15 results about "Cross-covariance" patented technology

The invention discloses a power distribution network state estimation method and device based on high-order volume Kalman filtering, and the method comprises the steps: firstly calculating a volume point of a state variable of a power distribution network at a current moment, calculating a propagation volume point according to a state equation of the power distribution network, and calculating the state variable of the power distribution network according to the propagation volume point; and calculating a state predicted value at the next moment and a predicted value of the state error covariance matrix, and performing volume transformation on the propagation volume point to obtain a volume point at the next moment. Then, propagation is carried out on a volume point at the next moment through a measurement function, and a measurement predicted value at the next moment, a measurement error covariance matrix and a cross covariance matrix are calculated; and finally, calculating a Kalman filtering gain according to the measurement error covariance matrix and the cross covariance matrix, correcting a state prediction value by adopting the calculated Kalman filtering gain, and updating the state error covariance matrix, so that the state estimation precision is improved, and the estimation accuracy is improved. And the robustness and the precision of the system facing bad data are effectively enhanced.

The invention discloses an electromagnetic inversion uncertainty evaluation method based on ensemble Kalman filtering, and belongs to the technical field of geophysical electromagnetic inversion, and the method comprises the steps: carrying out the time recursion of an initial sample set through the prediction step of ensemble Kalman filtering, and obtaining a prediction state variable; an observation variable of a prediction state variable is calculated through an updating step of ensemble Kalman filtering, a cross-covariance matrix is constructed based on the prediction state variable and the observation variable, and localization operation is performed on the cross-covariance matrix by using a localization weight matrix so as to correct estimation deviation of the cross-covariance matrix. A Kalman gain matrix is constructed based on the corrected cross covariance matrix, and prediction state variables are updated in combination with actual observation data; and performing electromagnetic inversion uncertainty evaluation based on the updated state variable. According to the method, the electromagnetic inversion precision and stability can be effectively improved, and uncertainty evaluation is provided for the magnetotelluric two-dimensional inversion result.

The invention relates to a power distribution network state estimation method and system in the technical field of power distribution networks, and the method comprises the following steps: building a power distribution network state estimation model which comprises a state equation and a measurement equation; performing state prediction based on the state equation to obtain a state prediction value and an error covariance prediction matrix, and performing measurement prediction to obtain an error covariance measurement initial matrix and a cross covariance initial matrix; calculating a fading factor, and updating the error covariance prediction matrix by using the fading factor to obtain an error covariance prediction adjustment matrix; performing measurement prediction based on the error covariance prediction adjustment matrix to obtain an error covariance measurement update matrix and a cross covariance update matrix; according to the method, the state estimation is updated, and the observation noise covariance is updated based on the updating result, so that the problem that the estimation precision and robustness are remarkably reduced due to the fact that an existing power distribution network state estimation method cannot accurately model unknown noise and is difficult to effectively track the rapid change of a system is solved.

This invention provides a radar target detection method and apparatus based on dual-polarization maximum eigenvalues. The method includes: receiving first polarization echo data and second polarization echo data, and determining a detection unit and at least two reference units based on the first polarization echo data and the second polarization echo data; for any unit, determining a cross-covariance matrix based on the first polarization echo data and the second polarization echo data, and determining the maximum eigenvalue corresponding to the unit based on the cross-covariance matrix; determining an average maximum eigenvalue based on the maximum eigenvalues ​​corresponding to each reference unit; and determining a target detection result based on the maximum eigenvalue corresponding to the detection unit, the average maximum eigenvalue, and a preset threshold factor, wherein the target detection result includes whether the target exists or does not exist. This reduces the computational complexity of radar target detection, fully leverages the distinguishability of target echo signals and sea clutter, and improves the detection accuracy of radar target detection.

The invention discloses a time-varying parameter identification method of a wind power mixed tower structure, a storage medium and equipment. The invention belongs to the field of new energy engineering structure health monitoring, and aims to solve the problem that time-varying parameters of a wind power mixed tower under the action of dynamic wind load are difficult to track in real time in an existing method. The method comprises the following steps: firstly, determining a covariance matrix of an initial state quantity, carrying out preliminary parameter identification based on a UKF algorithm, obtaining a sensitive parameter eta k corresponding to each step, drawing a time history curve, switching to an MAF-UKF algorithm for identification when peak pulses appear in the time history curve, dynamically adjusting a forgetting factor alpha k when eta k is greater than eta 0 in the process, and finally, carrying out identification according to the MAF-UKF algorithm. And the measurement prediction covariance, the cross covariance and the state quantity covariance are corrected according to the alpha k, a gain matrix is updated, iterative filtering is carried out until circulation is finished, a time-varying parameter identification result of the wind power mixed tower structure is obtained, parameters are updated through a sensor observation value in combination with a finite element model, a time history curve is output, and abnormal early warning is triggered.

