimensional echo signal to obtain a Toeplitz matrix of the target, and performing Vandermonde decomposition on the Toeplitz matrix of the target to obtain a distance super-resolution estimated value and an angle super-resolution estimated value of the target. According to the invention, high-precision detection and resolution of the unmanned aerial vehicle group can be realized.

The invention provides a continuous wave sonar direct wave suppression method, device and equipment and a storage medium, and the method comprises the steps: intercepting a target segment signal from a received signal, the target segment signal being composed of continuous Nw sampling points in the received signal; an estimation channel corresponding to the target segment signal is solved through a basis tracking algorithm, a dictionary matrix of the basis tracking algorithm is an Nw * Nw Toeplitz matrix, the first column is the first to Nw sampling points of the transmitted signal, and the second to Nw elements of the first row are zero; if the current estimation channel meets a judgment condition, the current estimation channel is determined as a final channel, and the judgment condition includes that the maximum element value in the estimation channel is larger than a first threshold value; reconstructing a direct wave and a multipath signal thereof according to the transmitted signal and the final channel; and subtracting the direct wave and the multipath signal thereof from the received signal to obtain a suppression result. According to the invention, on the premise of ensuring that the target echo energy is not lost, the sidelobe interference of the direct wave is effectively eliminated.

The invention discloses a mechanical fault diagnosis method based on maximum correlation kurtosis depth unrolling, and relates to the technical field of mechanical state monitoring, and the method specifically comprises the following steps: obtaining a vibration signal sliding window segmentation calculation kurtosis, and generating a window priority sequence through cross-correlation correction sorting; a high-quality window is selected to be combined with a rotating speed integral and a fast Fourier transform detection angle period to form an angle domain period boundary set, a segmentation signal calculation error is subjected to quasi-Newton optimization de-convolution to obtain a deep de-convolution fault feature signal, a Toeplitz matrix is constructed, and a multi-layer fault feature vector is weighted and extracted; according to the method, vibration signal sorting is segmented through a sliding window, so that impact positioning is enhanced, noise is reduced, definition is improved, an angle domain period is dynamically recognized, feature synchronization is achieved, error optimization unrolling is performed, impact recovery is enhanced, and feature dimensions are expanded through matrix weighted mapping. And the recognition precision and adaptability are improved through multi-scale aggregation.

The invention discloses an unmanned aerial vehicle coherent signal direction finding method based on a uniform circular array, and relates to a radio direction finding technology, and the method comprises the steps: defining related parameters, and receiving an unmanned aerial vehicle signal through the uniform circular array; preprocessing the matrix T, and converting a uniform circular array in an original array element space into a virtual uniform linear array in a mode space; defining a vector z, and constructing a response Toeplitz matrix through the vector z; on the basis of the transformed signal, a covariance matrix of a corresponding Hermitian Toeplitz matrix reconstructed from the observation space is calculated; according to the calculated covariance matrix, defining a MUSIC algorithm to search a maximum spectrum peak; and converting the spectral peak estimation of the MUSIC algorithm into a polynomial by using the ROOT-MUSIC algorithm, so as to determine the estimation of the direction of arrival by using the root of the polynomial. According to the method, the defect of direction finding of the unmanned aerial vehicle coherent signals by a traditional direction finding method is overcome, successful separation of the unmanned aerial vehicle coherent signals is ensured, and the direction finding precision of the unmanned aerial vehicle coherent signals is improved.

The invention provides an intelligent metasurface-assisted non-line-of-sight target DOA estimation method based on atom norm minimization. After a receiving end obtains an echo signal, the echo signal is left multiplied by a known channel vector pseudo-inverse, the influence of an RIS backward channel is eliminated, and an angle estimation problem is converted into a convex optimization problem; a convex optimization problem is solved by using a CVX toolbox in Matlab, two one-dimensional Toeplitz matrixes respectively containing DOD and DOA information are constructed, the two one-dimensional Toeplitz matrixes are respectively subjected to one-dimensional Vandermonde decomposition to solve a signal angle, and joint estimation of the DOD and the DOA is realized. According to the method, the active regulation and control capability of the RIS is enhanced, so that the problem that DOA estimation precision and calculation efficiency in a traditional non-line-of-sight scene are difficult to consider at the same time is effectively solved, and the real-time performance of azimuth angle estimation is remarkably improved while relatively low calculation complexity is kept; and particularly under the conditions of low signal-to-noise ratio and low snapshot number, high-precision estimation of the target orientation can still be realized.

