22 results about "Posterior probability density function" patented technology

The invention relates to a CVT error evaluation method and device, error evaluation equipment and a storage medium, and belongs to the technical field of mutual inductor error recognized.The CVT error evaluation method comprises the steps that a first data set is restored to a primary voltage level based on voltage proportionality coefficients of multiple CVTs to obtain a second data set, and a difference matrix is constructed based on the second data set; establishing a likelihood function based on the difference matrix, and converting the likelihood function into a posterior probability density function based on a Bayesian formula; extracting data corresponding to a preset number of CVTs from the second data set to construct a third data set, and determining a prior probability density based on the third data set; and based on a multi-chain MCMC algorithm fused with plant rhizome growth and a posterior probability density function, determining error values corresponding to a plurality of CVTs when the posterior probability density is maximum. According to the method, the accuracy of CVT error evaluation is effectively ensured, and meanwhile, the error evaluation efficiency is also improved.

The application relates to a weak underwater target azimuth detection pre-tracking method based on belief propagation, which comprises the following steps: converting a weak target tracking problem into solving a joint posterior probability density function (pdf) based on a Bayesian rule; factorizing the joint posterior pdf, constructing a corresponding factor graph through a graph model; solving the transmitted information in the factor graph by using a belief propagation (BP) algorithm, and converting multiple integrals into ordinary integrals by using a Goodman variable substitution principle; constructing a likelihood function by using a complex Wishart distribution to describe the relationship between original sonar data and target states, and simplifying the remaining pdf by using a Gaussian distribution to obtain a confidence approximation of a target state edge posterior pdf; and calculating the number of weak targets and corresponding states by using a minimum mean square error (MMSE) estimator. The application can simultaneously track multiple weak targets without a complex clustering algorithm, has fewer preset parameters, and can accurately and efficiently track target state information including an azimuth, an azimuth angular velocity and target intensity.

The invention provides an optimization method and device for a sensor array, and the method comprises the steps: building a linear array model, carrying out the discretization of a parameter space of an array element position, and generating an array arrangement position grid; acquiring a plurality of sampling points in a sampling space of a preset visual area; constructing a first sensing matrix according to the array position grid and the plurality of sampling points, and establishing a sampling signal model containing sparse signals according to the first sensing matrix; based on a preset multilayer prior model and a posterior probability density function, determining a posterior probability mean value of the sparse signals, and updating the sparse signals in the sampling signal model according to the posterior probability mean value; and performing linearization processing on the compressed sensing vector deviation corresponding to the updated sampling signal model through first-order Taylor approximation, constructing a target gradient matrix, correcting the first sensing matrix according to the target gradient matrix to obtain a second sensing matrix, and obtaining a target array arrangement position according to the second sensing matrix.

The application discloses a direct positioning method and system based on a single-bit signal, and the method comprises the following steps: a single-bit model and a space sparse model of a received signal are established; a probability density function of a single-bit received signal in the single-bit model of the received signal is obtained according to a prior distribution of noise, a prior distribution of observation data and a Laplace prior distribution; a mean value and a variance expression of a space sparse signal and a maximum posterior probability density function of a target radiation source position are obtained; an optimal target function of a hyperparameter is obtained; the optimal target function of the hyperparameter is solved to obtain an updating expression of the hyperparameter; the updating expression of the hyperparameter and the mean value and the variance expression of the space sparse signal are alternately iterated and solved to obtain the mean value and the variance of the space sparse signal, and the space position of the target radiation source is obtained according to the mean value of the space sparse signal. The application can reduce signal transmission cost and effectively improve the positioning speed and precision of multi-target radiation sources.

