19 results about "Gauss newton method" patented technology

The invention belongs to the technical field of geophysics. According to the truncation model space three-dimensional magnetotelluric inversion method and system, a three-dimensional inversion objective function is constructed according to an inversion initial model, the inversion objective function is optimized by adopting a Gaussian-Newton method, and a Gaussian-Newton increment equation is obtained, a sensitivity matrix in the Gaussian-Newton increment equation is a sparse matrix which is obtained after truncation is carried out by adopting a distance truncation threshold value and a sensitivity amplitude truncation threshold value, the Gaussian-Newton increment equation is converted into an unconstrained least square form, and an inversion updating direction is determined according to the least square form; and performing iterative optimization according to the initial inversion model, the inversion updating direction and the model updating step length to obtain an updated inversion model. According to the invention, units with small contribution to inversion updating are effectively cut, and the model space corresponding to each piece of data is reduced.

The fusion adaptive dynamic prior information magnetotelluric and magnetic method joint inversion method belongs to the field of geophysical data processing and inversion technology; by constructing a joint inversion objective function including data fitting term, model constraint term and physical coupling term, and using Gauss-Newton method for solving, the core innovation lies in introducing adaptive dynamic prior information extraction technology; the method can automatically generate and optimize prior constraints from data itself and intermediate results, without relying on external preset information, thereby significantly improving the accuracy and reliability of the boundary description and physical property recovery of underground abnormal bodies (such as resistivity, magnetic susceptibility anomaly); solve the problem that the traditional physical property coupling joint inversion technology has strong dependence on prior information and weak application generalization ability in information missing area.

The invention discloses a vehicle-mounted positioning method and system based on a tetrahedron UWB anchor point cluster. The method comprises the following steps: obtaining TDOA data and AOA data obtained by four UWB anchor points in a vehicle according to UWB signals sent by UWB equipment; constructing a hyperbolic equation according to the position information of the four UWB anchor points and the four groups of TDOA data; solving the hyperbolic equation to obtain the initial position of the UWB equipment; forming a non-coplanar tetrahedron according to the four UWB anchor points, and constructing a constraint condition of volume conservation of the tetrahedron; performing iterative optimization on the initial position based on a Gaussian Newton method, and introducing the constraint condition to constrain an iterative optimization process to obtain an optimized position; and performing weighted correction on the optimized position according to the four groups of AOA data to obtain a final position. Based on the data processing flow, error suppression is carried out by fusing TDOA fast convergence, AOA direction correction and volume constraint, so that four-anchor-point high-precision positioning is realized.

The invention relates to the technical field of automatic driving, and discloses a multi-sensor fusion SLAM method and system considering curvature constraints. According to the method, kinematics constraints of an Ackerman steering vehicle are fully considered on the basis of an LIO inertial laser radar tight coupling system, and the method comprises the steps that firstly, the rear axle wheel speed and the front wheel rotating angle are utilized and combined with a vehicle two-degree-of-freedom model to construct a vehicle odometer constraint factor; secondly, establishing a relationship among a yaw angle, a wheel speed, an X-axis displacement and a Y-axis displacement, and adding curvature constraint factors under the same turning direction; and finally, constructing SLAM rear-end optimization based on a factor graph method, adding an IMU pre-integration factor, a laser radar odometer factor, a vehicle odometer constraint factor and a curvature constraint factor, and solving by adopting a Gaussian Newton method. A test result in a real scene shows that compared with an LIO-SAM algorithm and an FAST-LIO2 algorithm, the precision of the algorithm provided by the invention is obviously improved.

The fusion adaptive dynamic prior information magnetotelluric and magnetic method joint inversion method belongs to the field of geophysical data processing and inversion technology; by constructing a joint inversion objective function including data fitting term, model constraint term and physical property coupling term, and using Gauss-Newton method for solving, the core innovation lies in introducing adaptive dynamic prior information extraction technology; the method can automatically generate and optimize prior constraints from data itself and intermediate results, without relying on external preset information, thereby significantly improving the accuracy and reliability of the boundary description and physical property recovery of underground abnormal bodies (such as resistivity, magnetic susceptibility anomaly); solve the problem that the traditional physical property coupling joint inversion technology has strong dependence on prior information and weak generalization ability in information missing area.

