35 results about "Matching pursuit algorithms" patented technology

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 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 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 application discloses a radar high-resolution imaging method based on compressed sensing. The application comprises the following steps: step 1, an imaging model is established according to the joint block sparse characteristics of a random sequence stepped echo signal; step 2, a criterion of a block sparse signal space matching pursuit (BSSMP) algorithm is determined; step 3, the BSSMP algorithm is constructed according to the criterion; step 4, a sensing matrix Θ is constructed, so that the column full rank is satisfied, and a column set T∪T k is represented in the sensing matrix Θ; step 5, a condition that all the indexes selected by the BSSMP in the k+1th iteration are correct is established under the premise that at least K-r correct support elements are selected in the first k iterations of the BSSMP algorithm; and step 6, a condition and a required number of times for reconstructing X by the BSSMP algorithm are given. The application can effectively recover a signal, the established model fully considers the structural characteristics of the block sparse and joint sparse of the sequence echo signal, realizes joint processing of multidimensional signals, improves the imaging performance, and has high application value.

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 invention provides an improved channel estimation method based on combination of an RIP criterion and an SOMP algorithm, relates to the technical field of channel estimation of an RIS auxiliary system, is suitable for a channel estimation algorithm of an RIS auxiliary millimeter wave large-scale MIMO system, and aims to solve the problems that a traditional SOMP algorithm lacks sparseness priori information and an atom selection strategy is rigid. According to the method, a sparseness estimation method based on the RIP criterion is innovatively designed when atom number selection of initial iteration is set in the SOMP algorithm, a result serves as the initial atom selection number, and the atom number in the subsequent iteration process is dynamically adjusted by using the residual energy ratio of two iterations of the SOMP algorithm; experiments show that compared with traditional matching pursuit algorithms such as an OMP algorithm, an SOMP algorithm and an StOMP algorithm, the method has obvious advantages, and support is provided for the SOMP algorithm to be more suitable for practical application.

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 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 invention provides a fast estimation method for ultrasonic thickness measurement sound time of a large-curvature cable insulation layer, and belongs to the technical field of ultrasonic measurement of the cable insulation layer, the method combines short-time Fourier transform (STFT), an improved grey wolf algorithm (IGWO) and a matching pursuit algorithm (MP), and fits ultrasonic echo signals based on the matching pursuit algorithm on the basis of a Gabor function model. The algorithm firstly utilizes short-time Fourier transform to optimize an echo parameter search range, then utilizes an improved grey wolf algorithm to replace traversal calculation in matching pursuit, and improves echo parameter search efficiency and estimation precision. The method can effectively solve the problems of insufficient precision and low efficiency in ultrasonic traditional acoustic time estimation and thickness estimation, and has better estimation precision and stability in an acoustic system containing frequency dispersion and phase shift, so that the method meets the application requirements of real-time online thickness measurement.

This invention proposes a joint optimization and sparse signal processing method based on an orthogonal frequency division multiplexing (OFDM) system, belonging to the field of non-destructive measurement. It solves the problems of balancing high resolution and anti-interference capability, and the trade-off between real-time performance and measurement accuracy. The method includes: receiving OFDM signals and performing time-frequency decomposition; decomposing the signal into multiple orthogonal subcarriers using a fast Fourier transform; dynamically disabling interfered subcarriers based on their signal-to-noise ratio (SNR); performing Bayesian phase compensation on the retained subcarriers; iteratively optimizing the subcarrier switching states and phase compensation parameters, dynamically adjusting the SNR threshold to separate signal and noise; reconstructing the sparse signal using compressed sensing technology; and improving ranging and imaging resolution using an orthogonal matched pursuit algorithm. It is mainly used in fields such as underground target detection and dynamic radar sensing networks.

The invention relates to a mine micro-seismic signal noise reduction method based on sparse decomposition. The method starts with priori knowledge analysis of mine micro-seismic signal characteristic waveforms, and comprises the following steps: firstly, selecting Laplace wavelet parameters which are most matched with the micro-seismic signal characteristic waveforms through a correlation filtering method, and constructing a Laplace wavelet parameter dictionary according to the Laplace wavelet parameters; secondly, sparsely reconstructing a characteristic waveform of the micro-seismic signal by combining an orthogonal matching pursuit (OMP) algorithm; in consideration of large calculation amount and long time of a correlation filtering method, a whale optimization algorithm (WOA) is used to carry out rapid global parameter optimization. The analysis result of the simulated micro-seismic signal and the actually measured signal shows that the characteristic waveform of the micro-seismic signal can be effectively reconstructed by the provided method, the noise reduction of the micro-seismic signal is realized, and the method has certain anti-interference capability on noise; compared with a common ensemble empirical mode decomposition (EEMD) method, the sparse noise reduction method based on the self-adaptive Laplace wavelet dictionary provided by the invention has certain superiority.