The invention discloses a combined self-adaptive parallel annealing source item inversion method for determining a nuclear radioactive source. The method comprises the following steps: acquiring a nuclear pollutant concentration measured value and meteorological data of each monitoring point; establishing an SRS (Sounding Reference Signal) matrix by using a reverse calculation diffusion process of the meteorological data by adopting an adjoint method; establishing an adaptive inversion model, including modeling a posterior probability density function of a source item parameter and determining a sampling method, the sampling method adopting an enhanced MCMC method, the enhanced MCMC method adopting an MCMC method and fusing a joint adaptive jump function and a parallel annealing algorithm, the joint adaptive jump function being a weighted set comprising an AM algorithm, an SCAM algorithm and a DE algorithm; and taking the SRS matrix and the pollutant concentration measured value as input data of an algorithm, substituting the input data into an inversion model for calculation, and performing inversion calculation to obtain nuclear radioactive source information. According to the method, the source item is quickly and stably reconstructed, and an effective calculation means is provided for source item determination and accident consequence evaluation.

This invention discloses a robust relative navigation method for aircraft based on a hybrid distribution under non-Gaussian noise, belonging to the technical field of computation, estimation, or counting. To address the problem of filter divergence caused by non-stationary heavy-tail noise in time-varying environments during relative navigation, this invention introduces a Dirichlet random mixture vector fusion of Gaussian, Student's t, and multivariate K-distributions, proposing a Gaussian-Student's t-multivariate K-distribution modeling of measurement likelihood. Then, by minimizing the KLD of the true posterior probability density function and the approximate posterior probability density function using variational Bayesian techniques, the approximate posterior estimates of the aircraft's relative motion state and filter parameters are obtained, yielding the target's state information relative to the aircraft and solving for relative position and velocity. Finally, a nonlinear filter based on the Gaussian-Student's t-multivariate K-distribution is derived to improve relative navigation accuracy for angle-only relative navigation of aircraft in time-varying environments.

The present application relates to a kind of CVT error evaluation method, device, error evaluation equipment and storage medium, belong to mutual inductor error identification technical field, wherein, the CVT error evaluation method includes: based on the voltage proportionality coefficient of multiple CVTs, first data set is restored to primary voltage level and obtains second data set, and difference matrix is constructed based on second data set;Based on difference matrix, likelihood function is constructed, and likelihood function is converted into posterior probability density function based on Bayes formula;From second data set, the data corresponding to the data of the extraction of the preset number of CVT is constructed third data set, and prior probability density is determined based on third data set;Based on the multi-chain MCMC algorithm of fusion plant rhizome growth and posterior probability density function, when the posterior probability density maximum, the error value corresponding to multiple CVTs is determined.The present application effectively guarantees the accuracy of CVT error evaluation, also improves the efficiency of error evaluation.

Methods for determining a physical property of a subterranean target volume, as well as systems and computer-readable media for performing the methods, are provided. According to the method, clues are mined from geographic data, and the method further relates to the improvement of sustainability and environmental development: we create a safe and livable world together. The method includes performing tomography inversion on the obtained empirical propagation time data. Performing the tomographic inversion includes obtaining a posterior probability density function corresponding to each of the first plurality of cells, where obtaining the posterior probability density function includes obtaining a prior velocity model, and defining, for each receiver pair in the subset, a prior probability density function indicative of a velocity of each of the first plurality of cells, a modeled propagation time is determined using initial velocity model values associated with two or more cells traversed by the surface wave path, and a posterior probability density function of the velocity model is determined based on the modeled propagation time and a prior probability density function, wherein the prior probability density function depends on the distance of each cell from a first point on the surface.

The invention discloses a single-base MIMO radar parameter joint estimation information amount calculation method, and the method comprises the steps: constructing a multi-dimensional joint probability density function of a uniform linear array through the property of an array receiving signal covariance matrix, and simplifying the joint estimation probability density function according to the vector form of a receiving signal; deducing a joint posterior probability density function of distance angle estimation under the condition of given received signals according to a Bayesian formula in combination with the joint probability density function, and obtaining distance direction information of multiple targets according to a distance-direction information formula; and deducing the distance direction information by using Taylor expansion under the condition of a high signal-to-noise ratio to obtain an upper bound of the distance direction information, and obtaining an entropy error, an entropy error lower bound and a Cramer-Rao bound according to the information amount and the information amount upper bound. According to the method, a posterior probability density function used for distance and angle joint estimation of multiple targets of the uniform linear array is deduced based on the information theory, and an information structure in radar observation data can be more completely described through joint distance-angle parameter modeling and mutual information theory analysis. Through introduction of mutual information indexes in an information theory, the perception capability of a radar system to target parameters can be quantified from a statistical perspective, and a theoretical basis is provided for system design.