The application provides a uniform plane array FDA-MIMO three-dimensional target positioning method and system based on complete decoupling atomic norm minimization. The method converts the problem into a semi-positive programming solution by constructing a low-dimensional observation matrix and using complete decoupling atomic norm, and realizes joint estimation of distance, pitch angle and azimuth angle through Vandermonde decomposition. Compared with high complexity algorithms (VANM, etc.), the application significantly reduces the calculation amount by decomposing the Toeplitz matrix, and improves the single snapshot precision by using the multi-slice constraint mechanism. Compared with low complexity algorithms (3D-DANM), the application introduces an auxiliary variable matrix to restore the distance-angle coupling structure, avoids the performance loss caused by excessive relaxation, and significantly improves the estimation accuracy and multi-target resolution capability while ensuring the calculation efficiency.

The invention discloses an ultra-wideband radar super-resolution imaging method based on spectrum mode decomposition. The method comprises the following steps: acquiring radar observation data; constructing a radar observation data vector and a frequency dependence factor set, and defining a frequency spectrum mode matrix for each frequency dependence factor, so as to obtain a Toeplitz matrix and a single-mode frequency spectrum vector corresponding to an optimal solution through solving a convex optimization problem; estimating scattering parameters of each scattering center in the scene according to the Toeplitz matrix and the single-mode spectrum vector; and carrying out spectrum extrapolation and radar image calculation according to the scattering parameters so as to output a super-resolution radar image of the target according to a calculation result. The radar spectrum is subjected to mode decomposition according to different frequency dependence factors, the estimation accuracy of target parameters is improved, the spectrum extrapolation precision under the large frequency span is improved, and then the radar super-resolution imaging quality is improved.

The invention belongs to the technical field of direction of arrival, and relates to a sparse array coherent signal source DOA estimation method based on Toeplitz matrix reconstruction. Comprising the following steps: S1, assuming that a coherent signal source is incident to a sparse array, further constructing a sparse array observation signal model, and further solving a theoretical covariance matrix of the sparse array observation signal model; s2, performing solution operation on the theoretical covariance matrix in the S1; s2, on the basis of the S2, further constructing a Hermitian Toeplitz matrix through virtual array interpolation, and further realizing DOA estimation based on the virtual array interpolation; and S3, obtaining a virtual ULA based on the sparse array of the S1 by combining the S2, performing solution operation on the virtual ULA, and then constructing a Toeplitz matrix based on physical array interpolation so as to realize DOA estimation based on virtual array interpolation. According to the algorithm provided by the invention, the coherent signal source can be accurately estimated under the condition that the degree of freedom and the array aperture are not lost. Simulation experiment results show that the proposed algorithm is superior to the existing algorithm in the aspect of estimating the coherent source.

The present invention discloses a distributed array single snapshot direction finding method based on a quantum cheetah optimization mechanism of the watch law, which relates to the technical field of array signal processing. The technical key points of the present invention include: constructing a Hankle matrix as a pseudo covariance matrix by using the single snapshot data received by the first sub-array for rough estimation to obtain an initial target range, and then constructing a Toeplitz matrix by using the single snapshot data received by the entire array in combination with the maximum likelihood estimation method. By combining the continuous quantum optimization theory with the cheetah optimization mechanism, the continuous quantum evolution mechanism is used to further accelerate the convergence speed of the cheetah optimization algorithm; finally, based on the watch law, the discrete quantum optimization theory and the cheetah optimization mechanism are combined. Under the condition that the target signal source is known, the most suitable distributed array structure for the algorithm is solved, realizing the single snapshot accurate direction finding of the distributed array, improving the convergence speed of the algorithm, reducing the computational complexity, and improving the direction finding accuracy.

The purpose of this invention is to provide a method for DOA estimation of acoustic vector arrays based on quaternion matrix dimensionality reduction, comprising the following steps: constructing a CAACIS acoustic vector coprime array at the receiver; establishing a quaternion model of the received signal based on the array output; estimating the quaternion covariance matrix; performing dimensionality reduction on the quaternion covariance matrix to obtain a virtual array received signal model of the acoustic vector coprime array; reconstructing the Hermitian-Toeplitz matrix; performing eigenvalue decomposition on the Hermitian-Toeplitz matrix and constructing a spatial spectral function; and obtaining the DOA estimation result through spectral peak search. This invention addresses the problem of how to achieve multi-objective DOA estimation with both low complexity and good performance based on a quaternion framework under acoustic vector coprime arrays.