The invention relates to the technical field of vehicle pose estimation, and particularly discloses a fast non-analytic robust adaptive filtering method for vehicle pose estimation, comprising the following steps: S1, collecting observation data of a vehicle pose; s2, constructing a nonlinear regression model according to the observation data, and calculating a residual error and a corresponding weight; s3, establishing a Gaussian distribution model according to the weight and the residual error, and calculating to obtain an observation covariance matrix by using a variational Bayesian inference method and a Gaussian Newton method; s4, reweighting the priori covariance matrix and the observation covariance matrix by using the weight; and S5, predicting an observation mean value, a covariance and a cross covariance based on the reweighted priori covariance matrix and the observation covariance matrix as well as a system state transition equation, and calculating a Kalman gain in combination with observation data to obtain state posteriori estimation of the vehicle pose. The method can better adapt to complex and changeable system state estimation requirements of vehicle pose estimation.

The invention relates to the technical field of hydrogen-electricity coupling, and provides a hydrogen-electricity coupling micro-grid energy management model adaptive updating method, which comprises the steps of generating a Sigma point based on unscented transformation, predicting a state and an observation value through a state and observation equation, and calculating statistical characteristics of the state and the observation value; according to the method, state-observation cross covariance and Kalman gain are further calculated, weighted fusion correction is carried out on state prior estimation by using actual observation residual, noise is effectively suppressed, nonlinear dynamics are accurately processed, and high-credibility state estimation is provided for energy management decision. A feature space is constructed based on real-time state estimation feedback information, fusion features are extracted through incremental principal component analysis and input into an energy management model to calculate a loss function, and a learning rate is adaptively adjusted by calculating first-order and second-order moments of gradient information in parameter vector loop iteration. The model parameters are updated according to the learning rate and the gradient of the loss function, and the problem that the model applicability in a traditional model updating mode is reduced is solved.

The invention provides a zero-trust intelligent network connection vehicle group trusted cooperative positioning method in a composite network attack scene, and the method specifically comprises the steps: firstly constructing a motion state model and a relative measurement model of a multi-vehicle system, and obtaining the vehicle state and measurement information through combining an IMU and a UWB sensor; distributed local prediction is realized through left and right decomposition of cross covariance between vehicles; a random attack model comprehensively considering DoS, FDI and hybrid attacks is designed, attack probability modeling is introduced, and a state error covariance upper bound is derived to quantify cooperative positioning credibility and ensure error convergence; in combination with a heterogeneous cooperation mechanism of distributed prediction and centralized updating, only a temporary master control agent broadcasts necessary updating parameters, so that the communication load is reduced, and the capability of adapting to dynamic topology is improved. Finally, according to the method provided by the invention, the accurate positioning and the system stability of the vehicle group can still be kept even under the condition of failure or hostile attack of a part of links.

This invention relates to energy storage technology and discloses a collaborative control method and system for an energy storage converter. The collaborative control method for the energy storage converter includes the following steps: acquiring multi-time-period electrical parameter measurements of the target energy storage converter's grid connection point through a data acquisition interface; dividing the electrical parameter measurements into a training dataset and a target dataset according to a program-defined partitioning rule; calling a covariance calculation module to calculate an autocovariance matrix based on the training dataset and a cross-covariance vector based on the training dataset and the target dataset; generating predicted values ​​of the target electrical parameters based on the autocovariance matrix and the cross-covariance vector using a prediction algorithm module; subtracting the predicted values ​​from the actual measured values ​​of the target dataset using a compensation processing module to obtain compensated electrical parameter commands; and controlling the energy storage converter to switch operating modes according to the compensated electrical parameter commands.