The present application relates to the technical field of signal processing, and provides a kind of catenary non-stationary vibration signal sparse decomposition method, the method comprises: collecting impact vibration response signal, and carrying out denoising pretreatment;Establish parametric impact response atom model, and construct over-complete parametric atom dictionary;Using matching pursuit algorithm and combining sparse decomposition algorithm, obtain a series of linear combination of sparse atom components;According to the best matching atom set and its corresponding parameter vector obtained in iteration process, the incident wave component set and the reflected wave component set are obtained, and the decoupled incident wave signal and reflected wave signal are reconstructed.The present application realizes the accurate separation and reconstruction of rigid catenary mixed vibration waveform by constructing over-complete parametric atom library containing frequency modulation factor, combined with matching pursuit algorithm.

The invention discloses an ultrasonic image reconstruction method and device based on OMP, equipment and a medium. The method comprises the following steps: acquiring an initial time-space domain radio-frequency signal of a target object acquired by using an ultrasonic transducer array, and decomposing to obtain an initial space domain radio-frequency signal and an initial time domain radio-frequency signal; respectively processing the initial spatial domain radio frequency signal and the initial time domain radio frequency signal by adopting a pseudo-random sampling mask to obtain a target spatial domain radio frequency signal and a target time domain radio frequency signal; performing sparse reconstruction on the target spatial domain radio frequency signal and the target time domain radio frequency signal based on an orthogonal matching pursuit algorithm to obtain a spatial reconstruction result and a time reconstruction result; and determining an ultrasonic image reconstruction result of the target object according to a fusion result of the spatial reconstruction result and the time reconstruction result. According to the scheme, dynamic ultrasonic time super-resolution enhancement is realized by utilizing an orthogonal matching pursuit algorithm and combining with spatio-temporal information, and low-cost frame rate enhancement is realized while the ultrasonic image quality is ensured.

Computing systems and computer-implemented methods for removing brain stimulation artifacts in neural signals are disclosed. The method makes use of a matching pursuit algorithm to accurately extract the stimulation artifact. The disclosed method removes the stimulation artifact associated with individual stimulation pulses without needing additional information from previous stimulation pulses or other electrodes. The disclosed method is compatible for use with various stimulation frequencies, does not need any filtering, and can recover neural signals almost immediately after stimulation. The disclosed method is compatible for use in has great potential in closed-loop systems used in various neurological diagnostic and therapeutic procedures.

The invention provides a gas-bearing property semi-quantitative characterization method and a gas-bearing property semi-quantitative characterization system for solid-flow-gas two-stage collaborative decoupling of a deep coal reservoir. The method comprises the following steps: firstly, acquiring frequency-expanded high-resolution seismic data through time-depth calibration and nonlinear superposition data processing by using logging and post-stack seismic data; using a matching pursuit algorithm to obtain ABV, LFG and other viscoelastic parameters; secondly, on the basis of pre-stack sub-angle gather conversion, P-domain Fatti elastic parameters are calculated, and solid-fluid decoupling gas-bearing data are obtained through fitting rotation; and then, under the constraint of a zoning phase belt, establishing a gas-liquid decoupling GID indication parameter and a gas-bearing probability body, and finally, dividing a rich gas region and a differential gas region through a GID threshold value to realize semi-quantitative evaluation of the gas-bearing property.

The invention discloses a target local RCS (radar cross section) measurement method based on a matching pursuit algorithm, which comprises the following steps that: a probe scans and acquires target back scattering echo information through a'transmitting and receiving 'rotary table, the matching pursuit algorithm is carried out on echo electric field data, the scattering point distribution condition of a target is acquired, and the high-precision RCS value of the target is acquired through inversion. In a turntable scanning system, initialization parameters are included; obtaining target scattering echo data; extracting an equivalent scattering center of the target; selecting local scattering points of the interested part; performing inversion to obtain a far-field scattered field of the target; measuring the calibration body by using the same method; and calibrating and calculating the real RCS of the target. Measurement is carried out at the scattering far field of the target, distribution of two-dimensional space scattering points of the target is obtained based on the matching pursuit algorithm, the RCS value of a local device of the target at a certain angle along with frequency change can be calculated, and the electromagnetic scattering characteristic of the target can be reflected more quickly and accurately.

