90 results about "Orthogonal matching pursuit algorithm" patented technology

The invention relates to a multi-dimensional transmission data compression sensing method and system for urban emergency rescue, and the method comprises the steps: obtaining multi-modal data, carrying out the normalization processing and sparse representation, and obtaining the sparse coefficient of each modal; constructing a block diagonal random matrix for each modal sparse coefficient to perform independent compression sampling, generating a low-dimensional observation value before coding, and performing arithmetic coding and low-density parity check code error correction coding to generate an anti-interference code stream; and performing low-density parity check code decoding and arithmetic decoding on the anti-interference code stream to obtain a decoded low-dimensional observation value, performing iteration by using an orthogonal matching pursuit algorithm, recovering sparse coefficients of each mode, performing reconstruction to obtain text, image and audio features, and projecting the text, image and audio features to a shared semantic space to obtain a shared semantic space. And calculating a joint semantic weight through a cross-modal attention mechanism, and generating a multi-modal fusion result with consistent semantics. The method disclosed by the invention provides an efficient, robust and semantic collaborative end-to-end solution for urban emergency rescue.

The invention discloses an ultra-harmonic rapid detection method based on compressed sampling and multiple measurement vectors, and solves the problems of high sampling rate, low reconstruction efficiency and insufficient spectrum resolution in the prior art. Sub-Nyquist compressed sampling is realized through a sparse random matrix, and the hardware burden is reduced; a dynamic step size sparse self-adaptive matching pursuit algorithm (DSSAMP) is adopted to reconstruct signals, the step size is adjusted in a self-adaptive mode in combination with residual energy changes, and the convergence speed and precision are balanced; an improved multi-measurement vector model (IMMV) is constructed through framing processing and introduction of interpolation factors and prior frequency point screening, and the calculation efficiency is improved; and finally, extracting super-harmonic frequency, amplitude and phase parameters based on an orthogonal matching pursuit (OMP) algorithm. According to the method, the high resolution is ensured, the sampling rate and the calculation complexity are remarkably reduced, and the real-time requirement is met.

Conventional Orthogonal frequency division multiplexing (OFDM) is unable to retain its orthogonality and suffers loss in performance in high Doppler circumstances. The present disclosure converts a signal received in delay-time domain into delay-Doppler domain and extracts a guard band region from the received converted signal. A 2-dimensional fast Fourier transform is performed on guard band region extracted from received converted signal. A 2-dimensional fast Fourier transform of the received converted signal is divided with the 2-dimensional fast Fourier transform of transmitted signal to extract phase information. A dictionary is created using a pre-defined set of values of delay and Doppler. A sparse recovery problem is formed for received converted signal using an orthogonal matching pursuit algorithm. One or more parameters are estimated by identifying one or more locations pertaining to the one or more columns corresponding to L significant non-zero locations comprised in sparse vector of sparse recovery problem.

The invention discloses a direct current charging pile detection system and method applied to the field, and relates to the technical field of power equipment on-line monitoring, and the method comprises the steps: inputting a three-dimensional data set into a Toeplitz cyclic measurement matrix for compressed sampling, reconstructing the three-dimensional data set into a high-resolution signal through employing an improved orthogonal matching pursuit algorithm, and carrying out the detection of the high-resolution signal; analyzing and calculating a local variable coefficient through a sliding window, marking abnormal positions, and integrating the abnormal positions into an abnormal position set; inputting the abnormal position set into a pre-trained long and short term memory prediction model, evaluating the insulation residual life and the fault risk level, and calculating the maximum allowable output current of the DC charging pile in real time; the load impedance is dynamically adjusted and detected through the memristor array, the output current of the direct current charging pile is collected in real time, and synchronous compression wavelet transform is adopted to analyze a harmonic ridge line and extract a harmonic component; the accuracy of fault early warning is remarkably improved, rapid dynamic adjustment of the output current is achieved, and the technical problem of multi-physics field coupling monitoring of the rapid charging pile is effectively solved.

