3550 results about "Covariance matrix" patented technology

A system disclosed herein applies a parameterized sensing and localization error model for use by connected devices to improve cooperative sensor fusion by estimating errors using key prediction factors (e.g., distance and velocity) at different tiers. The parameterized sensing and localization error model implements a local sensor fusion process for fusing measurements from sensors of a connected device and a global sensor fusion process for fusing measurements from many connected devices. The local sensor fusion process generates a covariance matrix that incorporates a measured distance and the global sensor fusion process generates a covariance matrix that incorporates a measured velocity combined with the local sensor fusion result. Results show an average improvement of 1.42× in RMSE vs. a typical fixed error model on a 1 / 10 scale test-bed.

The invention relates to the technical field of battery health state analysis, and discloses a battery health state comprehensive evaluation method, system and device and a storage medium, and the method comprises the steps: carrying out the preprocessing of data collected by a battery management system, and obtaining a standardized data sequence; calculating a first battery characteristic parameter according to the standardized data sequence; performing covariance matrix adaptive Kalman filtering processing on the first battery characteristic parameter to obtain a second battery characteristic parameter; constructing a capacity-internal resistance change rate index and a temperature-efficiency correlation index based on the second battery characteristic parameters to obtain a dual-threshold evaluation matrix; inputting the dual-threshold evaluation matrix into a deep reinforcement learning model, and performing forward propagation calculation and iterative training through an Actor network and a Critic network to obtain a feature fusion weight coefficient; and carrying out weighted calculation on the dual-threshold evaluation matrix to generate a battery health state value and a residual life prediction value, and realizing multi-dimensional evaluation of the battery health state.

The invention relates to the technical field of online calibration, in particular to a multi-parameter fusion intelligent electric energy meter online calibration method and system, and the method comprises the following steps: collecting voltage waveforms, current harmonics and active power data, dividing windows, calculating a covariance matrix, and generating a feature set; a power factor curvature extreme value and a temperature inflection point offset are analyzed to generate an interference identifier, current density distribution and a voltage distortion spectrum are jointly analyzed to extract a harmonic energy ratio to generate a feature vector, and phase compensation is performed on a pulse sequence to generate a calibration instruction set. According to the method, the covariance matrix is constructed by synchronously collecting voltage and current power parameters, the abnormal mark section is generated by combining the temperature change and the covariance difference value, the environment disturbance and the real deviation are effectively distinguished, and the temperature hysteresis effect is identified through the power factor curvature extreme value and the temperature inflection point offset. And combining current density and voltage distortion spectrum analysis to extract a fundamental wave and harmonic wave energy ratio, establishing a composite calibration reference, and dynamically adjusting a pulse duty ratio to realize harmonic wave energy compensation.

The invention discloses a three-dimensional Gaussian sputtering scene reconstruction method based on structure perception refined Gaussian, and aims to solve the problems of Gaussian drift, edge blur, structure artifacts and the like of a reconstruction model due to the fact that sparse point cloud contains outliers, Gaussian morphology and normal are mismatched and a multi-dimensional optimization target is lacked in an existing three-dimensional Gaussian sputtering reconstruction method. A key frame is extracted by collecting target scene video data, sparse three-dimensional point clouds are reconstructed by using an SfM algorithm, a depth map and a normal map are generated through a Lotus model, three-dimensional Gaussian distribution is initialized after the sparse point clouds are filtered, a Gaussian covariance matrix is adjusted by using a normal consistency regular term, and the sparse point clouds are extracted. And after structure attribute analysis is carried out, a comprehensive scoring function is constructed to screen Gaussian points, and finally, a combined training framework including luminosity, normal consistency and structure continuity loss is adopted to optimize and generate a three-dimensional Gaussian scene model. The method is mainly applied to the field of three-dimensional reconstruction and multi-view rendering, and scene reconstruction precision and geometric consistency can be improved.