This invention discloses a blind estimation method for frequency-hopping signal parameters based on atomic features. The method involves: first, windowing, compressed sampling, and atomic feature extraction of the frequency-hopping signal; then, roughly estimating the hopping time based on the distribution of atomic features within different windows; next, precisely estimating the hopping time, hopping period, and hopping speed using a block-diagonalized Fourier orthogonal matrix; and finally, estimating the frequency of each hop signal using an orthogonal matching pursuit algorithm, and obtaining the frequency-hopping pattern of the entire sample by concatenating each hop signal. This invention can adjust the search step size and compression ratio to change the computational complexity and estimation accuracy, adapting to different signal-to-noise ratio (SNR) environments. In high SNR environments, increasing the search step size and compression ratio effectively reduces computational complexity, while in low SNR environments, decreasing the search step size and compression ratio effectively improves estimation accuracy.

The invention discloses a three-dimensional stratum quality factor model construction method and related equipment, and relates to the technical field of geophysical exploration, the method comprises the following steps: obtaining target seismic data, the target seismic data comprising VSP logging data, migration velocity data, post-stack seismic data and pre-stack seismic data; obtaining quality factor models of different information sources based on the target seismic data; and based on the structure of the deep convolution combination model, fusing the quality factor models of different information sources to obtain a multi-information fused three-dimensional stratum quality factor model. According to the method, Q estimation and Q-V relation establishment are performed on VSP logging information through a complex field matching pursuit algorithm, so that Q models established by other methods are accurately calibrated. Secondly, providing a multi-information source Q value model by establishing pre-stack and post-stack Q value models; multi-source multi-information fusion is carried out based on the deep convolution combination model, and compared with a traditional mode, the method is more stable and efficient and has a better effect.

This invention belongs to the field of fiber optic distributed sensing and bridge structural health monitoring technology. It discloses a crack monitoring system and method for small-to-medium span bridges based on multidimensional enhanced OFDR. The method includes the following steps: acquiring Rayleigh scattering interferometry signals; converting the time-domain signal to a frequency-domain signal and processing the time-domain signal to obtain suspected crack regions; precisely aligning the frequency-domain signal to the time axis; for suspected crack regions, performing sparse decomposition on the normalized frequency-domain signal, and then reconstructing it using an orthogonal matching pursuit algorithm and wavelet inverse transform to obtain a denoised frequency-domain signal; segmenting the denoised frequency-domain signal and calculating the first frequency shift value through cross-correlation; constructing a search interval centered on the first frequency shift value, and performing global optimization using a particle swarm optimization algorithm to obtain an optimized second frequency shift value; and demodulating to obtain the crack width. This invention can balance measurement speed, measurement distance, and positioning accuracy.

The invention provides a deception jamming detection method based on sparse recovery. The deception jamming detection method comprises the following implementation steps: constructing a cyclic correlation matrix, an observation vector and a dictionary matrix; constructing a direction of arrival estimation spectrum based on a sparse recovery orthogonal matching pursuit algorithm; and obtaining the direction of arrival angle of each satellite signal and a deception jamming detection result. According to the method, the cyclic correlation matrix is obtained by carrying out cyclic autocorrelation on the data matrix, the cyclic correlation matrix is vectorized to obtain the observation vector, and the direction-of-arrival spectrum is constructed through the observation vector based on the sparse recovery orthogonal matching pursuit algorithm, so that the periodic repetition characteristic of satellite signals is fully utilized; according to the method, the number of snapshots required by the constructed direction-of-arrival spectrum is small, deception jamming detection is carried out by judging the amplitude ratio and the detection threshold in the direction-of-arrival spectrum, the defect that a network model adopted in the prior art is complex in structure is overcome, and the detection efficiency is improved.

This invention provides a sparse two-dimensional magnetic imaging method. The method includes: equating the magnetic anomaly field generated by a ferromagnetic target to the superposition of magnetic fields generated by multiple magnetic dipoles on a two-dimensional horizontal slice; wherein the ferromagnetic target includes targets such as underground pipes, ironware and metal fragments in archaeological work, and landmines and shells in military applications; uniformly dividing the two-dimensional horizontal slice into small rectangular grid units, where the magnetic moments of the magnetic dipoles in the grid where the target is located are non-zero, and the magnetic moments of the spatial magnetic dipoles without the target are all zero; and estimating the parameters of the magnetic dipoles using an orthogonal matching pursuit algorithm to achieve two-dimensional magnetic imaging of the target. This embodiment simplifies the complex magnetic field distribution to the superposition of multiple magnetic dipoles, reducing the complexity of the problem; through modeling and parameter estimation of the magnetic anomaly field, the location and identification of ferromagnetic targets can be achieved, which has important application value in fields such as geological exploration, archaeology, and landmine detection.