The invention discloses a distribution network area emergency insurance supply method and system based on vehicle network interaction, and relates to the technical field of distribution network area emergency insurance supply, and the method comprises the following steps: obtaining a distribution network area fault position and electric vehicle distribution data, and constructing a vehicle scheduling instruction set; driving the electric vehicle to move to a target area according to the scheduling instruction set, and transmitting magnetic field disturbance signals of different frequency bands through a vehicle-mounted coil array to obtain a magnetic field disturbance signal set; constructing a dynamic sensing matrix for each magnetic field disturbance signal, and performing compression reconstruction by adopting an orthogonal matching pursuit algorithm to obtain a line impedance feature vector group; constructing a security constraint set based on the line impedance feature vector group, and respectively applying the security constraint set to a multi-objective optimization algorithm to solve the optimal capacity calling proportion of the electric vehicle battery pack; and controlling the electric vehicle to output reverse power to the distribution network area based on the optimal capacity calling proportion, thereby solving the problem that power supply is difficult to optimize due to the fact that difference between multi-vehicle cooperation and a power line is not considered in a traditional method.

The invention discloses an underwater sound environment sensing method based on multi-array-element sparse channel estimation. An underwater sound receiving end receives signals transmitted through an underwater sound multipath channel through a multi-array-element array; performing Hilbert transform on the receiving signal of each array element to obtain an analysis signal, and calculating a cross-correlation function of the analysis signal and the transmitting signal; based on the cross-correlation function, sparse channel parameters, including path amplitude and time delay, of each array element are estimated by adopting an orthogonal matching pursuit algorithm combined with a constant false alarm detection dynamic threshold value; the method comprises the following steps: constructing an array response vector by using sparse channel parameters of a multi-array element array, and searching and estimating angles of arrival, including a direction angle and a pitch angle, of a multipath signal through a spatial spectrum peak value; and based on the arrival angles, amplitudes and time delays of the direct path and the reflection path, inverting an underwater environment structure through a ray acoustic theory, including a reflection point distance and a reflection surface normal vector, so as to realize three-dimensional perception of the underwater reflector. According to the invention, multipath resolution can be improved, and false alarm and missing detection can be effectively reduced.

The invention provides a comprehensive noise reduction performance evaluation method for a nonlinear ultrasonic detection signal noise reduction algorithm, and the method comprises the steps: firstly constructing a multi-working-condition noise reduction performance pre-screening mechanism based on error band analysis based on early-stage experimental data, and then introducing a radar map as an evaluation tool for the comprehensive noise reduction effect of the signal noise reduction algorithm. The noise reduction effects of a moving average method (MA), a spectral subtraction method (SS), a short-time Fourier transform method (STFT), a wavelet transform method (WT) and an orthogonal matching pursuit algorithm (OMP) are compared, and finally a signal noise reduction algorithm with the optimal comprehensive efficiency is screened out. On the basis of algorithm optimization, quantitative mapping rules between microcrack three-dimensional geometric parameters and relative nonlinear coefficients are analyzed through regression modeling, and the significant level of the correlation degree of the relative nonlinear coefficients and microcrack size parameters is effectively improved; and a high-confidence theoretical support is provided for quantitative nondestructive detection of the microcracks in engineering practice.

The invention relates to the technical field of electrical variable measurement, in particular to a self-adaptive power quality monitoring method for a power cabinet, which comprises the following steps of: starting a low-frequency acquisition unit arranged at a monitoring node of the power cabinet, and acquiring a low-frequency coarse scanning voltage signal of the power cabinet in a current monitoring period at a preset low-frequency sampling rate; according to the method, compressed observation vectors are transmitted, sparse basis inversion calculation is executed by using an orthogonal matching pursuit algorithm, high-precision power quality time domain waveform data are reconstructed, rich high-frequency transient details are restored under the condition of low-data-volume transmission, distortion feature analysis is carried out based on reconstructed waveforms, disturbance types are identified, and a monitoring analysis report is generated and uploaded. The real-time dynamic adjustment of the sampling scale and the matrix structure along with the signal fluctuation state is realized, the optimal balance of the data compression ratio and the signal reconstruction quality is achieved, the contradiction between the storage and transmission bandwidth limitation of massive high-frequency data in a complex power grid environment is solved, and the sensitivity for transient abnormality is improved.

