188 results about "Covariance" patented technology

The invention discloses a point cloud reconstruction method and system based on three-dimensional Gaussian sputtering, and is used for solving the technical problem that the structural stability of a final three-dimensional point cloud model is not good enough due to the fact that a traditional point cloud reconstruction method causes gradient propagation abnormity, and the optimization process is not convergent or falls into a local minimum value. The method comprises the following steps: firstly, acquiring a multi-view image and a reference view image, and constructing a covariance degradation risk probability graph; generating a plurality of Gaussian three-dimensional points to be regulated and controlled, performing three-dimensional point screening and fitting credibility score calculation, outputting secondary regulation three-dimensional points and corresponding scores, and constructing an initial three-dimensional point cloud model; performing secondary adjustment on the three-dimensional point by combining the multi-view image and fractional optimization to obtain a target Gaussian three-dimensional point, and updating the initial model into an intermediate model; and updating the intermediate model through a covariance updating gating mechanism based on gradient convergence dynamic monitoring, and outputting a target three-dimensional point cloud model.

The invention discloses a heat pump system state anomaly detection method based on a depth auto-encoder, and the method comprises the steps: collecting compressor data, and carrying out the standardization processing to construct a multi-dimensional time sequence; a spatial-temporal feature extraction depth auto-encoder with a thermodynamic coupling attention mechanism is constructed, coupling attention is utilized to calculate physical parameter coupling strength weights to extract spatial features, time features are extracted in combination with a long and short-term memory network, and normal state data are reconstructed and predicted through a decoder after fusion; residual vectors of predicted normal state data and original data are calculated, and a weighted mahalanobis distance is calculated by using a covariance matrix to generate an abnormal score; and constructing a sliding probability distribution model based on historical normal data, calculating a current score occurrence probability, and comparing the current score occurrence probability with a preset threshold to output an anomaly detection result. According to the method, a multi-physical parameter space coupling relationship and a time evolution rule are captured through a thermodynamic coupling attention mechanism, and the anomaly detection accuracy and robustness are improved.

The invention discloses a GRACE and Swarm time-varying gravity field fusion filtering method based on a state space model, and the method comprises the steps: taking a spherical harmonic coefficient as a state quantity, constructing a random walk process equation, introducing three types of observations, employing a quantization parameter for observation noise and process noise covariance, constructing according to an order / power law, and carrying out the self-adaptive updating along with the monthly; a Nelder-Mead method is adopted to search for spectral index parameters, and an EM algorithm and a statistical method are utilized to update other parameters in a closed / quasi-closed mode; obtaining the state and posterior covariance of a full time sequence by using Kalman filtering and RTS smoothing; in the GRACE and GRACE-FO window period, continuous reconstruction is carried out by means of a process model and Swarm; and outputting quality evaluation information including monthly gravity field coefficients, posterior covariance, innovative variance ratio, residual whitening test, space power spectrum, uncertainty band and the like. According to the method, while physical rationality and calculation feasibility are ensured, a continuous and stable monthly time-varying gravitational field sequence with quantifiable uncertainty is realized.

The invention discloses a mobile robot accurate docking method based on edge calculation, and aims to solve the problems that the dynamic docking precision is reduced and the collision risk is increased due to micro-motion or drifting of a target station. According to the method, unified time reference alignment is carried out on data of a camera, a laser radar, an inertial measurement unit, an ultra-wideband range finder, a station encoder and a programmable logic controller at an edge node, and a three-dimensional special Euclidean group equivariant multi-source fusion network is used for outputting relative pose estimation and covariance; the estimation in the time window is further used as a condition to be input into a conditional diffusion short-time prediction model to obtain a time-varying mean value and a time-varying covariance, an anisotropic probability tube is constructed, and prediction-measurement joint correction is carried out based on a score function; scenarized opportunity constraints are constructed under correction probability tube constraints, a tubular nonlinear model is adopted to predict, control and solve a reference trajectory, an actuator command is generated in combination with depth visual servo and compliance control, and the technical effects of high precision, robustness and safe docking under the station dynamic disturbance condition are achieved.

