9 results about "Weighted least squares estimation" patented technology

The invention discloses an InSAR (Interferometric Synthetic Aperture Radar) and GNSS (Global Navigation Satellite System) robust adaptive fusion method for high-precision three-dimensional surface deformation monitoring, which belongs to the technical field of space geodetic survey and remote sensing, and comprises the following steps of: constructing a time sequence deformation field based on a small baseline set time sequence InSAR processing technology, and resampling GNSS observation data to a grid consistent with the InSAR by using Kriging spatial interpolation; a Helmert variance component estimation method is adopted to adaptively estimate variance components of InSAR and GNSS data, and reasonable weight fixing of a multi-source observation value is achieved; iterative reweighted least square estimation is introduced, outlier influences in various observation data are dynamically restrained through a Tukey double-weight function, robust optimization of a fusion model is achieved, and therefore a high-precision three-dimensional deformation field is reconstructed. The method comprises the steps of Helmert weight fixing, IRLS robust, variance component updating and a closed-loop feedback mechanism of weight matrix optimization.

The invention discloses a method for realizing fine terrain analysis through image fusion by integrating multivariate surveying and mapping data, and relates to the technical field of remote sensing surveying and mapping data processing and image fusion of terrain analysis, and the method comprises the steps: generating grid elevation, ground point density and visual angle information based on a point cloud, constructing a risk index, and extracting a residual error; residual errors are calculated in a track transverse window and are related to view angle cosine, and a strip-shaped error mask is generated; estimating a deviation field through weighted least squares based on observation fitting and smoothing along an air route, correcting the earth surface and obtaining a pixel-level weight; the correction result and the exogenous elevation are fused according to the weight, and the fused elevation, the gradient, the curvature and other indexes are output. The method can improve the precision and reliability of the terrain index under the complex earth surface.

The invention discloses a robot high-sensitivity collision detection method based on a base six-dimensional force sensor. The robot high-sensitivity collision detection method comprises the steps that 1, a robot linearization dynamic model based on base six-dimensional force is constructed; 2, designing a minimum inertia parameter set and a regression matrix of the robot kinetic model; 3, designing a robot periodic excitation track based on regression matrix condition number weighted optimization, and collecting joint motion data and base six-dimensional force sensor data; 4, designing a weighted least square estimation algorithm of kinetic parameters based on measurement noise distribution, identifying the kinetic parameters and obtaining a kinetic identification model; and 5, calculating a base reaction force through a dynamic identification model, comparing the base reaction force with an actual measurement value to obtain an external acting force estimation value, and realizing real-time detection of robot collision by judging whether the estimation value exceeds a safety threshold value or not. The system can realize real-time detection of collision of any part of the whole body of the robot, and has the advantages of high sensitivity and high reliability.

This invention discloses a PID control method and related apparatus based on topology data analysis, belonging to the field of PID control technology. The method includes: mapping the time series of feedback signals representing the output state of the controlled object in a closed-loop control system into a two-dimensional point cloud; extracting the topological complexity features of the point cloud; generating fitting weights for each point cloud data point; constructing a weighted least squares estimator to extract the differential estimate of the two-dimensional point cloud; and replacing the differential term in the traditional PID controller with the differential estimate. Through point cloud mapping and weighted fitting, noise points can be suppressed, high-frequency noise amplification can be avoided, and inherent signal jitter can be suppressed. Dynamically adjusting the weights using topological features overcomes the defect that fixed differential gain cannot respond to signal changes. The adaptive weight adjustment mechanism of this invention can balance noise suppression and response speed without relying on a large fixed smoothing window, thus avoiding the introduction of large phase lag.

