62 results about "Matrix estimation" patented technology

The invention discloses a motion sensing game linear acceleration anti-drifting method fusing space anchor points, and belongs to the technical field of motion sensing game pose tracking. The method comprises the following steps: synchronously acquiring IMU (Inertial Measurement Unit) original data and a space anchor point absolute pose; the method comprises the following steps: preprocessing IMU data, resolving an attitude matrix in real time by utilizing the angular velocity of a gyroscope, estimating attitude, velocity and displacement, and constructing a state vector; by taking the anchor point pose as observation, through fusion of extended Kalman filtering and state estimation, the optimal estimation is corrected, and the sensor error is calibrated on line; the six-degree-of-freedom pose and the linear acceleration are output to drive a game role to interact with an object, meanwhile, the correction state is fed back to a filter to form a closed loop, integral drift is remarkably restrained, and long-term precision and robustness are improved.

This invention discloses a method and system for separating AIS aliasing signals using a four-circular array antenna. The steps are as follows: S100: AIS signals are received through multiple antennas to obtain the spatial baseband signal matrix X, and the phase error Δφ is calculated using interval calculation. k S200: Calculate the phase compensation matrix p; S200: Compensate the baseband signal according to the phase compensation matrix to obtain the baseband signal X. comp S300: For baseband signal X comp By expanding the spatial and temporal dimensions, we obtain the spatiotemporal snapshot matrix X. st (t); S400: The covariance matrix R is obtained by estimating the spatiotemporal snapshot matrix. xx S500: For the covariance matrix R xx Eigenvalue decomposition is performed to obtain the signal subspace and noise subspace; S600: DOA estimation is performed using the MUSIC algorithm to construct the spatial spectrum P(θ); S700: The beam w at the optimal solution is obtained using the space-time constrained minimum variance algorithm; S800: The separated signal is output through the sparse separation method of protocol features. This method can significantly improve the availability of AIS self-organizing network and provide more reliable protection for ship navigation safety.

The invention discloses a control method and device for an active folding arm deformation aircraft, a medium and a product, and relates to the field of aircraft control, and the method comprises the steps: obtaining a real-time output angle value of a servo motor through PID control based on expected deformation configuration; designing an inertia self-adaptive estimation mechanism based on a coordinate system represented by kinematics and kinetic equations and a variable theoretical value during attitude determination, determining a current inertia matrix estimation value by adopting a recursive least square method, adding a kinetic model, and setting a sliding mode backstepping control rate; damping and feed-forward compensation optimization are carried out by adopting a set control rate, a current rotating speed value of the power servo motor is determined by combining a time-varying distribution matrix, an actual position and an attitude angle are determined by considering a real-time output angle value, and the actual position and the attitude angle are fed back to a position and attitude controller. According to the method, the control rate and the feedback term related to the inertia in the controller design can be corrected in real time, and the sudden change problem of the inertia is solved, so that the adaptability to the uncertainty of the aircraft structure is effectively enhanced.

The present application belongs to the technical field of distributed speech enhancement, and particularly relates to a distributed speech enhancement system based on maximum likelihood. In order to expand the diversity of speech enhancement technology in WASN and complete good noise elimination performance, the system comprises a discrete Fourier transform module, a speech activity detection module, a steering vector estimation module, a data compression module, a result output module, a signal construction module, a weighted correlation matrix estimation module, a filter update module, and a discrete inverse Fourier transform module. The present application is a distributed speech enhancement technology which can be applied to a wireless acoustic sensor network without a data processing center. The technology estimates a weighted correlation matrix through a local signal constructed by a node and a variance of an output result, and updates a filter by combining the estimated weighted correlation matrix with a constructed local steering vector, so as to complete distributed speech enhancement.

The invention provides a graph neural network training method and device, a medium, equipment and a program product, and the method comprises the steps: obtaining sample graph data, and enabling a plurality of nodes of the sample graph data to comprise a plurality of nodes with noise marks; iteratively training the graph neural network and the matrix estimator based on the sample graph data and the noise marks; in any round of iteration, the sample graph data and the noise marks are input into the graph neural network, so that the graph neural network outputs original prediction marks of all the nodes in the multiple nodes, the original prediction marks are corrected based on the current noise transfer matrix, and corrected prediction marks are obtained; performing parameter adjustment on the graph neural network based on the corrected prediction mark and the noise mark; and after parameter adjustment of the graph neural network is completed, inputting the original prediction mark and the noise mark into a matrix estimator, so that the matrix estimator outputs a noise transfer matrix, and performing parameter adjustment on the matrix estimator based on the noise transfer matrix of the adjacent nodes.

