16 results about "Linear least squares" patented technology

This application discloses a deconvolution method for gas chromatography-mass spectrometry (GC-MS) data, relating to the field of GC-MS data analysis technology. This method utilizes a non-negative matrix factorization function to initially decompose the time-mass-charge ratio two-dimensional intensity matrix, obtaining an initial elution curve matrix and an initial mass spectrometry matrix. An exponentially modified Gaussian peak shape modeling function is then used to fit the initial peak shape parameter set. For each component: based on the initial peak shape parameter set, an exponentially modified Gaussian peak shape modeling function is used to generate the original elution curve matrix. Non-negative least squares optimization and non-linear least squares optimization methods are used to iteratively optimize the mass spectrometry matrix, peak shape parameter set, and elution curve matrix. Finally, a comprehensive scoring function is used to select the optimal component set, and the optimal peak shape parameter set, mass spectrometry matrix, and elution curve matrix for the optimal component set are sorted and output. This application enables precise separation of co-eluted components, improving the accuracy and reliability of complex mixture analysis.

The application discloses a kind of based on track keeping's unmanned aerial vehicle thermal infrared image splicing method and system, it is related to image splicing technical field, including steps: constructing image set;Obtain the matching feature point of any pair of to-be-spliced unmanned aerial vehicle's thermal infrared image respectively, and the re-projection error of matching feature point is generated alignment item by the similar transformation matrix of to-be-spliced thermal infrared image;The actual distance between the center of reference image and the thermal infrared image of any unmanned aerial vehicle is calculated, and the track trajectory protection item is obtained between the center point of similar transformation matrix and reference image by pixel distance;Alignment item and track trajectory protection item are weighted and summed to construct global optimization objective function, and global optimization objective function is converted into linear least square model with similar transformation constraint to solve, and generate spliced thermal infrared panoramic image.The application minimizes the cumulative error of splicing process, while ensuring the integrity of global structure.

The invention discloses a target harmonic signal complex frequency estimation method based on frequency domain least square, and belongs to the technical field of signal processing, and the method comprises the steps: obtaining a geophysical observation time sequence containing a target harmonic signal; windowing Fourier transform is adopted to obtain an initial power spectrum, and a spectrum peak of the target harmonic signal is identified; selecting power spectrum values at a plurality of frequency points near the target spectrum peak, and obtaining complex frequency parameters of the target harmonic signal by using linear least square fitting; zero-filling expansion is adopted to increase effective frequency points of a power spectrum, and the estimation precision of complex frequency parameters is improved through multiple least squares. According to the method, Fourier transform and linear least square fitting are combined, high-precision estimation of the complex frequency of the target harmonic signal can be achieved, the method is simple, efficient and easy to implement, and the method is successfully applied to scenes such as earth normal mode frequency extraction and ocean partial tide parameter inversion and can be expanded to any time sequence signal analysis such as communication and biomedicine.

The invention discloses a deception jamming method and device for an azimuth phase coding SAR (synthetic aperture radar), and belongs to the technical field of electronic support system synthetic aperture radar countermeasure. The method comprises the following steps: intercepting an SAR signal, carrying out two-dimensional structured phase stripping by constructing an inverse distance modulation operator and an inverse azimuth modulation operator, and estimating to obtain an azimuth phase sequence; carrying out complex domain differential operation on the estimated azimuth phase sequence, constructing a phase consistency measurement index and minimum consistency order mapping, and identifying a coding structure of the phase consistency measurement index and the minimum consistency order mapping; constructing a linear least square problem based on an identification result, and reconstructing an azimuth phase sequence; and performing phase consistent modulation including convolution modulation on the intercepted signal to generate a deception jamming signal. According to the method, complete matching of the interference signal and the SAR receiving filter is realized, and effective deception interference can be implemented on a new system SAR adopting random, primary, secondary and grouped phase coding.

The application discloses a kind of pure angle target trajectory estimation methods, this method will typical target motion model be converted into the autoregressive moving average model of position component to realize state decoupling, by adjusting the relationship between pseudo-linear least square cost function and process noise energy to obtain regularized pseudo-linear least square model.To reduce the estimation deviation generated by the correlation between pseudo-linear measurement matrix and noise, the regularized instrumental variable least square position estimation is obtained by using instrumental variable estimation method.The method of the application obtains the analytical expression of batch estimation of azimuth position component in the framework of regularized least square estimation.The method has linear time calculation complexity, and its estimation performance is irrelevant to initial state.

