108 results about "Matrix estimation" patented technology

The invention relates to the technical field of attitude control, in particular to a moving object attitude control method based on an improved LKF. Predicting prior attitude estimation at the next moment; calculating a process error covariance matrix; calculating a measurement attitude; calculating a quaternion measurement error covariance matrix; constructing feature vectors of acceleration interference and geomagnetic interference amplitude levels, and calculating an interference amplitude level value by using a Gaussian function model; updating the process error covariance matrix and the measurement error covariance matrix by using the interference amplitude level value; calculating a priori attitude estimation error covariance matrix by using the updated process error covariance matrix; calculating a Kalman gain matrix by using the priori attitude estimation error covariance matrix and the updated measurement error covariance matrix; calculating attitude posteriori estimation at the next moment; and performing attitude estimation at the next moment. According to the method, the problems of insufficient estimation precision and poor interference suppression capability of the attitude observation error covariance matrix of the existing LKF are solved.

The invention discloses an electroencephalogram signal source space function connection estimation system and method based on deep learning, and the system comprises a training data set construction module which is used for constructing a training data set containing N training sample pairs, and each training sample pair comprises a sensor space function connection matrix and a source space function connection matrix corresponding to the sensor space function connection matrix; the signal preprocessing module is used for acquiring a real electroencephalogram signal and preprocessing the real electroencephalogram signal; the sensor space function connection matrix calculation module is used for calculating a corresponding sensor space function connection matrix true value; and the deep learning mapping module is used for learning a mapping relation from the sensor space function connection matrix to the source space function connection matrix, receiving a true value of the sensor space function connection matrix and outputting a predicted value of the source space function connection matrix. According to the method, the source space function connection matrix can be efficiently and accurately estimated.

The invention provides a joint broadband DOA and frequency band estimation method based on sparse Bayesian learning, and the method comprises the steps: calculating a covariance and a mean value of signal parameters based on obtained array receiving data through a broadband signal model defined based on sparse Bayesian learning, and calculating an intermediate parameter estimation value based on a variance vector; obtaining a binary matrix estimation value and a potential feature matrix estimation value based on the intermediate parameter estimation value, and calculating a variance vector estimation value based on the covariance and the mean value; judging whether an iteration ending requirement is met or not, if not, updating the covariance and the mean value, and further updating the binary matrix estimated value and the potential characteristic matrix estimated value until the iteration ending requirement is met so as to obtain a target binary matrix estimated value and a target potential characteristic matrix estimated value; obtaining DOA preliminary estimation results and frequency band information of the plurality of broadband signals based on the target potential feature matrix estimation value and the target binary matrix estimation value; and obtaining a DOA final estimation result of the corresponding broadband signal by adopting a maximum search criterion.

The invention discloses a large-scale MIMO multi-satellite positioning and direction estimation method and system based on angle information. The method comprises the following steps: firstly, obtaining channel matrix estimation between each satellite and a user by sending a pilot signal; then, obtaining estimated values of channel parameters by using a tensor-ESPRIT algorithm, wherein the estimated values comprise an angle of departure and an angle of arrival; according to the estimated departure angles, direction vectors between the user and all satellites are obtained, an optimization problem on spherical manifold is established, altitude constraints are considered, and a Riemann conjugate gradient method is used for solving and obtaining a user position estimation value; and finally, according to the estimated user position and arrival angle, establishing an optimization problem on the SO (3) manifold, and solving by using a Riemannian gradient descent method to obtain an estimated value of a user array rotation matrix. According to the method, positioning and direction estimation are performed by combining channel information between a plurality of satellites and users, and altitude constraints are considered during positioning, so that compared with single-satellite and non-constraint conditions, the positioning performance can be remarkably improved, and meanwhile, the method has good direction estimation performance.