The invention discloses a mode identification method based on an unsupervised optimization covariance random subspace method. The method comprises the following steps: arranging a vibration sensor on a to-be-detected structure with noise interference or a weak excitation mode to obtain a structure dynamic response; constructing a Hankel matrix, calculating a covariance matrix Ri, and constructing a Toeplitz matrix based on the Ri; calculating an extended observable matrix Oi and an extended controllable matrix Gamma i based on the weighted Toeplitz matrix; defining the range of the row block number i and the model order N of the Toeplitz matrix; analyzing the sensitivity of the row block number i of the Toeplitz matrix and the model order N based on a parameter optimization index kP (i, N); the singular entropy increment of the weighted Toeplitz matrix T1i is calculated; calculating the singular entropy increment curvature of the weighted Toeplitz matrix T1i, and determining a critical model order Nc (i); calculating an accumulated parameter optimization index kappa P value from a minimum model order Nmin to a critical model order Nc (i) under the condition of different Toeplitz matrix row block numbers i; selecting a parameter combination {iopt, Nopt} corresponding to the minimum parameter as an optimal parameter; identifying a system matrix and determining modal parameters, and drawing an original stability diagram; and on the basis of DBSCAN clustering, automatically identifying each order of physical modality from candidate modalities containing noise interference. A corresponding system is also disclosed.

The application provides a self-adaptive signal beam forming method, device, equipment and storage medium. The method is based on a 2N+1 element uniform linear array receiving signal. The method comprises the following steps: a multiple Toeplitz matrix is constructed, and a covariance matrix is obtained by merging; a signal direction vector is calculated according to an eigenvalue and an eigenvector, and an expected signal direction vector is obtained by optimization; a noise power is estimated by using a minimum eigenvalue, a corresponding relationship between a signal power and the eigenvector is established, and an expected signal power is determined; a covariance matrix of mutually independent noise is reconstructed according to the noise power, the corresponding relationship and the expected signal power; and the expected signal direction vector and the reconstructed covariance matrix are brought into a Cap on beam former, and an optimal weight vector is calculated to control beam forming. The method improves the performance and reliability of the beam forming system in a complex signal environment by constructing a multiple Toeplitz matrix and using the characteristics of the multiple Toeplitz matrix for signal decorrelation and covariance matrix reconstruction.

A gridless coherent signal direction-of-arrival estimation method based on alternating projection according to the present invention is characterized in that: the patented method uses a hydrophone array installed on an underwater vehicle to receive signal data to construct a positive semi-definite matrix S0, and uses the projection method to alternately project S0 onto a rank-constrained set and a Toeplitz matrix with averaged main diagonal elements. Secondly, the convergence degree of S0 is judged, and then T i (u) is subjected to reduced-dimensional space smoothing-singular value decomposition. Finally, z is obtained by using the polynomial root-finding method, and the incident angle of the coherent signal is solved by using the corresponding mapping relationship. The patented method effectively realizes the angle estimation of coherent signals. Secondly, the polynomial root-finding method is used instead of spectral peak search, which effectively reduces the computational amount, improves the robustness and real-time performance of the algorithm, and has high estimation accuracy, having important engineering application value.

The application belongs to the technical field of radio positioning, and particularly relates to a kind of intelligent metasurface assisted user positioning method based on modified dimensionality reduction MUSIC.The method utilizes the phase reconfigurable characteristics of intelligent metasurface, constructs zero-mean orthogonal phase coefficients, separates out base station-user direct channel and base station-intelligent metasurface-user cascade channel, estimates intelligent metasurface-user channel under the assumption that base station-intelligent metasurface has good line-of-sight path, thereby calculates the left and right correlation coefficients of two channel sections, and reconstructs left and right pseudo-covariance matrix using the Toeplitz matrix of correlation coefficient, further obtains pseudo-covariance matrix, then performs eigenvalue decomposition on pseudo-covariance matrix, constructs modified dimensionality reduction MUSIC spatial spectrum using the orthogonality of signal space and noise space, thereby estimates the azimuth of base station-user and intelligent metasurface-user, and then calculates the user position using least square method.The application trades off higher estimation accuracy for complexity.