The invention relates to the technical field of network engineering and artificial intelligence, in particular to a network traffic matrix estimation model, method and system. In the training process, the reviewer module is introduced to serve as a self-supervision guider, the flow generation network module is guided to learn spatial-temporal correlation in a flow matrix, a reasonable evaluation signal for unobserved source point convection is provided for an estimation model, the problem of supervision missing caused by training data sparseness is effectively solved, and the prediction accuracy is improved. The model obtained by training can realize high-precision traffic matrix estimation based on link load and loss information hypothesis.

The application discloses a time-frequency domain underdetermined blind source separation method and system based on double sensors, which comprises two stages: in the first stage, a single source point with high clustering characteristics is detected by using a clustering and matching tracking algorithm, and a mixing matrix is estimated, so that high-precision mixing matrix estimation is realized; in the second stage, a time-frequency domain signal recovery problem is converted into a sparse recovery model with a relaxed sparse condition, the constraint of the number of sources on the self-source point in the traditional blind source separation algorithm is broken, and good separation effect is realized. The application is suitable for the double-sensor underdetermined blind source separation scene with more source numbers, low mixing signal-to-noise ratio and serious aliasing, and has strong applicability and practicability.

The invention provides a signal processing method and device, electronic equipment, a chip and a storage medium, and relates to the field of communication, and the method comprises the steps: carrying out the noise estimation of a receiving signal received by at least one receiving antenna, so as to obtain an initial noise correlation matrix of the receiving signal; according to the initial noise correlation matrix, determining an interference scale factor used for representing the proportion of white noise and interference in the received signal; according to the interference scale factor, correcting the initial noise correlation matrix to obtain a target noise correlation matrix; and performing noise and interference suppression on the received signal based on the target noise correlation matrix to obtain a demodulation signal. Therefore, the difference between the estimated noise correlation matrix and the real noise correlation matrix can be reduced, the accuracy of noise correlation matrix estimation is improved, and on the basis, noise and interference suppression is performed on the received signal based on the accurate noise correlation matrix, so that the finally obtained demodulation signal is closer to the real sending signal.

The invention discloses a fine-grained urban crime pattern mining method and system. The method comprises the following steps: combining and organizing crime data into a three-dimensional data structure according to areas, time periods and situations; constructing a first objective function containing a multi-factor matrix on the basis of the structure, and constructing a second objective function containing a spatial-temporal correlation matrix in combination with spatial-temporal correlation; superposing two objective functions and setting constraint conditions to convert the two objective functions into an optimization problem; iteratively solving by adopting an alternating optimization mode until the objective function is converged; and obtaining each matrix estimation value after convergence, and mining crime modes under different regions, time periods and situation combinations. Wherein the construction of the space-time correlation matrix, the setting of the optimization constraint and the alternative iteration solution all comprise specific sub-steps. According to the method, multi-dimensional data are integrated, time-space correlation characteristics are described, the problems of incomplete modes and insufficient precision are solved, an accurate basis is provided for urban crime prevention and control, a collaborative guarantee method of all units of the system is integrated, and refined urban safety management is assisted.

Aspects of the subject technology relate to systems, methods, and computer readable media for estimating acoustic spectra. Acoustic data can be received at a hydrophone array from a first acoustic source and a second acoustic source in a downhole environment. An initial noise spatial correlation matrix estimation can be generated based on the acoustic data. The initial noise spatial correlation matrix estimation can be applied to a beamformer to generate a first source spectra estimation for the first acoustic source and the second acoustic source. A revised noise spatial correlation matrix estimation can be generated based on the first source spectra estimation. The revised noise spatial correlation matrix estimation can be applied to the beamformer to generate a second source spectra estimation for the first acoustic source and the second acoustic source in the downhole environment based on the first source spectra estimation.