The invention discloses a multi-source remote sensing reflectivity fusion method based on orthogonal matching pursuit and physical constraint, and the method comprises the steps: taking a multi-source remote sensing image as a data basis, and constructing a multiband sparse dictionary through time sequence alignment and overlapping sampling; then, an orthogonal matching pursuit algorithm introducing physical constraints is utilized to gradually select an image atom which is most matched with a target date in a sparse reconstruction process, and physical consistency of a reconstruction result in the aspects of hydrological conditions, phenological characteristics, spectral reflectivity and the like is ensured; and finally, through residual error return correction, day-by-day seamless 30-meter six-waveband reflectivity reconstruction is realized. According to the method, the stability and the physical interpretability of a reconstruction result are remarkably improved while efficient calculation is kept, continuous, multi-band and high-resolution time sequence data generation can be achieved in complex ecological environments such as lake wetlands, and high-precision remote sensing data support is provided for hydrological process simulation, ecological environment evaluation and carbon cycle research.

The invention relates to the technical field of wireless communication perception, in particular to a user perception method based on a multi-layer intelligent omnidirectional metasurface, and proposes to realize accurate perception of reflection and refraction user orientations by utilizing heterogeneous characteristics of propagation paths on two sides of a multi-layer intelligent omnidirectional metasurface structure. Based on the architecture, a user reflection and refraction type judgment algorithm based on uplink pilot frequency information is provided, and the user reflection and refraction types are determined based on the heterogeneous characteristics of user channels on the two sides of the multi-layer intelligent omni-directional metasurface and based on least square optimization problem solving and target function size comparison. The propagation path angle estimation algorithm specifically comprises the following steps: firstly, based on uplink pilot frequency information, designing a channel estimation algorithm comprising alternating optimization, constant-rank manifold optimization, convex relaxation and other methods to estimate and obtain a channel matrix by utilizing channel constant-rank and angular domain sparse characteristics; and finally, based on a user type determination result, obtaining user angle information based on each channel matrix by using an orthogonal matching pursuit algorithm.

The invention discloses a GNSS (Global Navigation Satellite System) signal sparse compression capture method for a low-correlation measurement matrix, which comprises the following steps of: firstly, constructing a code phase matrix and a frequency offset matrix, and carrying out sparse transformation on a downsampled GNSS signal to obtain a correlation matrix; the method comprises the following steps of: firstly, carrying out iterative optimization on a measurement matrix by taking a Gramb matrix as a matrix, then taking a Frobenius norm of a difference between the Gramb matrix and an equiangular tight frame matrix as a target function, alternately using singular value decomposition (SVD) and a shrinkage operator to carry out iterative optimization on the measurement matrix, and carrying out compression measurement on a correlation matrix through the optimized measurement matrix. Secondly, a correlation matrix of compression measurement is quickly and accurately reconstructed by using a least square operator and an orthogonal matching pursuit algorithm; and finally, when the peak value ratio of the primary peak to the secondary peak of the reconstructed correlation matrix is greater than a threshold value, outputting code phase time delay and Doppler frequency offset corresponding to the primary peak. And if the ratio of the primary peak value to the secondary peak value of the reconstructed correlation matrix is smaller than the threshold value, capturing the GNSS signal again. According to the method, the maximum correlation coefficient and the average correlation coefficient of the measurement matrix can be reduced, the GNSS signal capturing sensitivity is improved, and hardware resources in the signal capturing process in a GNSS receiver can be reduced.

