22 results about "Radon transform" patented technology

The methods may include obtaining a frequency-domain seismic wavefield associated to grid points and organized into slices. Each slice is organized into a sequence of overlapping windows. The methods may further include, for each slice, determining a value of a first intermediate function using the frequency-domain seismic wavefield for each grid point in a first overlapping window, determining a value of the first intermediate function using the frequency-domain seismic wavefield for each incremental grid point in each n-th overlapping window, where n is an integer greater than one, and determining a value of the first intermediate function by selecting the previously-determined value of the first intermediate function for each common grid point in each n-th overlapping window. The methods may still further include determining, using a second intermediate function, a Radon-transformed seismic wavefield using the value of the first intermediate function for the grid points. A non-transitory computer-readable memory and systems are also disclosed.

The invention discloses a wing aerodynamic optimization design method based on wavelet and Radon transform, which comprises the following steps: acquiring a plurality of wing shape parameter vectors of a to-be-optimized wing, and taking each wing shape parameter vector as the input of a trained wing aerodynamic optimization model to obtain a plurality of aerodynamic performance index vectors corresponding to each wing shape parameter vector; according to the method, a wing aerodynamic optimization model is designed, the wing aerodynamic optimization model comprises a wavelet neural operator and Radon transformation, the wavelet neural operator carries out kernel integration in a wavelet domain, cross-scale features from a macroscopic structure to microscopic turbulence in a flow field are accurately captured, then the multi-scale features are projected to a Sinogram domain through Radon transformation, and the wing aerodynamic optimization model is obtained. The characterization capability of key flow structure directions such as shock waves and separation zones is enhanced by using angle reweighting, and direction-sensitive compact flow field characterization is constructed. On the premise that the calculation cost is remarkably reduced, rapid and high-precision prediction and optimization design of the aerodynamic performance of the wing are achieved.

The application provides a multiple wave suppression method, device, equipment and storage medium, and belongs to the technical field of seismic data processing. The method comprises the following steps: determining first seismic data of a plurality of first seismic trace sets; determining the curvature and frequency of each seismic trace in the first seismic trace set; determining the maximum curvature and minimum curvature in the curvatures of a plurality of seismic traces, and determining the maximum frequency in the frequencies of a plurality of seismic traces; generating a three-dimensional conical body filter based on the maximum curvature, the minimum curvature and the maximum frequency; performing three-dimensional Radon transform on the first seismic data of the first seismic trace set to obtain three-dimensional Radon transform data; filtering the three-dimensional Radon transform data based on the three-dimensional conical body filter to obtain three-dimensional Radon multiple wave data; and determining target seismic data after suppressing multiple waves based on the difference between the first seismic data of a plurality of first seismic traces and the three-dimensional Radon multiple wave data of a plurality of first seismic trace sets. The method improves the efficiency of suppressing multiple waves.

The present disclosure provides a data reconstruction method based on Radon domain sparse representation and related equipment, relating to the technical field of communication. The method comprises: obtaining low-resolution Radon coefficients; inputting the low-resolution Radon coefficients into a neural network of an encoding-decoding structure to obtain first high-resolution Radon coefficients; and increasing the sparsity of the first high-resolution Radon coefficients through an adaptive soft threshold function cascaded at the back end of the neural network to obtain second high-resolution Radon coefficients. The method can reduce the data size, operation time, and operation cost of time-domain inversion of reduced-time variable Radon transform, and improve the stability and resolution of reconstructed data.

This invention provides a system and method for enhancing the trajectory of a moving object based on a grating array. The system comprises: an acquisition module for acquiring raw vibration signals based on the grating array; a filtering module for filtering the raw vibration signals to remove environmental noise, resulting in a filtered signal; a matrix generation module for combining the filtered signal data to form a multidimensional spatiotemporal signal matrix based on grating points and sampling time; a noise cancellation module for comparing and analyzing the multidimensional spatiotemporal signal matrix and performing local processing to eliminate abnormal grating noise and accompanying noise; and a trajectory enhancement module for performing Radon transform on the noise-cancelled signal to enhance the trajectory of the moving object. This invention can eliminate uncorrelated noise signals in the grating array vibration signals of the moving object's trajectory, effectively eliminate accompanying noise in the non-driving channels of the moving object, and enhance the trajectory of the moving object, making the trajectory more continuous in time and space.

A method for performing maneuvering in a land vehicle using synthetic aperture radar (SAR) is provided. The method includes: receiving digitized radar echo data from a vehicle-mounted SAR radar transmission; accumulating multiple frames of the digitized radar echo data; applying a RADON transform to the accumulated multiple frames of digitized radar echo data and odometer data from the vehicle to generate a transformed frame of data for each three-dimensional point, wherein the RADON transform is configured to perform coherent accumulation on each three-dimensional point, project a radar trajectory onto each three-dimensional point, and project Doppler information onto each three-dimensional point; generating a two-dimensional map of the area covered by the radar transmission from the SAR radar based on the transformed frames of data for each three-dimensional point; and performing maneuvering using the land vehicle by applying the generated two-dimensional map.