The invention discloses a health degree estimation method for a pneumatic actuator. According to the invention, system identification is carried out at a stable working point and a discrete state space model is obtained. And comprehensively considering disturbance and excitation signals and the health condition of the actuator, and constructing a discrete state space expression form suitable for the augmented Kalman filter. The health information is modeled as an unknown input disturbance variable and is taken as part of a state variable. And an augmented Kalman filter is adopted to carry out combined prediction on the system state quantity and the unknown input interference quantity. And maintaining the acquired control input sequence and the unknown input disturbance variable sequence output by the filter by adopting a sliding window mechanism, and obtaining an estimated value of a health matrix of the pneumatic actuator by adopting a least square method for a data sequence in a window. And finally, performing feature extraction and analysis on the health matrix estimation value to realize real-time evaluation and dynamic tracking of the health state of the pneumatic actuator. According to the method, accurate, stable and real-time health degree information can still be provided under the condition that the pneumatic actuator has faults, disturbance or model mismatch, and powerful technical support is provided for fault-tolerant control and predictive maintenance of a complex system.

The invention discloses an apple recognition method, system and device and a storage medium, and relates to the technical field of apple recognition and detection, and the method comprises the following steps: calculating feature points on each edge contour in an original infrared thermal image and a visible light image based on the curvature of the edge contour; determining the main direction of the feature point based on the position information of each feature point on the edge contour; performing parametric coding on the position and the main direction of each feature point to generate a feature vector of each feature point; matching based on the Euclidean distance of the feature vector of each feature point on the two images to obtain a plurality of matching pairs; determining a transformation matrix through the coordinates of the plurality of matching pairs; performing image registration on the original infrared thermal image and the visible light image based on the transformation matrix to obtain a registered image; and carrying out image fusion and target identification on the registered image and the visible light image to obtain the position, the category and the confidence of the apple. According to the invention, the accuracy of transformation matrix estimation and the precision of registration are improved.

The application discloses a kind of sewer silting disease intelligent diagnosis method, data is collected using full-scale test, one-dimensional flow characteristic index is upgraded to two-dimensional image data with spatial information using data processing module based on gram angle and field. Data set containing pipeline silting information is constructed. Convolutional neural network module is constructed through convolution layer and pooling layer to extract spatial information in image. Time characteristics of one-dimensional data are extracted using gated recurrent unit. Adaptive matrix estimation optimizer is used to train sewer silting disease intelligent diagnosis model, and hyperparameters are automatically optimized using adaptive mutation particle swarm optimization algorithm during training process. The sewer silting disease intelligent diagnosis model is trained using optimized hyperparameters, and a diagnosis method is proposed that can intelligently predict whether pipeline silting occurs and its silting degree. The application can improve the efficiency of water pipeline silting inspection and meet the needs of modern city management.

The invention provides a water surface micro-amplitude wave detection method based on nonlinear reconstruction and a high-dimensional decoupling technology. The method is used for a water surface micro-amplitude wave detection task. According to the method, a time-frequency domain constraint module and a blind source separation precision feedback module are introduced into a U-NET adversarial network to carry out nonlinear reduction on a micro-amplitude wave signal, then feature extraction is carried out based on a subspace translation covariance matrix, a non-metric scaling technology is introduced to improve hybrid matrix estimation, and a source signal is recovered through an optimal submatrix in a sparse space. And efficient and accurate water surface micro-amplitude wave detection is realized.

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.

The present application relates to the field of audio processing, in particular to a sound separation method and system based on a vector microphone, the method comprising: constructing a fourth-order cumulant matrix of a sound signal received by the vector microphone, solving sound source direction angle estimation and sound source pitch angle estimation of each incident signal; constructing a spatial characteristic matrix of each incident signal at different frequencies; based on the spatial characteristic matrix, calculating a spatial correlation matrix estimation of the sound signal at different frequencies and different frame numbers, and constructing a spatial correlation matrix of the sound signal at different frequencies and different frame numbers; constructing an orthogonal MNMF model; adjusting the variables of the MNMF through the multiplication update method, so that the difference between the spatial correlation matrix estimation and the spatial correlation matrix is less than a preset target, to obtain an optimal spatial correlation matrix estimation; and separating the sound signal based on the optimal spatial correlation matrix. The present application effectively solves the sound separation problem when the distance between sound sources is close, and improves the accuracy of sound separation.