The invention relates to the technical field of battery management systems, and particularly discloses a battery signal acquisition precision optimization method of a BMS (Battery Management System), which comprises the following steps of: dynamically adjusting a covariance matrix of process noise and measurement noise by monitoring the voltage, current and temperature of a battery in real time so as to cope with the change of the working state of the battery; according to the change trend of the charge and discharge rate and the temperature, noise parameters are predicted and corrected, and the adaptability of the system in an extreme environment is improved; in combination with data from a plurality of sensors, weighted average and a self-adaptive adjustment mechanism of a Kalman filter are adopted, so that evaluation of the system on signal quality of different sensors and effective suppression of noise are enhanced; aiming at the sudden change of the battery state, the system can be ensured to quickly respond by automatically adjusting the filtering algorithm, the accurate monitoring of the battery health state is ensured, the stability and the accuracy of the battery management system can be improved, and the problem of estimation error under the extreme working condition of a traditional method is solved.

The invention relates to the field of civil engineering construction safety monitoring, and discloses an open caisson construction soil gushing dynamic early warning system based on multi-parameter fusion, which comprises a sensor array module, a data processing module, a dynamic parameter correlation analysis module, a self-adaptive covariance optimization module, a risk index generation module and an early warning output module, collecting data in real time through a multi-source sensor and generating a parameter vector; the dynamic parameter correlation analysis module updates the dynamic matrix on line and represents real-time correlation among soil pressure, displacement and water level; the adaptive covariance optimization module dynamically adjusts a covariance matrix and suppresses noise interference; the risk index generation module fuses the dynamic matrix, the covariance inverse matrix and the parameter vector calculation weight, and corrects the risk index in combination with the vibration state of the construction machinery; and the early warning output module triggers grading alarm and feeds back to form a closed loop optimization link. According to the invention, real-time accurate early warning and self-adaptive prevention and control of the soil gushing risk are realized.

The invention provides an automobile magnesium alloy pre-twin crystal deformation data analysis system, and relates to the technical field of data processing, and the system comprises a twin crystal correction module which is provided with a first target monitoring point and a second target monitoring point in a pre-twin crystal area, the first target monitoring point is located at a crystal boundary intersection, and the second target monitoring point is located at the center of a twin crystal zone; acquiring orientation difference angle and strain energy density difference data of the two monitoring points in real time, calculating a covariance matrix of change characteristics of the two monitoring points, and correcting a proliferation rate parameter and an orientation rotation parameter in the dynamic evolution matrix according to a correlation coefficient of the covariance matrix to obtain a corrected dynamic evolution matrix; and the optimization module is used for adjusting a hot extrusion process parameter group according to the twin crystal density gradient distribution output by the corrected dynamic evolution matrix, the parameter group comprises a mold temperature, an extrusion speed and a strain path, and the twin crystal density is dynamically controlled in a target interval. According to the invention, the complex stress state of the automobile part under the actual working condition can be accurately simulated.

The invention discloses a single-epoch GNSS-oriented PAR ambiguity precision factor determination method, and relates to the technical field of satellite navigation and positioning. Comprising the following steps: dividing pseudo-range and carrier wave observed quantities in a GNSS double-difference mathematical model into two groups, and constructing a function relationship between a floating point solution # imgabs1 # and a floating point solution # imgabs2 # variance-covariance matrix of an ambiguity vector # imgabs0 #; constructing a single-frequency single-epoch single-system P-ADOP expression and establishing a shrinkage factor k de-inversion transformation analytic expression, and simultaneously obtaining a single-frequency single-epoch single-system GNSS non-inverse transformation P-ADOP approximation formula; and performing analogy derivation on the single-frequency single-epoch single-system non-inverse transformation P-ADOP approximate formula to obtain a single-frequency multi-system or multi-frequency single-epoch GNSS non-inverse transformation P-ADOP general approximate formula. The method has important theoretical value and practical guiding significance for GNSS optimal ambiguity subset rapid selection and positioning and partial ambiguity fixing success rate influence mechanism analysis.