The invention discloses an underwater sound double-expansion channel parameter estimation method based on Newton orthogonal matching pursuit. The method comprises the following steps: firstly, realizing frame synchronization by using a cross-correlation peak of an LFM signal and a received signal; a coarse estimation value of a Doppler coefficient is obtained on a discrete time frequency grid through an ambiguity function method by utilizing the self-correlation characteristic of m-sequence pilot frequency, multipath time delay of a channel is detected by utilizing a matched filtering peak value of a signal after frequency offset compensation, and a coarse estimation result of the channel is obtained; executing Newton iteration optimization in a continuous parameter space by adopting a Newton orthogonal matching pursuit algorithm to realize super-resolution off-grid estimation of the Doppler coefficient of the first data block; and finally, proposing an iterative block tracking strategy, initializing the compensation of the rear block through the estimated value of the front block, and updating the time-varying Doppler coefficient by using the balanced signal feedback to realize the dynamic tracking of the time-varying Doppler coefficient. According to the invention, the Doppler estimation root-mean-square error is obviously reduced, and the calculation efficiency is high.

The invention discloses a power distribution network fault signal reconstruction method, system and device based on K-SVD and orthogonal matching pursuit, and a medium, and belongs to the technical field of power systems, and the method comprises the steps: obtaining a fault signal from a monitoring device of a power distribution network, and carrying out the preprocessing of the signal; a K-SVD algorithm is used for dictionary learning, a sparse dictionary suitable for power distribution network fault signals is generated, and the dictionary is optimized through training of a signal data set; performing sparse representation on the signal by adopting an orthogonal matching pursuit algorithm, and realizing sparse decomposition of the signal by searching an optimal sparse representation coefficient; based on the sparse representation coefficient, reconstructing the signal from the sparse dictionary, calculating a reconstruction error, comparing the reconstruction error with an actual fault signal, evaluating a reconstruction effect and an error, and performing parameter optimization adjustment; and accurately judging the type of the fault according to the reconstructed signal. According to the method, the key information of the fault signal is effectively extracted in a complex noise environment, and the signal reconstruction precision is remarkably improved.

The invention discloses a compressed sensing coherent signal DOA estimation method based on Toeplitz reconstruction, and the method comprises the steps: carrying out the forward and reverse smoothing operation of a covariance matrix of a received signal, obtaining a new covariance matrix, carrying out the Toeplitz reconstruction of the covariance matrix, enabling the rank of the covariance matrix of the received signal to be recovered to the number of information sources, not to be affected by the signal correlation, and carrying out the estimation of the DOA of the compressed sensing coherent signal. And the azimuth estimation performance is improved. The method comprises the following steps of: firstly, reconstructing a covariance matrix by using Toeplitz, then performing eigenvalue decomposition by using the covariance matrix after Toeplitz reconstruction, and obtaining a DOA (Direction of Arrival) estimated value of a signal in combination with an orthogonal matching pursuit (OMP) algorithm. Simulation results show that compared with a traditional DOA estimation algorithm, the coherent signal DOA estimation method can accurately estimate the coherent signal DOA, has better estimation precision and resolution capability, and does not cause reduction of spatial resolution.

The invention belongs to the technical field of Raman spectrum detection, and relates to a mixture qualitative and quantitative analysis method and system based on Raman spectrum. The method comprises the following steps: taking spectral information of a mixture and a qualitative dictionary as input, adopting a sparseness-based adaptive step-by-step orthogonal matching pursuit method for analysis, preliminarily determining various pure substance components in the mixture and corresponding spectrums and weights thereof, and adding constraint conditions to the pure substance components to obtain a final qualitative analysis result. And finally, taking the mixture spectrum, the qualitative component list and the quantitative dictionary as input of an orthogonal matching pursuit algorithm, and carrying out iterative loop according to qualitative analysis steps to obtain a quantitative analysis result. The components and the concentration of the mixture can be quickly and accurately judged through an orthogonal matching pursuit algorithm; meanwhile, by adding constraint conditions, the problem that analysis results are affected by factors such as baseline drift and noise interference due to the reasons of spectrums is effectively avoided, and the accuracy and efficiency of the analysis results are effectively improved.