This invention relates to a shape control method and system for a flexible robot. The method inputs the desired shape of the flexible robot and the current tendon actuation data set into an offline-constructed shape Jacobian matrix estimation model to estimate the shape Jacobian matrix, obtaining the current shape Jacobian matrix and predicted motion velocity. The method then corrects the shape Jacobian matrix estimation model online by calculating the velocity error between the real-time motion velocity and the predicted motion velocity. Based on the online-corrected shape Jacobian matrix estimation model, the shape control problem of the flexible robot is constructed as a constrained quadratic programming problem. A neurodynamics optimizer is used to solve the quadratic programming problem to obtain the optimal driving command that satisfies the constraints, driving the flexible robot to achieve the desired shape. Therefore, this invention achieves high-precision shape control of a flexible robot while also considering adaptability and real-time computational efficiency.

The application belongs to the field of network engineering, artificial intelligence and network measurement, and discloses a traffic matrix estimation method combined with observation traffic and link load constraints, comprising the following steps: step 1, obtaining a traffic matrix dataset and a routing matrix dataset; step 2, obtaining a complete traffic matrix sample; step 3, performing main training on a diffusion model module, and updating a Transformer denoising network parameter by minimizing a loss function until convergence; step 4, generating a traffic matrix subjected to known traffic constraints; step 5, repeating steps 4-5 T times, and finally obtaining an output traffic matrix; and step 6, performing post-processing optimization on the traffic matrix output in step 5, and finally outputting a complete estimated traffic matrix. The application utilizes the probability generation capability of the diffusion model module, incorporates the constraint conditions such as observable traffic and link load into the reverse sampling process thereof, and cooperates with a training data preprocessing module to realize high-precision recovery of the traffic matrix.

The application discloses a method for suppressing main lobe interference of a multi-station radar system based on oblique projection. The method aims to solve the problem that the estimation of noise subspace is not accurate due to the energy leakage of interference, so that the noise subspace is no longer orthogonal to the interference subspace, and the orthogonal projection anti-interference performance is invalid. The implementation steps of the application include: directing the receiving antenna beams of each radar station in the multi-station radar system to the target and the area where the jammer is located; aligning the echo baseband signal vectors of each radar station in the multi-station radar system in the time domain to obtain a receiving signal matrix of the radar system; generating a covariance matrix; estimating a signal subspace; constructing an oblique projection operator; and multiplying the receiving signal matrix of the radar system by the oblique projection operator to obtain a signal after interference suppression. The application has the advantage that the suppression type interference signal entering from the main lobe of the radar station antenna can be effectively suppressed in a low signal-to-noise ratio scene.

Systems and methods include receiving network data from a network; estimating large flows from the network data utilizing a first statistical approach; responsive to the network being large flow dominant, recursively estimating flows in the network utilizing the first statistical approach until an exit condition is reached; and combining the recursively estimated flows and forming a traffic matrix based thereon. The recursively estimating flows can include estimating a set of large flows utilizing the first statistical approach and freezing a resulting estimate while leaving smaller flows unsolved; repeating the estimating for a next set of large flows from the unsolved smaller flows; and continuing the repeating until the exit condition is reached.

The application discloses a mobile manipulator control method and device based on online learning and fuzzy neural dynamics, and relates to the technical field of mobile manipulator control. The method comprises the following steps: acquiring an actual pose of an end effector, a desired pose trajectory and joint speed; introducing an excitation signal formed after superimposing random noise, updating a current Jacobian matrix estimation value of the mobile manipulator; inputting each data into a fuzzy neural dynamic solver; the fuzzy neural dynamic solver is configured to take a quadratic form as an optimization objective, take a differential kinematics tracking equation as an equality constraint, and take a non-convex feasible region as an inequality constraint; and finally outputting a control signal of the joint speed obtained by solving the fuzzy neural dynamic solver to drive the mobile manipulator to move. The application solves the technical problems of low control precision of the mobile manipulator under the conditions of parameter uncertainty and non-convex constraints without an accurate prior model, and realizes high-precision model-free pose control.