The invention relates to the technical field of underwater intelligent navigation and integrated navigation, in particular to a submersible integrated navigation multi-source data fusion method based on an elastic random model, which comprises the following steps: preprocessing multi-source navigation data output by an inertial navigation system (INS), a Doppler velocimeter, an ultra-short baseline / long baseline, an altimeter and a depthometer; constructing a state space model containing navigation parameter errors and sensor errors, and screening an optimal sensor combination mode; the method comprises the following steps: constructing an elastic correction parameter model and a dynamic adjustment state and observation noise covariance matrix by combining an elastic PNT framework, performing local filtering on multi-source data by adopting a plurality of parallel elastic self-adaptive robust volume Kalman filters (CKF), outputting a fusion resolving result, and realizing optimal estimation of a global navigation state through a dynamic information distribution strategy. The method has relatively high anti-interference performance, self-adaptive capability and combination switching robustness, and is suitable for navigation and positioning tasks of a high-precision submersible in a complex underwater environment.

A covariance accumulation-based direction of arrival estimation method for an S-mode signal at a low signal-to-noise ratio, relating to the technical field of information processing. According to the method, a delayed covariance matrix of array output data is comprehensively utilized, a weight is determined by means of a correlation feature of the S-mode signal, and the delayed covariance matrix of array output data is accumulated, so that the direction of arrival estimation precision of a MUSIC method for the S-mode signal in a low signal-to-noise ratio environment is improved, establishing more accurate angle tracking for an S-mode target in the low signal-to-noise ratio environment, effectively reducing the false alarm probability, and expanding the effective working ranges of angle measurement and a tracking system.

The invention relates to the technical field of three-dimensional modeling in engineering surveying, in particular to a point cloud building component modeling method and system based on feature extraction. Three-dimensional point cloud data of building components are obtained, a multi-scale local neighborhood calculation covariance matrix is constructed with each point as the center, eigenvalues are decomposed, an edge probability graph is generated, corresponding component categories are semantically output, and corresponding key feature point sets are screened; taking the feature point cloud as a control point, constructing a three-level B-spline surface model, and adjusting the corresponding spatial distribution density; a weighted complete graph is formed through a key feature point set, a non-planar area is identified and processed through a Kurtowski theorem, a QEM algorithm is applied to carry out lightweight processing on a three-dimensional model, screening and optimization are carried out through automatically extracting key feature points of building components, and a concise and accurate lightweight model is constructed based on feature point topological optimization and a graph theory algorithm. The automation degree and efficiency of modeling are improved, and the contradiction between model lightweight and precision is effectively solved.

The invention relates to the technical field of battery state monitoring, and discloses a sightseeing vehicle battery state monitoring method, device and equipment and a storage medium, and the method comprises the steps: collecting the voltage, current and temperature data of a sightseeing vehicle battery, and carrying out the preprocessing, and obtaining a battery operation parameter data set; constructing a battery equivalent circuit model, and performing global and local two-stage optimization to obtain an optimal parameter set of the battery equivalent circuit model; constructing a state equation and an observation equation, and carrying out initialization setting on a covariance matrix of the triple forgetting factor adaptive extended Kalman filter to obtain a battery state observation model; priori state estimation is obtained through time updating operation, posterior state estimation is obtained through measurement updating operation, hierarchical estimation is carried out on the charge state and the polarization voltage of the battery, and the state characteristic quantity of the battery is obtained; according to the method, the reliability and the safety of the battery management system of the sightseeing vehicle are improved.

The invention relates to the technical field of heat dissipation control, and discloses a heat dissipation fan multi-order filtering control method and device, and the method comprises the steps: collecting the operation parameters of a multi-fan heat dissipation system, and carrying out the construction of a dynamic state space model, and obtaining a system operation state equation and a temperature observation equation; inputting the system operation state equation and the temperature observation equation into an expectation maximization sliding window iteration extended Kalman filter to obtain a system state estimation value and a prediction error covariance matrix; performing three-stage series design on the cut-off frequency of the low-pass filter, the passband range of the band-pass filter and the coefficient of the adaptive filter to obtain a system multi-order filtering control model; based on a system multi-order filtering control model, an airflow coupling influence coefficient matrix among multiple fans is calculated, the total energy consumption, the noise level and the heat dissipation efficiency are subjected to optimization solution, a fan control optimization parameter set is obtained, the heat dissipation efficiency is guaranteed while the energy consumption is reduced, and dynamic balance among all control units is achieved.

