18 results about "Unscented transform" patented technology

The invention discloses a generator state estimation method and system considering noise and parameter uncertainty constraints. The method comprises the following steps: acquiring model parameters and dynamic state vectors of a generator, and establishing augmented state vectors; performing unscented transformation on the augmented state vector to obtain a particle set; improving to obtain robust mixed Kalman particle filtering, and in the process of performing unscented Kalman filtering on an augmented state vector, taking correlation entropy maximization of a measurement information sequence as a target function, and solving by adopting a fixed point iteration method to obtain a filtering gain; determining a filtering gain according to the updated state of the measurement information; robust mixed Kalman particle filtering is executed, physical constraints of model parameters serve as a feasible region, after resampling, projections of the model parameters in new-generation particles exceed the feasible region, the model parameters are set to be closest boundary points, and then resampling is conducted again; a weighted average value of the particle set is an optimal joint estimation value, a dynamic state estimation value and a model parameter identification result are separated, and reliable uncertainty quantization is provided for state and parameter estimation.

The invention discloses a probabilistic optimal power flow calculation method based on a second-order cone and unscented transformation, belongs to the field of power system optimization, and efficiently processes the uncertainty of new energy output and load by coupling the probabilistic sampling capability of unscented transformation (UT) and a convex optimization framework of second-order cone programming (SOCP). According to the method, unscented transformation efficient sampling is utilized, a covariance matrix is directly embedded to simplify correlation processing, and accurate probability distribution of network loss, voltage and line power can be output. Test results show that the calculation efficiency is improved by dozens of times compared with Monte Carlo simulation, the error is lower than 3%, and the method is suitable for risk assessment and operation optimization of the power system.

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 present application belongs to the technical field of unmanned aerial vehicle navigation, and particularly relates to an online trajectory prediction and parameter optimization method coupling multiple output Gaussian processes. The method first constructs a multiple output coupling kernel function fusing a square exponential kernel and a neural network kernel, and uses a symmetric positive definite output covariance matrix to explicitly model the dynamic coupling relationship between the lateral position (axis) and the longitudinal position (axis) of the unmanned aerial vehicle, thereby overcoming the coupling information loss caused by the traditional independent modeling method. Secondly, a recursive Gaussian process online learning framework is established, and an augmented state space model containing the flight trajectory latent function value and the kernel hyperparameter is constructed. Finally, the unscented transformation technology is introduced to process the nonlinear propagation of the hyperparameter, and the real-time prediction of the flight state of the unmanned aerial vehicle and the online adaptive update of the model hyperparameter are realized through the block Kalman filtering mechanism. The present application significantly improves the precision, real-time performance and robustness of the unmanned aerial vehicle trajectory prediction under complex flight environment and high maneuvering task.

The invention belongs to the technical field of rigid body posture estimation, and discloses a rigid body posture estimation method, system and device based on generalized correlation entropy geometric filtering, and the method comprises the steps: obtaining target motion information, and building a discrete nonlinear kinematics model; the discrete nonlinear kinematics model is initialized; sigma points of a posterior state covariance matrix and a process noise covariance matrix are generated respectively, and the sigma points are propagated and updated through manifold fast unscented transformation; obtaining a prediction state mean value, a priori state covariance matrix and a square root of the priori state covariance matrix; updating the central parameters based on the information of the historical moments; calculating a pseudo measurement matrix and updating the gain; correcting the state estimation by using the gain; judging whether convergence occurs or not, and if not, continuing iteration; otherwise, outputting a posterior state estimation value as a final rigid body posture estimation result at the moment. According to the method, the problem that the robustness and precision of rigid body posture estimation in a non-zero mean value and non-Gaussian noise environment are reduced is effectively solved.

The invention provides an inertia and acoustics integrated navigation method based on enhanced hybrid minimum error entropy and unscented Kalman filtering. The method comprises the following steps: establishing a state equation model of an inertia and acoustics integrated navigation system; establishing an enhanced mixed minimum error entropy measurement model considering a sound ray bending effect and a multi-modal noise characteristic; based on a state equation model and an enhanced hybrid minimum error entropy measurement model, an EnMMEE-UKF framework is constructed, nonlinear mapping is processed through unscented transformation, an attenuation factor in a strong tracking filtering theory is introduced to correct an error covariance matrix, and an expectation maximization algorithm is adopted to adaptively adjust a hybrid coefficient of a hybrid kernel function. And state estimation and a covariance matrix are recursively updated in combination with a fixed point iteration method, so that real-time updating and feedback of errors are realized.

The invention discloses a power distribution network prediction auxiliary state estimation method and system based on iteration process improvement. The method comprises the following steps: establishing a nonlinear discrete time state space model; generating a group of Sigma points through unscented transformation, substituting the Sigma points into a state transition equation, and calculating a state prediction value and a state prediction error covariance matrix at the current moment; substituting the Sigma point into a measurement equation to obtain a measurement predicted value, and calculating a Kalman gain; calculating a process noise covariance matrix at the next moment; and correcting the state prediction value and the state prediction error covariance matrix at the current moment to obtain an optimal state estimation value and an error covariance matrix thereof. According to the method, the risk that the covariance matrix loses the semi-positive qualitative property in the iteration process is avoided from the algorithm mechanism, the numerical stability and robustness of the UKF algorithm are remarkably enhanced, and reliable application in high-dimensional complex power distribution network state estimation is ensured.