The invention discloses an array mutual coupling self-correction DOA method based on sparse off-network, and belongs to the field of array signal processing. The method comprises the following steps: constructing a sparse off-network DOA estimation system model considering an array mutual coupling effect; initializing a mutual coupling coefficient vector and an off-grid parameter vector, and reconstructing a signal sparse matrix by using a smooth norm according to a system model; estimating an off-network parameter vector according to the reconstructed signal sparse matrix and a known initialized (or after last iteration) mutual coupling matrix; estimating a mutual coupling matrix from a system model by using a method based on gradient descent by using the reconstructed signal sparse matrix and the estimated off-network parameter vector; and after iteration meets a stop condition, obtaining DOA estimation of the signal according to the signal sparse matrix, the mutual coupling coefficient matrix and the off-network parameter vector after iteration. According to the method, a better DOA estimation effect can be obtained on the premise that the array antenna has an unknown mutual coupling effect.

The application discloses a transient electromagnetic inversion method based on deep neural network reparameterization regularization, belongs to the technical research field of geophysical electromagnetic data processing and analysis, and comprises the following steps: constructing an untrained deep neural network; inputting random hidden vectors conforming to the input layer structure of the deep neural network and boundary values of resistivity and stratum thickness required by transient electromagnetic inversion into the deep neural network to output a geological model containing a regularization effect; performing iterative updating on a transient electromagnetic inversion objective function by using a self-adaptive matrix estimation algorithm, judging whether a convergence condition is met, if the convergence condition is not met, taking the weight of each layer of the deep neural network as a variable needing to be updated, iteratively updating the weight of each layer, and making the deep neural network generate a new geological model containing the regularization effect. The application not only makes it easy to apply the regularization effect on the generated geological model in the physical constraint without external training data, but also has good anti-noise performance.

An OD estimation method based on flow data of an ETC gantry, which method solves the problem of it being difficult to reflect traffic mobility by means of an existing OD estimation method, and belongs to the field of traffic engineering and intelligent traffic systems. The method in the present invention comprises: determining that the research objective is region-level OD estimation, city-level OD estimation or expressway-level OD estimation; dividing constitution regions involved in the research objective into direct traffic impact regions and indirect traffic impact regions according to the degree of influence; dividing the direct traffic impact regions and the indirect traffic impact regions into traffic regions according to administrative region units and expressway sections; acquiring flow data of selected ETC gantries in real time, so as to obtain the hourly traffic volume of each ETC gantry, and then in view of the positions of the ETC gantries, matching the obtained hourly traffic volumes into an existing road network, so as to obtain road network section flows; using the obtained traffic regions as origins and destinations, selecting road network section flows of ETC gantries for OD estimation, and performing OD matrix estimation; and verifying an estimated OD matrix.

The application provides a short-circuit current zero point prediction system fusing a deep learning algorithm, relates to the field of phase control breaking of a large-capacity vacuum circuit breaker, and comprises a current signal acquisition module, a model construction processing module, a deep learning prediction module and a phase control instruction execution module. The current signal acquisition module is used for collecting short-circuit current data of the system in real time and performing time synchronization and cache processing. The model construction processing module initializes model parameters by constructing a short-circuit current mathematical model containing direct current and alternating current components. The deep learning prediction module adopts an alternating optimization method fusing a recursive least square algorithm and a self-adaptive matrix estimation algorithm, fits a current waveform in real time and predicts a zero point moment. The phase control instruction execution module generates a control instruction to realize phase control breaking according to a prediction result and a circuit breaker opening time. The system can effectively improve breaking synchronization and breaking reliability of a multi-break vacuum circuit breaker, is suitable for real-time zero point prediction of a power grid system fault short-circuit current, and is especially suitable for protection and breaking of a large-capacity power generator motor, such as a pumped storage power station, in a high-frequency opening and closing scene.

The application provides a scattering correction method and device, a digital device and a computer readable storage medium. The method comprises: obtaining a down-sampling response line based on detection data; obtaining an estimated target function corresponding to each down-sampling response line by using a matrix estimation method based on a maximum likelihood-expectation maximization iterative algorithm; solving the estimated target function to obtain a number of scattering coincidence events corresponding to each down-sampling response line; performing up-sampling processing on the down-sampling response line to obtain an up-sampling response line; and calculating the number of scattering coincidence events corresponding to each up-sampling response line. When the embodiment of the application performs scattering correction, it does not depend on activity images and attenuation images, and the method does not require a hyperparameter, so that the hyperparameter no longer needs to be set in a determined range, and the method is simple and accurate.