The invention relates to a sliding element having a base layer and a coating arranged on an outer surface of the base layer, a profile is defined such that the profile forms an outer surface of the coating, and a Radon transform g (theta, rho) of a square segment of the profile is defined, the value domain of which is standardized to a value between 0 and 1, theta describes the angle and Rho describes the distance from the origin of the coordinates, and the Radon transform g (Theta, Rho) is shortened in the Rho direction in factorial symmetry. For any angle [theta], the standard deviation [sigma] [rho] ([theta]) of the Radon transform and the value of the Radon transform are defined, where is the average value of all g ([theta], [rho]) for the respective angle [theta]. [sigma] max, [sigma] min, being a maximum standard deviation, a minimum standard deviation, and an average value of each standard deviation, being a maximum value of each value, a minimum value of each value, and an average value of each value, parameter A being defined and the profile having a parameter Alt; 1.0, preferably lt; 0.9, particularly preferably lt; and 0.7.

The invention discloses a load identification method, system and device based on Radon transformation and deep learning and a storage medium, and the method comprises the steps: intercepting load measurement data, and converting the load measurement data into a load image through a Markov transfer field; de-noising processing is carried out on the load image containing noise by using wavelet transform; extracting image features of the de-noised load image by using Radon transform; dividing the image features into a training set and a test set, and performing normalization processing on the training set and the test set; and constructing a convolutional neural network, inputting the training set after normalization processing into the convolutional neural network to train the model to obtain a load identification model, testing the load identification model by using the test set after normalization processing, and performing load identification by using the tested load identification model. The method has the advantages of being easy to implement and high in recognition precision, and can be applied to the field of load regulation and recognition.

This disclosure relates to the field of seismic data processing technology, and particularly to a Radon transform method, apparatus, device, storage medium, and computer program. The method includes: acquiring target seismic data to be processed; constructing a time-domain Radon transform operator between the Radon domain model vector and the time-domain seismic data; constructing an objective function based on the time-domain Radon transform operator, wherein the objective function includes a data fitting term and a sparsity constraint term, the data fitting term being used to measure the error between the time-domain seismic data and the fitted seismic data, the fitted seismic data being generated by projecting the Radon domain model vector onto the data domain using the time-domain Radon transform operator, and the sparsity constraint term being used to apply norm sparsity constraints to the Radon domain model vector; and solving the objective function based on the target seismic data to obtain the target Radon domain model vector. Implementing the technical solution of this disclosure can improve the resolution of the Radon transform.

This invention discloses a method, apparatus, storage medium, and electronic device for seismic profile imaging. The method includes: performing a Radon transform on each first seismic wavefield at the shot point to obtain a first plane wave component and obtaining the value of a first ray parameter, thereby obtaining the incident ray angle corresponding to the first ray parameter; performing a Radon transform on each second seismic wavefield on one side of the receiver point to obtain a second plane wave component and obtaining the value of each second ray parameter, thereby obtaining the scattered ray angle corresponding to each second ray parameter; obtaining each dip angle based on the incident angle and each scattered angle; obtaining the single-shot imaging function for each dip angle through a single-shot imaging calculation formula; iteratively executing the imaging function for all shot points to obtain dip gathers; and superimposing the dip gathers to obtain seismic profile imaging. This method can efficiently obtain accurate dip gathers, thereby obtaining high-resolution, high signal-to-noise ratio seismic profile imaging.

The application discloses a multi-source data separation method and device based on wideband Radon transform, relates to the technical field of oil and gas resource exploration, and comprises the following steps: determining pseudo-separated data, performing frequency domain conversion on multi-source aliasing data and the pseudo-separated data to obtain a frequency-wavenumber spectrum, performing Radon transform calculation by using each theoretical bandwidth value in the frequency-wavenumber spectrum to obtain each Radon transform operator, and calculating corresponding each Radon parameter based on the Radon transform operator; constructing a self-adaptive filter by using each theoretical bandwidth value in the frequency-wavenumber spectrum, performing multi-source noise suppression based on each Radon parameter and the self-adaptive filter, and obtaining a coherent signal; performing calculation on the coherent signal by using an aliasing operator to obtain multi-source noise; and performing data separation on the multi-source aliasing data and the pseudo-separated data based on the multi-source noise. The application can avoid the situation that the amplitude is not preserved and noise is left in multi-source data separation, realizes multi-source noise suppression, and better preserves the horizontal propagation characteristics of seismic data.