The invention discloses a federal causal portrait and teaching intervention optimization method for a multi-school online education scene. The method comprises the following steps: constructing a local sample set and a local variable matrix; correlation calculation is carried out on education variables in the local variable matrix, and a local candidate neighborhood matrix is constructed; constructing a local candidate father node set; constructing a father variable set and a target variable, and performing linear least square fitting on the relationship between the father variable set and the target variable; obtaining a first residual dependency degree between the residual and the parent variable matrix; constructing a local causal adjacency matrix according to the local direction score; obtaining a global causal direction score by adopting federal causal structure weighted aggregation; constructing a global causal skeleton matrix; mining a causal path set pointing to the learning result node from a teaching related node set, and obtaining path causal intensity; calculating an overall causal influence score of learning result nodes of the teaching action set; and sorting according to the overall causal influence scores to obtain an intervention strategy.

This invention relates to the field of optical inspection technology, and in particular provides a method and apparatus for sub-aperture stitching inspection of large-aperture optical elements. The method includes the following steps: S1, setting up an inspection system, including an electrically controlled rotary table, a display screen, and a camera; S2, establishing a mirror coordinate system with the center of the mirror surface under test as the origin; S3, rotating the mirror under test and transforming the coordinates of sampling points within each sub-aperture to the world coordinate system; S4, ray tracing to calculate the local slope data of the sampling points; S5, uniformly transforming the slope data of each sub-aperture to the mirror coordinate system, and constructing a full-aperture slope field based on a slope stitching fusion method; S6, constructing an overdetermined linear least squares equation system based on Zernike polynomials to obtain the full-aperture reconstructed surface shape of the mirror surface under test. This invention acquires sub-aperture data through rotational scanning and uses a slope stitching fusion method to achieve full-aperture surface shape reconstruction and high-precision complete inspection of the surface of large-aperture optical elements.

The invention belongs to the technical field of magnetic measurement, and particularly relates to a calibration method of a magnetic dipole array. The method comprises the following steps: S1, obtaining an independent calibration model of each magnetic source unit; and S2, according to the independent calibration model, measuring and modeling an additional magnetic field generated by coupling between the magnetic source units, and establishing a compensation model. According to the method, the problem is decomposed into N low-dimensional nonlinear fitting and one linear least square problem, the calculation complexity and time consumption are reduced in order of magnitude, and high efficiency in engineering application is achieved.

A method is provided for using Light-Field Dynamic Homography (DH) to generate a large virtual focal plane from a non-rigid camera array with narrow overlaps between their fields of view (FOV). The method incorporates the 3D geometry of the cameras and employs non-linear least square optimization to dynamically estimate the inter-view homography transformations. Remarkably, only two feature correspondences are required between adjacent views to stitch the images and generate a wide virtual focal plane array, eliminating the need for significant FOV overlaps between the multiple cameras.

The invention discloses a surface shape anti-vibration measurement method for a large-aperture high-resolution phase-shifting interferometer. According to the method, vibration offset iterative estimation and high-resolution wavefront reconstruction are decoupled, and anti-vibration phase reconstruction is completed only by using a single group of high-resolution interference images collected in a vibration environment. The method comprises the following steps: acquiring a high-resolution interference image in a vibration environment and carrying out noise suppression and contrast compensation preprocessing; down-sampling the preprocessed image, and iteratively estimating the vibration offset of each frame of interference image in a low-resolution domain; and taking the vibration offset as a known parameter, carrying out linear least square fitting on the full-resolution pre-processed image, and reconstructing the measured wavefront phase distribution. According to the method, the calculation time is shortened by 8-10 times while the phase reconstruction precision is ensured, low-resolution data are not required to be collected, hardware is not required to be changed, and the method is suitable for the phase-shifting interferometer with the caliber being greater than or equal to phi 100mm and the resolution being greater than or equal to 1000 * 1000 pixels.

The invention discloses a porous medium seepage field rapid prediction method based on principal component analysis and a random neural network. Firstly, principal component analysis dimensionality reduction is performed on a permeability field and a pressure field, and an encoder and a decoder are constructed; then, a random neural network is constructed, the hidden layer weight and bias of the random neural network are fixed after being initialized, and the output layer weight is trained only by solving the linear least square problem so as to learn the potential space mapping relation; preferably, the prediction stability and precision can be improved by integrating a plurality of networks; furthermore, a prediction interval with statistical guarantee can be provided for the integrated model by using a conformal prediction method, and the prediction uncertainty is quantified. According to the method, the training time is shortened to the second level, the calculation cost is remarkably reduced while high prediction precision is kept, high robustness is achieved, and an efficient tool is provided for real-time simulation and rapid decision making of oil and gas exploitation, underground water simulation and other scenes.