A channel estimator circuit receives in one slot a multiplexed representation of TD-CDM, spread received signals; an initial estimator circuit processes a pre-estimation signal and outputs an initial estimate signal. A frequency-domain processing circuit receives and processes a first set of TD-CDM spread channel estimate signals, representative of the initial estimate signal, and outputs a second set. An error estimator circuit receives a first set of error estimation reference signals representative of a base pilot signal; a second set of error estimation reference signals representative of the second set of TD-CDM spread channel estimate signals. A noise variance estimation circuit may be configured to use the first and second error estimation reference signals and perform noise variance estimation thereon. A covariance matrix estimation circuit may use the error estimation reference signals and the second set of error estimation reference signals and perform covariance matrix estimation on the multiplexed representation.

The application discloses a kind of automatic perspective correction circular instrument's method, including correction matrix estimation and image correction two steps.Correction matrix estimation, including with camera acquisition source image, and it carries out gray processing;Through existing ellipse fitting method, the gray image is fitted;Screen out the fitting ellipse of instrument profile, obtain profile coordinate point set;With coordinate point set, geometry matrix is constructed and carries out eigenvalue decomposition, calculates correction matrix;Image correction is using correction matrix, using bilinear interpolation to source image is carried out interpolation transformation, obtains the instrument image after correction.This method not only small, but also with the coordinate point of entire profile as key point calculates correction matrix, correction error is small, effectively solve the correction problem of circular instrument.The correction accuracy of this method is improved by 2%~6% than existing method, realizes low cost, and has practical application value to the pre-processing of automatic reading of circular instrument.

The application discloses a multi-sound source positioning and imaging method based on sparse representation and variational Bayesian inference, and steps are as follows: (1) an initial sound intensity matrix is generated under low resolution by using a conventional beamforming algorithm to estimate the sound source position; (2) the signal intensity gradient is calculated, the search position is updated along the gradient direction, and the accurate sound source position and high-resolution sound intensity matrix are obtained; (3) a sparse dictionary learning algorithm is used for sparse coding and dictionary update optimization of the high-resolution sound intensity matrix; (4) a variational Bayesian inference model is constructed based on the sparse coefficient, the lower bound of variation is optimized, and the posterior positioning estimation of the multi-sound source is carried out; and (5) the positioning result is fused with the camera image to obtain the visualized position of the sound source. The method realizes high-precision real-time positioning and imaging in a complex sound field by combining low-resolution positioning, gradient optimization, sparse dictionary learning and Bayesian inference, and has high spatial resolution and strong anti-interference capability.

The present application relates to the technical field of computer graphics and computer vision, and discloses a homography matrix estimation method and system, the method comprising the following steps: S1, collecting images with different overlap rates as a training data set; S2, constructing a neural network model, including an overlap monitoring network and a homography matrix estimation network; S3, using a loss function to guide and optimize the neural network model, calculating the loss value between the result of image transformation by the network output homography matrix and the target image; S4, training the neural network model to generate a trained neural network model; S5, using the overlap detection network to crop out the common area of the two estimated images; S6, using the deep homography matrix estimation network to achieve coarse-to-fine homography matrix estimation. The system comprises an acquisition unit, a model construction unit, a model training module and a homography matrix estimation module. The present application also discloses an electronic device and a computer readable storage medium.

The application relates to a sound source positioning method, device and electronic device under an incomplete observation scene, the sound source positioning method comprising: acquiring actual measurement data collected by a linear array; the actual measurement data comprising actual measurement data of inter-element direction arrival angle and actual measurement data of arrival time; based on the actual measurement data, a mixed parameter positioning model of multiple error sources is constructed; the multiple error sources comprising inter-element direction arrival angle measurement noise, arrival time measurement noise and linear array reference element position error; based on the mixed parameter positioning model, an optimization objective function for estimating the sound source position is constructed; a matrix estimation of the gradient of the optimization objective function is calculated, and bias correction is performed to obtain a corrected matrix estimation; based on the corrected matrix estimation, the estimated value of the sound source position is iteratively updated until a preset convergence condition is met, and the final sound source position is obtained. The sound source is accurately positioned by constructing the mixed parameter positioning model and combining the matrix estimation.