The invention discloses a probabilistic optimal power flow calculation method based on differential evolution and a discrete Fourier matrix, which efficiently processes the uncertainty of new energy output and load by coupling the probabilistic sampling capability of a discrete Fourier matrix method and a differential evolution optimization framework. According to the method, a discrete cosine transform matrix (DCTM) sub-method in DFTM is utilized to generate sample points, and the sample size is reduced to m + 1 from 2m + 1 of a traditional unscented transform (UT) method. The probabilistic optimal power flow problem of the island micro-grid containing high-proportion renewable energy sources can be efficiently and accurately solved, and meanwhile, the adaptability to asymmetrically distributed random variables is ensured.

The invention discloses a self-adaptive UKF fusion positioning method for an outdoor cleaning robot. The method comprises the following steps: acquiring inertial measurement unit IMU data, point cloud data and RTK positioning data through a sensor; according to the system state of the robot, unscented Kalman filtering (UKF) is adopted for unscented transformation, IMU data serve as control input, a predicted sigma point set is obtained, and sigma points serving as the system state are updated; evaluating the quality of the point cloud data; according to the point cloud data quality evaluation and the RTK positioning data, a current positioning mode is adaptively selected through a state machine, and the positioning mode comprises a first mode with the point cloud data quality as the main mode, a second mode with the RTK positioning data as the main mode and a third mode fusing the point cloud data quality and the RTK positioning data; and according to a corresponding mode, observation updating and time-sharing dimension multiplexing are carried out based on unscented Kalman filter (UKF).

The application discloses a kind of water unmanned vehicle trajectory tracking method and system based on distributed fusion strategy, belong to automatic control field.First, the state estimation problem of water unmanned vehicle based on multiple radars is constructed;Then, by means of radar measurement vector, the mean and variance of random variable after transformation by nonlinear radar measurement model are approximated using unscented transformation, and a local nonlinear filter is designed under the least mean square error criterion;Further, the distributed fusion estimation value is obtained by fusing local estimates based on distributed fusion strategy, and the real-time tracking of unmanned vehicle motion trajectory is realized.The simulation results show that the application realizes the real-time tracking of water unmanned vehicle trajectory.

This invention discloses a visual inertial wheel velocity meter SLAM method based on an improved adaptive UKF in the field of visual navigation technology, including the following steps: 1) establishing the state equation for the robot SLAM system; 2) establishing the measurement model of the visual sensor and wheel velocity meter; 3) determining the unscented transformation sampling points and weights according to the state space model; 4) transferring the sampling points through a nonlinear function and updating the system state prediction and state prediction covariance matrix; 5) updating the measurement and updating the process noise and measurement noise covariance matrix using an improved Sage-Husa algorithm; 6) introducing a convergence factor to update the state estimation, filter gain, and state estimation covariance matrix. This invention uses an improved Sage-Husa adaptive Kalman filter, which optimizes the filtering effect by adaptively adjusting the parameters of the UKF algorithm, thereby improving the real-time performance and accuracy of the robot SLAM system.

The present application relates to a method, device and equipment for evaluating the performance of intelligent dialogue based on large language models, which solves the problem of performance evaluation of large language models by research, distinguishes the topics of questions and answers of large language models, studies the uncertainty of large language models under different topics, establishes the uncertainty expression of large language models in each topic based on information entropy under the assumption of introducing multivariate Gaussian distribution, and then uses unscented transformation estimation to solve and obtain the estimated entropy value, so as to obtain the uncertainty of large language models in different topics, and accurately determine the performance of intelligent dialogue of large language models at the topic level.

The application discloses a pose solution method based on complementary filtering and unscented Kalman fusion, and particularly relates to the technical field of waterways, and the specific solution steps are as follows: S1: for a nonlinear system, the system state vector is iteratively updated by using unscented Kalman filtering; the application fuses Mohony complementary filtering and unscented Kalman filtering (UKF) algorithm, replaces the pose angle directly calculated from acceleration and magnetic field intensity information with the pose angle obtained by solving based on the complementary filtering algorithm from the perspective of making the measurement information more accurate, improves the overall pose solution accuracy; meanwhile, the framework of the fusion algorithm is based on the unscented Kalman filtering algorithm, the probability distribution of the nonlinear function is approximated by unscented transformation, the high-order truncation error caused by Taylor expansion of the extended Kalman filtering algorithm is avoided, and the pose solution accuracy for the nonlinear system is improved.