The application provides a regional crop yield estimation method and system based on particle filtering data assimilation, which comprises the following steps: running each particle at a fixed time interval during the critical growth period of crops until each particle runs to the mature period of crops, and obtaining the first LAI output by each particle; updating the initial weight of each particle according to the posterior probability density function, the updated first LAI of each particle and the likelihood function, and obtaining the updated weight of each particle; resampling each particle in the initial particle set and updating the initial particle set under the condition that the particle divergence is less than a preset value; and obtaining the estimated value of the crop yield according to the crop yield and the maximum weight of each particle under the condition that each particle runs to the mature period of crops. The application can improve the sequential sampling efficiency of particles, reduce the resampling frequency, maintain the diversity of particle input parameters, and improve the speed and accuracy of crop yield prediction.

The invention discloses a single-base MIMO radar multi-target DOA estimation method, and belongs to the field of signal processing. According to the method, a Shannon information theory method is combined, and a joint conditional probability density function of a received signal of the MIMO radar, a target direction angle and a target reflection coefficient is deduced by utilizing the property of noise in the received signal; according to a Bayesian formula, firstly, a posterior probability density function of a single-base MIMO radar multi-target reflection coefficient based on a uniform linear array is derived, and a posterior probability density function of a multi-target direction angle when a signal is received is further obtained by combining a derivation result. The azimuth angles of a plurality of targets can be obtained at the same time by performing spectral peak search on the posterior probability density function of the target direction angle. The invention provides a DOA (direction of arrival) detection method based on a posterior probability density function and aiming at the condition that a plurality of targets are densely distributed. According to the method, the posterior probability density function used for uniform linear array monostatic MIMO radar multi-target DOA estimation is deduced based on the information theory, all available information is organically combined through the Bayesian theory, and therefore more comprehensive angle estimation is provided.

The embodiment of the invention provides a noise filtering method and system for a sensor, and the method comprises the steps: carrying out the modeling of the noise statistical characteristics of a signal of the sensor through employing Gaussian-Gaussian-Gaussian inverse index mixed distribution for a sensor used in an automatic driving scene, obtaining a noise model, and carrying out the calculation of the noise model based on a system state equation and a measurement equation, constructing a joint probability density function of a system state, and forming the joint probability density function into a joint posterior probability density function by adopting a variational Bayesian method; solving an optimal approximate probability density function through a KL divergence between a minimum approximate probability density function and a joint posterior probability density function, and alternately updating q (xk), q (yk), q (pi k) and q (lambda k) by using a fixed point iteration method until iteration convergence; and obtaining the optimal estimation value of the system state xk according to the converged approximate probability density function. The fitting degree of noise description is improved from the source, and precise adaptation and efficient suppression of multi-mode noise of the sensor are achieved.

This invention discloses a method, apparatus, and equipment for pre-stack elastic parameter inversion based on the converted wave domain. The method includes: determining the converted wave incident angle distribution based on the P-SV wave pre-stack offset gather, converted wave velocity, P-wave / S-wave velocity ratio, and converted wave travel time; performing angle-separated stacking of the converted wave incident angle distribution to obtain angle-separated stacked gathers; extracting data from the angle-separated stacked gathers to obtain wavelets at different angles; determining seismic traces of the incident angle based on the theory of P-SV wave amplitude variation with incident angle, the seismic synthetic record of the convolution model, and the wavelets at different angles; determining the relationship between observed seismic data and elastic parameters based on the seismic traces; and determining the inversion results of the elastic parameters based on the relationship between observed seismic data and elastic parameters, prior geological information, and the posterior probability density function. This method enables direct inversion of reservoirs in the converted wave domain, reducing computational load and improving inversion reliability.

