13 results about "Data covariance matrix" patented technology

The invention discloses an automatic history fitting method based on multiple data assimilation of an improved set smoother, and relates to the technical field of petroleum engineering. The method comprises the following steps: firstly, constructing a plurality of oil reservoir models, setting a prior model, a real model and an initial expansion factor, obtaining the yield of each oil reservoir model by utilizing oil reservoir model numerical simulation, and calculating a residual error; based on corrected data covariance matrix singular value decomposition, production observation data disturbance enhancement, adaptive learning rate matrix scaling and geological boundary constraint, improving a set smoother, updating the permeability of each oil reservoir model, performing numerical simulation again, updating an expansion factor, judging whether the expansion factor meets a preset condition or not, continuing iteration if the expansion factor meets the preset condition, and if the expansion factor does not meet the preset condition, continuing iteration until the expansion factor meets the preset condition; and if not, updating the expansion factor of the iteration, ending the iteration, and outputting the permeability field of each updated oil reservoir model, thereby solving the problems of parameter overshoot, covariance statistical deviation and low calculation efficiency in oil reservoir history fitting, and facilitating history fitting of complex oil reservoir production parameters.

The invention relates to the technical field of underwater acoustic signal processing, in particular to a robust adaptive beam forming method based on prior information. Comprising the steps of performing dimension reduction processing on a data covariance matrix according to dimension reduction quantity parameters in historical data to obtain a data dimension reduction covariance matrix, and solving a data dimension reduction beam forming expression; deriving a preset value range of the loading amount according to the data dimension reduction covariance matrix; setting an initial value of a loading amount according to an optimal loading amount parameter in the historical data of the wave beam, and solving a current optimal loading amount; and if the current optimal loading amount is within the preset value range, solving a driving vector formed by the robust adaptive beam. According to the method, the data dimension reduction order and the initial value in the iteration process are set by utilizing the historical optimal data dimension reduction amount and the historical optimal loading amount, so that the convergence speed can be accelerated and the calculation complexity can be reduced. Meanwhile, the value range of the loading capacity is deduced to judge whether the diagonal loading capacity is effective or not, and divergence of the iteration process is avoided.

The application discloses a constant beam adaptive beam forming method and system based on frequency difference, and the method comprises the following steps: segmenting FFT of time domain data of a target signal, converting the time domain data into frequency domain data, and calculating a data covariance matrix for each frequency point; estimating the azimuth of the target by using the data covariance matrix of each frequency point to obtain an estimated value of the target azimuth; generating a Gaussian random sequence with the same length as the array receiving signal as a reference signal, and reconstructing the array output of the reference signal according to the target azimuth estimated value; performing conjugate multiplication on the array output containing the target signal and the reconstructed array output of the reference signal to obtain the array output based on the frequency difference, and calculating the covariance matrix of the frequency difference output; calculating the weight value of the adaptive beam according to the covariance matrix of the frequency difference output to form the constant beam width adaptive beam. The application is simple and convenient, has small calculation amount, and can improve practicability.

The application discloses a pretreatment method for coherent signal DOA estimation of a logarithmic spiral array, wherein step 1) first receives coherent signals by using the logarithmic spiral array to obtain a real signal covariance matrix; step 2) according to the positions of the signals, an observation region is divided into Θ1~Θ2; step 3) an array flow matrix of a virtual array is determined according to the observation region; step 4) a transformation relationship between the real array and the virtual array is obtained; step 5) a data covariance matrix of the virtual array is obtained; step 6) in order to restore the rank of the signal covariance matrix, the data covariance matrix of the virtual array is processed; step 7) eigenvalue decomposition is performed on R to obtain a signal subspace U and a noise subspace U; step 8) a MUSIC spatial spectrum is calculated; and step 9) through spectrum peak searching, the angle corresponding to the maximum value of the peak value is the angle of the coherent signal source positioning. k X s N ; step 7) a MUSIC spatial spectrum is calculated; and step 8) through spectrum peak searching, the angle corresponding to the maximum value of the peak value is the angle of the coherent signal source positioning.Through establishment of a logarithmic spiral array array model, the application discloses the pretreatment method for coherent signal DOA estimation of the logarithmic spiral array.​​

The invention provides a saline-alkali cultivated land quality evaluation method and system, and relates to the technical field of saline-alkali cultivated land quality evaluation method.The saline-alkali cultivated land quality evaluation method comprises the steps that a sodium ion concentration gradient coefficient of each grid point is calculated through a central difference method, and a chloride ion space variation coefficient of each grid point is constructed through a regional averaging method; constructing a data characteristic matrix by adopting a principal component analysis method, and determining a sodium ion concentration gradient coefficient and a weight coefficient occupied by values of grid points; and calculating the quality index of any position of the grid by adopting a bilinear interpolation method, and calculating the comprehensive quality index of the whole saline-alkali cultivated land by adopting a global weighted integral method. On the basis of characteristic decomposition of a data covariance matrix, principal component weights of sodium ion gradients and values are automatically extracted, and human intervention is eliminated; capturing spatial heterogeneity through a region averaging method, and identifying local salinization hotspots; and bilinear interpolation is adopted to ensure the continuity and smoothness of the quality index field.