The invention discloses a regional numerical forecasting method and system based on multi-scale background field error covariance. The method comprises the following steps: firstly, constructing a multi-scale background field error covariance model containing different spatial scale error structures, then obtaining an initial atmospheric state and a boundary condition of a forecast area, and generating a background field set sample by using an ensemble forecast algorithm; thirdly, performing multi-scale decomposition on a background field set sample based on the model to obtain error components of different spatial scales, and determining a corresponding error covariance matrix; and combining the matrixes to form a complete multi-scale background field error covariance. Then, meteorological observation data are obtained, a variational assimilation algorithm is applied, optimal combination of the observation data and a background field set sample is achieved through multi-scale background field error covariance, and an optimal initial field is obtained; and finally, performing regional numerical forecasting based on the optimal initial field to obtain a future weather condition forecasting result of the forecast region. According to the method, the forecasting accuracy and reliability are improved.

The invention provides a multi-source spatio-temporal data fusion processing method and device. The method comprises the following steps: preprocessing multi-source spatio-temporal data; performing standardization processing on the preprocessed multi-source spatio-temporal data to obtain standardized data; performing alignment processing on the standardized data by using a space-time registration technology to obtain registration data; constructing a multi-layer network structure, and performing data arrangement on the registration data by using the multi-layer network structure to obtain structured data; and establishing a unified geographic element classification system according to the structured data. Sampling the standardized feature vectors by adopting an MCMC urban sampling algorithm to obtain feature distribution parameters; calculating a covariance matrix based on the feature distribution parameters and performing feature value decomposition; and defining geographic element categories according to the distinction degree of the feature combination. The method also comprises a corresponding device implementation. According to the method, the problems of low efficiency and poor precision of multi-source spatio-temporal data fusion in the prior art are solved, the precision of data fusion is improved, and the method has relatively high practicability and adaptability.

The invention relates to a multi-source data fusion method and application system based on digital twinning, and the method comprises the steps: collecting real-time and historical data from various sensors, devices, networks and other information sources, and carrying out the cleaning and format standardization preprocessing of the collected data; the data set fusion uses a Kalman filter algorithm to perform data fusion, and the prediction step comprises: predicting a state equation at a current moment based on a state equation and a covariance matrix at a previous moment, and predicting the covariance matrix and an observation vector; the updating step comprises the steps of calculating Kalman gain and updating state estimation and a covariance matrix. The feature set fusion uses deep learning line feature data fusion, features are extracted from different data sources by using CNN, and different feature vectors are subjected to weighted fusion to form a single feature vector; decision set fusion adopts a deep learning method for decision fusion, so that the accuracy, stability and reliability of decisions are improved; the method improves the efficiency and precision of data fusion, and has a good market application prospect.

A partial ambiguity resolving method suitable for a complex dynamic environment belongs to the field of high-precision dynamic satellite navigation relative positioning, and comprises the following steps: constructing a single-epoch carrier phase double-difference difference observation equation; and calculating a weight factor corresponding to each satellite through a standardized residual error and an IGG3 method, and substituting the weight factors into the double-difference factor matrix to obtain a corrected observation value. And resolving the updated observation equation through a weighted iterative least square method. And carrying out partial ambiguity fixation on the corrected ambiguity floating point solution to obtain a whole cycle ambiguity fixed solution. And carrying out Ratio-based posteriori consistency traversal detection, monitoring the number of satellites participating in calculation in a traversal search process, and if the number of the satellites is smaller than a threshold value, outputting a floating point solution. And if the number of satellites after traversal search is greater than or equal to And substituting the fixed solution of the integer ambiguity into the updated observation equation to obtain a fixed solution of a three-dimensional relative position vector and a covariance matrix of the three-dimensional relative position vector.

The invention relates to the technical field of cross-modal data fusion analysis of soil component detection and improvement agent preparation, in particular to a preparation method of a soil improvement agent based on component detection, which comprises the following steps: acquiring soil component data through a near infrared spectrometer, a high performance liquid chromatograph and an inductively coupled plasma mass spectrometer; an incremental principal component analysis algorithm is combined with an oscillation suppression function to update a covariance matrix in real time, a dynamic defect factor priority list is generated, a multi-objective optimization model improved based on NSGA-II is guided to integrate the soil pH value, humidity and organic matter content to construct a dose response curved surface, and a Pareto optimal solution set is solved. A sensor array collects environment feedback data, drives a transfer learning algorithm to calibrate parameters, a dynamic incidence matrix attenuation rate and optimal weight adaptive adjustment, iteratively outputs a modifier synergistic effect solution set through a closed-loop control mechanism, and solves the problems of difficult data fusion, principal component weight offset and component synergistic deviation. And the proportioning accuracy and stability are improved.

