20 results about "Total least squares" patented technology

The invention discloses a robust robust sequence extraterrestrial celestial body terrain reference surface fitting method and system, and relates to the technical field of terrain reference surface fitting, and the method comprises the steps: collecting three-dimensional laser point cloud data to construct a reference plane geometric model, and building a joint error model of an observation value error and a design matrix error; adopting a sequence PEIV total least square framework to carry out grouping iteration adjustment on the point cloud data, and recursively updating parameter estimation by utilizing an error propagation matrix and a matrix inversion formula; introducing a two-factor robust mechanism, dynamically calculating a weight according to a residual error and estimating a robust scale; and circularly optimizing the parameters through an iterative strategy until a convergence condition is met, and outputting reference plane parameters. According to the method, different terrain complexity, data scales and error distribution scenes can be covered, the adaptability and engineering practicability of terrain datum plane fitting are remarkably improved, and reliable geometric reference is provided for subsequent obstacle detection and safe area decision making.

The invention belongs to the technical field of near-field RIS target positioning, and particularly relates to a non-line-of-sight near-field source parameter estimation and correction method based on decoupling atom norms. According to the method, through cross-correlation operation and pseudo-snapshot processing, angle information and distance information are decoupled while the array aperture is expanded. In addition, the single-snapshot decoupling atom norm algorithm based on the virtual aperture is provided while the advantage of super-resolution performance of the atom norm under the single snapshot is reserved. And through a decoupling atom norm method, the pitch angle and the azimuth angle on the RIS are further decoupled, and the algorithm complexity is reduced. And finally, designing a total least square parameter compensation algorithm based on an accurate model so as to reduce system errors caused by Fresnel approximation.

The invention belongs to the technical field of image detection, and discloses a finite element cloud picture crack fitting method based on direction priori and dark quantile ROI, which comprises the following steps: calculating a preliminary direction angle of a crack according to a reference point coordinate as the direction priori; taking the direction prior as a central axis direction and one of the reference points as a benchmark, constructing a strip-type ROI, extracting brightness values of pixels in the strip-type ROI, and screening the pixels with the brightness values lower than or equal to a threshold value as a preliminary crack candidate point set; calculating the local gradient direction of each pixel point in the candidate point set, screening out the pixel points of which the deviation between the local gradient direction and the direction prior is smaller than a preset direction tolerance, and forming a direction consistency set; and carrying out total least square fitting on the coordinates of the pixel points in the direction consistency set to obtain a main direction straight line and fitting parameters of the crack, and superposing the main direction straight line on the original finite element cloud picture to generate a crack fitting result. According to the method, robust and high-precision extraction of the crack direction in the complex finite element cloud picture is realized.

The application provides a space positioning method for mapping, and relates to the technical field of laser radar positioning. The method comprises the following steps: projecting color grid light to a region to be mapped by color coding, forming a positioning grid, and collecting two images of the on-off grid light by using an image acquisition device, extracting the grid intersection points as control points and obtaining the pixel coordinates of the control points by color recognition; emitting measuring light to the control points from at least three measuring light projection points, and solving the three-dimensional coordinates of the control points by space distance intersection; combining the pixel coordinates and the three-dimensional coordinates of the control points, and inversely calculating the pose of the image acquisition device by using a heteroscedastic total least squares model; identifying the pixel offset of a target feature point and its adjacent control point, combining the pose to convert the actual distance offset of the target feature point, and calculating the three-dimensional coordinates of the target feature point. The application effectively fuses optical active projection and optical ranging, and realizes efficient three-dimensional space positioning.

The application discloses an AR model parameter estimation method and device based on forward and backward linear prediction, and the method comprises the following steps: obtaining an interference signal of an infrared spectrometer and converting the interference signal into a matrix form, so as to complete construction of an AR model; a forward and backward expansion matrix of the interference signal is constructed by using a forward and backward prediction total least square method, and a forward and backward linear prediction equation is formed based on the forward and backward expansion matrix; the forward and backward expansion matrix is converted into a homogeneous linear equation by considering the noise disturbance influence of an observation vector and a data matrix, and a total least square problem of the homogeneous linear equation is constructed; and a singular value decomposition method is used to solve the solution of the total least square problem, so as to obtain parameter coefficients of the AR model. The application has the advantages that the problem of spectrum line separation of a restored spectrum is solved, the influence of noise on parameter estimation is reduced, and the accuracy of the restored spectrum is improved.