The invention relates to an efficient forward modeling transient electromagnetic three-dimensional inversion method and device and computer equipment. The method comprises the following steps: randomly generating a plurality of transient electromagnetic three-dimensional geoelectric models, carrying out emission source decomposition on each three-dimensional geoelectric model, constructing an independent sub-grid for each independent source and each receiving point according to the skin depth of each time channel, converting a large-scale global problem into a large number of small-scale sub-problems, and generating a plurality of transient electromagnetic three-dimensional geoelectric models; and the sub-problems are efficiently distributed to a large number of computing cores for parallel processing. Based on the efficient parallel forward modeling capability, it is possible to construct a large-scale and high-density three-dimensional training data set, and a foundation is laid for subsequent application of a deep learning model. And then, a high-precision prior model of deep learning prediction is used as a hot start initial model, and is introduced into a traditional inversion process based on a Gaussian Newton method, so that rapid, stable and high-resolution transient electromagnetic three-dimensional inversion is realized. According to the invention, the precision and efficiency of underground structure imaging are significantly improved.

The invention discloses a multi-source information fusion integrated navigation method and system independent of satellite navigation, and belongs to the field of navigation and positioning of autonomous mobile platforms. Establishing a vision-laser-inertial odometer and polarized light sensor measurement model, establishing an inertial navigation system motion model, initializing the integrated navigation system, and determining the attitude of the integrated navigation system according to a carrier state node position constraint, a course constraint and an adjacent state node pose transformation constraint at the current moment; establishing an integrated navigation system state vector and a measurement error function to obtain a to-be-optimized factor graph, and optimizing the to-be-optimized factor graph by using a Gaussian-Newton method to obtain optimal estimated values of the carrier position, speed and attitude under a world coordinate system with the minimum error function. According to the method, multi-source information is fused, information contributions are balanced through optimization of the factor graph, the robustness and reliability of the system in a satellite navigation signal rejection environment are remarkably improved, and the method has good practical engineering significance.

The application discloses a kind of unmanned aerial vehicle cluster cooperative target encirclement methods based on brain-like computing, belong to autonomous navigation robot and multi-robot coordination control technical field, method includes: obtaining multi-view image under camera visual angle, obtain the local pose and covariance of each unmanned aerial vehicle;According to the local pose and covariance of each unmanned aerial vehicle, obtain the global consistent relative pose of unmanned aerial vehicle cluster;Using unscented Kalman filtering algorithm, the state of target is estimated, and the state information of target in unified coordinate system is obtained;Using Bezier curve generation algorithm, the target motion trajectory under a period of history state is obtained;The method of centroid extension is used to expand the unmanned aerial vehicle cluster, and the encirclement queue of target encirclement is obtained, the least encirclement cost is solved using Gauss Newton method, and the encirclement position of each unmanned aerial vehicle is generated;The motion trajectory of unmanned aerial vehicle is generated using hybrid A* search, and the final encirclement trajectory sequence is generated, which significantly improves the accuracy of motion state estimation.

The present application relates to a kind of unmanned aerial vehicle low-altitude visual navigation method based on motion blur auxiliary, comprising: acquisition monocular camera image and IMU data, quantification the image blur degree of the monocular camera image, to extract the blur parameter of dominant motion, for calculating global measurement blur data;The IMU data is pre-integrated, obtains the relative motion amount between adjacent frames, and based on system prior state estimation, the image plane motion field of current time is predicted, for simulating global theoretical blur data;The global measurement blur data and the global theoretical blur data are compared, and after being combined with adaptive weight, as motion blur consistency error, construct nonlinear least squares optimization objective function;According to the nonlinear least squares optimization objective function, Gauss-Newton method iteration is solved, generates the state vector of minimum total error, for calculating health score, to implement three-level mode switching.

The application provides a multi-level fusion optimization method and system for oblique photogrammetry space three processing, and relates to the technical field of photogrammetry, wherein the method comprises: acquiring multi-view image data and constructing an image connection graph, using weighted spectral clustering combined with multi-constraint spectral clustering for dynamic blocking to form a subnetwork with internal connection and overlapping relationship. Based on the image quantity, point cloud scale and matching complexity evaluation and calculation load, the subnetwork is distributed to distributed computing nodes, and the subnetwork parameters are optimized in parallel. The target overlapping area is determined through multi-level subnetwork fusion, the public points and camera pose are extracted to construct a pose graph model, and the Gauss-Newton method is used to solve the global consistent transformation parameters. Under the constraint of ground control points, overall optimization is carried out through weighted least squares adjustment, a global model is finally constructed, light beam adjustment is performed, and the space three processing result is generated. The application improves the automation degree and three-dimensional reconstruction precision of oblique photogrammetry data processing.