The invention discloses a gas compressor near stall state voiceprint continuous monitoring method and system, a medium and equipment, and the method comprises the steps: randomly selecting a preset installation number of microphone measuring points at an inlet to form a microphone array, and carrying out the synchronous collection of multi-channel sound pressure time domain signals; the method comprises the following steps: adding a rectangular window to a multi-channel sound pressure time domain signal, constructing an acoustic time domain signal segment, carrying out fast Fourier transform on the multi-channel acoustic time domain signal segment in a current time domain window to form a plurality of sound pressure frequency spectrums, judging whether a plurality of asynchronous single-tone characteristic frequencies which are non-integer multiples of current rotation frequency exist in the sound pressure frequency spectrums, and if yes, judging whether the sound pressure frequency spectrums exist in the sound pressure frequency spectrums; the method comprises the following steps: constructing a multi-channel microphone continuous frequency domain observation matrix of an asynchronous single-tone characteristic frequency, constructing an orthogonal Fourier transfer matrix based on a microphone sensor installation azimuth angle and a modal highest order, and establishing a compressor near-stall characteristic modal reconstruction model under a non-uniform few-measurement-point condition; and the amplitude is solved based on a block orthogonal matching pursuit algorithm, and compressor near-stall working condition monitoring based on voiceprint features is achieved.

The invention relates to an unmanned aerial vehicle physical layer secret key secret communication method based on an orthogonal time-frequency space. Comprising the following steps: establishing an unmanned aerial vehicle air-ground communication system consisting of a multi-antenna unmanned aerial vehicle base station, ground single-antenna legal users and ground passive eavesdropping illegal users; obtaining channel estimation vectors of all dominant paths by adopting a three-dimensional structured orthogonal matching pursuit algorithm; the method comprises the steps of obtaining a channel characteristic value of a communication channel through a path gain self-adaptive weighted moving average coding method, carrying out double-bit uniform quantization, carrying out Winnow key negotiation based on dynamic grouping and carrying out SHA-2 hash function confidentiality enhancement, and thus obtaining a physical layer key. According to the method, the channel dimension is converted through the orthogonal time-frequency space technology, the influence of Doppler frequency shift is effectively resisted, the channel reciprocity is improved, the key inconsistency rate is reduced, and the safety and stability of a communication system can be remarkably improved.

The invention discloses a millimeter wave scattering aperture sparse three-dimensional imaging method and a millimeter wave scattering aperture sparse three-dimensional imaging system, and aims to realize high-resolution three-dimensional reconstruction through a sparse reconstruction algorithm aiming at a non-vision field target imaging problem under a rough scattering surface condition. The method comprises the following steps: firstly, improving the spectral resolution of an echo signal by using a time domain zero padding technology, accurately positioning a scattering aperture position based on spectrum peak estimation and completing distance compensation, converting a non-vision field echo into an equivalent vision field echo, and retaining complete information of a target; secondly, in combination with target space scattering point sparsity and scattering aperture sparse sampling characteristics, a virtual array and a measurement matrix are constructed, an imaging problem is converted into a sparse reconstruction task, and a target scattering coefficient is efficiently solved by adopting a generalized orthogonal matching pursuit algorithm; the dependence on a mirror reflection condition is broken through, and a non-vision target can be accurately reconstructed in a diffuse reflection scene; compared with an existing scattering aperture algorithm, the method has the advantage that the imaging resolution and the anti-noise capability are remarkably improved through double sparsity constraints. The method has important application value.