The invention discloses a wind power mixed tower outer surface flatness deviation positioning correction method and system, and the method comprises the steps: obtaining three-dimensional point cloud data and thermal imaging data of a wind power mixed tower, and carrying out the spatial synchronization processing; constructing a high-robustness reference curved surface and a space-time composite deviation field: performing cylinder fitting through a robust weighted overall least square method to obtain a reference surface, and fusing geometric and thermal information to generate the space-time composite deviation field; extracting a deviation region by adopting a multi-feature clustering and graph theory segmentation technology; aiming at the extracted deviation area, carrying out deviation cause auxiliary diagnosis by fusing a deep network and Bayesian reasoning; and in combination with a cause diagnosis result, performing adaptive correction decision optimization based on a multi-objective evolution algorithm, and outputting an optimal correction scheme considering quality, efficiency, cost and safety. The external surface deviation of the wind power mixing tower can be accurately positioned and corrected, and the detection efficiency and the correction reliability are improved.

The non-integer linear array DOA estimation method based on accelerated iterative hard threshold value comprises the following steps: constructing a model comprising a non-integer linear array and received signals thereof; generating an estimated value of an ideal covariance matrix of the received signal model; constructing a difference co-array and a sparse optimization model of the non-integer linear array according to the received signal model; solving the sparse optimization model by using an iterative hard threshold algorithm combined with an Armijo type step length criterion to automatically adjust the step length and a support set, and obtaining an initial angle estimation; and generating an accurate angle estimation by performing a first-order Taylor expansion on the initial angle estimation. The application expands the array degrees of freedom by constructing a difference co-array, takes a single fast sample covariance vector as a sparse representation input, adaptively accelerates the convergence by combining the iterative hard threshold algorithm and the Armijo step length criterion, and compensates for the off-grid error caused by grid division by using a Taylor expansion total least square method, so that high-precision DOA estimation is realized under a small number of fast shots.

A self-calibration method for frame non-orthogonality error and accelerometer error in a dual-axis frame inertial system is proposed. Under static base conditions, the platform is locked at 10 frame angular positions, accelerometer measurements are collected, and the specific force measurement of the inertial system in the platform coordinate system at each locked position is calculated. The attitude matrix of the inertial system from the platform coordinate system to the local horizontal coordinate system at each locked position is also calculated, yielding the acceleration of the inertial system in the local horizontal coordinate system at each locked position, which is used as the error observation. The residuals of the error coefficients are calculated using a total least squares algorithm, and the estimated values ​​of the measured parameters are corrected. Iterative calculations are performed until the iterative convergence criterion is met, obtaining the self-calibration results for each error coefficient. Through the self-calibration and compensation of the error coefficients, the accuracy of attitude measurement of the dual-axis frame inertial system carrier or base affected by the non-orthogonality error of the axis system can be improved.

The invention discloses a lithium ion battery charge state estimation method based on a parallel battery neural network, and the method comprises the steps: obtaining battery charge and discharge data of a parallel battery under different working conditions, carrying out the preprocessing, carrying out the parameter identification through employing a total least square method, carrying out the branch current estimation through employing a DNN deep neural network, and carrying out the calculation of the branch current. And after the estimated current is obtained, SOC prediction is carried out based on a Sage-Husa adaptive filter in combination with an SRCKF (root mean square cubature Kalman filter) algorithm, and the state of charge of the battery is obtained. According to the fusion algorithm, firstly, a first-order ECM is constructed to capture the dynamic response characteristics of each parallel unit, and model parameters are identified by adopting a TLS (Total Least Squares) method, so that parameter deviation caused by measurement noise and system errors is reduced, and the modeling precision is improved. And finally, on the basis of estimating branch current, identifying model parameters and measuring voltage signals, introducing a Sage-Husa adaptive filtering algorithm and combining with a square root cubature Kalman filtering algorithm to realize online estimation of the SOC of each parallel battery.