The invention discloses a distillate oil property prediction method based on deep learning feature extraction and partial least squares regression. The method comprises the following steps: firstly, carrying out classification training on a near infrared spectrum through a convolution-attention double-branch fusion network, and extracting high-dimensional spectral features with local and global information; then, historical samples are retrieved from a database based on prediction categories, a plurality of most similar samples are selected by adopting cosine similarity measurement to construct a correction set, and the spectral features and property labels are subjected to standardization processing; and finally, carrying out partial least squares regression modeling on the correction set, extracting latent variables to maximize covariance between spectral features and physicochemical properties, and inputting feature vectors of an oil sample to be detected into the trained PLS model to obtain a corresponding property prediction result. According to the method, the modeling requirement and the category specificity characteristics under the small sample condition are considered while the prediction precision is guaranteed, and the method is suitable for rapid property detection and intelligent analysis in the refining process.

Auto-tuning covariances associated with a set of noise models for a variety of sensor modalities and / or perception components such that the covariances are leveled respective to one another may include whitening the covariances and / or error models and determining scalars to apply to the covariances. Determining these scalars may comprise using the residuals that result from generating the set of noise model (e.g., such as may be determined as part of least squares estimation) along with the hat matrix of the process model to determine the scalars. The covariances may iteratively be updated until the scalar adjustments converge or until another end condition is met.

The invention relates to the technical field of data processing, in particular to a covariance inverse matrix recursive updating method for on-satellite hyperspectral anomaly detection, which comprises the following steps: acquiring a hyperspectral data stream; carrying out adaptive dimension reduction processing on the data stream and loading an initial background model; extracting local background statistics by adopting a sliding window mechanism, generating a background spectrum dictionary by utilizing online dictionary learning, calculating a reconstruction error between a current pixel and the dictionary and a Mahalanobis distance between the current pixel and a background model, and fusing to generate an abnormal score; performing abnormal confidence coefficient evaluation by combining the spatial context information and the spectral angle matching degree, and updating a spectral mean vector by using an exponential weighted moving average algorithm based on non-abnormal pixel data; and the updated model is injected into the next round of processing to form a recursive chain. According to the method, the high calculation complexity of direct inversion of a covariance matrix is avoided, real-time anomaly detection on a satellite is realized, the downloading amount of original data is remarkably reduced, and the congestion of a satellite-ground communication link is relieved.

The invention discloses a motor multi-parameter identification method based on sampling noise excitation and bias compensation, and the method comprises the steps: firstly obtaining the voltage and sampling current of a motor, and enabling the sampling current to comprise sampling noise; then constructing an information evaluation index, and adjusting a proportional parameter of a current controller to improve the identification information amount; then, shaft voltage feed-forward compensation is constructed to suppress electromagnetic torque pulsation; recursion is carried out by adopting a least square method based on the discrete linear parameterization model to obtain a parameter initial value and a covariance matrix; secondly, estimating offset-containing parameters on line, and calculating a sampling noise variance by using an estimated residual error; and finally, obtaining unbiased parameter estimation according to bias compensation iteration updating so as to output online estimation values of stator resistance, direct-axis inductance Ld, quadrature-axis inductance Lq and permanent magnet flux linkage.

The invention belongs to the technical field related to deep learning, and discloses a training method and application of a three-dimensional flow field prediction system of an underwater vehicle, and the training method comprises the steps: calculating the mass center of a neighbor point set for each surface grid point of a vehicle model, constructing a covariance matrix between the neighbor point set and the centroid of the neighbor point set, and performing eigenvalue decomposition to obtain a normal vector of the point; calculating a normal vector included angle between the surface grid point and each point in the neighbor point set, taking the obtained maximum included angle as the geometric feature measurement of the point, and converting the geometric feature measurement into a weight through a Sigmoid function, thereby obtaining the sampling probability of each point; randomly extracting surface grid points from the original surface grid of the aircraft based on the obtained sampling probability to obtain a point cloud of a corresponding model; and training a point cloud neural network by using the sampled point cloud data to obtain the three-dimensional flow field prediction system of the underwater vehicle. Based on the method, the prediction precision and the flow field detail recovery capability can be improved while the calculation efficiency is maintained.