The invention relates to the technical field of smart communities, and discloses a building low-carbon operation management method based on data analysis, which comprises the following steps: acquiring building data, preprocessing the data to obtain a multi-dimensional data set, performing feature extraction by adopting a principal component analysis method aiming at the multi-dimensional data set, and calculating a feature value and a feature vector of a covariance matrix to obtain a building low-carbon operation management result. Selecting the first k principal components with relatively large feature values as new feature spaces to obtain a feature data set after dimension reduction, taking the feature data set after dimension reduction as input, constructing a credit rating prediction model by using a random forest algorithm, calculating importance scores of all features to rating results, and sorting the results according to the importance scores of the features to obtain a credit rating result. And selecting the first n features with higher scores as key feature factors, and constructing a credit evaluation feature vector. According to the method, a more accurate and comprehensive evaluation result is provided for building credit rating, and green building development and realization of energy conservation and emission reduction targets are effectively supported.

The invention belongs to the technical field of medical image processing, and particularly relates to a double-encoder semi-supervised heart MRI double-ventricle segmentation method based on improved SAM. A teacher model and a student model which are used both comprise double encoders, prompt encoders and mask decoders, each double encoder comprises a ViT branch and a CNN-Mama branch which are parallel, parameter fine tuning of the ViT branch is realized through a position adapter and a feature adapter, the CNN-Mama branch mainly comprises a convolution fusion Mama block and a second-order dimension attention block, and the CNN-Mama branch is used for realizing fine tuning of parameters of the CNN-Mama block. The convolution fusion Mama block introduces an enhanced convolution block on the basis of a VSS block, and the second-order dimension attention block uses a covariance matrix to mine second-order statistical information of data; carrying out weighted summation on the output features of the ViT branch and the CNN-Mama branch to obtain the output features of the double encoders; the output features of the double-encoder and the prompt encoder are input into a mask decoder to be decoded, and a segmentation result is obtained; and performing joint training on the teacher model and the student model based on interpolation consistency regularization semi-supervision, and using the trained student model as a segmentation model for double-ventricle segmentation of heart MRI. The segmentation model has strong feature extraction capability, can effectively extract the edge information of the ventricles and the boundary information of the double-ventricle connection region, and improves the segmentation precision.

The invention provides a breakwater monitoring data preprocessing method and system based on Kalman filtering, and relates to the technical field of breakwater structure safety monitoring. The method comprises the following steps: acquiring original motion data of acceleration, inclination and displacement through a motion attitude sensor to obtain an original data sequence; initializing a state vector and an error covariance matrix; dynamically correcting the state transition matrix and calculating a prediction state vector and a prediction error covariance matrix; a Kalman gain is generated; updating a state vector and an error covariance matrix; and extracting the filtered motion data as a preprocessing result. According to the method, the state transition matrix is dynamically corrected by introducing the wave force feedback, so that the Kalman filtering algorithm can adapt to the wave impact environment, noise interference in monitoring data is effectively inhibited, and the accuracy and reliability of key motion parameter data of the breakwater are remarkably improved.

The invention relates to the technical field of data filtering and data fusion, in particular to an improved weighted Kalman data fusion algorithm. The algorithm comprises the following steps: establishing a state equation, calculating a state prediction value at the current moment according to state estimation of current / voltage data at the previous moment to become priori, and calculating an error covariance matrix at the same time; obtaining a prediction covariance matrix at the current moment through matrix operation; calculating an observation noise covariance matrix; calculating to obtain a Kalman gain; calculating to obtain an updated optimal state estimation value; calculating a current optimal estimation value and an optimal estimation value covariance matrix; calculating a weighting factor, and compensating and updating the covariance matrix of the current / voltage data; calculating to obtain an updated covariance matrix; and then loop iteration is carried out on the steps, so that the estimation precision of the system state is gradually improved. The fusion algorithm provided by the invention can improve the data precision and accuracy.