The application discloses a method for predicting seismic spatial structure parameters, comprising the following steps: reading observed seismic data; defining a prior probability density function of spatial structure parameters based on a variogram type of model parameters and the number of spatial structure parameters; defining a seismic forward operator and calculating a likelihood function between the spatial structure parameters and the observed seismic data based on the seismic forward operator; and calculating a posterior solution of a posterior probability density function of the spatial structure parameters based on the prior probability density function and the likelihood function through a Monte Carlo sampling method. The application also discloses a device for predicting seismic spatial structure parameters, a computer device and a readable storage medium. The application combines the Bayesian theory with the Monte Carlo inversion method, fully utilizes the volume constraint characteristics of seismic data, and simultaneously infers the longitudinal and lateral spatial structure of the underground medium in a data-driven manner.

The invention provides a space manipulator actuator load optical fiber reconstruction method and system based on self-adaptive non-negative Bayesian regularization and application, and relates to the technical field of load reconstruction. The method comprises the following steps: arranging a plurality of fiber grating sensors on an end effector of the space manipulator; constructing a dynamic load model representing a reverse mapping relation between the external load of the end effector and the optical fiber strain response; constructing a load history reconstruction posterior probability density function of the end effector of the space manipulator, and performing variation approximate calculation on the load history reconstruction posterior probability density function; and carrying out iterative solution on the posterior probability density function to obtain dynamic load history data of the space manipulator end effector. According to the method, data fitting residual and parameter posteriori distribution are monitored in real time, regularization intensity is adjusted in a self-adaptive mode, the algorithm can accurately track the dynamic characteristics of the time-varying load, the defect that a fixed parameter method is insufficient in adaptability under complex working conditions is overcome, and high-precision dynamic load process data reconstruction is achieved.

The present application relates to the technical field of wireless communication, especially to a channel estimation method based on sparse Bayesian learning, comprising the following steps: step A, using the sparse characteristics of the millimeter wave channel to model the channel, and constructing the corresponding RIS assisted MIMO communication system model; step B, first constructing two-layer sparse prior information of the channel, then factorizing the joint posterior probability density function of the parameters, and finally drawing the corresponding factor graph model according to the factorization; step C, for the factor graph model in step B, combining the unitary transformation approximate message passing, and using the sparse Bayesian algorithm framework to perform channel estimation; step D, repeating step C until the algorithm converges; the method in the present application has low complexity, reduces the pilot overhead of the system, and has high algorithm universality.

The embodiment of the invention discloses a deep learning short-term and medium-term runoff forecasting method fusing a physical mechanism, and relates to the technical field of hydrological forecasting, and the method comprises the steps: obtaining the hydrometeorological data and weather forecasting data of a target drainage basin; inputting the hydrometeorological data and the weather forecast data into the trained multivariable mixed downscaling model to obtain model weather forecast data of a forecast period; inputting model weather forecast data and hydro meteorological data into the trained hybrid model to obtain forecast runoff data of a forecast period; determining a posterior probability density function through back calculation forecasting, and determining a probability distribution result of the actual runoff data based on the posterior probability density function; and performing flood early warning based on a probability distribution result. The method is suitable for scenes of runoff forecasting of large and medium-sized drainage basins, mountain torrent early warning of small and medium-sized drainage basins, reservoir optimization scheduling, hydropower station operation management and the like, and can provide key technical support for water safety, water resource management and water ecological protection.