The invention relates to the technical field of data processing, in particular to an electric power project data fusion method and system. The method comprises the following steps: collecting and preprocessing multi-dimensional time sequence data of a distributed power supply and an energy storage system; extracting features of the preprocessed data; an adaptive forgetting factor is introduced to carry out adaptive Kalman filtering processing on the characteristic data, the adaptive forgetting factor combines a current error, an error change trend and a historical error weighted sum difference value, and a prediction covariance matrix and a measurement noise covariance matrix are dynamically adjusted, so that the response capability of filtering to system mutation is improved; and fusing the optimal estimation state of each feature by adopting a D-S evidence theory so as to accurately judge the operation state of the electric power project. According to the method, the problem that estimation is lagged when the state is suddenly changed in traditional Kalman filtering is effectively solved, the accuracy and the reliability of judging the operation state of the electric power project are improved, and an effective guarantee is provided for stable operation of a micro-grid system.

The invention discloses a marine ecological early warning management system and method based on multi-source data, and relates to the technical field of marine data monitoring. The method comprises the following steps: presetting sampling time and synchronously acquiring water body parameters and environmental parameters of each sampling layer at a sampling point; calculating a standard vertical gradient, and constructing an incidence matrix; calculating association strength to form an interlayer association vector; extracting a trend slope and a time difference degree of the inter-layer association vector, and splicing to obtain a space-time association vector; a sliding window method is adopted to calculate covariance between the space-time correlation vector and each environment parameter time sequence, a dynamic coupling coefficient is obtained, and a coupling coefficient matrix is constructed; and comparing the coupling coefficient matrix with the reference matrix element by element, and realizing accurate early warning of the red tide risk through a preset threshold value. According to the method, the time-space correlation characteristics of the multi-source data are fused, so that the early-stage and accurate early warning of the red tide is realized.

The invention discloses a data set distillation method and system based on semantic relation decoupling, and belongs to the technical field of data distillation. According to the method, the feature distribution of the distillation data is aligned with the category-level statistics of the original data stored in the memory bank to realize efficient and fine-grained category-level optimization of the distillation data, so that the representativeness and semantic consistency of the distillation data are improved; in addition, for the problem that distribution in the last layer of feature distribution of the pre-training model is too concentrated, the problem of downstream task gradient disappearance caused by insufficient sample diversity in the feature space is effectively relieved by maximizing diagonal elements of a covariance matrix and minimizing non-diagonal elements at the same time. Therefore, the generalization ability and the learning effect of the model on downstream tasks are improved.

The invention relates to the technical field of attitude estimation, in particular to an attitude estimation method in a magnetic interference environment, which comprises the following steps of: obtaining a conversion relation between an Euler angle and a quaternion; calculating the quaternion at the next sampling moment; respectively solving a first state transition matrix, a first process noise matrix and a first prior covariance according to the first state equation; respectively solving a second state transition matrix, a second process noise matrix and a second prior covariance according to a second state equation; elements in the nb-series rotation matrix are used as observed quantities of state variables in the parameter-series rotation matrix, a corresponding Kalman gain is calculated, and a Jacobian matrix is designed by using the observed quantities; calculating an observation noise covariance matrix by using the Jacobian matrix; updating the Kalman gain by using the observation noise covariance matrix; and updating the state equation and the posterior covariance by using the updated Kalman gain and the observed quantity. The method solves the problem of how to prevent the calculation of the roll angle and the pitch angle from being influenced by magnetic interference when the magnetic interference exists.