The application provides a wireless communication channel estimation method and system based on holographic multiple-input and multiple-output, comprising: equipping a uniform planar array for a holographic multiple-input and multiple-output channel, and sending a pilot signal by a user equipment for uplink channel estimation; predefining an antenna element index of the uniform planar array, and establishing a coordinate system with the center of the uniform planar array as the origin; constructing an angle covariance matrix, so that the original channel including three-dimensional angle and distance information is converted into the current covariance matrix only having angle information; constructing a distance covariance matrix based on the estimated angle information, so that the original channel including three-dimensional angle and distance information is converted into the covariance matrix containing distance information; and based on the constructed angle covariance matrix and distance covariance matrix, informing the user equipment by a transmission and reception point that the two matrix estimation tasks are completed, so that the construction of the wireless communication channel is realized. The application solves the problem of high cost of antenna control technology in the next generation of wireless communication.

The invention discloses an amplitude-phase error self-calibration direction of arrival greedy estimation method. The method comprises the following steps: S1, establishing an array receiving signal model containing an amplitude-phase error; s2, calculating an effective grid size and an iteration stop threshold value of the search grid; s3, performing coarse search on the search grid, and detecting an initial direction of arrival and an initial signal corresponding to the maximum projection energy; if the maximum projection energy is lower than an iteration stop threshold, skipping to S6; s4, performing Newton iteration refinement on the initial direction of arrival to obtain more accurate continuous angle estimation and signal estimation; s5, alternately updating the signal waveform of the signal source and the diagonal calibration matrix, updating the residual signal according to the signal waveform and the diagonal calibration matrix, rejecting the current signal source, and returning to S3; and S6, removing a weak source through pruning, performing optimization, and outputting a final direction of arrival and diagonal calibration matrix estimation. According to the method, error transmission is avoided, and the direction-of-arrival estimation precision under the condition of unknown array errors is remarkably improved.

The application discloses a kind of large-scale MIMO multi-star positioning and direction estimation method and system based on angle information.The application first obtains the channel matrix estimation between each satellite and user by sending pilot signal;Then the estimated value of channel parameter is obtained using tensor-ESPRIT algorithm, including angle of departure and angle of arrival;According to the estimated angle of departure, the direction vector between user and each satellite is obtained, the optimization problem on spherical surface is established, considering the altitude constraint, the user position estimation value is obtained by using Riemann conjugate gradient method;Finally, according to the estimated user position and angle of arrival, the optimization problem on SO (3) manifold is established, and the estimated value of user array rotation matrix is obtained by using Riemann gradient descent method.The application combines the channel information between multiple satellites and users for positioning and direction estimation, and considers the altitude constraint during positioning, compared with single satellite and unconstrained case, can significantly improve the positioning performance, while having good direction estimation performance.

The invention provides an array signal processing method and device, electronic equipment and a storage medium, and relates to the technical field of radar signal processing, receiving signals of a non-uniform array are mapped into receiving data of a virtual uniform array, and a covariance matrix is reconstructed by using a neural network model based on the data, so that the array signal processing efficiency is improved. And finally, estimating the direction of arrival of the signal through subspace decomposition and polynomial rooting operation, so that the problems of low estimation precision and narrow application range caused by the fact that a non-uniform array structure does not meet application conditions of a traditional algorithm, covariance matrix estimation is greatly influenced by signal conditions and subspace discrimination is insufficient in the prior art can be solved. The technical effects of adapting to a non-uniform array scene, improving the accuracy and robustness of covariance matrix estimation under a wide signal-to-noise ratio, enhancing the distinction degree of a signal and a noise subspace, further improving the direction-of-arrival estimation precision and resolving power and having physical interpretability are achieved.

The application discloses a kind of based on domain self-adapting migration's battery multi-scene health state estimation method, comprising steps 1, deduce the health state quantity of battery;Step 2, filter out and battery health state related coefficient absolute value is greater than or equal to the feature of set threshold as model input;Step 3, source domain data and target domain data are extracted;Step 4, calculate the distribution distance of source domain data and target domain data in the high-level feature output of convolutional neural network model, and the total loss function is obtained by weighted summation to the loss function and mean square error loss function, the total loss function is optimized by adaptive matrix estimation optimizer, realize the domain self-adapting training of unlabeled battery data;Step 5, the performance of model is evaluated using validation set, and it is applied to test set to output health state estimation result.The application effectively solves the estimation precision decline problem caused by data distribution difference under cross-domain scene.