The invention provides a foundation pit support structure lateral displacement prediction method based on dictionary learning fusion monitoring data. The foundation pit support structure lateral displacement prediction method comprises the steps that S1, a physical parameter database is established; s2, performing conversion to obtain weak prior distribution of a compressibility parameter ES; s3, sampling by using Latin hypercube to obtain N alternative parameter combinations of the soil body; s4, establishing a finite element model of the target foundation pit; s5, inputting a parameter combination to calculate corresponding side displacement data of the enclosure structure, and forming an over-complete dictionary D; s6, reading measured side displacement data of the enclosure structure to form a monitoring data matrix Y; S7, extracting corresponding data in the over-complete dictionary D, and forming a dimension transformation matrix A matched with the dimension of the monitoring data matrix Y; s8, obtaining an important atom set and a sparse solution vector x by using an orthogonal matching pursuit algorithm program; and S9, obtaining a side displacement prediction curve of the building envelope corrected by combining the monitoring data. The method has the advantages of high prediction precision, low calculation cost and high response speed.

The invention discloses an off-network channel estimation method and device based on backtracking momentum gradient descent. The method comprises the following steps of: acquiring a frequency domain observation symbol after pilot frequency normalization by using a transmitting pilot frequency symbol and a receiving pilot frequency symbol through least square; iteratively estimating channel parameters based on an orthogonal matching pursuit algorithm until the number of iterations is equal to preset channel sparseness; the estimated channel time delay serves as prior information, channel parameters are iteratively optimized in an off-network mode on the basis of a backtracking momentum gradient descent algorithm, and in the off-network optimization process, an Armijo backtracking line search method is used for adaptively adjusting hyper-parameters of the backtracking momentum gradient descent algorithm. According to the method, the backtracking momentum gradient descent algorithm and the orthogonal matching pursuit channel estimation are combined, and the channel estimation precision is improved through a momentum item acceleration mechanism of the backtracking momentum gradient descent algorithm and a parameter updating mechanism imitating a damping Hamiltonian differential equation; and the convergence rate and the hyper-parameter robustness of the high-precision off-network channel estimation method are effectively improved.

This invention belongs to the field of radar signal sorting technology, specifically relating to a radar signal sorting method, program, device, and storage medium based on improved sparse subspace clustering. This invention constructs a complex network of radar pulse sequences based on the self-representation properties between radar pulse data points in the same subspace. It utilizes a generalized orthogonal matching pursuit algorithm to reduce the number of connections between pulses from different radars in the complex network, thereby reducing computational complexity. Borrowing the idea of ​​graph segmentation, it uses Laplace spectral decomposition to mine the geometric correlations of radar pulse signals from the same source, achieving efficient sorting of non-ideal radar signals under complex electromagnetic environments. Simultaneously, this invention designs an algorithm based on an energy correction severance threshold, which can effectively solve for the accurate calculation of the number of cluster centers. This invention can effectively solve the problem of mis-sorting caused by aliasing or missing parameters of complex radar operating conditions.

The invention discloses a multi-parameter cooperative monitoring method for sulfur hexafluoride insulation equipment, and relates to the technical field of equipment monitoring, original parameters are collected, and the original parameters comprise a sulfur hexafluoride concentration value, a sulfur hexafluoride density value and an original partial discharge signal; performing discharge preprocessing on the original partial discharge signal to obtain a partial discharge signal; constructing a point spread function matrix, and performing sparse reconstruction on the partial discharge signals by combining an orthogonal matching pursuit algorithm to obtain coordinates and discharge quantity of a partial discharge source; based on the sulfur hexafluoride concentration value, the sulfur hexafluoride density value, the coordinates of the partial discharge source and the discharge quantity, threshold judgment is carried out, and a grading early warning result is obtained. According to the invention, the early discovery capability and fault positioning precision of the insulation defect of the large sulfur hexafluoride insulation equipment are obviously improved, and reliable technical support is provided for equipment state evaluation and intelligent operation and maintenance.