The invention discloses a robust adaptive fusion filtering astronomical attitude determination method and system, and belongs to the technical field of radio navigation. The method comprises the following steps: constructing an integrated navigation model of a starlight and inertial integrated navigation system based on Lie group description; obtaining an observation residual error and an observation residual error covariance of a corresponding mode; robust and adaptive mode parallel sub-filtering is carried out, and posterior states and covariances in the two modes are updated; updating the mode probability; and fusing the updated posterior state with the covariance to obtain fusion output of parallel sub-filtering, taking the fusion output of the parallel sub-filtering as final estimation of the starlight and inertial integrated navigation system based on Lie group description, and outputting an astronomical attitude determination result containing an azimuth angle, a pitch angle and a roll angle. The high-precision astronomical attitude determination method has strong adaptability, strong robustness and accuracy when coping with complex noise and measuring outliers, and can realize high-precision astronomical attitude determination of starlight and inertial integrated navigation in a complex dynamic environment.

The invention discloses a pulse signal source tracking method based on a robust Kalman filter. The method comprises the following steps: reading a target state vector and error covariance matrix at a previous moment, and observing a noise covariance matrix; a variational Bayesian parameter before iteration, a target state vector and an error covariance matrix are initialized; starting iteration, and calculating the quadratic form sufficient statistics of the observation error and the forecast error; updating variational Bayesian parameters; updating the forecast error covariance matrix and the observation noise covariance matrix; calculating a target state vector and an error covariance matrix of the iteration; when the variation of the target state vector of the current iteration and the target state vector of the last iteration is smaller than the tolerance, ending the iteration, and outputting the target state vector at the current moment, the error covariance matrix and the observation noise covariance matrix; compared with a deterministic integral sampling method, the method provided by the invention adopts adaptive volume sampling to realize higher tracking precision under lower calculation cost.

The invention relates to a river channel sand body random model generation method and device based on hidden Markov model data filling, a medium and equipment. The method comprises the steps that logging data of a target area are collected and arranged; dividing logging data according to a logging gas-bearing interpretation conclusion, and carrying out analytical statistics on the velocity and density of longitudinal and transverse waves in the logging data; initializing an initial state probability, a state transition matrix, and a mean value and a covariance of Gaussian distribution in the hidden Markov model; inputting a three-dimensional observation vector composed of the velocity and density of the longitudinal and transverse waves into the initialized hidden Markov model for iterative training; performing down-sampling on the lithology data, then constructing a state transition probability matrix, and further constructing a lithology sequence conforming to a geological law through simulation; according to the lithologic sequence, calling a hidden Markov model to generate longitudinal wave velocity, transverse wave velocity and density corresponding to the lithologic sequence; and calculating a reflection coefficient of the synthetic stratum, setting a Ricker wavelet dominant frequency, and obtaining synthetic seismic response data through convolution.

The invention discloses a wide-working-condition steam turbine system operation data correction method based on digital twinning, and relates to the technical field of steam turbine equipment digitalization, and the method comprises the steps: collecting operation data of a wide-working-condition steam turbine system in real time; constructing a digital twinborn model of the steam turbine system; performing prior covariance estimation on the operation data according to the statistical distribution condition and the design parameters; generating a Sigma point by adopting an unscented Kalman filtering algorithm, iteratively updating a state vector and a covariance in combination with a state transition equation and an observation equation, and outputting an updated known variable; performing smoothing processing on the updated known variable in a plurality of time point regions by adopting an RLOESS algorithm to obtain corrected operation data; according to the method, the real dynamic characteristics of the wide-working-condition steam turbine are approached by building the digital twinborn model, the calculation complexity of dynamic data fusion of a steam turbine system can be remarkably reduced, and the problems that the convergence speed is low and calculation is complex due to strong nonlinearity of a traditional method are solved.