The invention provides an orthogonal propagation operator direction-of-arrival estimation method based on a Newton interpolation method, a medium and equipment. Firstly, an array antenna of a uniform linear array structure is used for receiving a signal, and a received signal is obtained at a receiving end; then, calculating to obtain a received signal covariance matrix by using the received signal; then, constructing a Toeplitz matrix by using the received signal covariance matrix; secondly, using off-diagonal elements in the first row and the first column of the Toeplitz matrix, estimating diagonal elements by using a Newton interpolation method, and replacing the original diagonal elements; and finally, DOA estimation is carried out by using an orthogonal propagation operator method. Compared with cubic spline interpolation and linear prediction interpolation methods, the method provided by the invention not only maintains the original DOA estimation performance, but also has lower algorithm complexity.

The invention discloses a coherent signal source DOA estimation method based on Toeplitz reconstruction and Schur decomposition, and the method comprises the following steps: S1, array receiving signal acquisition and covariance matrix calculation, S2, first Toeplitz matrix construction, S3, covariance matrix Schur decomposition and signal matrix construction, S4, second Toeplitz matrix construction, and S5, matrix fusion and DOA estimation. A first Toeplitz matrix is constructed; the method comprises the following steps: constructing a first Toeplitz matrix, performing Schur decomposition on a covariance matrix, constructing a signal matrix, constructing a second Toeplitz matrix by taking a mean value of each diagonal line of the signal matrix as a reference, fusing the two Toeplitz matrixes, performing subspace separation and spectral peak search on the fused matrix to realize DOA estimation, and performing simulation verification to obtain the DOA estimation result. The algorithm provided by the invention can effectively reduce the DOA estimation error of the coherent signal source under the scenes of low signal-to-noise ratio and few snapshots, and can effectively improve the stability of the estimation result.

The present invention provides a method and apparatus for optical property modeling. The method includes dividing all microstructures in the periodic space into a plurality of thin slices in the z direction; obtaining each closed region projected by the thin slices of the same layer on the xy plane of the periodic space, and representing the boundaries of the respective closed regions as respective polygons; obtaining the x coordinates and y coordinates of the vertices of each polygon to form an x coordinate set and a y coordinate set, respectively forming candidate integral subintervals from the coordinates in the x coordinate set and the y coordinate set, obtaining a target integral subinterval from the candidate integral subintervals, and calculating the Fourier coefficients of the dielectric constant of the periodic medium and the Toeplitz matrix of the dielectric constant of the periodic medium according to the target integral subinterval, and finally realizing the optical property modeling of the periodic medium. According to the target integral subinterval, the present invention calculates the theoretical spectral data, which improves the efficiency of obtaining the theoretical spectral data while ensuring the accuracy.

The invention relates to the technical field of water source monitoring systems, in particular to a water source monitoring system based on intelligent matching and inspection management. The cloud platform module is wirelessly connected with the user side module; based on the user data and the water source information, intelligent matching and scheduling assignment are executed; the intelligent matching module is in data connection with the cloud platform module and encodes the water source position based on the coherence group theory of topology; quantizing a water source state by using a Bes sel function; constructing an intelligent matching scoring model by using the Lie group and Lie algebra theories; performing optimal matching selection by applying an elliptic curve theory; a matching result is optimized by using a Toeplitz matrix; the inspection management module is used for generating and assigning inspection tasks; recording and analyzing an inspection result; updating water source state information in real time; the data analysis module is in data connection with the cloud platform module and the inspection management module; and a water source use report and early warning information are generated, so that water source matching can be quickly and accurately performed.

The present application relates to the technical field of signal processing, in particular to a kind of sparse low-rank decomposition DOA estimation method based on virtual array interpolation, establish receiving signal model;The sampling covariance matrix of receiving signal model is calculated, and vectorization operation is carried out to sampling covariance matrix, since there is hole for a non-uniform virtual array in virtual array, virtual sensor is inserted at the hole position of virtual array, form virtual uniform array, reconstruct virtual uniform array receiving signal model as Toeplitz matrix, and carry out low-rank and sparse matrix decomposition into signal covariance matrix and noise covariance matrix;Signal covariance matrix is reconstructed using low-rank matrix recovery theory, and the expected signal covariance matrix is recovered, it is carried out eigenvalue decomposition using MUSIC algorithm, and the target DOA is estimated.This application makes full use of all virtual array element information, avoids the inaccuracy of DOA estimation caused by information loss.