The present invention relates to a multi-region collaborative control method and device for a high-precision pressure sensor array. The method: based on a first collaborative sampling parameter, data is collected from multiple sensor units in a pressure sensor array to obtain a pre-processed pressure data set and a sensor unit spatial position matrix; a spatial weighted pressure covariance matrix is ​​constructed; eigenvalue decomposition and information entropy calculation are performed, and the number of target pressure points is determined according to the minimum description length criterion and the information gain ratio; a pressure feature subspace and a local perception domain are constructed; the pre-processed pressure data set is projected onto the pressure feature subspace to generate a sensor unit state data set; a multi-layer attention encoder is input for processing, and the first collaborative sampling parameter of the pressure sensor array is adjusted to obtain a second collaborative sampling parameter. The implementation of the present invention improves the system's response sensitivity to local pressure changes, thereby realizing multi-region collaborative control of pressure sensing.

The invention discloses a vehicle state estimation method based on adaptive strong tracking extended Kalman filtering. The method comprises the following steps: constructing a nonlinear three-degree-of-freedom dynamic model containing a state equation and an observation equation based on an extended Kalman filtering algorithm to describe longitudinal, lateral and yaw states of a vehicle; state variables of the state equation are a side slip angle, a yaw velocity and a longitudinal vehicle speed; constructing a time-varying measurement noise statistical estimator based on a Sage-Husa algorithm to adaptively correct a measurement noise covariance matrix in the extended Kalman filtering algorithm; state prediction is carried out on the vehicle through the state equation, and a state prediction covariance matrix is calculated; and calculating the ratio of the sum of quadratic terms of the information sequence to the trace of the variance matrix of the information sequence based on an extended Kalman filtering algorithm, and judging whether filtering is really divergent or not. According to the method, the state quantity which is difficult to measure in the vehicle driving process is estimated in real time by establishing an adaptive strong tracking extended Kalman filter, and the vehicle state is accurately estimated.

The invention discloses a PPP-RTK time delay processing method and device in conversion from SSR to OSR, and relates to the technical field of global navigation satellite system positioning, and the method comprises the steps: converting orbit, clock error, ionosphere and troposphere delay SSR parameters provided by a server into OSR virtual observation values through geometric distance calculation and projection function mapping; based on a constant velocity model and a static random process, predicting a correction amount of a future moment, and correcting an OSR virtual observation value through state transition matrix compensation time delay; combining server side parameters and user side observation data, recursively generating an error covariance matrix, dynamically adjusting a state prediction weight, and obtaining covariance information through adaptive filtering optimization positioning calculation; and realizing ambiguity rapid fixation and centimeter-level positioning by using the corrected OSR observation value and covariance information. According to the method, real-time centimeter-level positioning in a high-delay scene is realized through a dynamic correction value prediction method and a recursive covariance estimation method.

The invention discloses a sparse dynamic graph convolution-based electroencephalogram signal decoding method and system. The method comprises the following steps of: acquiring a multi-channel electroencephalogram signal and preprocessing the multi-channel electroencephalogram signal; performing multi-band filtering on each channel signal, extracting statistical characteristics on each band signal, calculating a covariance matrix of a task electroencephalogram signal, constructing image electroencephalogram data by taking an electroencephalogram channel as an image node, the multi-band spliced statistical characteristics on the channel as a node feature vector, and the covariance matrix between the channels as an adjacent matrix; finally, a dynamic graph convolutional neural network model is constructed, the model constructs a graph convolutional neural network based on an autoregression moving average filter, graph electroencephalogram data is used as input, the category of electroencephalogram signals is used as output, an adjacency matrix is dynamically generated in combination with bilinear mapping, fuzzy label learning and sparse constraint are added to improve the decoding capacity of the model, and the dynamic graph convolutional neural network model is obtained. And the frequency domain response capability and robustness of the model to the graph structure are enhanced.

