82 results about "Toeplitz matrix" patented technology

A post-processing-based quantum random number extraction method, apparatus and device, and a computer readable storage medium, the method comprising: converting an analog signal output by a quantum entropy source into a digital signal through a preset high-speed ADC to obtain an original random number; calculating a minimum entropy according to the obtained original random number; updating a preset Toeplitz large matrix by taking the original random number as a seed so as to obtain a first random number sequence and a first extraction proportion output by the updated preset Toeplitz large matrix; according to the minimum entropy and the first extraction proportion, quantum random numbers are extracted from the first random number sequence, the technical problem that in the related technology, a Toeplitz matrix is usually generated through a pseudo-random number generator or cyclically calls stored seeds, and consequently the safety of the extracted random numbers is poor is solved, and the safety of the extracted random numbers is improved. The random number seed of the Toeplitz matrix is updated in real time by using the post-processed random number, the possibility that a random number generator is controlled and cracked by an attacker is avoided, and the security of the random number is guaranteed.

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.

A multi-carrier signal angle and time delay estimation method based on a nested array belongs to the field of wireless communication, and comprises the following steps: firstly, designing a nested array set according to the number of radio frequency links and the number of pilot frequency carriers, and designing a beam forming matrix into a random banded Toeplitz matrix; meanwhile, selection of pilot frequency carriers is determined according to the nested array of the pilot frequency carriers. At a transmitting end, each pilot carrier transmits a unit matrix signal in each pilot block period time, at a receiving end, the signal received by each pilot carrier is vectorized, and all pilot carrier vectors are gathered to form a new pilot vector. The method comprises the following steps of: calculating a covariance of a new pilot frequency vector, selecting a maximum continuous holeless vector, improving a rank of the holeless vector by using a smoothing technology, finally solving a time delay, a departure angle and an arrival angle of each path by using a 3-D ESPRIT algorithm, and pairing all the time delays, departure angles and arrival angles through a MUSIC algorithm. After the method is used for processing data, a super-resolution method 3-D ESPRIT can be used for time delay and angle estimation, and the angle and time delay estimation precision is effectively improved.

The invention discloses a seismic data time-frequency analysis method and system based on time sparse S transformation, and the method comprises the steps: collecting seismic data, carrying out the time-frequency analysis of the collected seismic data through S transformation, and obtaining a time-frequency spectrum containing the time-frequency characteristics of the seismic data; based on sampling duration and sampling rate characteristics of the seismic data, obtaining a window function of each frequency of the seismic data by using S transformation time-frequency analysis; and constructing a Toeplitz matrix based on a window function, carrying out reverse convolution along the time direction by using the time-frequency spectrum and the Toeplitz matrix, and traversing all frequencies to obtain a seismic data analysis result. The method is applied to time-frequency analysis of seismic data for the first time, a good effect is achieved in multi-scale analysis of two-dimensional actual seismic data, the time resolution of time-frequency analysis of the seismic data is remarkably improved through de-windowing processing, and thin-layer identification of transient signals and seismic data is more accurate especially in a high-frequency part.

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 a gear vibration signal compressed sensing method based on adaptive sparse representation, and particularly relates to the technical field of signal processing and mechanical fault diagnos.The method comprises the steps that firstly, a multi-channel vibration sensor is used for collecting gear vibration signals, and the gear vibration signals are preprocessed to obtain high-quality signals; then, an improved K-SVD algorithm is adopted to construct a self-adaptive sparse dictionary, and the sparse representation capability of the signals is improved; thirdly, designing an optimized measurement matrix meeting RIP (Restricted Isometric Property), including a combination of a weighted random Toeplitz matrix and a cyclic matrix, and realizing efficient compressed sampling; in the signal reconstruction stage, a regularization item is introduced, and an improved orthogonal matching pursuit (OMP) algorithm is adopted, so that the reconstruction precision and the anti-noise performance are improved. The method significantly improves the signal reconstruction precision and processing efficiency, and is suitable for the field of real-time monitoring and fault diagnosis.

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 discloses a heterodyne detection-based self-evaluation quantum random number generation system and method, and the method comprises the steps: extracting two corresponding orthogonal component fluctuations of a quantum state at the same time based on a heterodyne detection method, and enabling the two corresponding orthogonal component fluctuations to serve as an entropy source of a random number generator; two-component measurement original data is randomly extracted based on a synchronous clock to reconstruct quantum state phase space distribution in real time, entropy source safety is comprehensively evaluated, then the result is fed back to a hardware hash post-processing link, the scale of a Toeplitz matrix constructed in an FPGA is adjusted in real time, and the real-time safety of generated quantum moments is guaranteed; meanwhile, on the basis that quantum fluctuations of two orthogonal components of a quantum state are extracted at the same time to serve as entropy sources, multiple high-frequency quantum sideband modes in a balanced zero beat detection bandwidth are extracted in parallel from the two orthogonal components to serve as sub-entropy sources, and the quantum random number generation rate is doubled in a double parallel quantum entropy source extraction mode.

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.