The invention discloses an FDA-MIMO sonar fast meshless joint distance-angle estimation method and product based on a two-dimensional generalized fast interior point method, and the method comprises the steps: s1, carrying out the frequency mixing and matched filtering of a received signal, and obtaining an expanded virtual received signal model; s2, an estimation problem is concluded as an NsCP problem, and related parameters of an interior point method are initialized; s3, updating an original variable and a dual variable by using a limited memory BFGS algorithm and an augmented KKT condition, performing 2D-FFT fast feasible region cone verification on the obtained variables, repeating the step until a result is in a feasible region, and turning to s4; s4, carrying out convergence check on the obtained variables, if convergence is carried out, obtaining an optimal solution of the NsCP problem, and turning to s5, otherwise, turning to step 3; and s5, according to the optimal solution, sparse atoms are retrieved through Vandermonde decomposition of the multi-stage Toeplitz matrix, and distance and angle estimation of the target is extracted by using a linear corresponding relation.

The invention relates to a meshless azimuth super-resolution sparse aperture ISAR (inverse synthetic aperture radar) imaging method and a meshless azimuth super-resolution sparse aperture ISAR imaging system, which are used for performing one-dimensional imaging processing on all effective distance units of received echo to obtain a super-resolution one-dimensional azimuth image of a target and further obtain a super-resolution ISAR image of the target. Sparse representation of missing azimuth echoes in a single distance unit is established based on a meshless compressed sensing framework, super-resolution one-dimensional azimuth imaging is modeled into an underdetermined inverse problem based on OANM and solved through a fast algorithm, and after a positive semidefinite Toeplitz matrix and an echo signal of the azimuth echoes of the distance unit are reconstructed, the sparse representation of the missing azimuth echoes in the distance unit is obtained. Vandermonde decomposition is carried out on the reconstructed Toeplitz matrix to obtain a super-resolution one-dimensional azimuth image, and a super-resolution ISAR image with a complete target is drawn, so that the technical effects of avoiding an off-grid error, being not constrained by a minimum atomic distance and improving the ISAR image quality under sparse aperture and short aperture radar observation conditions are achieved.

The present invention discloses a method for DOA estimation of a MIMO array based on double Toeplitz matrix completion. The received echo signals of the MIMO array radar with element failures are subjected to matched filtering to obtain a virtual array output matrix; the covariance matrix of the virtual array of the MIMO array radar with element failures is calculated; by jointly utilizing the low-rank and double Toeplitz structural characteristics of the covariance matrix of the MIMO array radar, a low-rank matrix completion model is constructed; the low-rank matrix completion model with constraint conditions is transformed into an augmented Lagrangian function form without constraint conditions; the augmented Lagrangian function is decomposed into several sub-problems by using the alternating direction method of multipliers (ADMM) for alternating iterative solution; and the DOA of the target is estimated from the complete covariance matrix by using the dimensionality reduction ESPRIT algorithm. The present invention can effectively recover the missing data of the failed elements and improve the DOA estimation performance of the MIMO array radar under element failures.

The invention relates to the field of quantum key distribution, and discloses a key distribution anti-attack method and system based on decoy state coding and an irrelevant protocol, and the method comprises the steps: Alice generates a signal state quantum pulse and a three-intensity decoy state quantum pulse in a mixed manner, and transmits and monitors channel parameters in real time through a quantum channel; bob randomly selects a measurement base for measurement, records original data and delays to obtain a state type identifier; the Charlie transmits a random test state, collects Bell inequality verification parameters, and implements equipment-independent security verification; the two parties screen matched data through base comparison, evaluate channel security and correct a signal state bit error rate by using a decoy state bit error rate; and based on the screened keys, a Toeplitz matrix negotiation protocol is adopted for compression to generate a final key, 10% of the keys are randomly extracted for cross validation, and after the cross validation is passed, the keys are stored, and the decoy state strength and the measurement base group parameters are updated. According to the method, an Alice, Bob and Charlie three-party system is constructed to form an iterative security closed loop, attack is comprehensively resisted, and a security key is generated.

The invention discloses an array signal direction-of-arrival phase recovery method and system based on meshless compressed sensing, a computer and a storage medium, and the method comprises the steps: collecting the amplitude observation data of a target signal through a linear sampler, and forming an observation vector; a matrix variable is introduced, a linear relation between the observation vector and the matrix variable is established, and x is an array signal to be recovered; constructing a joint optimization model, wherein the model is a positive semidefinite programming model containing an atomic norm minimization constraint and a phase lifting constraint; performing numerical solution on the positive semidefinite programming model to obtain an optimal solution; and constructing a Toeplitz matrix according to the optimal solution to perform frequency estimation, and obtaining direction-of-arrival information of the target signal. According to the invention, under the condition of only signal amplitude measurement, phase information loss or serious distortion, high-precision recovery and direction-of-arrival estimation of array signals can be realized.