The invention discloses a total station free station setting three-dimensional adjustment method taking reference point errors into account, which comprises the following steps: S1, constructing a station center coordinate system O-XYZ, and calculating station center coordinates of free station setting measurement points such as a reference point, a broken part point and the like; s2, constructing a coordinate transformation model considering the reference point priori coordinate and the station center coordinate error, then performing iterative solution through a Gaussian-Newton method, and obtaining a transformation parameter estimation reference point station center coordinate correction V station between the reference point priori coordinate and the station center coordinate; and S3, calculating three-dimensional space coordinates of the broken part points. According to the method, a space rectangular coordinate conversion adjustment model considering reference point prior coordinates and station center coordinate errors is constructed, two sets of reference point three-dimensional space coordinate conversion parameters conforming to the actual free station setting measurement are obtained through iterative solution by adopting a Gaussian-Newton method, and the three-dimensional space coordinates of broken part points are fitted and estimated; the method solves the problem that the three-dimensional adjustment of free station setting of the total station cannot consider the influence of the datum point error at present.

The invention provides a wireless access point positioning method based on received signal strength. The wireless access point positioning method comprises the steps that S1, RSS original data of a target wireless access point are collected through receivers, and corresponding RSS observation values are obtained through processing according to the RSS original data; s2, constructing a to-be-estimated parameter vector containing a target position coordinate, a path loss index and reference power of the target wireless access point; s3, based on a path loss model, establishing a nonlinear model function between the RSS observed value and the to-be-estimated parameter vector; and S4, constructing a least square problem according to the nonlinear model function, and carrying out iterative solution by adopting a Gaussian-Newton method to obtain a least square estimated value of the to-be-estimated parameter vector as a target position coordinate of the target wireless access point. The method has the advantages that the method can automatically adapt to different indoor positioning scenes, tedious parameter calibration in advance is not needed, and high positioning precision and calculation efficiency can be kept.

The application discloses an adaptive weight-adjusting high-precision joint inversion method based on electromagnetic gradient data driving and relates to the technical field of electromagnetic signal inversion. The application comprises the following steps: calculating the spatial gradient data of the original electromagnetic field observation data by using a Gaussian weighted neighborhood method; combining a conventional data residual term, a data residual term and a regularization term to construct a joint objective function; embedding a gradient sensitive scale matrix into the data residual term of the joint objective function to establish an inversion equation; iteratively calculating the inversion equation and updating model parameters to obtain a three-dimensional inversion result reflecting the spatial distribution of resistivity. The application extends the classic Gauss-Newton method, optimizes the inversion by using the residual terms of the electromagnetic field data and the spatial gradient, introduces the scale matrix sensitive to the gradient in the residual weighting, so that only the model parameters related to the abnormal area can be updated in each inversion iteration, and the model parameters are mapped to the three-dimensional finite element domain for forward calculation.

The invention discloses a multi-layer casing conductivity and magnetic conductivity inversion method, which belongs to the field of electromagnetic nondestructive testing, and comprises the following steps: determining a transient response expression of a multi-layer ferromagnetic casing under pulsed eddy current excitation by adopting a pulsed eddy current testing rapid analysis model; converting an electromagnetic parameter measurement problem of the multilayer ferromagnetic sleeve into an optimization problem, wherein the optimization problem aims at minimizing an error between an actually measured transient response and a theoretical transient response calculated by a transient response expression; solving the optimization problem by adopting a Gaussian-Newton method to obtain coupling parameters related to the conductivity, the relative permeability and the wall thickness of the sleeve; based on the known wall thickness of each layer of the multi-layer ferromagnetic sleeve, the coupling parameters and the wall thickness of the corresponding layer are calculated, and the conductivity and the relative permeability of each layer of sleeve are obtained; according to the sequence from the bottom of the well to the ground, operation is carried out on different levels in the multi-layer ferromagnetic casing in sequence, and inversion of the conductivity and the relative permeability of the casing of all the levels is completed.

