7 results about "Matching pursuit algorithms" patented technology

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 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.

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 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.

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.

This invention provides a method for monitoring abnormal vibration in elevator cars, belonging to the field of elevator monitoring technology. This invention utilizes sparse representation theory to construct an overcomplete dictionary and uses a matching pursuit algorithm to sparsely reconstruct the impact component from mixed vibration signals. It then combines a spectral kurtosis map to locate the optimal filtering frequency band, converts the impact component into a spatial domain signal based on the car speed integral result, constructs a multi-dimensional vibration feature matrix sample covariance matrix, and uses random matrix theory to derive a collaborative monitoring algorithm for elevator group vibration status to locate abnormal elevators. Finally, it inputs the impact component characteristics, spatial location coordinates, and degradation rate assessment value into a physical constraint, embedding them into a temporal inference artificial intelligence model to output fault category and severity score. Finally, it generates a maintenance decision report based on a comprehensive risk index. This invention solves the technical problem of accurately locating the spatial location of elevator guide rail defects and assessing their degradation trend in real time under conditions of multiple concurrent impact sources and insufficient signal-to-noise ratio.