The invention discloses an ATR engine control system state estimation method based on adaptive robust UKF. The method comprises the steps that a nonlinear discrete model of an ATR engine control system is established; the time updating process comprises the steps of initializing an engine state quantity and a state error covariance matrix, selecting a sampling point, constructing a state quantity of the sampling point, and predicting an estimated value of the engine state quantity and the error covariance matrix; the measurement updating process comprises the steps of updating the state quantity of the sampling point, converting the updated state quantity of the sampling point, calculating an engine output quantity estimated value and a covariance matrix through unscented transformation, and calculating a state-measurement cross covariance matrix; calculating a Kalman gain matrix process through a maximum correlation entropy criterion; and updating a state estimation and covariance matrix process. The method can solve the problems that an existing nonlinear system state estimation method is insufficient in robustness in a complex noise environment, sensitive to abnormal measurement values, difficult to cope with the influence of non-Gaussian noise and the like.

The invention discloses an SOC (State of Charge) estimation method of deep fusion neural network unscented Kalman filtering, relates to the technical field of all-vanadium redox flow batteries, and aims to realize high-precision estimation of the state of charge of the battery. The method comprises the following steps: establishing a second-order equivalent circuit model of a battery, and performing parameter identification on the second-order equivalent circuit model; obtaining a group of sigma points through unscented transformation, calculating propagation state estimation and propagation state prediction of each sigma point, obtaining a first-order statistical moment of prior state estimation, a first-order statistical moment of prior state prediction and a cross covariance through unscented transformation, and obtaining an observation difference and a state updating difference; and inputting the observation difference and the state updating difference into a neural network system to obtain a Kalman gain, and performing state updating on the priori state estimation through the Kalman gain to obtain prediction of the state of charge of the battery.

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 provides a hydrological variable valuation method, system, equipment and medium, and belongs to the technical field of random hydrology and geostatistics, and the method comprises the following steps: obtaining a random variable sequence of a plurality of hydrological variables, and removing a deterministic component from the random variable sequence of each hydrological variable to obtain a plurality of random residual components; constructing a cross covariance function and a cross variation function of different random residual components according to covariances of the random residual components of different hydrological variables at a set time interval; according to the cross covariance function or the cross variation function, obtaining co-Kriging weights of different random residual components at different moments, and according to the co-Kriging weights, constructing an interpolation model; and in combination with the deterministic component, performing interpolation estimation on the unknown random residual component at any moment by using an interpolation model. According to the method, the precision and the reliability of multivariable space-time estimation can be improved by combining a multivariable cooperative relationship and time dynamic characteristics.

The application discloses a diversity gain and decoding mode switching method of a low-frequency wireless communication system, which comprises the following steps: collecting two pieces of data in a time period T1 before code sending and a time period T2 during code sending and carrying out band-pass filtering to obtain N3, N4, X3 and X4; calculating a noise covariance matrix C1 of N3 and N4, a sample covariance matrix C2 of a known sequence section in X3 and X4 and a cross covariance matrix C3; expanding C1 to the dimension of C2 to obtain C4; multiplying the sum of C4, C2 and a regularization coefficient and a unit matrix to obtain two filters after inverting, and then multiplying the two filters with C3 to obtain X7 after filtering X3 and X4; accumulating the total energy of the carriers of each frequency of the known sequence section of X7 to obtain a detection coefficient; comparing the relative signal-to-noise ratio of each detection coefficient with the size of a deep fading threshold to determine the applicable decoding mode of X7. The application can improve the signal-to-noise ratio and adaptively switch the decoding mode based on the carrier detection state.

The invention relates to the technical field of data processing, in particular to an abnormal behavior accurate identification method based on multistage safety management. According to the method, time delay is obtained according to a mapping relation between a target speed instruction sequence and an observation speed feedback sequence. The method comprises the following steps: acquiring an instruction track point set representing expected motion and a feedback track point set representing actual motion based on speed information, acquiring feedback track form entropy according to distribution chaos of singular values in a cross covariance matrix of the instruction track point set and the feedback track point set, calculating entropy gain, a non-rigid deformation residual error and an entropy-time delay coupling coefficient as multi-stage working condition feature vectors, and determining whether an abnormal working condition behavior is generated or not through comparison with a standard multi-stage working condition feature vector. According to the invention, before the motion deviation of AMR reaches a danger threshold value, early warning signals can be resisted in advance and abnormal behaviors can be diagnosed based on the meaning of each characteristic representation, so that a multi-stage safety management system can be converted from passive event response to active risk prevention.