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 speech presence probability calculation method, system, speech enhancement method and headphones provided in this specification correct the speech presence probability and speech non-existence probability in the iterative process by comparing the entropy of the speech presence probability and the entropy of the speech non-existence probability to obtain faster convergence speed and better convergence results, thereby making the speech presence probability and noise covariance matrix estimation more accurate, thereby improving the speech enhancement effect of MVDR.

This invention relates to an adaptive Kalman noise estimation method and system based on data fusion. The method includes using multiple sensors to track a dynamic target and acquiring observations from each sensor; performing a first data fusion based on the observations from the first two sensors to obtain first fused data; performing a second data fusion using the first fused data and the observations from the next sensor to obtain second fused data; and so on, completing the data fusion of observations from multiple sensors to obtain fused observations from multiple sensors; and obtaining estimates of the process noise covariance matrix and measurement noise covariance based on the fused observations from multiple sensors. This invention improves the accuracy of measurement data by fusing measurement sequences obtained from multiple sensors, and solves the filtering problem of unknown second moments in the noise statistical characteristics of the Kalman model.

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 photovoltaic module shielding detection method based on noise label learning, and belongs to the technical field of photovoltaic energy systems, and the method comprises the steps: collecting an RGB image of a photovoltaic module, carrying out the preprocessing of the RGB image of the photovoltaic module, and obtaining an environment parameter at an image shooting moment; constructing a multi-modal feature extraction module, and outputting a power loss prediction result based on the RGB image and the corresponding environmental parameters; constructing a noise label distribution model based on a clustering noise transfer matrix estimation method; and integrating the noise transfer matrix on an output layer of the shielding detection model, applying linear transformation on a prediction probability output by a softmax layer to obtain prediction distribution with noise labels, and accurately predicting a shielding type and a corresponding power loss level of the photovoltaic module.

The application discloses a likelihood-based cognitive diagnosis Q matrix estimation method, belongs to the field of cognitive diagnosis evaluation, and comprises the following steps: obtaining response data of students to questions and a partially defined Q matrix, fitting a G-DINA model to obtain a posterior distribution of a student attribute vector, and calculating a marginal likelihood based on the posterior distribution. Furthermore, the fitting degrees of different q vectors are evaluated through AIC or BIC values, the optimal q vector is selected to gradually fill unknown parts of the Q matrix, and the process is continued until convergence. The method reduces the dependence on experts, improves the accuracy and efficiency of Q matrix estimation, and is suitable for large-scale education evaluation scenarios.

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 present application discloses a data-driven terminal sliding mode decoupling control method and device for a high-speed train, which relates to the technical field of automatic driving of high-speed trains. The method comprises: establishing a distributed mathematical model of multiple power units of a high-speed train system, converting it into a dynamic linearized data model, splitting the parameter matrix in the dynamic linearized data model, and obtaining a decoupled dynamic linearized data model; designing a parameter matrix estimation algorithm for estimating the time-varying parameter matrix in the decoupled dynamic linearized data model, and designing an adaptive extended state observer for estimating uncertain terms; designing a nonlinear sliding mode function and a hyperbolic sliding mode convergence law; deriving a data-driven terminal sliding mode decoupling control method, and obtaining the control force and speed results of each power unit of the high-speed train system. The present application can alleviate the system control force chattering phenomenon, and has fast convergence and strong anti-interference ability.

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 present application relates to radar signal processing technical field, especially to a kind of radar adaptive detection method based on improved large-dimensional precision matrix estimation.The method for the low problem of existing radar detection accuracy is as follows: radar data is collected and radar data is preprocessed, to obtain the radar data after preprocessing;According to the radar data after preprocessing, determine the unit to be detected and reference unit;According to reference unit, construct sample covariance matrix, then inverse sample covariance matrix, to obtain sample precision matrix;According to sample precision matrix, obtain improved precision estimation matrix;According to the improved precision estimation matrix, judge whether there is target in the distance unit of the unit to be detected, the detection performance of radar system in the case where dimension N and sample size K are in the same order of magnitude can be significantly improved by using the present application.The low problem of radar detection accuracy caused by poor detection performance of adaptive detector in the large-dimensional asymptotic system of existing method is solved.

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.