The invention relates to a self-adaptive quantum random number generation method based on real-time entropy estimation, and belongs to the technical field of quantum random number generation. According to an existing integrated quantum random number generator, due to the non-stationary characteristic of a physical entropy source, the entropy rate dynamically drifts, and after-processing parameters are fixed, randomness safety and generation efficiency are difficult to guarantee at the same time. According to the method, the state transition probability of the original random bit stream is counted in real time, the confidence interval correction is introduced to calculate the minimum entropy, the input bit width is dynamically adjusted accordingly, the Toeplitz matrix is reconstructed for random extraction, and self-adaptive adjustment of the compression ratio is achieved. According to the method, the compression ratio is increased to ensure the security of the output random number when the entropy source quality is reduced, and the compression ratio is reduced to improve the generation efficiency when the entropy source quality is improved, so that the dynamic balance between the randomness security and the generation efficiency is realized on the premise of keeping integration and low cost.

The invention relates to the technical field of ultrasonic communication, in particular to a design method of an ultrasonic cross-metal communication channel echo cancellation filter, which comprises the following steps of: acquiring a composite signal which is received by a receiving end and comprises a direct signal and an echo signal; creating a corresponding filter mask according to the length of a pre-selected filter; generating a Toeplitz matrix of the composite signal, and according to the filter mask, rejecting a column corresponding to a filter coefficient invalid for echo elimination in the Toeplitz matrix to form a new Toeplitz matrix; constructing a sparse optimization problem of a filter coefficient of the filter; solving the sparse optimization problem to obtain a sparse vector, and recovering the sparse vector into a corresponding filter coefficient according to the filter mask; and the filter is designed according to the filter coefficient obtained through recovery, so that the echo signal in the composite signal is filtered through the designed filter, interference can be avoided, and the filtering performance is improved.

The application provides a low-complexity target DOD-DOA and Doppler frequency joint estimation algorithm based on space-time nested sampling, and the joint estimation algorithm comprises the following steps: step 1: configuring a bistatic MIMO radar system into a space-time nested sampling model, and sampling a received signal {x q (l)} by using the space-time nested sampling model; q Step 2: performing multi-stage delay sampling on the received signal {x q (l)} to obtain {y(l)}; step 3: performing matched filtering on the received signal {y(l)} to obtain {y(t)}; step 4: solving a target echo signal covariance matrix R; step 5: vectorizing and de-redundantizing the target signal covariance matrix, and obtaining a new observation signal according to the obtained observation signal; step 6: performing three-dimensional Toeplitz matrix iterative reconstruction on the new observation signal to obtain an equivalent covariance matrix R xx in a virtual domain; and step 7: solving DOD-DOA and Doppler frequency parameters of the target by using an improved multi-dimensional ESPRIT algorithm.

A distributed acoustic sensing (DAS) system may include an optical fiber, a phase-sensitive OTDR (φ-OTDR) coupled to the optical fiber, and a processor cooperating with the φ-OTDR. The processor may be configured to generate a series of covariance matrices for DAS data from the φ-OTDR, and determine an acoustic event based upon comparing the series of covariance matrices with a corresponding Toeplitz matrix.

The invention discloses a near field source positioning method based on covariance fitting. Calculating a spatial correlation function of an array element and constructing single-snapshot equivalent data by utilizing a symmetric characteristic of a uniform linear array, and eliminating signal correlation by utilizing spatial smoothing; performing convex optimization reconstruction on the smoothed covariance matrix based on a covariance fitting criterion, and recovering an idealized Toeplitz matrix structure; solving the reconstructed covariance matrix by adopting a rooting MUSIC algorithm, and resolving a near-field source angle parameter; and constructing a distance sensitive steering vector according to an angle estimation result, and establishing a distance polynomial equation by using noise subspace orthogonality to realize joint optimization estimation of the angle and the distance. Through the covariance matrix reconstruction and polynomial rooting technology, the estimation precision under the conditions of low signal-to-noise ratio and few snapshots is improved, the operand is reduced, meanwhile, the cumulative effect of angle errors to distance estimation is avoided, and efficient matching of near-field source positions is achieved in a complex scene.