33 results about "Data covariance matrix" patented technology

The invention discloses a phase interferometer DOA and polarization parameter joint estimation method based on multiple linearly polarized antennas, and belongs to the technical field of radar signal processing. According to the method, the problems of low ambiguity resolution probability and low signal DOA and polarization parameter estimation precision of an existing method are solved. Noise variance and signal power estimation is carried out by adopting a method of receiving data covariance matrix high-order power, and the complexity is reduced. Aiming at the problem that the defuzzification probability is limited due to polarization mismatch, the method firstly adopts a method of solving a short base line by a long base line to reduce a phase difference error, then utilizes a multi-base-line phase weighted defuzzification method, and utilizes phase differences and amplitude information of all base lines to perform polarization domain phase difference and spatial domain phase difference decoupling, so that the defuzzification probability is limited. And the power estimation error is reduced through the LMMSE method, and the precision of signal DOA and signal polarization parameter estimation is improved. The method can be applied to joint estimation of DOA and polarization parameters of the phase interferometer.

The invention discloses a low signal-to-noise ratio DOA estimation method based on an auto-encoder and a deep neural network, comprising a low signal-to-noise ratio DOA estimation method and a DOA estimation method combining DAE and CNN, the low signal-to-noise ratio DOA estimation method comprises the following steps: giving parameters such as array radar receiving antenna array element number M, array element position dm, signal-to-noise ratio SNR, input signal sin and the like; obtaining an array receiving data covariance matrix R; taking a real part and an imaginary part of a received signal covariance matrix R in an incidence angle [-60 degrees, 60 degrees] range as input Y1 of the neural network, constructing a data set, and distinguishing a training set and a test set according to a proportion of 8: 2; dAE network parameters are initialized, data in the training set are imported, forward propagation is completed, meanwhile, back propagation is conducted on errors, and network weights are synchronously updated; a DAE network structure is further introduced to optimize signal processing, the DOA estimation accuracy is improved, and the requirement for complex computing resources is lowered.

The invention discloses a method for suppressing magnetotelluric mixed noise in a shallow water area. The method comprises the following steps: reading a noisy ocean MT time sequence; setting phase space reconstruction parameters; constructing a phase-space vector to obtain a noisy data matrix; noise priori information is obtained through noise pre-estimation; constructing an original noisy data matrix after noise whitening; performing singular value decomposition to obtain a feature vector and a feature value matrix of a data covariance matrix after noise whitening; selecting a low-order principal component after noise adjustment principal component transformation to reconstruct a denoised data matrix; recovering the denoised data matrix into a time sequence; carrying out Fourier transform to obtain an ocean MT magnetic field component amplitude spectrum, and combining to obtain an ocean MT four-component amplitude spectrum; detecting the frequency point of the pulse in the four-component amplitude spectrum; and setting all amplitudes corresponding to pulse frequency points in the four-component amplitude spectrum to be 0, and performing inverse Fourier transform to obtain a processed four-component time sequence. According to the invention, the ocean magnetotelluric signals can be better processed, and the data quality is improved.

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 discloses a co-prime array robust DOA estimation method based on a small number of auxiliary array elements. According to the scheme, three auxiliary array elements are added on an original co-prime array according to array element spacing, received data of a new co-prime array is decomposed into two sub-array data, and amplitude-phase error estimation is performed on the two sub-arrays by using amplitude-phase characteristics of each element in a covariance matrix; and the amplitude-phase errors of the sub-arrays are sorted and recombined to obtain the amplitude-phase error of the new co-prime array. The method comprises the following steps: firstly, receiving data, then compensating a covariance matrix error of the received data and performing Toeplitz reconstruction, relaxing a non-convex low-rank matrix reconstruction problem through atomic norm optimization, solving by utilizing a convex optimization CVX tool box to obtain a rank recovery matrix, and finally performing angle estimation by utilizing an orientation estimation method. Compared with a traditional method, the method has the advantages that errors of the array are effectively corrected, the number of auxiliary array elements is reduced, resources are effectively saved, and the method has important application value in subsurface buoys or mobile observation platforms for ocean observation.