The invention belongs to the field of magnetic resonance sounding signal noise filtering, and particularly relates to a magnetic resonance sounding signal de-noising method based on backtracking generalized OMP, and the method comprises the steps: employing a magnetic resonance sounding water detector to carry out the sampling of a sparse signal at a frequency lower than the Nyquist frequency, and obtaining a noisy MRS signal; converting the noisy MRS signal into a two-dimensional matrix signal; dictionary learning is carried out on the two-dimensional matrix signals through a KSVD algorithm, and an updated dictionary is obtained after iteration; calculating a recovery matrix and measurement data by using an independently distributed Gaussian random matrix; performing iterative operation on the measurement data by using a backtracking generalized orthogonal matching pursuit algorithm, selecting an atomic sequence of maximum projection of the measurement data and a recovery matrix module, and obtaining a sparse coefficient matrix of the measurement data under the recovery matrix; and calculating a recovery signal according to the dictionary and the sparse coefficient matrix of the measurement data under the recovery matrix.

This invention relates to a method for secure physical layer communication for unmanned aerial vehicles (UAVs) based on orthogonal time-frequency space. The method includes the following steps: establishing an UAV air-to-ground communication system consisting of a multi-antenna UAV base station, a legitimate ground-based user with a single antenna, and an unauthorized ground-based user passively eavesdropping; obtaining channel estimation vectors for all dominant paths using a three-dimensional structured orthogonal matching pursuit algorithm; obtaining channel eigenvalues ​​of the communication channel using a path gain adaptive weighted moving average coding method; performing double-bit uniform quantization; and enhancing security through Winnow key negotiation based on dynamic grouping and SHA-2 hash function, thereby obtaining the physical layer key. This invention effectively combats the effects of Doppler shift by transforming the channel dimension using orthogonal time-frequency space technology, improving channel reciprocity, reducing key inconsistency rate, and significantly improving the security and stability of the communication system.

The invention discloses a broadband oscillation suppression method of a wind power flexible direct current transmission grid-connected system, and relates to the technical field of broadband oscillation suppression of a power system, and the method comprises the steps: firstly, recognizing the frequency information of broadband oscillation according to a multi-frequency oscillation frequency online recognition link; then, designing a multi-band wave trap by adopting a windowing orthogonal matching pursuit (OMP) algorithm; and finally, providing a real-time center frequency for the multi-frequency wave trap according to a frequency identification link so as to realize broadband oscillation self-adaptive suppression. According to the method, the time-varying oscillation frequency can be effectively tracked through an online identification algorithm, so that the method adapts to oscillation scenes under different system working conditions. The windowing OMP method provided by the invention can avoid the problem that the coefficient of the wave trap needs to be redesigned when the center frequency of the wave trap changes, reduces the calculation amount, improves the real-time performance of the algorithm, and achieves the self-adaptive suppression of broadband oscillation.

A compressor near stall state voiceprint continuous monitoring method, system, medium and equipment, in the method, a predetermined number of microphone measuring points are randomly selected at the inlet to form a microphone array, and multi-channel sound pressure time domain signals are synchronously collected; a rectangular window is added to the multi-channel sound pressure time domain signal to construct an acoustic time domain signal segment, and a fast Fourier transform is performed on the multi-channel acoustic time domain signal segment in the current time domain window to form multiple sound pressure spectra; it is judged whether there are multiple non-synchronous single-tone characteristic frequencies which are non-integral multiples of the current rotation frequency in the sound pressure spectrum; a multi-channel microphone continuous frequency domain observation matrix of the non-synchronous single-tone characteristic frequency is constructed; an orthogonal Fourier transfer matrix is constructed based on the installation azimuth angle of the microphone sensor and the highest order of the mode; a compressor near stall characteristic mode reconstruction model under non-uniform few measuring points is established; the amplitude is solved based on the block orthogonal matching pursuit algorithm, and the compressor near stall working condition monitoring based on the voiceprint characteristics is realized.

