53 results about "Orthogonal matching pursuit algorithm" patented technology

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 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 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.

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.

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.

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.

The invention relates to an improved Newton orthogonal matching far-field sound source tracking method based on mobile cluster equipment, and belongs to the technical field of sound source positioning. Aiming at the problems of difficulty in time delay estimation, low positioning precision and inconvenience in fixed array deployment in a low signal-to-noise ratio environment in a traditional sound source positioning technology, the method comprises the following steps: extracting a target sound source fragment signal through a sound source activity detection scheme; acquiring a narrow-band signal by utilizing low-pass filtering and band-pass filtering; determining array element space coordinates based on global positioning system equipment; constructing a maximum likelihood function, and realizing initial direction finding and fine correction of a sound source by combining density-based noise application spatial clustering and a Newton orthogonal matching pursuit algorithm; and searching the mapping relation between the array element number and the spatial distribution and the positioning capability. According to the method, the positioning precision and the anti-noise robustness are remarkably improved, the deployment flexibility is enhanced, and efficient guidance is provided for scientific arrangement of mobile cluster equipment.

The application discloses a kind of fast high-resolution imaging methods based on vortex electromagnetic wave, which constructs the two-dimensional imaging model of pitch angle and azimuth angle based on concentric circular ring array, realizes low-rank decomposition to the received echo and observation matrix by means of spherical harmonics function, introduces orthogonal matching pursuit algorithm to the scattering point in observation plane for target reconstruction, so as to realize the two-dimensional joint imaging of radar target pitch angle and azimuth angle.The imaging mode of joint OMP algorithm and spherical harmonic decomposition proposed in the application can realize higher imaging resolution compared with the traditional generalized inverse solution mode, while greatly reducing the operation amount of imaging process, effectively saving calculation time.

The invention particularly relates to a sparse array DOA (direction of arrival) estimation method based on an optimized array manifold dictionary, and the method comprises the steps: firstly carrying out the layout optimization of an initial uniform area array through combining a genetic algorithm, constructing an eight-element sparse array structure, and maintaining a directional diagram which is close to a full array while greatly reducing the number of array elements; performing energy screening on multichannel observation signals acquired by the sparse array, and extracting a dominant frequency band to improve an effective signal-to-noise ratio; and an optimized array manifold dictionary is constructed based on a phase compensation mechanism and an amplitude weighting mechanism, and the internal correlation of the dictionary is reduced by minimizing off-diagonal elements of a dictionary Gramer matrix. And on the basis, sparse reconstruction is carried out on the optimized dictionary by using an orthogonal matching pursuit algorithm, so that a direction-of-arrival estimation result of the sound source signal is obtained. According to the scheme, inherent direction finding fuzziness and grating lobes of the sparse array are effectively suppressed, the direction estimation precision is improved, and high reliability and high robustness are still kept under the condition that the number of array elements is remarkably reduced.

The application discloses a power distribution network protection method and device, electronic equipment and computer program product. It relates to the technical field of power distribution network protection. The method comprises the following steps: acquiring node voltages of H target node partitions of a power distribution line; dividing multiple domains according to the H target node partitions; constructing node voltage fault components of the domains based on the node voltages of each domain; determining node impedance matrices of each domain; determining node current fault components to be solved according to the node voltage fault components and the node impedance matrices; calculating the node current fault components to be solved based on an orthogonal matching pursuit algorithm, and solving the node current fault components of each domain; and controlling nodes at both ends of a fault area to be disconnected according to the relationship between each node current in the node current fault components of each domain and a node current fault threshold. The application solves the problem that the traditional power distribution network protection technology takes a long time to locate a fault point and has low accuracy, thereby affecting the safety of the power distribution network.

The invention discloses a folding clutter suppression method based on a frequency agility radar, and the method comprises the steps: setting the pulse number of a frequency agility pulse sequence, a frequency hopping interval and a frequency hopping coefficient of each frequency agility pulse signal, and transmitting a plurality of frequency agility pulse signals; calculating the carrier frequency of the echo signal received by the frequency agility radar according to the frequency hopping interval and the frequency hopping coefficient; filtering the echo signal by using a frequency domain filter corresponding to the echo signal carrier frequency, and outputting the filtered echo signal; respectively performing impulse response and convolution processing on the time domain and the frequency domain of the filtered echo signal by using a matched filter, and outputting the echo signal after matched filtering; and sparse reconstruction is carried out on observation signals in the sparse reconstruction model by using an orthogonal matching pursuit algorithm or an alternating direction multiplier method to obtain sparse vectors, so that target signal energy is enhanced, non-matching interference components are suppressed, significant enhancement of weak and small targets is realized, and folding clutters and noise are suppressed.