This invention discloses a cooperative localization method for aircraft in ad hoc networks, relating to the field of aircraft cooperative localization. It utilizes the relative distance and angle measurements between aircraft within a two-hop link and the positions of observable anchor nodes within that two-hop link to construct a multi-hop link joint observation. A weight matrix is ​​constructed by combining the influence of ranging and angle measurement errors on the localization error. Cooperative localization of the aircraft in the ad hoc network is then achieved based on a weighted linear least squares algorithm. Compared to conventional cooperative localization methods, this method does not require aggregating all link measurement results to a central computing node for position calculation. It only needs to collect measurement results within a two-hop link to autonomously calculate the position of its own nodes, resulting in low communication overhead and low localization latency between aircraft.

Described herein is NLS using a bounded linear initial search space and a fixed grid with pre-calculated variables. An initial first elevation angle corresponding to a direct reflection from an object and an initial second elevation angle corresponding to a multi-path reflection from the object are received along with a grid comprising pre-calculated variables for each of a plurality of grid points. The initial elevation angle pair is then refined by, for a plurality of iterations, determining a closest grid point to a current elevation angle pair and performing a non-linear least squares iteration on the closest grid point using the pre-calculated variables for the closest grid point to determine a next elevation angle pair. When the iterations have converged, a height of the object is calculated based on a final elevation angle pair.

The application discloses a target harmonic signal complex frequency estimation method based on a frequency domain least square, and belongs to the technical field of signal processing, and comprises the following steps: acquiring a geophysical observation time sequence containing a target harmonic signal; acquiring an initial power spectrum by adopting a windowed Fourier transform, and identifying a spectral peak of the target harmonic signal; selecting power spectrum values at multiple frequency points near the target spectral peak, and obtaining a complex frequency parameter of the target harmonic signal by utilizing a linear least square fitting; and adopting zero padding extension to increase effective frequency points of the power spectrum, and improving estimation accuracy of the complex frequency parameter by a multiple least square. The application combines the Fourier transform and the linear least square fitting, can realize high-precision estimation of the complex frequency of the target harmonic signal, is simple, efficient and easy to realize, has been successfully applied to scenes such as extraction of earth normal mode frequencies and inversion of ocean tide constituent parameters, and can be extended to communication, biomedical and other arbitrary time series signal analysis.

This invention relates to the field of indoor positioning technology, specifically to a UWB and IMU tightly coupled positioning method and system based on sliding window and motion constraints, comprising the following steps: S1, deploying UWB base stations in the space to be measured, installing UWB and IMU units on a ground robot, acquiring distance observation values ​​through bilateral bidirectional ranging, and establishing a set of distance equations in conjunction with the base station coordinates, and solving for the initial position using linear least squares; S2, initializing the initial position input error state Kalman filter, and predicting the position, velocity, and attitude based on IMU data; S3, constructing an innovation vector based on the UWB observation vector and the predicted state, updating the observation noise covariance through a sliding window, and fusing UWB and inertial information; S4, constructing a Z-axis pseudo-zero velocity observation model and configuring the observation noise covariance, updating the error state through the input filter, and correcting the nominal state. This invention achieves the collaborative fusion of UWB and IMU information, thereby improving the accuracy and stability of ground robot positioning.

The invention relates to a coal preparation plant stock bin coal quality point cloud boundary point data processing method and related components, and is applied to the technical field of coal preparation plant data processing. Calculating the normal direction of each original point in the original point set; determining a first boundary point and a second boundary point based on the normal direction, the distance between the original points and a preset point interval; fitting the xy plane coordinate of the first boundary point through a linear least square method and a standard equation of a circle, and calculating a circle center coordinate; scaling the first boundary point based on the circle center coordinate to determine a projection point; determining an interpolation point based on the projection point, the first boundary point and a preset point interval; correcting z values of the projection point and the interpolation point based on the first boundary point and the second boundary point; and determining a target point set based on the original point set, the corrected projection points and the corrected interpolation points. The method has the effect of improving the comprehensiveness and accuracy of the coal quality point cloud data.