The application discloses a kind of based on multi-array sparse channel estimation underwater acoustic environment perception method, underwater acoustic receiving end is received through multi-array array signal that is propagated through underwater acoustic multipath channel;The received signal of each array element is subjected to Hilbert transform, and analytic signal is obtained, and the cross-correlation function of analytic signal and transmitting signal is calculated;Based on cross-correlation function, using the orthogonal matching pursuit algorithm of combining constant false alarm detection dynamic threshold, the sparse channel parameters of each array element are estimated, including path amplitude and time delay;Utilize the sparse channel parameters of multi-array array, construct array response vector, estimate the angle of arrival of multipath signal, including direction angle and pitch angle by space spectrum peak value search;Based on the angle of arrival, amplitude and time delay of direct path and reflected path, the underwater environment structure is inverted by ray acoustics theory, including reflection point distance and reflection surface normal vector, realize the three-dimensional perception of underwater reflector.The application can improve multipath resolution, effectively reduce false alarm and miss detection.

This invention discloses a non-cooperative multi-user localization method in near-field scattering environments, comprising two stages: signal source acquisition and user identification. When the number of users and the transmitted pilot signals are unknown, the method leverages the system's inherent characteristics and machine learning algorithms to achieve multi-user localization in complex scattering environments. In the first stage, utilizing the spatial orthogonality between signals from different locations in the near field, a synchronous orthogonal matching pursuit algorithm based on a deep convolutional neural network (CNN) is used, with a stop iteration indication provided, to acquire the positions of all users and scattering points. In the second stage, a signal energy-assisted k-means clustering algorithm is used, simultaneously incorporating the distance between data points and signal energy information into the cluster center update criterion to accurately identify users. This invention effectively solves the problem of non-cooperative multi-user localization in near-field scattering environments.

The present application belongs to the technical field of photoelectric countermeasure, and relates to a detection technology for a hidden camera device, and particularly provides a long-distance detection method for a hidden camera device based on sparse representation, which is used to overcome the problem of low recognition accuracy in the prior art; in the present application, after sparse representation of an image through wavelet transform, a measurement matrix group is obtained by compressing and projecting the sparse image using a random measurement matrix; a measurement matrix without static background and dynamic background information is obtained by performing a series of difference addition operations on the matrix group; then, the orthogonal matching pursuit algorithm is used to reconstruct and recover the measurement matrix to obtain a recovered image; finally, the cat-eye target area is screened out according to the image pixel value and the connected area area judgment method, so as to realize the cat-eye effect target recognition method based on sparse representation; in this way, the image data redundancy is greatly reduced, the recognition accuracy of the long-distance cat-eye target is effectively improved, and the anti-detection of the hidden camera device is realized, so as to eliminate the potential information leakage danger.

The application provides a seismic data processing method and device based on multiple types of prior information, and belongs to the technical field of oil and gas seismic exploration. The method comprises the following steps: based on original seismic data to be processed and corresponding well logging data, a seismic sparse representation target function coupled with multiple types of prior information is constructed, wherein the multiple types of prior information comprise low-frequency impedance prior information of the well logging data, seismic amplitude envelope prior information of the original seismic data, and support position prior information; a three-dimensional multi-channel Bayesian orthogonal matching pursuit algorithm is used to solve a maximum a posteriori probability density solution of the seismic sparse representation target function, wherein the maximum a posteriori probability density solution comprises a target reflection coefficient sequence of the original seismic data; a target wideband Ricker wavelet is obtained, and seismic data reconstruction is performed based on the target wideband Ricker wavelet and the target reflection coefficient sequence, so that target seismic data is obtained. According to the application, multiple types of prior information can be coupled to reconstruct seismic data, and the resolution of the seismic data is improved.