The application discloses a method for estimating parameters of a water acoustic double-spreading channel based on Newton orthogonal matching pursuit, which comprises the following steps: first, frame synchronization is realized by using the cross-correlation peak of an LFM signal and a received signal; second, the coarse estimation value of a Doppler coefficient is obtained on a discrete time-frequency grid by using the autocorrelation characteristics of an m sequence pilot and a blurring function method, the multipath time delay of a channel is detected by using the matching filter peak value of a frequency offset compensated signal, and a coarse estimation result of the channel is obtained; third, the Newton orthogonal matching pursuit algorithm is used to perform Newton iteration optimization in a continuous parameter space, and the super-resolution off-grid estimation of the Doppler coefficient of a first data block is realized; and finally, an iterative block tracking strategy is proposed, the compensation of a latter block is initialized by using the estimation value of a former block, the time-varying Doppler coefficient is updated by using the feedback of an equalized signal, and the dynamic tracking of the time-varying Doppler coefficient is realized. The application significantly reduces the root mean square error of Doppler estimation and has high calculation efficiency.

The invention relates to a compressed sensing-based remote sensing image full-process adaptive coding and decoding method, and relates to the technical field of space remote sensing imaging. The method comprises the following steps of: calculating an image content complexity self-adaptive selection coding mode by utilizing a maximum between-class difference method; extracting an image feature factor, establishing a significance model of a corresponding mode, and guiding the coding tree block to carry out adaptive form partitioning; detecting the sparsity of the coding block through an information density function to mine the sparsity of the image, and realizing the adaptive sampling rate distribution of the image; and adaptively selecting a reconstruction matrix and a sparse base according to the category of the coding block, setting an iteration threshold according to the known sparseness, and reconstructing the coding block by using an orthogonal matching pursuit algorithm. According to the method provided by the invention, the compression ratio does not need to be manually preset, the sampling mode can be selected according to the image content, the compression efficiency is adaptively set by detecting the sparseness, and the requirements of on-satellite rapid compression and ground high-fidelity reconstruction are met.

The invention belongs to the field of radar signal processing, discloses a method for acquiring a one-dimensional range profile of a high-speed moving target by sub-pulse linear frequency modulation emission and inter-pulse frequency agility radar, and realizes frequency agility waveform echo high-speed target detection and correction based on sparse representation. In the prior art, distance and Doppler parameter estimation of target echoes can be generally realized by adopting sparse representation and sparse processing, and orthogonal matching pursuit (OMP) is one of common solution algorithms. The carrier frequency of a frequency agility radar jumps randomly between pulses, and the acquisition of a one-dimensional range profile of a high-speed moving target not only needs to correct inter-pulse range migration, but also needs to compensate intra-pulse range-Doppler coupling generated by the change of the carrier frequency. According to the method, firstly, compensation of intra-pulse distance-Doppler coupling is carried out, then distance migration correction is carried out on each frequency agility pulse based on a KeyStone method, sparse processing is carried out on a corrected echo signal by adopting an orthogonal matching pursuit algorithm, and a high-resolution one-dimensional distance image of a target is obtained. Experimental results show that the one-dimensional range profile processing method for performing inter-pulse range migration correction through the KeyStone algorithm is beneficial to target detection in high-speed motion.

The application provides a time delay-Doppler joint estimation method under polar pulse noise interference. On the basis of the orthogonal matching pursuit algorithm, the maximum correlation entropy criterion and the l1 norm constraint are introduced to enhance the robustness under the pulse noise, and a fast solving algorithm based on a distributed iterative optimization strategy is used to improve the calculation efficiency. Compared with the existing same type estimation method, the method has a wider application range, higher estimation accuracy and stronger robustness.

The invention discloses a millimeter wave large-scale MIMO adaptive-fast convergence sparse recovery algorithm, which is an adaptive multi-dimensional joint correlation synchronous orthogonal matching pursuit algorithm. According to the algorithm, a plurality of atoms are selected in parallel in single iteration, so that the convergence process is remarkably accelerated. Moreover, an adaptive termination criterion can be realized without prior information such as channel sparseness and noise power. Simulation results show that the convergence speed of the algorithm is increased by about 50% compared with that of a traditional SOMP algorithm, and a normalized mean square error equivalent to that of the traditional SOMP algorithm can be obtained under the condition of lacking channel sparseness and noise power prior information. Especially in an environment with a signal-to-noise ratio of 8-23dB, even if channel noise power information does not exist, sparseness estimation errors can be effectively reduced, and the method is superior to a traditional algorithm. Therefore, the algorithm provided by the invention has strong self-adaptability and can effectively cope with the dynamic change of the channel.

The application provides a multi-modal resonant signal decoupling separation and fault ranging method, comprising the following steps: building an electromagnetic transient simulation model of a to-be-observed line; constructing a mapping database of time difference and wave velocity; obtaining a multi-modal resonant frequency spectrum database under a non-fault condition; determining a fault line section and a preliminary position based on the mapping database and a time difference method; setting a simulation fault near the preliminary position, obtaining a resonant frequency spectrum database of the fault line section through frequency domain simulation; converting a fault traveling wave signal to a frequency domain to obtain a measured frequency spectrum; reconstructing and separating the measured frequency spectrum by using an orthogonal matching pursuit algorithm to obtain a fault line frequency spectrum; and determining a fault position by calculating a correlation coefficient of the fault line frequency spectrum and the resonant frequency spectrum database through weight weighting. The application eliminates a detection blind area of a short-distance cable under a long-distance overhead line background, and improves the reliability of mixed line fault ranging.