The invention discloses a transient electric energy quality detection method for wide-area protection of a power grid. The method comprises the following steps: acquiring a fault signal containing a fundamental wave, a harmonic wave, an inter-harmonic wave and an attenuation direct current component; performing frequency spectrum estimation to obtain an estimated value of a fuzzy attenuation direct-current component, establishing an exponential function model to iteratively calculate a reconstructed attenuation direct-current component, solving a least square problem, taking the reconstructed attenuation direct-current component with the minimum error with the fuzzy attenuation direct-current component as final estimation, and removing the reconstructed attenuation direct-current component from a fault signal; a Taylor Fourier multi-frequency model is reconstructed for a fault signal without an attenuation direct current component to change the frequency resolution, then a dynamic phasor estimation problem is converted into a compressed sensing solving problem, and finally an improved sparsity self-adaptive matching pursuit algorithm is used for solving to obtain estimated values of phasor, frequency and frequency change rate. According to the method, the attenuation direct current component can be quickly estimated, and accurate voltage and current phasors can be calculated within an extremely short time window after a fault occurs.

The application discloses a multi-sound source positioning method based on spatiotemporal feature adaptive matching pursuit. The method first acquires audio signals collected by a microphone array, extracts amplitude difference and phase difference features, and constructs a three-dimensional time-frequency tensor; the feature tensor is input into a deep neural network containing a residual convolution block and an attention mechanism injection, and a ratio mask of a single sound source dominant area is output; the mask is used to screen effective time-frequency points, and an improved MUSIC algorithm is combined to perform spatial spectrum estimation, and a spatial histogram is generated; finally, according to the obtained histogram, an adaptive double-atom width matching pursuit algorithm is invented to analyze the total number and direction of arrival of target sound sources. The application utilizes the nonlinear feature filtering capability of deep learning to accurately divide the single sound source dominant area, combines spatial spectrum and matching pursuit algorithm, and combines deep learning with traditional methods, so that the positioning accuracy is ensured and the calculation load of the system is reduced.

This invention discloses a terahertz-laser speckle-ultrasound composite nondestructive testing method for key components of an engine combustion chamber. The method includes: using a terahertz time-separable convolutional network nondestructive testing method, based on the reflection characteristics of terahertz waves at the interlayer interface of composite materials, to image and locate debonding defects in the aramid fiber layer of the shell; using a laser speckle interferometry nondestructive testing method, based on the speckle interferometry measurement principle, combined with high-quality image reconstruction and deep learning technology to detect surface and internal defects in the shell; using an ultrasonic nondestructive testing method combined with a matching pursuit algorithm, based on the reflection principle of ultrasonic waves at discontinuous impedance interfaces, to achieve deep localization and imaging of debonding defects in discontinuous impedance layers; and visually assessing the defect damage status based on the composite test results. This invention solves the problem of traditional nondestructive testing methods being unable to comprehensively and accurately detect defects in complex structures and multilayer materials, achieving high-precision detection of defects in key components of the engine combustion chamber.

The application provides a generalized S transform time-frequency analysis method based on dictionary learning and matching pursuit decomposition, comprising the following steps: step 1, inputting an original seismic signal set, and constructing a wavelet library based on K-SVD dictionary learning; step 2, combining the fast complex domain matching pursuit algorithm with the wavelet library constructed based on K-SVD dictionary learning to improve the speed and accuracy of seismic signal decomposition; step 3, obtaining the time-frequency spectrum of independent wavelets based on the generalized S transform, and superimposing the time-frequency spectrum of all independent wavelets to perform time-frequency joint analysis of the high-resolution time-frequency spectrum; step 4, studying the change of reservoir characteristics by using single-frequency attributes based on the high-resolution time-frequency spectrum; and step 5, eliminating the high-resolution time-frequency spectrum representing the change of reservoir characteristics after strong reflection. The generalized S transform time-frequency analysis method based on dictionary learning and matching pursuit decomposition forms a high-resolution time-frequency analysis technology according to the development of actual application requirements and the characteristics of non-stationary signals.