According to an embodiment, an information processing device includes a processor that divides input data based on photons received via a quantum cryptographic communication channel into a plurality of pieces of first divided data, obtains first numeric theory translation data of the first divided data by performing numeric theory translation on the plurality of pieces of first divided data, divides a Toeplitz matrix into a plurality of pieces of second divided data, obtains second numeric theory translation data of the second divided data by performing numeric theory translation on the plurality of pieces of second divided data, and performs privacy amplification processing on the input data based on a result obtained by performing inverse numeric theory translation on a multiplication result obtained by multiplying the first numeric theory translation data and the second numeric theory translation data.

The invention relates to an FDA-MIMO radar target parameter estimation method based on a mobile nested array, and belongs to the field of radar signal processing. The method comprises the following implementation steps: constructing a mobile nested array model of a monostatic FDA-MIMO radar, and carrying out angle and distance decoupling; a sample covariance matrix of an observation matrix is calculated, an optimization problem based on low-rank matrix reconstruction is established, a Toeplitz matrix is solved and obtained by using an alternating projection method, and a target angle and distance estimation value is obtained by using a two-dimensional subspace method. The method has the advantages that the mobile nested array is applied to FDA-MIMO, the number of virtual array elements of a differential common array is remarkably increased, the array aperture is larger, the target parameter estimation precision is higher by solving the low-rank matrix reconstruction problem, and the problems that an existing FDA-MIMO radar is low in parameter estimation precision and resolution, limited in detectable target number and the like can be solved.

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 relates to the technical field of information security, in particular to a real-name authentication method for a global quantum security encryption machine. According to the scheme of the invention, real-name binding is carried out on the user and the client device thereof and the quantum security encryption machine based on the quantum security CA certificate, and each user and device are ensured to have a unique and unforgeable identity label, so that unauthorized access is effectively prevented, and the system security is improved. And meanwhile, through a periodic verification mechanism, the identity can be confirmed regularly after the equipment is accessed, illegal use and potential security threats are avoided, and the user information security is further guaranteed. Besides, a Toeplitz matrix is utilized to calculate a hash value, so that the hash value generated each time depends on a random number S and an irreducible polynomial P provided by the quantum security encryption machine, and high randomness and variability are ensured, thereby greatly reducing the risk that a pretending user and equipment send real-name access confirmation to the quantum security encryption machine, and enhancing the overall security.

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.

The invention provides an adaptive signal beam forming method and device, equipment and a storage medium. The method receives signals based on a uniform linear array of 2N + 1 array elements. The method comprises the following steps: constructing multiple Toeplitz matrixes, and combining the multiple Toeplitz matrixes to obtain a covariance matrix; calculating a signal steering vector according to the characteristic value and the characteristic vector, and optimizing to obtain an expected signal steering vector; estimating noise power by using the minimum eigenvalue, establishing a corresponding relation between signal power and eigenvectors, and determining expected signal power; reconstructing a covariance matrix of uncorrelated noise according to the noise power, the corresponding relation and the expected signal power; and substituting the desired signal steering vector and the reconstructed covariance matrix into a Cap on beam former, and calculating an optimal weight vector to control beam forming. According to the method, multiple Toepli tz matrixes are constructed, and the characteristics of the multiple Toepli tz matrixes are utilized to carry out signal decoherence and covariance matrix reconstruction, so that the performance and reliability of a beam forming system in a complex signal environment are improved.

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 present invention provides a method for a Chebyshev Toeplitz chaotic sensing matrix for CSE, characterized in that it includes an encryption process and a decryption process; the encryption process includes: image sparse representation, Arnold scrambling operator, compressed sensing operator, bilateral diffusion operator and chaotic scrambling operator; the decryption process is the reverse operation of the encryption process; that is, the encryption process and the decryption process are inversely proportional; the image encryption performed by the present invention through compressed sensing can not only protect information security, but also greatly reduce memory and calculation complexity, and is easy to implement on hardware, the Toeplitz matrix is ​​used to compress and sample the perturbed image to reduce the transmission bandwidth and data volume, and finally, the bilateral diffusion operator and the chaotic encryption operator are used to disturb and expand the image pixels, the pixel position and pixel value of the compressed image are changed, and the encrypted image is finally obtained; the algorithm not only improves the image transmission efficiency, but also introduces higher security, and is suitable for popularization and application.

A DOA estimation method based on novel array optimization is provided; according to the implementation mode, after a system is initialized, an improved non-uniform array is firstly constructed, then the non-uniform array is used for receiving narrow-band far-field incoherent signals, in order to make full use of data collected by the non-uniform array, a difference sum common array is generated according to a VCAM algorithm, time delay sampling processing is conducted, then a conjugate augmentation vector is constructed, and the non-uniform array is used for receiving the narrow-band far-field incoherent signals. Secondly, constructing a pseudo data matrix, performing vectorization operation on the pseudo data matrix, constructing a covariance matrix, obtaining difference set and sum set elements containing a novel non-uniform array, performing redundancy removal and sorting, applying a matrix reconstruction thought for recovering a rank of the matrix, and performing Toeplitz matrix reconstruction, so as to obtain a covariance matrix in a conventional MUSIC algorithm; therefore, DOA estimation can be carried out by directly using the MUSIC algorithm to obtain angle information.

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.