The application provides a kind of rotary body part installation error identification method based on elliptical error model, and the analysis result can judge rotary body part geometric eccentricity, phase, end face swing angle, direction simultaneously, to provide conditions for next step accurate detection or processing.The detection principle is to measure the complete contour line of a circle in radial direction of rotary body part by displacement sensor (such as contact scanning probe, non-contact optical probe, etc.) to obtain original relative data set, to obtain contour change data set by wavelet decomposition, to obtain relative data set for optimization by further processing.Due to the influence of rotary body part swing, the measurement section which is theoretically circular will become elliptical, and it is easy to know that the short axis length b of elliptical section is consistent with the actual radius of the measured rotary body part.Using improved Gauss-Newton method to optimize the objective function combined with the data set for optimization, and adjusting the threshold value and increment coefficient of each parameter of increment matrix according to the different iteration convergence speed of optimization parameters.

The invention provides a coal bed gas reservoir brittleness index earthquake prediction method and device, and relates to the technical field of geological prospecting, and the method comprises the steps: constructing an objective function used for solving model parameters under the L1 norm constraint based on seismic trace set data and a reflection coefficient approximation formula under an isotropic medium; then solving the target function based on an LM algorithm and initial model parameters extracted from the logging data to obtain target model parameters, and finally calculating the brittleness index of the coalbed methane reservoir according to the target model parameters and the position information of the coalbed methane reservoir. As the L1 norm constraint can apply stronger constraint to the target function and suppress the influence of noise on the inversion process, the accuracy of the inversion result can be effectively improved. Moreover, the LM algorithm has the local convergence of the Gaussian-Newton method and the global characteristic of the gradient descent method, so that the method has the advantages of high convergence speed and stability.

The application provides a kind of vehicle-mounted multi-line laser radar and IMU external parameter automatic calibration method and device, the method is by digging round hole as laser radar recognizable feature on calibration board, the coordinates of four corner points of calibration board under laser radar coordinate system are calculated, the same point pairing with WGS-84 control point on calibration board is realized in combination with the absolute pose information of vehicle-mounted GPS / IMU, and finally the external parameter matrix of laser radar and IMU is solved by Gauss Newton method. Through the scheme, the calibration scene requirement can be reduced, the automatic external parameter calibration can be realized, the calibration efficiency can be effectively improved, the influence of human factors can be avoided, and the accuracy and reliability of calibration result can be ensured.

The application relates to the technical field of automatic driving, and discloses a multi-sensor fusion SLAM method considering curvature constraints and a system thereof. The method fully considers the kinematic constraints of Ackerman steering vehicles on the basis of an LIO inertial laser radar tight coupling system, first constructs a vehicle odometer constraint factor by using rear axle wheel speed and front wheel steering angle and combining a two-degree-of-freedom vehicle model; secondly, establishes the relationship among a yaw angle and wheel speed, X-axis displacement and Y-axis displacement, and adds a curvature constraint factor under the same steering; finally, constructs a SLAM back-end optimization based on a factor graph method, adds an IMU pre-integration factor, a laser radar odometer factor, a vehicle odometer constraint factor and a curvature constraint factor, and solves by using a Gauss-Newton method. Test results in a real scene show that, compared with LIO-SAM and FAST-LIO2 algorithms, the accuracy of the application algorithm is obviously improved.

The application discloses a kind of concrete dam real-time monitoring inversion method and system, the method includes that mechanical parameter sample set is calculated by finite element model, the displacement response matrix corresponding to each sample is obtained, then with mechanical parameter sample set composition sample pair set, establish POD-RBF proxy model and objective function, obtain initial extreme point, with initial extreme point or new extreme point of last step as initial value, using Gauss-Newton method carries out local optimization, obtains new extreme point and substitutes into finite element model, whether the single displacement response vector obtained is less than the allowable error set, if not, the current new extreme point and corresponding single displacement response vector are added to sample pair set, update POD-RBF proxy model, repeat calculation until the single displacement response vector obtained and measured displacement response vector is less than the allowable error set, output current new extreme point as concrete dam mechanical parameter inversion value, effectively improve concrete dam monitoring inversion precision.