This invention discloses a data processing method and system for time-series synthetic aperture radar interferometry (InSAR), comprising: performing differential interferometry processing on N SAR images to obtain M interferograms and P highly coherent points; correcting the terrain residual phase, orbital error, and vertical atmospheric phase at the highly coherent points using a function model; and re-establishing a function model between the corrected phase and surface deformation. The core innovation lies in the fact that this invention deeply considers and establishes the variance and covariance matrices of turbulent atmospheric noise and decoherent noise in the spatiotemporal domain, thereby constructing a stochastic model that accurately characterizes the spatiotemporal correlation of noise. Based on this stochastic model, weighted least squares estimation is used to inversely derive the time-series surface deformation results and their uncertainties. This invention aims to improve the accuracy and reliability of time-series InSAR deformation monitoring by refining the modeling of the noise characteristics of the interferometric phase.

This invention proposes a multi-station passive sensor target localization method based on angle measurement. The steps are as follows: constructing a target localization scenario based on angle measurement; an information acquisition module collects angle information and sensor position observation information; an information processing module constructs a pseudo-linear observation matrix and a pseudo-linear measurement vector; obtaining the least squares estimate of the target position coordinates; calculating the total measurement variance of the azimuth and elevation angles; calculating the weight matrix; calculating the weighted least squares estimate of the target position coordinates; and performing deviation compensation on the weighted least squares estimate. This invention effectively improves the target localization accuracy by compensating for the deviation of the weighted least squares estimate of the target position coordinates. Furthermore, when using weighted least squares to estimate the target position coordinates, it considers both angle measurement errors and the sensor's own localization errors, further enhancing the target localization accuracy.

The application discloses a power grid monitoring and risk early warning method and system based on digital twinning and AI, which comprises the following steps: comparing, under the same time slice, the synchronous acquisition data with the nodal and branch flow calculation results and reference quantities of the digital twinning power grid model, and generating a residual vector; constructing an influence relationship matrix based on the power flow Jacobian matrix, the branch power balance relationship and the upstream and downstream power conservation relationship; solving each cause group disturbance variable by using weighted least squares estimation and sparse group constraint, and determining the confidence; correcting the digital twinning power grid model according to the confidence, updating the data reliability and labeling the historical acquisition samples; training the load prediction model based on the labeled samples, generating representative working conditions, performing power flow calculation, counting the out-of-limit conditions and obtaining the risk ranking. The application can improve the accuracy of power grid anomaly attribution, load prediction and risk early warning.

The invention discloses a double-layer network atmospheric phase error correction and height estimation method based on an optimized reference network. The method comprises the following steps: acquiring data of a plurality of preprocessed two-dimensional single-view complex images; sPS candidate points are extracted, an initial Delaunay reference network is constructed, correction of atmospheric phase errors is realized by using the constructed and connected phase difference signals, minimum spanning tree optimization is carried out based on the residual energy ratio of each side, and a final reference network is generated; performing height estimation on the SPS point in the final reference network relative to a certain reference SPS point by adopting weighted least square estimation; pS candidate points are extracted to be connected with adjacent SPS points in the reference network, a star network is generated, and height estimation of the PS points is completed after atmospheric phase error correction; and generating an elevation map result based on the height information of all the points acquired by the double-layer network. The atmospheric phase does not need to be separated in advance, and atmospheric phase error correction and PS point recognition can be directly achieved by performing phase difference on adjacent pixels; the three-dimensional imaging result is more accurate, and noisy points are fewer.

This invention provides a distributed power data acquisition method, medium, and system, belonging to the field of distributed measurement technology of electrical variables. The method includes: First, establishing a topology diagram of the power system under test and acquiring real-time data such as voltage, current, frequency, and three-phase imbalance from power data acquisition terminals at each node. Then, establishing a set of data relationship equations between nodes based on the topology diagram and fitting these equations using historical data. Next, constructing a node correlation matrix and a node state estimation matrix, including indicators such as voltage, phase angle, frequency, and three-phase imbalance. Integrating these matrices into a weighted least squares estimation model to optimize the real-time acquired power data, obtaining corrected power data. Finally, updating the state estimation matrix based on the corrected power data and storing the distributed power acquisition results. This invention solves the problem of high uncertainty in the quality of power data acquired by existing technologies.