The application provides an underwater DOA estimation method, system, device and medium based on SKLD convex modeling, which comprises the following steps: constructing a sensor array received signal model according to a steering vector of a signal source; based on the difference between an observation data covariance matrix and a parameterized model covariance matrix, selecting a symmetric divergence as an initial objective function for measuring the difference; imposing a sparsity constraint on the initial objective function to form a regularized optimization problem, introducing a linear matrix inequality constraint, equivalently reconstructing the regularized optimization problem into a convex semi-definite programming problem, and taking the convex semi-definite programming problem as an optimization objective function; based on the sensor array received signal model, constructing a positive definite matrix by using sampling data; inputting the positive definite matrix into the optimization objective function to obtain an optimal objective function, obtaining a spatial spectrum distribution result based on the optimal objective function, and taking a spectral peak value of the spatial spectrum distribution result as an estimation result of a target direction.

The application discloses an automatic history matching method based on improved ensemble smoother multiple data assimilation, and relates to the technical field of petroleum engineering.The application firstly constructs multiple reservoir models, sets prior models, real models and initial inflation factors, obtains the production of each reservoir model by using numerical simulation of the reservoir model, calculates the residual error, improves the ensemble smoother based on the singular value decomposition of the modified data covariance matrix, the disturbance enhancement of the production observation data, the adaptive learning rate matrix scaling and the geological boundary constraint, updates the permeability of each reservoir model and performs numerical simulation again, judges whether the inflation factor meets the preset condition after updating the inflation factor, continues iteration if the preset condition is met, uses the updated inflation factor of the current iteration if the preset condition is not met, and ends the iteration, and finally outputs the updated permeability field of each reservoir model, so that the problems of parameter overshoot, covariance statistical deviation and low calculation efficiency in reservoir history matching are solved, and the history matching of complex reservoir production parameters is facilitated.

An array information source number estimation method based on variable granularity interval information granulation comprises the steps that a uniform linear model is established, and a received signal column vector is acquired; calculating a data covariance matrix of a received signal column vector to obtain characteristic values and a characteristic value sequence formed by the characteristic values; selecting a division reference point, and dividing the characteristic value sequence into two subsequences; calculating variable granularity interval information particles corresponding to the two subsequences and the volume sum of the variable granularity interval information particles; judging whether the variable granularity interval information particle volume sum with the characteristic value number minus 1 is obtained or not; if not, returning to the third step; if yes, comparing the size of the total volume of the information particles in the variable granularity interval with the number of the characteristic values minus 1, and recording the minimum value; extracting a corresponding division mode serial number; dividing a characteristic value sequence to obtain a sub-sequence I and a sub-sequence II; counting the number of characteristic values in the first sub-sequence, namely the number of information sources; according to the method, the adaptability to small snapshot number and low signal-to-noise ratio scenes is enhanced, and the success rate of array signal source number estimation is improved.

The invention discloses a low-complexity azimuth estimation method based on noise eigenvalue reconstruction, and belongs to the field of array signal processing. The method comprises the following steps: firstly, carrying out sound pressure and vibration velocity combined processing on array output signals to construct a covariance matrix, constructing a pseudo-data covariance matrix with non-divergent noise power aiming at the problem of divergence of noise characteristic values, constructing a matrix containing real and symmetric target information by introducing symmetric target angle information, and introducing a scanning source to form an expansion matrix; on the basis, a spatial spectrum function is designed by utilizing the characteristic value relation of the two, angle search is carried out in a half spectrum, and a real target angle is discriminated and screened out. According to the method, the spectral peak distortion problem caused by noise characteristic value divergence in a traditional DOA estimation method is effectively suppressed, the DOA estimation stability and the anti-jamming capability are improved, meanwhile, the calculation speed of the algorithm is effectively improved in combination with half-spectrum search, and the method is particularly suitable for real-time application scenes with limited operation resources.