The embodiment of the application provides a kind of telesignalling detection method and device, belong to telesignalling detection field;The method and device include by chaos mapping generation first non-uniform sampling point;Obtain the first telesignalling signal of the first non-uniform sampling point, input the joint feature vector into reinforcement learning model, output sampling density adjustment coefficient;Adjust the sampling interval of first non-uniform sampling point, obtain second non-uniform sampling point, utilize improved orthogonal matching pursuit algorithm to carry out second telesignalling signal reconstruction, obtain second telesignalling signal reconstruction signal;Based on the second telesignalling signal reconstruction signal carries out abnormal discrimination, completes telesignalling detection.The application generates non-uniform sampling point by chaos mapping, extracts signal time-frequency feature in combination with deep learning model, and optimizes sampling density dynamically using reinforcement learning, finally realizes the adaptive reconstruction and abnormal detection of telesignalling signal through fractal dimension and energy analysis, can improve the efficiency and accuracy of power equipment telesignalling signal detection.

The application discloses a bearing weak fault feature extraction method based on a parameter dictionary and an OMP algorithm, and establishes a sparse representation model of a rolling bearing vibration signal according to collected rolling bearing vibration data; the rolling bearing vibration data comprises rolling bearing vibration signals with useful fault information and useless background noise information; according to a Laplace wave parameter dictionary, an orthogonal matching pursuit algorithm is adopted to solve a sparse coefficient matrix of the signal sparse representation model of the rolling bearing; the sparse coefficient matrix is reconstructed with the Laplace wave parameter dictionary, envelope analysis is performed on the reconstructed signal, weak fault features are extracted, and bearing fault diagnosis is realized. The application can not only effectively reduce the algorithm complexity of the original method, but also more accurately extract bearing weak fault features under strong background noise, and can simply and effectively perform rolling bearing fault diagnosis in engineering practice.

The invention provides an orthogonal matching pursuit algorithm-based ultrasonic non-linear metal material microcrack size detection method. The method comprises the following steps: step 1, preparation before detection; step 2, ultrasonic signal emission and reception for microcrack detection; step 3, signal processing and analysis; according to the invention, through a one-transmitting and one-receiving ultrasonic excitation and receiving mode, data processing is carried out on a received signal, so that the size of the microcrack on the surface of the metal material is detected; an orthogonal matching pursuit algorithm is introduced to research an upper computer signal processing method, and accurate calculation of a nonlinear coefficient is achieved. The method has the advantages of high microcrack detection precision, high detection applicability and wide detection range, and is suitable for accurate detection of the microcrack size of the material in service.

The invention provides an offshore wind power medium-low speed unit main bearing state monitoring method and system, and relates to the technical field of wind power equipment monitoring. The method comprises the four steps of multi-source signal synchronous acquisition, signal preprocessing, fault feature extraction and fault and lubrication state diagnosis, wherein signals are synchronously acquired through multiple types of sensors according to preset parameters, and signal quality is improved through time-frequency conversion, denoising, standardization and pseudo-color processing; a POPgram method is adopted to extract fault features, and an RGB three-color square diagram is generated to realize visualization; fault classification and amplitude feature retention are realized based on improved sparse Bayesian learning and an orthogonal matching pursuit algorithm; and the multi-signal fusion is combined to realize the staged diagnosis of the lubrication state. The technical problems of multi-source data fusion, complex working condition fault extraction, lubrication state monitoring and the like are solved, the diagnosis precision and reliability are improved, the method is adaptive to multiple manufacturers and multiple models of units, the operation and maintenance time can be shortened, the cost is reduced, and guarantee is provided for safe operation of the offshore wind power main bearing.

The invention belongs to the technical field of underwater acoustic communication, and discloses an underwater acoustic channel estimation method based on combination of time-varying tracking and virtual signals, which is applied to an OFDM (Orthogonal Frequency Division Multiplexing) underwater acoustic communication system adopting a cyclic prefix. The method comprises the following steps: firstly, performing singular value decomposition denoising preprocessing on a received pilot signal, and setting a threshold value based on an energy function to screen an effective path; secondly, path clusters are divided through a clustering algorithm by using historical channel time delay parameters; estimating a time-varying offset parameter of each cluster by adopting an orthogonal matching pursuit algorithm; finally, after a virtual channel signal is constructed, the virtual channel signal and an actual observation signal are jointly input into a time sequence multiple sparse Bayesian learning algorithm for joint channel estimation. According to the method, the noise robustness is enhanced through a dual-signal mechanism, the fast time-varying channel characteristic is accurately tracked by using the cluster-level offset parameter model, and the channel estimation accuracy and stability in the low signal-to-noise ratio and fast time-varying environment are remarkably improved.