The application discloses a kind of multi-carrier underwater acoustic communication channel estimation methods under time delay-scale double expansion, for general multi-carrier underwater acoustic channel model, the influence of insufficient characterization of Doppler effect, and the precision of traditional channel estimation method is limited, it is proposed to utilize time delay-scale double expansion channel model to characterize the influence of Doppler effect on multi-carrier underwater acoustic communication system, and utilize orthogonal matching pursuit algorithm to separate multipath signal, and the delay and size parameter estimation results of each path of channel are corrected using Newton method, while realizing off-grid channel estimation, reduce algorithm complexity.The characterization of the present application to Doppler effect is more in line with the real scene, and realizes off-grid channel estimation and low complexity algorithm, so that the channel estimation result is more accurate.

The invention discloses a sea area deep layer flat lake group sandstone reservoir-oriented gas-bearing prediction method and device, and the method comprises the steps: determining gas-bearing sensitive parameters of a reservoir in a research area through surface rock physical analysis, and determining a fluid factor expression; an elastic parameter body is obtained through pre-stack inversion, and a fluid factor expression is adopted for calculation to obtain a fluid factor data body; superposing the pre-stack gathers of the research area into a plurality of part angle superposition bodies, analyzing the frequency spectrum characteristics of a gas-bearing layer section, a water-bearing layer section and a non-reservoir layer section of a three-dimensional data body around a well of each part angle superposition body, and then performing frequency spectrum decomposition by adopting a high-precision matching pursuit algorithm; analyzing the high-frequency attenuation gradient attribute characteristics of the plurality of partial angle superposition bodies, and performing differentiation processing to obtain a final sensitive attenuation attribute body; and based on the fluid factor data volume and the sensitive attenuation attribute volume, carrying out comprehensive prediction on the gas-bearing property of the research area. According to the method, the multiplicity of solutions of gas-bearing prediction can be reduced, and the gas-bearing prediction precision is improved.

This invention relates to the field of electrical variable measurement technology and discloses a power quality monitoring system for distribution cabinets based on Internet of Things (IoT) communication. The system includes edge acquisition nodes for the distribution cabinet, an IoT communication link, and a cloud-based reconstruction platform. The edge acquisition nodes acquire voltage and current signals, separate the steady-state fundamental signal to generate a sparse basis matrix, and use a pseudo-random matrix to compress and sample the original mixed signal to generate a low-dimensional measurement vector. The low-dimensional measurement vector is combined with the feature parameters of the steady-state fundamental signal to form a multi-mode data packet, which is then transmitted via the IoT communication link. The cloud-based reconstruction platform extracts the feature parameters to reconstruct the sparse basis matrix and combines it with the low-dimensional measurement vector to execute an orthogonal matching pursuit algorithm to reconstruct the high-frequency power quality disturbance waveform. This invention reduces the amount of high-frequency sampled data transmitted in the IoT link, overcomes the narrowband communication bandwidth problem, preserves the complete waveform characteristics of high-frequency disturbances, and avoids power quality characteristic distortion.

The application discloses an offline sleep slow wave detection system, comprising: a sleep EEG collection module for collecting and storing sleep EEG signals of a target to be measured, and transmitting the sleep EEG signals to a data processing and visualization module via a data transmission module; the data processing and visualization module is used for processing the received sleep EEG signals, selecting specific sleep EEG signals in the processed sleep EEG signals, and detecting slow waves in the specific sleep EEG signals according to an improved matching pursuit algorithm based on a pre-constructed dictionary; the application constructs the dictionary by limiting the base function to a preset function similar to the shape of the sleep slow wave, and completes the slow wave detection by combining the dictionary with the matching pursuit algorithm; the detection process depends on the waveform rather than the hard amplitude index, so it is suitable for people with low amplitude, has a wider application scenario, can greatly reduce the labor cost, and has high sleep slow wave detection efficiency, accuracy and reliability.

The invention discloses a mud pulse communication channel estimation method, equipment, a medium and a product, relates to the field of mud pulse communication channel estimation, and aims to apply a Newton orthogonal matching pursuit method to mud pulse communication channel estimation for the first time and optimize the mud pulse communication characteristics. According to the Newton orthogonal matching pursuit method, the sparse recovery advantage of the orthogonal matching pursuit algorithm and the optimization capability of the Newton method are combined, the off-grid effect can be effectively overcome, more accurate channel time delay and gain estimation can be provided, the performance of a communication system can be improved, and compared with a traditional mud pulse communication channel estimation method, the performance of the communication system is improved. According to the mud pulse communication channel estimation method based on Newton orthogonal matching pursuit, an accurate channel estimation result can be provided for high-speed communication, so that the problem that mud pulse communication channel estimation is not accurate enough is solved.