Access system (28) for a vehicle (30, 200, 108, 5200, 7800, 790), wherein the access system comprises: a plurality of antennas (414, 1102, 1104, 1410, 1424, 1428, 1438, 1440, 1502, 1504, 3102, 3104, 5300, 5302, 5304, 5306, 5412, 7000, 7602) configured to each receive a signal transmitted from a portable access device (32, 34, 400, 5206) to the vehicle (30, 200, 108, 5200, 7800, 7900), one of the plurality of antennas being a circularly polarized antenna (1104, 1428); and an access module (36, 210) that is configured to Down-conversion of the received signal to generate an in-phase signal and a quadrature-phase signal, Executing a Music algorithm to determine the arrival angles of the received signal as received by the multitude of antennas, where "Music" stands for "Multiple Signal Classification", Determining a distance between the portable access device and the vehicle based on the arrival angles, and Allowing access to the vehicle based on distance, where the access module (36, 210) is configured during execution of the Music algorithm, to: Collecting analytical signal samples of the signal received at each of the multitude of antennas to generate a received data matrix; Estimating a data covariance matrix based on the received data matrix; Using an eigenvalue decomposition process to determine an MxM matrix based on the covariance matrix, where M is an integer greater than or equal to 2; Determining a number of incoming signals; Splitting the MxM matrix into a multitude of matrices; Calculating a music spectrum based on one of the many matrices; and Performing a peak search on the music spectrum to determine the arrival angles, the access module (36, 210) is further configured to: Generating a time vector corresponding to the in-phase and quadrature-phase sampling vector; Discarding some of the analytical signal samples taken near antenna switching times; Unwinding each repeat portion of remaining samples with a step size π; Averaging a frequency of sine waves from the remaining samples; Finding an average slope of the remaining samples; Measuring a standard deviation of the average slope; Determine which of the many antennas is misaligned, based on the measured standard deviation; For each of the multitude of antennas, interpolating a straight line of points on a time vector to generate a reconstructed phase angle vector; If the standard deviation is greater than a predetermined threshold, check which of the many antennas has an inaccurate alignment; and for one of the many antennas, remeasuring the standard deviation of the average slope.

This invention discloses a meshless DOA estimation method for coherent signals based on coprime matrices. The method first averages the diagonal elements of the covariance matrix of the received data from the array, then reconstructs the Toeplitz matrix based on this average, and finally reconstructs the low-rank matrix using its Toeplitz structure. Next, the trace norm of the positive semi-definite matrix is ​​used to relax the non-convex low-rank matrix reconstruction problem, and the rank recovery matrix is ​​obtained using the convex optimization CVX toolbox. Finally, the DOA is estimated using the ESPRIT algorithm. Compared with traditional methods, this method does not require mesh generation, has stronger decoherence capabilities, and can accurately estimate the azimuth of coherent signals even under conditions of low signal-to-noise ratio, few snapshots, and small signal incident angle intervals. It has significant application value in underwater moorings or mobile observation platforms for ocean observation.

The invention discloses a co-prime array-based suppression-deception mixed interference detection and suppression method, which comprises the following steps of: firstly, calculating a data covariance matrix after noise weakening based on the periodic stationary characteristic of a navigation signal, then, carrying out vectorization and redundancy elimination processing on the covariance matrix, and further constructing a virtual array equivalent receiving signal model; the method comprises the following steps of: selecting a continuous part of the matrix to carry out spatial solution smoothing operation to recover the rank of the matrix, and then taking the discrete condition of a characteristic value of the matrix as a deception jamming detection quantity, and finishing the detection and suppression of the suppressing jamming and the deception jamming in cooperation with a multiple signal classification (MUSIC) algorithm and a signal subspace projection algorithm. The method can give consideration to the detection and suppression of suppressing interference and deception interference in a suppressing-deception mixed interference scene, and has the advantages of high interference detection rate and high-precision DOA estimation.

The application discloses a phase interferometer DOA and polarization parameter joint estimation method based on multiple linear polarization antennas, and belongs to the technical field of radar signal processing.The application solves the problems of low demodulation probability, low signal DOA and polarization parameter estimation precision of the existing method.The application adopts a high-order power method of a received data covariance matrix to estimate noise variance and signal power, and reduces the complexity.Aiming at the problem that polarization mismatch leads to limited demodulation probability, the application first adopts a long-baseline demodulation short-baseline method to reduce phase difference error, and then adopts a multi-baseline phase weighting demodulation method to utilize phase difference and amplitude information of all baselines to demodulate polarization domain phase difference and space domain phase difference, so that the demodulation probability is improved, and the LMMSE method is used to reduce power estimation error, and the signal DOA and signal polarization parameter estimation precision is improved.The method can be applied to joint estimation of phase interferometer DOA and polarization parameter.