The application relates to a three-dimensional imaging method for a spatial spin group target based on Kalman filtering, and relates to a three-dimensional imaging method for a spatial spin group target.The application aims to solve the problems of large search calculation amount, long operation time, and limited parameter estimation accuracy caused by search range and search parameter setting in the prior art three-dimensional imaging method for a spatial spin group target.The process is as follows: I. obtaining a one-dimensional range image based on a spatial spin group target; II. performing binaryzation processing on the one-dimensional range image, and performing skeleton extraction processing on the binaryzation image to obtain an image; III. extracting each sinusoidal curve in the image; IV. obtaining spin frequency estimation values of scattering points corresponding to each sinusoidal curve; V. estimating the z-axis direction coordinate values of the corresponding scattering points according to the values of the centers of each sinusoidal curve deviating from the center of the distance dimension; and VI. estimating the x-axis and y-axis direction coordinate values of the scattering points by using an I Radon transform.The application belongs to the technical field of radars.

The invention discloses a high-resolution self-adaptive seismic data multiple suppression method, electronic equipment, a storage medium and a high-resolution self-adaptive seismic data multiple suppression device. The method comprises the following steps: constructing a prediction multiple model by adopting a nonlinear regularized diagonal matrix based on least square parabola Radon transformation; and solving a minimum value of primary wave energy by adopting an adaptive multiple suppression algorithm based on the prediction multiple model, and completing seismic data multiple suppression. According to the method, through least square parabola Radon transformation, a nonlinear regularization diagonal matrix is adopted to construct a prediction multiple model, the resolution of the Radon transformation is improved, the focusing degree of a velocity spectrum is improved, and therefore effective signals and multiple energy are effectively distinguished; the minimum value of the primary wave energy is solved through the adaptive multiple suppression algorithm, so that the influence of human intervention on multiple suppression is reduced, the multiple energy is effectively suppressed, and the resolution of the seismic section is improved.

The invention discloses a first arrival refraction area linear interference suppression method, a product, a medium and equipment, and belongs to the technical field of seismic data processing. The method comprises the following steps: designing different parameters aiming at inconsistent linear characteristics of different frequency band ranges; the multi-scale and multi-directional representation capability of curvelet transform is utilized to decompose seismic data into subsets with different scales and directions, and then accurate wave field separation is realized through Radon transform in each curvelet domain, so that refraction interference with relatively strong energy in a first arrival refraction region is eliminated, and effective reflection signals in a large offset range are recovered. The method can accurately suppress first-arrival refraction interference, recover effective signals, and improve seismic imaging quality.

This invention relates to the field of seismic exploration technology, specifically to a "ground-to-ground combined acquisition" seismic exploration method and system. It includes exploration acquisition, wavefield separation, and fusion imaging. The invention ensures high-precision imaging of regional geological structures and macroscopic velocity fields through a first observation system; the second observation system meets the requirement of spurious-frequency sampling of high-frequency components of scattered waves, overcoming the spatial spurious-frequency problem caused by traditional large-area-cell systems, laying a data foundation for high-resolution imaging. Based on common-scattering point gathers and sparse-constrained Radon transform, it ensures that the separation process conforms to the physical laws of wavefield propagation. A deep learning model intelligently denoises and enhances the separation results, greatly improving the ability to extract weak signals in complex noise backgrounds. An adaptive fusion algorithm guided by signal-to-noise ratio dynamically optimizes the output results of physical and intelligent methods, promoting seismic exploration technology from structural imaging to a refined and transparent description of geological bodies.

The invention discloses an RCS two-dimensional imaging calculation method and system based on Radon transformation, and the method comprises the steps: obtaining to-be-tested target test data on a frequency domain at a t1 moment, obtaining background test data on the frequency domain at a t2 moment, and carrying out the vector background cancellation of the to-be-tested target data through a background vector subtraction mathematical model under a time-varying system; performing fast Fourier transform to transform the data from a frequency domain to a space domain; carrying out distance window adding processing on the airspace data; carrying out Radon transformation on the data after distance window adding processing, and carrying out filtering processing; and performing Radon inverse transformation on the filtered data to obtain two-dimensional RCS data of the to-be-measured target. According to the method, RCS two-dimensional imaging can be quickly calculated on the premise that the resolution ratio is not reduced.