The invention discloses a correction method, device, equipment and medium for an inertial measurement unit of an unmanned aerial vehicle, and relates to the field of data processing, the correction method for the inertial measurement unit of the unmanned aerial vehicle comprises the following steps: constructing a sigma point at a k-1 moment through unscented transformation according to posterior state data at the k-1 moment of the inertial measurement unit of the unmanned aerial vehicle, the sigma point covers the sensor parameters of the inertial measurement unit and the probability distribution of the state of the unmanned aerial vehicle; performing prediction processing on the posterior state data at the k-1 moment by using unscented Kalman filtering based on the sigma point to obtain prior state data at the k moment of the inertial measurement unit of the unmanned aerial vehicle; according to the preset measurement data of the plurality of sensors at the moment k, constructing a sensor measurement model at the moment k; projecting the sigma point at the k-1 moment to a measurement space of a sensor measurement model, and determining a pre-measurement mean value and a measurement covariance of the sensor at the k moment; determining the state correction of the inertial measurement unit according to the pre-measured mean value, the measurement covariance and the sigma point; performing state parameter correction on the prior state data at the k moment according to the state correction quantity, and determining posterior state data of the inertial measurement unit of the unmanned aerial vehicle at the k moment; and determining the state information of the unmanned aerial vehicle at the moment k and the zero offset of the inertial measurement unit at the moment k according to the posterior state data, and performing sensor parameter correction on the inertial unit of the unmanned aerial vehicle according to the zero offset at the moment k.

This invention belongs to the field of rigid body pose estimation technology, and discloses a rigid body pose estimation method, system, and device based on generalized correlation entropy geometric filtering. The method includes: acquiring target motion information and establishing a discrete nonlinear kinematic model; initializing the discrete nonlinear kinematic model; generating sigma points for the posterior state covariance matrix and the process noise covariance matrix, respectively, and propagating and updating the sigma points through a manifold fast unscented transformation; obtaining the predicted state mean, the prior state covariance matrix, and the square root of the prior state covariance matrix; updating the center parameters based on the information from historical moments; calculating the pseudo-measurement matrix and updating the gain; correcting the state estimate using the gain; determining whether convergence has occurred, and if not, continuing the iteration; otherwise, outputting the posterior state estimate as the final rigid body pose estimation result for that moment. This invention effectively solves the problems of robustness and accuracy degradation in rigid body pose estimation under non-zero mean and non-Gaussian noise environments.

The application provides a kind of attitude monitoring method and system in aircraft navigation, in each filtering period, the high frequency energy component of current gyroscopic measurement angular velocity signal is used to determine process noise covariance matrix, according to the state vector of last time and the angular velocity obtained by current gyroscopic measurement and process noise covariance matrix, the prior state vector and prior covariance matrix of current time are predicted;Asymmetric Sigma point set is generated and measurement noise covariance matrix is determined;The asymmetric Sigma point set is substituted into measurement model, and the predicted measurement value and predicted measurement covariance are obtained by unscented transformation;Based on the predicted measurement covariance and the measurement noise covariance matrix, the Kalman gain is calculated, and the innovation composed of real measurement value and predicted measurement value is weighted gate inspection;Based on the adjusted Kalman gain and innovation, the state vector and covariance matrix are updated, and then the attitude information of current time aircraft is obtained.

The application discloses a SOC estimation method based on a deep fusion neural network and a UKF (Unscented Kalman Filter), relates to the technical field of all-vanadium redox flow batteries, and aims to realize high-precision estimation of the state of charge of a battery. The method comprises the following steps: a second-order equivalent circuit model of the battery is established, and parameter identification is performed on the second-order equivalent circuit model; a group of sigma points are obtained through unscented transformation, the propagation state estimation and the propagation state prediction of each sigma point are calculated, the first-order statistical moments of the prior state estimation and the prior state prediction are obtained through unscented transformation, the cross covariance is obtained, and the observation difference and the state update difference are obtained; the observation difference and the state update difference are input into a neural network system to obtain Kalman gain, the prior state estimation is updated through the Kalman gain, and the state of charge of the battery is predicted.

The invention relates to the technical field of power system evaluation, and discloses a power transmission line lightning resistance evaluation method based on an unscented transformation method, and the method comprises the following steps: building an electromagnetic transient model of a lightning stroke power transmission system, and obtaining the core parameters of a power transmission line; generating a Sigma point set based on an unscented transformation method, and enabling the allocated Sigma point set to meet mean and covariance characteristics of original probability distribution of the core parameters; an exponential regression model is adopted to carry out curve fitting on the insulator Voltage-Time critical curve, and an intersection point of the fitted curve and the insulator Voltage-Time critical curve is calculated; and calculating the lightning trip-out rate of the power transmission line in combination with the ground lightning density and the transverse attraction width of the lightning conductor to complete the lightning resistance evaluation of the power transmission line. According to the method, the unscented transformation method and the transient program are combined, the limitation of a traditional Monte Carlo method is broken through, the number of evaluated samples in the process is greatly reduced, the calculation time is remarkably shortened, and the calculation efficiency is improved.