The invention discloses a water supply pipe network pressure estimation method based on second-order graph signal reconstruction, and relates to the technical field of water supply pipe network reconstruction, and the method comprises the steps: obtaining water supply pipe network information, constructing a first pipe network graph Laplacian matrix, and carrying out the spectrum analysis to obtain a spectrum base; the method comprises the following steps: presetting a high-frequency term of a graph Laplacian frequency spectrum on a frequency spectrum base, limiting to obtain a low-frequency term of the graph Laplacian frequency spectrum, reconstructing a first pipe network water head on the basis of inverse graph Fourier transform on the low-frequency term of the graph Laplacian frequency spectrum, and obtaining a pseudo monitoring value; and constructing a second pipe network graph Laplacian matrix according to the pseudo monitoring value and the actual monitoring value, and repeating the reconstruction process to obtain a second pipe network water head. The innovation lies in that through a double constraint framework, the low-frequency component and the measured data covariance matrix are fused while the pipe network topological characteristics are reserved, and the problems that a traditional method is sensitive to parameters, the hydraulic relation is ignored and the like are effectively solved.

The invention discloses a synthetic aperture radar (SAR) image interference suppression method based on frequency domain signal recovery, which comprises the following steps of: receiving synthetic aperture radar image data containing radio frequency interference, and performing Fourier transform on the image data along a distance dimension to obtain two-dimensional frequency spectrums of a distance frequency domain and an azimuth time domain; kurtosis is calculated for the two-dimensional frequency spectrum, and an outlier detection threshold value is set based on the kurtosis; interference detection is carried out on the kurtosis, sampling points larger than a threshold value are regarded as interference, and a one-dimensional mask is constructed; constructing an effective data selection matrix based on the one-dimensional mask; constructing an amplitude guiding matrix; extracting frequency domain pulses of each row in the two-dimensional frequency spectrum, and constructing an amplitude spectrum and an effective data covariance matrix by using the frequency domain pulses, the amplitude guiding matrix and the effective data selection matrix; reconstructing time domain pulses corresponding to the frequency domain pulses; the reconstructed time domain pulse combination corresponding to all the frequency domain pulses is the SAR image after interference suppression. According to the invention, fine and effective suppression of the SLC image with interference is realized.

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 kind of based on system disturbance response's self anti-interference controller setting method, comprising the following steps: determining the model order of controller and controlled object;According to model order, data vector and model parameter vector are set;Select the initial value of calculation of feedback gain matrix and data covariance matrix;Recursive calculation feedback gain matrix, data covariance matrix and the estimated value of the model parameter vector, until the estimated value of model parameter vector tends to be stable;According to stable value, calculate the coefficient estimate of controlled object model;Based on each coefficient estimate, design self anti-interference controller parameter.The method of the application can guarantee system closed loop and system set value unchanged, only according to the disturbance response of system setting self anti-interference controller parameter, overcome the problems existing in prior art, the method of the application has good tracking and anti-interference performance, help to further promote the field application of self anti-interference controller in thermal process control.

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 invention discloses a near-field terahertz large-scale MIMO target positioning method under non-uniform noise, and the method comprises the steps: building a receiving end system model based on an array-of-sub-array (AoSA) mixed structure, and obtaining a signal transmitted by a terahertz signal source; the method comprises the following steps: preprocessing a received signal, and obtaining full-array information from a few radio frequency chains by increasing time slots; calculating a covariance matrix of full array information, and decomposing the data covariance matrix to obtain a diagonal matrix containing noise power; the method comprises the following steps of: selecting a minimum value in diagonal elements to replace other diagonal elements to obtain a reconstructed data covariance matrix, and processing the reconstructed data covariance matrix by using a multi-signal classification (MUSIC) method to obtain an angle and a distance of a terahertz signal source. According to the method, the non-uniform noise interference can be effectively suppressed with relatively low calculation complexity, the positioning precision is improved, and the hardware cost can be reduced by increasing time slots.

The application provides a fast MUSIC method for enhancing radar angle resolution, comprising the following steps: step one, establishing an array model equivalent to a radar antenna, and then calculating the distance and direction between each antenna in the array; step two, constructing a spatial spectrum function, and estimating the parameters of a signal through a spectrum peak search; step three, setting one transmitting array element, transmitting chrip signals in one frame, and then the signal received by a single receiving array element in one frame is, and point sampling is conducted on the received signal to obtain the maximum likelihood estimation of a finite-length data covariance matrix. The technical scheme can effectively accelerate the estimation speed of the arrival angle of a target, and can be applied to actual point cloud acquisition.