The invention relates to a gravity background field construction method based on iterative optimization Kriging. The method comprises the following steps: S1, obtaining in-situ gravity measurement data as a known point set; s2, based on the known point set, performing preliminary interpolation on grid points of the gridding background image by adopting a Kriging method to obtain a Kriging estimated value of each grid point; s3, by introducing a local gravity anomaly covariance and a Kriging variation function, evaluating the random uncertainty of a Kriging estimated value in an interpolation process, and dividing each grid point into a reference point and a point to be corrected based on the random uncertainty; and S4, combining the reference point and the known point set to construct a continuous distribution constraint curved surface, carrying out local continuity correction on the continuous distribution constraint curved surface based on the obtained reference point, and carrying out iterative expansion on the basis of the corrected local constraint curved surface until the continuous correction of the whole continuous distribution constraint curved surface is completed. And the construction of the gravity background field is realized.

The embodiment of the invention discloses a fault solution trap boundary identification method and device, equipment and a medium. The method comprises the following steps: acquiring seismic data of a to-be-identified area; determining at least two covariance matrixes based on the seismic time slice data body of the sub-region in the to-be-identified region; a covariance value in the covariance matrix is determined according to seismic data of two adjacent seismic channels in a seismic time slice data body; determining crack probability values corresponding to the sub-regions according to the at least two covariance matrixes; the crack probability value reflects the probability that the sub-region comprises a crack; and according to the fracture probability value corresponding to each sub-region in the to-be-identified region and the rock physical chart of fracture development, determining a fault-karst trap boundary in the to-be-identified region. According to the technical scheme, the crack probability value capable of reflecting the crack information is determined through the covariance matrix, so that the fault solution trap boundary can be accurately and rapidly determined in the to-be-identified region based on the crack probability value.

The invention discloses a Riemannian manifold-based space-time joint anti-interference direct positioning method. The method comprises the following steps of: acquiring signals emitted by an interference source and a radiation source by using a plurality of distributed antenna arrays; segmenting a received signal, constructing a sample covariance sequence, and establishing a model covariance matrix at each frequency; determining a Riemannian distance expression between the sample covariance sequences by constructing a Riemannian manifold on the basis of the characteristic that the sample covariance sequences are located on an Ermitt positive definite matrix manifold, and solving a Riemannian mean value of the sample covariance sequences; utilizing Riemannian geometric characteristics of a sample covariance matrix, and taking a Riemannian distance on an HPD matrix manifold as a judgment criterion of fitting accuracy; replacing an Euclidean distance with a Riemannian distance, performing approximate processing on the Riemannian distance by using a logarithm Euclidean distance, and constructing a direct positioning cost function based on the logarithm Euclidean distance; and two-step spectrum peak searching is carried out through the direct positioning power spectrum, and a direct positioning result of the direct positioning cost function is determined.

This application discloses a method and system for estimating the direction of arrival (DOA) of weak targets based on Riemannian manifold background suppression and adaptive sparse Bayesian learning. The method includes: acquiring time-series signals using a sensor array to construct a series of sample covariance matrices, mapping them to a point sequence on a Hermitian positive definite matrix manifold space; iteratively calculating the background interference covariance matrix using the non-Euclidean geometric properties and logarithmic shielding effect of the Riemannian metric; mapping the background interference covariance matrix back to Euclidean space, adaptively performing background subtraction based on an energy decision mechanism to reconstruct a positive definite covariance matrix to be measured; inputting the covariance matrix to be measured into a sparse Bayesian learning framework, first iteratively recovering the signal power through adaptive mesh refinement sparse Bayesian learning, then performing a closed-loop iteration of subspace noise cleaning while keeping the mesh fixed to recover the sparse spatial spectrum of the target signal; and finally, using local analytical interpolation techniques to eliminate mesh quantization errors and calculate the precise DOA of the target.