The invention discloses an ISAR image typical component pointing detection method and device based on morphological processing, and the method comprises the steps: carrying out the preprocessing of an ISAR image, and extracting a first edge image; performing long edge detection on the first edge image by adopting a non-maximum suppression Radon transformation straight line detection method to obtain an edge fitting result of two long edges; further preprocessing the first edge image according to the edge fitting result of the two long edges, and extracting a second edge image; performing binary classification on the second edge image by adopting a graph clustering method to obtain a short edge, detecting two end points of the short edge by adopting a breakpoint detection method, and obtaining a short edge fitting result according to the two detected end points; and taking the length and the direction of the edge fitting result of the two long edges as the length and the direction of the long edges, and taking the length and the direction of the edge fitting result of the short edges as the length and the direction of the final short edges. According to the invention, the edge detection precision and robustness of typical parts are improved.

The invention relates to the technical field of active source surface wave exploration, and particularly discloses a multi-modal surface wave frequency dispersion curve extraction method and system, and the method comprises the steps: obtaining a surface wave seismic record, carrying out the self-adaptive signal separation of the surface wave seismic record through Shearlet transformation, and obtaining a first signal with a primary base-order signal and a second signal containing a high-order signal; a high-precision linear Radon transformation inversion model based on multi-term regularization constraint is constructed; solving the inversion model by using an alternating direction multiplier method to obtain a base-order signal and a high-order signal in the Radon domain; converting the base-order signal and the high-order signal in the Radon domain into a frequency-speed domain, and generating a base-order frequency dispersion energy diagram and a high-order frequency dispersion energy diagram; according to the method, the anti-noise performance of high-precision Radon transformation can be improved while high-order modal frequency dispersion energy is highlighted, and a complex inverse problem under the constraint of multiple regularization terms is decomposed into a plurality of sub-problems easy to solve by adopting an ADMM algorithm, so that an optimal solution is efficiently obtained.

The application relates to a SAR image ship target sidelobe removing method based on distance constraint, which is used for removing sidelobes and tails around strong scattering points in a SAR ship image due to tailing effect and sidelobe effect. In a traditional step-by-step approximation method, the length and width of an initial circumscribed rectangle need to be used, which may lead to incomplete sidelobe removal, and in a Radon transform method, a maximum peak area in a transform domain needs to be found, which is not easy to find in practice. The application avoids using initial circumscribed rectangle information and finding a peak area in a complex transform domain, is completely based on an original binary image, respectively carries out ship average distance estimation and farthest distance estimation according to the distribution characteristics of the ship and the sidelobe, and finally iteratively compares the farthest distance and the average distance to remove the sidelobe. The algorithm uses the difference between the ship target and the sidelobe, avoids using the geometric structure information of the ship target with the sidelobe, and is good for large sidelobes.

A method for place re-recognition of a mobile robot based on a lidar estimable pose, said method comprising: using radon transform to convert rotation and translation changes into translation changes on two axes of a sinusoidal graph, and, on the basis of an amplitude spectrum of a spectrum, performing spectrum cross-correlation calculation on translation invariance and two images, so as to solve the translation property of the images. Translation invariance is used to generate a position descriptor and thereby perform candidate matching for place re-recognition; and cross-correlation calculation may be performed together with radon transform to solve relative rotation and translation. In the described method, a time-varying environment is considered, and by using a multi-channel feature BEV for representation, the capability of representing a local feature in a laser point cloud can be improved.

This invention discloses a robust detection method and system for high-speed, highly maneuverable targets based on Radon-LVD transform, belonging to the field of radar signal processing. The method estimates the target's velocity and acceleration from the pulse-compressed echo signal using Radon-LVD transform; constructs a slow-time-range frequency domain quadratic phase compensation function using the velocity and acceleration estimates; performs migration compensation on the slow-time-range frequency domain of the echo signal; and then performs moving target detection in the range-Doppler domain. The two searches during this process improve the accuracy of target parameter estimation. This invention combines Radon transform and LVD transform, which can compensate for range migration and Doppler migration respectively, achieving coherent accumulation of energy in the echo signal of a uniformly accelerated target.

This invention provides a photovoltaic module operation monitoring method and system based on AI vision, belonging to the field of photovoltaic module condition monitoring and fault diagnosis technology. This invention synchronously acquires surface images and electrical time-series data of photovoltaic modules through hardware, constructing a sub-millisecond spatiotemporal alignment mechanism for multi-source heterogeneous data, laying a reliable foundation for the correlation analysis of electrical anomalies and visual representations. It utilizes Radon transform and a one-dimensional convolutional neural network to extract run-length encoding features of the grid lines, transforming grid line continuity into quantifiable and comparable structured strings, significantly improving feature robustness under complex scenarios such as light variations and dirt interference, while reducing the computational complexity of subsequent processing. Based on a neighborhood similarity comparison strategy using the state matrix, it fully exploits the spatial consistency constraints of adjacent modules in the photovoltaic array, enabling rapid location of modules with abnormal grid line structures, effectively avoiding the high false alarm rate caused by traditional single-point detection.