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 present invention provides a fast DOA estimation method for underwater acoustic broadband signals based on spatio-temporal adaptive interference suppression. The spatio-temporal processing structure adopted by this method is formed by removing the pre-steering delay unit in the Frost broadband adaptive beamformer. The method includes: Step 1: Orthogonally demodulate the original array element data x m to obtain the corresponding complex envelope signal #imgabs0# Step 2: Evenly divide #imgabs1# into L frequency band complex envelope broadband signals #imgabs2# through a filter bank, and then select the detection snapshot samples by D-fold decimation for each frequency band complex envelope broadband signal #imgabs3#; Step 3: Calculate the data covariance matrix #imgabs4# and the spatio-temporal steering vector a(θ p , f q ) in each frequency band; Step 4: Calculate the azimuth spectrum #imgabs6# of each frequency band according to the data covariance matrix #imgabs5# and the spatio-temporal steering vector a(θ p , f q ); Step 5: Integrate the azimuth spectra of each frequency band into an azimuth spectrum #imgabs7#

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.

A method, system, and non-transitory computer readable media for seismic imaging of a subterranean formation. The operations include receiving data representing seismic traces corresponding to seismic waves propagating in the subterranean formation, and determining, from the seismic traces, travel time data and a data covariance matrix. The operations include determining a prior velocity model and a prior model covariance matrix for common midpoint locations and generating an objective function based on the travel time data, the data covariance matrix, the prior velocity model, and the prior model covariance matrix. The operations include determining, by minimizing the objective function, values for one-dimensional velocity models and a set of accuracy values. The operations include generating a pseudo-3D model and a set of accuracy values for the pseudo-3D model. Based on the pseudo-3D model and the set of accuracy values for the pseudo-3D model, a seismic image representing the subterranean formation is generated.

The invention discloses a radar broadband coherent signal DOA (direction of arrival) estimation method based on mirror symmetry, which belongs to the field of array signal processing, and comprises the following steps: processing and sampling echo signals of each channel of a uniform linear array to obtain echo intermediate frequency signal data, and performing digital down-conversion processing to obtain echo signal baseband data; decomposing the data into data of a plurality of frequency points in a frequency domain, and calculating a data covariance matrix of each frequency point; performing decorrelation processing on the data covariance matrix of each frequency point, and performing characteristic decomposition to obtain a signal subspace and a noise subspace; performing spectrum peak search by using a mirror symmetry method to obtain a spatial spectrum on each frequency point, and fusing the spatial spectrums on the frequency points to obtain a final power spectrum; and carrying out spectrum peak search by using a mirror symmetry method to obtain a final DOA estimation result. According to the invention, the DOA angle of the broadband coherent signal can be accurately estimated, and the mirror symmetry method reduces the calculation amount of spectrum peak search in the algorithm.

The invention discloses a signal arrival angle estimation method, electronic equipment and a readable storage medium, and the method comprises the steps: constructing a data covariance matrix corresponding to a to-be-estimated signal according to the received signal data of the to-be-estimated signal; obtaining a plurality of feature values of the data covariance matrix and feature vectors corresponding to the feature values; based on the multiple feature values and feature vectors corresponding to the feature values, a target space matrix is obtained, the target space matrix is composed of a maximum signal feature vector and a noise feature vector, the maximum signal feature vector is the feature vector corresponding to the maximum feature value in the multiple feature values, and the noise feature vector is the feature vector corresponding to the maximum feature value in the multiple feature values; the noise feature vector is a feature vector corresponding to a noise feature value in the plurality of feature values, and the noise feature value is smaller than a preset threshold value; based on the target space matrix, obtaining a target corresponding relation between the target space spectrum and an incident angle of the to-be-estimated signal; and estimating a signal arrival angle corresponding to the to-be-estimated signal based on the target corresponding relation.

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.

The present invention provides an array self-calibration direction finding method based on local optimization search, belonging to the technical field of array signal processing. By decomposing the eigenvector of the array data covariance matrix into a signal subspace and a noise subspace, the present invention uses the orthogonality between the product of the steering vector matrix and the error matrix and the noise subspace for cyclic iteration, and improves the convergence speed and direction finding stability by introducing local optimization search, so as to achieve a better angle estimation result.