The application discloses a power distribution network prediction auxiliary state estimation method and system based on an improved iteration process, and the method comprises the following steps: establishing a nonlinear discrete time state space model; generating a group of Sigma points through an unscented transformation, and substituting the Sigma points into a state transition equation to calculate a state prediction value and a state prediction error covariance matrix at a current time; substituting the Sigma points into a measurement equation to obtain a measurement prediction value, and calculating a Kalman gain; calculating a process noise covariance matrix at a next time; and correcting the state prediction value and the state prediction error covariance matrix at the current time to obtain an optimal state estimation value and an error covariance matrix thereof. The application avoids the risk of losing semi-positive definiteness of the covariance matrix in the iteration process from the algorithm mechanism, significantly enhances the numerical stability and robustness of the UKF algorithm, and ensures reliable application in high-dimensional complex power distribution network state estimation.

The invention provides a gyroscope-assisted geomagnetic calibration method of layered parameter adjustment EKF, and relates to the technical field of sensor data processing and attitude calculation. According to the method, under a traditional EKF framework, a hierarchical selection mechanism for a process noise control parameter alpha and an observation noise adjustment parameter beta is introduced, a historical attitude quaternion and a geomagnetic observation value are subjected to statistical analysis through a sliding window mechanism, a mean value and a variance are calculated respectively, expert voting is carried out according to the deviation degree, and a process noise control parameter alpha and an observation noise adjustment parameter beta are obtained; parameters alpha and beta are dynamically adjusted according to the voting condition, and then the parameters alpha and beta act on a prediction noise covariance matrix Qk and an observation noise covariance matrix Rk respectively. According to the method, real-time optimization of the filter parameters is realized under the condition of not adding extra hardware and complex calculation, the precision and stability of soft magnetic and hard magnetic parameter calibration are improved, and the method has the advantages of being simple to implement, suitable for embedded deployment, high in external magnetic interference robustness and the like.

This application discloses a one-dimensional variational inversion method and apparatus for GNSS occultation data. The method includes background field preparation: interpolating temperature, air pressure, and water vapor pressure from the grid field to the latitude and longitude of the occultation data; simultaneously, interpolating the liquid water content and ice water content in the clouds to the latitude and longitude of the occultation data to obtain the background field profile required for inversion; and inputting observation data and background field: inputting the occultation data, background field profile, observation error covariance matrix, and background field error covariance matrix into the one-dimensional variational inversion system. By adding liquid water content and ice water content terms to the observation operator, the deviation between the observation data and the background field data is calculated, and the distribution characteristics of the deviation are statistically analyzed. After adding liquid water and ice water, the average refractive index deviation between the two types of data is significantly reduced.

This invention provides a dynamic weighted fusion system and method based on real-time error covariance closed-loop feedback. Addressing the contradiction between underlying physical coupling interference and the need for high-bandwidth dynamic response in non-stationary random environments for large-scale arrays, this invention utilizes a first-order Markov model to quantify the statistical coherence strength of individual observation units. Secondly, it implements physical-layer regularization constraints, eliminating bus capacitance interference and geometric phase deviation through a slope-constrained driving strategy and spatial consistency mapping. In high-frequency sampling environments of 208Hz and above, combined with transient discrimination logic based on full-axis spatial correlation, an analytical solution from a Lagrange optimized functional is used to establish a negative correlation mapping function between weight allocation and the real-time posterior error covariance trace, achieving dynamic gain suppression for non-stationary observation units. This invention achieves optimal fusion of large-scale redundant information while ensuring high-bandwidth real-time response, significantly improving the system's measurement accuracy and robustness.

The application discloses a multi-target tracking method based on joint probability association of observation value number of high-probability association, which comprises the following steps: obtaining multiple observation values of different targets through a sensor; estimating the association probability of the observation values and the targets; setting an effective observation range; performing an exhaustive search on the possible association conditions of the observation values and each target to obtain all feasible joint events; obtaining the association probability density of the observation values to the targets for the feasible joint events in the effective observation range; and updating the posterior state quantity and covariance of each target through Kalman filtering according to the association probability density of the observation values to the targets to realize target tracking. Compared with the traditional JPDA method, the multi-target tracking method based on joint probability association of observation value number of high-probability association can greatly improve the real-time performance and better adapt to different engineering environment requirements, although the target tracking precision is slightly reduced.