The application discloses a transformer partial discharge positioning method based on multi-sensor abnormal time difference elimination, S1: installing a sensor for receiving a partial discharge signal on the surface of a transformer, and extracting the time when the sensor receives the partial discharge signal; S2: establishing a spherical positioning equation set under a space rectangular coordinate system and linearizing; S3: solving the linearized equation set, and obtaining an initial solution set after screening; S4: using a DBSCAN clustering algorithm to perform clustering analysis on the initial solution set, and obtaining multiple clusters and outliers; S5: classifying the clusters according to residuals, counting the measurement time difference frequencies corresponding to the clusters and the outliers, and identifying good time differences, ordinary time differences and abnormal time differences; S6: eliminating the abnormal time differences, considering the measurement noise of the remaining time differences, and establishing a constrained total least squares model; S7: selecting an iterative initial solution, and using a Newton iteration method to calculate a final positioning solution. The application can eliminate abnormal time differences and reduce noise influence, and thus still has higher positioning precision in a higher noise environment.

The invention provides a state monitoring and fault diagnosis system and method for a primary flue gas electrostatic dust collector of a converter. The system comprises a multi-source data acquisition module, a signal preprocessing and feature extraction module, an intelligent diagnosis core module and a man-machine interaction and early warning module. The invention further relates to a state monitoring and fault diagnosis method for the converter primary flue gas electrostatic dust collector. According to the invention, an improved PRONY algorithm is adopted to extract damping factors, oscillation frequency and amplitude characteristics, and a total least square method is adopted to improve parameter estimation accuracy; an integrated deep learning model combining a convolutional neural network, a long-short-term memory network and an attention mechanism is adopted to perform fault recognition, and a probability graph model based on a Bayesian network is adopted to perform fault positioning and reason reasoning; and the man-machine interaction and early warning module generates multi-level early warning information according to the diagnosis result and visually displays the multi-level early warning information. The fault diagnosis accuracy of the system reaches 94.5%-97.2%, the early fault early warning time is advanced by 12-72 hours, and the intelligent level of the dust removal system is effectively improved.

The application discloses a vehicle roll angle estimation method and device based on point cloud under critical working conditions and belongs to the technical field of intelligent automobile environment perception. Laser radar point cloud data and vehicle kinematics signals are acquired; a region of interest is dynamically adjusted according to the kinematics signals to adaptively focus on a ground plane; ground plane robust estimation is carried out in the dynamic region of interest based on a weighted total least squares method and a Huber loss function to obtain a ground plane normal vector; and the vehicle roll angle is solved according to the geometric relationship between the ground plane normal vector and a reference normal vector. The application solves the point cloud distortion problem caused by the violent movement of the vehicle body under critical working conditions, realizes stable and reliable estimation of the roll angle without increasing hardware, and effectively improves the adaptability of the perception system under extreme working conditions.

The invention discloses a non-integer linear array DOA estimation method based on an accelerated iteration hard threshold. The non-integer linear array DOA estimation method comprises the following steps: constructing a non-integer linear array and a received signal model thereof; generating an estimated value of an ideal covariance matrix of the received signal model; according to the received signal model, constructing a differential common array and a sparse optimization model of a non-integer linear array; an iterative hard threshold algorithm is utilized, the step length and the support set are automatically adjusted in combination with an Armijo type step length criterion to solve the sparse optimization model, and initial angle estimation is obtained; and performing first-order Taylor expansion on the initial angle estimation to generate accurate angle estimation. According to the method, the degree of freedom of an array is expanded by constructing a differential common array, a single-snapshot sample covariance vector is used as sparse representation input, convergence is adaptively accelerated in combination with an iterative hard threshold algorithm and an Armijo step length criterion, and off-grid errors caused by grid division are compensated through a Taylor expansion total least square method, so that the accuracy of grid division is improved. And high-precision DOA estimation is realized under a small amount of snapshots.

The invention belongs to the technical field of power distribution network planning, and particularly relates to a power distribution network acceptance capability evaluation method and system based on data driving, and the method comprises the steps: collecting the historical time sequence measurement data of each node of a power distribution network, calculating the instantaneous voltage sensitivity and the change rate of the instantaneous voltage sensitivity through the ratio of the measurement voltage variation to the measurement power variation, and calculating the acceptance capability of the power distribution network; combining the deviation between the voltage amplitude and the voltage safety upper limit to construct a dual coupling weight; constructing an improved total least square objective function, weighting the objective function in the whole time period by using double coupling weights, and identifying a dynamic voltage sensitivity sequence under the condition of applying physical constraints; and calculating a fitting residual error and a standard deviation thereof based on the dynamic voltage sensitivity sequence obtained by identification, constructing a global confidence upper limit of the sensitivity, and calculating the acceptance capability of the power distribution network by combining a voltage safety upper limit and the current operating voltage. According to the method, the admitting ability robust evaluation under the condition that parameters are unknown is realized.