The invention discloses a robust DOA estimation method based on a generalized co-prime array. According to the scheme, the front array element and the rear array element of the two uniform sub-arrays are corrected auxiliary array elements, the distance between the uniform sub-arrays 1 is compressed, and the two sub-arrays are nested to form the generalized co-prime array. Receiving data of the array is decomposed into two sub-array data, amplitude-phase error estimation is carried out on the two sub-arrays according to the amplitude-phase characteristics of all elements in the covariance matrix, and the amplitude-phase errors of the sub-arrays are sorted and recombined to obtain the amplitude-phase error of the generalized co-prime array. Then error compensation is carried out on a received data covariance matrix, continuous virtual array elements are selected after the covariance matrix is vectorized, a Toeplitz matrix is constructed, and finally DOA estimation is carried out. Compared with the traditional method, the method provided by the invention firstly corrects the array error, expands the freedom degree of the array, does not need to carry out interpolation optimization on the hole, reduces the calculation cost, and has important application value in a subsurface buoy or a mobile observation platform for ocean observation.

The present invention discloses a method for suppressing mixed magnetotelluric noise in shallow waters, comprising the following steps: reading a noisy marine magnetic field time series; setting phase space reconstruction parameters; constructing a phase space vector to obtain a noisy data matrix; pre-estimating the noise to obtain prior information about the noise; constructing the original noisy data matrix after noise whitening; performing singular value decomposition to obtain the eigenvectors and eigenvalue matrix of the noise-whitened data covariance matrix; selecting low-order principal components after noise-adjusted principal component transformation to reconstruct a denoised data matrix; restoring the denoised data matrix to a time series; performing Fourier transform to obtain amplitude spectra of marine magnetic field components, merging these to obtain a four-component amplitude spectrum of marine magnetic field components; detecting the frequency points of pulses in the four-component amplitude spectrum; setting all amplitudes corresponding to the pulse frequencies in the four-component amplitude spectrum to 0, and performing inverse Fourier transform to obtain a processed four-component time series. The present invention can better process marine magnetotelluric signals and improve data quality.

The invention discloses an interference incoming direction estimation method and device, equipment and a storage medium, and is applied to the technical field of satellite navigation. The method comprises the following steps: constructing a spherical coordinate system by taking the center of a uniform circular array as a coordinate origin; obtaining a space steering vector of the array according to a phase relation of each array element in the uniform circular array relative to the origin of coordinates; constructing a unitary matrix to transform the array manifold of the uniform circular array to obtain a received data covariance matrix; obtaining an interference signal subspace through eigenvalue decomposition; constructing a generalized eigenvector reconstruction covariance matrix based on the interference signal subspace; defining a new interference signal subspace; and estimating the interference direction by using the new interference signal subspace and the space steering vector. According to the method, unitary matrix transformation array flow patterns are constructed, decorrelation processing is carried out on a covariance matrix after transformation based on the corresponding relation of feature vectors before and after transformation, generalized feature vectors and steering vector space, and estimation of the interference direction is achieved through a subspace algorithm.

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.

A method for constructing a reference signal for a fan duct ANC system based on a microphone array. A microphone array device is used as a reference signal sensor for a feedforward ANC system. The algorithm estimates the rotation noise frequency in the duct fan noise based on FFT and quadratic interpolation. After completing array data preprocessing such as rotation noise component extraction, a circular harmonic transform is first performed for a uniform annular array. For a uniform linear array or a virtual linear array obtained by the transform, subarray division is performed and forward and backward spatial smoothing is performed. The smoothed array data covariance matrix is ​​diagonally loaded, and then MPDR or LCMP beamforming is performed. Finally, the amplitude and phase of the data sequence obtained by beamforming are estimated based on the least squares method, and a high-quality fan rotation noise reference signal is constructed in conjunction with a timer. The method of the present invention improves the stability of feedforward ANC algorithms such as FxLMS and the suppression effect of fan rotation noise.

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 present invention discloses a SAR image interference suppression method based on frequency domain signal recovery, comprising: receiving synthetic aperture radar image data containing radio frequency interference, performing Fourier transform on the image data along the range dimension to convert it into a two-dimensional spectrum in the range frequency domain and the azimuth time domain; calculating the kurtosis of the two-dimensional spectrum and setting an outlier detection threshold based on the kurtosis; performing interference detection on the kurtosis, wherein sampling points greater than the threshold are considered to be interference, and constructing a one-dimensional mask; constructing an effective data selection matrix based on the one-dimensional mask; constructing an amplitude steering matrix; extracting the frequency domain pulses of each row in the two-dimensional spectrum, constructing an amplitude spectrum and an effective data covariance matrix using the frequency domain pulses, the amplitude steering matrix, and the effective data selection matrix; reconstructing the time domain pulses corresponding to the frequency domain pulses; and then the reconstructed time domain pulse combination corresponding to all the frequency domain pulses is the interference suppressed SAR image. The present invention achieves refined and effective suppression of SLC images with interference.