The invention discloses a Gaussian splash covariance matrix optimization method based on a Hamiltonian Monte Carlo algorithm, and belongs to the technical field of 3D Gaussian splash modeling. According to the method, a dynamic system is constructed through the Hamiltonian Monte Carlo algorithm, a dynamic equation is further constructed to improve and optimize the processing capacity and processing precision of a covariance matrix on Gaussian ellipsoid parameters in 3D Gaussian splashing, and the Gaussian ellipsoid parameters are obtained through the modes of constructing Hamiltonian amount, introducing momentum variables, simulating dynamics through a leapfrog method and the like. A brand new covariance matrix optimized by a quality matrix is realized and obtained, and an optimization and analysis method of a Gaussian splash covariance matrix based on a Hamiltonian Monte Carlo algorithm is provided. Meanwhile, a peak signal-to-noise ratio (PSNR) evaluation mechanism is provided and is used for quantifying the optimization result.

This filtering method comprises steps in which: a Kalman gain is calculated; a system calculates an estimated value on the basis of the Kalman gain; a predicted value and a measured value of an error covariance are compared and analyzed to calculate the error covariance; an exponentially weighted average and an exponentially weighted covariance for the estimated value are calculated; and the exponentially weighted average and the exponentially weighted covariance of the calculated estimated value are reflected in the subsequent Kalman gain calculation. Therefore, performance superior to that of a conventional Kalman filter can be achieved in non-Gaussian noise conditions.

The application discloses a subspace parameter iterative estimation space-time adaptive detection method for a strong clutter environment. In view of the problem that in a space-time adaptive processing system, a target steering vector falls in a clutter subspace, main and auxiliary data exist power mismatch, and existing methods are difficult to effectively maximize the marginal likelihood, the application projects the main and auxiliary data to a low-dimensional coordinate domain to obtain sufficient statistics, regards the clutter coefficient as a hidden variable, and under two kinds of assumptions, respectively uses EM and ECME algorithms to iteratively maximize the marginal log-likelihood function, and obtains the stationary point estimation of the clutter covariance matrix, the power mismatch factor and the target complex amplitude after convergence, and simultaneously constructs three detection statistics, GLRT, Rao and Wald, according to the stationary point estimation, and compares with a pre-calibrated threshold to complete the judgment. The iterative process of the application has the guarantee of the monotone non-decreasing of the marginal log-likelihood, can effectively compensate for the main and auxiliary power mismatch, and can still maintain good detection performance under the condition of small training samples.

The invention discloses a pseudo-range precision optimization quantification method based on GNSS redundant pseudo-range observed quantity, and relates to the field of satellite navigation and positioning. Comprising the following steps: constructing a double-difference mathematical model according to GNSS pseudo-range and carrier phase observed quantity, and obtaining a baseline vector floating point solution variance-covariance matrix by using a least square method; adding the redundant pseudo-range observed quantity to the double-difference mathematical model, obtaining a baseline vector floating point solution variance-covariance matrix after the redundant pseudo-range observed quantity is added by using a least square method, and obtaining a baseline vector floating point solution variance-covariance matrix after the redundant pseudo-range observed quantity is added according to the baseline vector floating point solution variance-covariance matrix before and after the redundant pseudo-range observed quantity is added; constructing an original pseudo-range precision optimization quantization formula after the redundant pseudo-range observed quantity is added; according to a given ADOP value, obtaining a value required by the precision optimization quantization of the original pseudo-range; and adding the redundant pseudo-range observed quantity to the double-difference mathematical model until an original pseudo-range precision optimization quantization calculation value is not greater than an original pseudo-range precision optimization quantization required value, so as to realize a given ADOP value and GNSS reliable precision positioning.