The application relates to the technical field of antennas and discloses a high-precision two-dimensional DOA estimation method based on optimal redundant linear arrays, 1) input: array receiving data y i (t), optimal redundant parallel array two-subarray element position coordinates P S1 and P S2 ; 2) parameter calculation ① calculating array autocovariance matrices of two optimal redundant linear arrays according to a formula; ② calculating array cross-covariance matrices between the optimal redundant linear arrays according to a formula. The high-precision two-dimensional DOA estimation method based on optimal redundant linear arrays proposes an extended covariance matrix reconstruction algorithm, the algorithm constructs an extended covariance matrix containing autocovariance matrices and cross-covariance matrices, further constructs an extended covariance matrix reconstruction model, simultaneously realizes high-precision estimation of initial autocovariance matrices and initial cross-covariance matrices, and based on the initial autocovariance matrices and the initial cross-covariance matrices, utilizes a covariance vector sparse representation and a total least squares method in sequence to realize high-precision two-dimensional DOA estimation.

A method for rapidly detecting a straight line, a plane and a hyperplane in a multi-dimensional space. The new method has two important advantages: firstly, the model corresponds to a total least square fitting algorithm and has better tolerance to data noise, so as to solve the problem of the precision of detecting a target on a parameter space segmentation line by means of fast Hough transform being too low; and secondly, in the integrated fast Hough transform, targets that are close to each other in a data space are gathered together in a parameter space, and a calculation process in the parameter space can be displayed by using a visual graph, thereby rapidly determining the number of targets, guiding the setting of system parameters, and distinguishing a target that is repeatedly recognized.

The invention relates to an underwater Internet of Things positioning method and system based on constrained total least squares, and the method comprises the steps: obtaining the position coordinates of intelligent sensing devices and the sound wave propagation time from a target node to each intelligent sensing device, and carrying out the data preprocessing; based on the observation model and the algebraic equation, establishing a linearized total least square model; through derivation, the total least square problem is converted into a fractional programming problem, and the fractional programming problem is converted into a semi-definite programming problem; according to the position coordinates and the sound wave propagation time, solving a semi-definite programming problem to obtain an optimal regularization parameter; in combination with a regularization objective function and predefined quadratic constraints, converting the fractional programming problem into a generalized trust region sub-problem; and solving the generalized trust region sub-problem by adopting a Lagrange multiplier method to obtain a final target node position estimation value. Compared with the prior art, the target positioning method provided by the invention has the advantages of high robustness and high precision.

The application belongs to the technical field of image detection, and discloses a finite element cloud atlas crack fitting method based on direction prior and dark subinterval ROI, which comprises the following steps: calculating a preliminary direction angle of the crack according to the reference point coordinates, and taking the preliminary direction angle as the direction prior; taking the direction prior as the central axis direction, and taking one of the reference points as the benchmark to construct a strip type ROI; extracting the brightness values of the pixels in the strip type ROI, and screening the pixels with brightness values lower than or equal to a threshold value as a preliminary crack candidate point set; calculating the local gradient directions of the pixel points in the candidate point set, screening out the pixel points with a deviation between the local gradient direction and the direction prior less than a preset direction tolerance to form a direction consistency set; performing total least squares fitting on the pixel point coordinates in the direction consistency set to obtain a main direction straight line of the crack and fitting parameters, and superimposing the main direction straight line on the original finite element cloud atlas to generate a crack fitting result.

The invention discloses a sub-pixel edge detection method and device and a storage medium in the technical field of computer vision and image processing, and the method comprises the steps: carrying out the weighted total least square straight line fitting based on a joint weight, and obtaining a local main direction straight line; calculating an orthogonal residual error of the candidate sample based on the local main direction straight line, calculating a Huber type robust weight according to the size of the orthogonal residual error, and carrying out primary reweighting and refitting based on the Huber type robust weight and a joint weight to obtain a stable main direction straight line; and carrying out linear interpolation type smooth updating based on the current position and the target position of the sub-pixel point, carrying out chaining according to direction continuity and geometric adjacency, and outputting a sub-pixel edge curve set. According to the invention, the technical problems of positioning jitter, wrong connection / disconnection and outlier traction in a scene with complex texture and low signal-to-noise ratio in the existing sub-pixel edge detection technology can be solved.