A motion information and visual information fused mechanical arm space pose self-adaptive estimation method comprises the steps that firstly, an internal reference matrix of a binocular camera is combined, and three-dimensional reconstruction of two-dimensional features of an image is achieved based on an epipolar constraint method; then, hand-eye calibration is completed in a laser tracker and binocular camera combined measurement mode, and the relative position relation between the machining tail end of the mechanical arm and the center of the workpiece is calculated based on the calibration relation; and finally, a self-adaptive extended Kalman filter is provided, a known binocular camera is combined to measure a noise covariance matrix, state noise covariance matrixes of the tail ends of the mechanical arms at different positions are adjusted in a self-adaptive mode, and smooth estimation of the tail end positions in the movement process of the mechanical arms is achieved. And finally, the tail end of the mechanical arm accurately moves to the effective view field range of the binocular camera. According to the method, the problem that the tail end position of the mechanical arm is difficult to accurately estimate under the complex working condition is solved, and self-adaptive accurate estimation of the tail end position of the mechanical arm can be achieved without priori accurate observation and motion noise covariance matrixes.

The invention provides a Gaussian modeling method combined with laser scanning point cloud data processing, and relates to the technical field of three-dimensional modeling, and the method comprises the steps: carrying out the registration of a plurality of point cloud data sets through employing a point cloud fusion algorithm, and obtaining a target point cloud data set; performing data precision, position deviation and noise influence analysis on the plurality of point cloud data sets, determining point cloud data precision, a position deviation coefficient and a noise influence coefficient, and comprehensively evaluating and determining point cloud acquisition precision; setting a data dimension reduction proportion according to the point cloud acquisition precision, and performing covariance matrix dimension reduction on the target point cloud data set; and selecting a predetermined kernel function, performing data fitting and Gaussian process regression modeling on the dimension reduction point cloud data set, and outputting a surface fitting model. According to the method, the technical problems that the requirements for storage and computing resources are too high and efficient three-dimensional modeling cannot be realized due to the fact that a large number of covariance matrixes need to be calculated in a traditional Gaussian modeling method can be solved; the dimensionality of data can be effectively reduced, and the efficiency of three-dimensional modeling is remarkably improved.

The invention provides a sea wave magnetic interference suppression method and device for ocean electromagnetic detection, and relates to the technical field of electromagnetic signal processing, and the method comprises the steps: synchronously collecting original electromagnetic signals through a plurality of annularly arranged magnetic sensors, generating interference parameters through employing the calculated interference signal amplitude and phase information, and carrying out the subsequent interference suppression processing. The method comprises the following steps of: calculating a covariance matrix, performing principal component analysis, extracting spatial characteristics of low-frequency magnetic interference, projecting a signal to an orthogonal subspace outside an interference subspace, removing a low-frequency magnetic interference component, retaining a target signal, performing Fourier transform to convert the signal to a frequency domain, analyzing a frequency spectrum by using a band elimination filter, and setting an interference threshold value. According to the identified interference frequency, an interference suppression signal is generated, the interference suppression signal is returned to the time domain through inverse Fourier transform and is superposed with the target signal to generate a standard electromagnetic signal, the magnetic interference in the ocean detection area can be effectively suppressed, and accurate acquisition and analysis of the standard electromagnetic signal are ensured.

A GNSS positioning slowly-varying fault detection method based on residual-SVR regression comprises the steps that an observation information sequence is acquired based on a Kalman filter, and a covariance matrix of the observation information sequence is calculated; accumulating multi-step information through a sliding window, and constructing chi-square statistics; based on the fault-free data, constructing a training set by taking an innovation sequence as input and chi-square statistics as output, and generating an innovation-statistics mapping function; and fitting a normal slope threshold value based on an SVR predicted value, carrying out least square fitting on an observation statistic curve by sliding a window in real time, and judging whether to start a slow change fault alarm or not. According to the method, the residual error sequence is directly used as model input, and the dynamic chi-square statistical magnitude is used for replacing a traditional dichotomy label, so that the detection delay is reduced; an SVR detection model based on grid search and cross validation collaborative optimization is utilized, and an optimal parameter combination of a minimum mean square error (MSE) is screened through logarithm uniform sampling, interval linear sampling and five-fold cross validation, so that the average absolute error of slowly varying fault detection is reduced.