The application discloses a kind of communication perception channel construction method, device, equipment, storage medium and program, applied to wireless communication technical field, wherein, the method comprises: according to the scattering signal of acquisition, constructs quasi-static multipath channel model, and determines the prior distribution of the frequency domain channel gain vector of quasi-static multipath channel model;Perception channel model and communication channel model are established based on the quasi-static multipath channel model;The posterior probability density function of the channel gain of the perception channel model is determined based on the prior distribution;Based on the posterior probability density function and the communication channel model are soft fused, obtain the communication perception channel.The embodiment of the application can realize the fusion of communication stage and perception stage, reduce signal loss, can improve communication quality, enhance perception accuracy, can improve the satisfaction degree of user to system.

The invention belongs to the technical field of radar signal processing and array amplitude-phase error correction, and provides a large-scale millimeter wave array amplitude-phase error Bayesian calibration method, which comprises the following steps of: establishing an array receiving signal model containing an amplitude-phase error; constructing a layered Bayesian probability model, and introducing hyper-parameters to carry out layered Bayesian modeling on the Laplacian sparse prior model; solving posterior distribution by adopting variational Bayesian inference, and approximately solving a posterior probability density function by adopting a variational Bayesian expectation maximization algorithm and maximizing an evidence lower bound; and iteratively updating until convergence, and outputting a correction result. The method can overcome the defects of an existing amplitude-phase correction method, does not need to depend on a calibration piece, effectively calibrates the amplitude-phase error of a system channel, remarkably reduces the calibration complexity of the system, and improves the calibration convenience.

This invention discloses an odor source localization method based on adaptive spatial perception information. The method includes the following steps: First, the search environment is divided into a fine grid, and a gas diffusion model that fits the actual scene is established to obtain the predicted concentration for each grid. Then, the real-time gas sampling data from the robot is binarized, and a sensor response model based on Poisson distribution is established to obtain the sampling concentration. The robot performs Bayesian inference using the predicted and sampled concentrations to update the likelihood function, thereby obtaining the posterior probability density function of the entire map. The reward function for each movable direction in the allowable action set is calculated using the posterior probability density function. The robot finds the direction with the largest change in the reward function, calculates the movement step size based on the current information entropy, and moves the robot a specified step size in the direction with the largest change in the reward function. This method solves the problems of existing odor source localization strategies easily getting trapped in local optima and the low search efficiency of fixed step sizes.

The application belongs to the technical field of seismology and geophysical tomography, and relates to a surface wave dispersion three-dimensional tomography method based on adaptive dictionary constraint, which comprises the following steps: performing three-dimensional grid dissection on a research area, constructing a model parameter vector for representing a three-dimensional shear wave velocity field, constructing a linearized sensitivity matrix equation between a travel time residual and a shear wave velocity disturbance, obtaining a sensitivity matrix and a column norm distribution of the sensitivity matrix, equivalently converting maximum joint posterior probability density function into minimization according to Bayes theorem, obtaining a joint inversion objective function composed of a travel time data fitting term, a global-assisted coupled constraint term and an image block sparse representation error term, and solving the joint inversion objective function by using an alternating minimization strategy, and outputting a final three-dimensional shear wave velocity structure model after iterative convergence. The optimal balance between data constraint and structure prior is achieved, and high-fidelity imaging of the three-dimensional shear wave velocity structure is realized.

The application provides a robust multi-target tracking method based on a Gaussian hypothesis probability density filter, belongs to the technical field of robust multi-target tracking, and solves the problem that a traditional GF method is difficult to stably estimate target states when measurement is abnormal; the application increases Student's t distribution with heavy tail noise on the basis of original Gaussian measurement by modifying a measurement model; meanwhile, in order to overcome the limitation of the traditional GF on the measurement model, an affine function is applied to construct a pseudo measurement model, and a posterior probability density function is calculated by minimizing KL divergence, a prediction result is updated, weights in the prediction result are corrected, and a robust multi-target tracking process is completed; compared with the traditional GF method, the application improves the robustness of the algorithm in an environment with abnormal value measurement, and the finally obtained target state information is more reasonable.