12 results about "Fixed-point iteration" 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 an unmanned aerial vehicle state estimation method, a readable storage medium and a navigation device, and belongs to the technical field of unmanned aerial vehicle navigation. The method comprises the following steps: establishing a linear state space model with multi-cluster measurement noise, modeling the measurement noise as multivariate Gaussian distribution, and introducing a measurement noise covariance matrix coefficient; joint prior updating is carried out based on posterior information of a previous moment, and prior estimation of a state, a state covariance, a measurement noise covariance matrix coefficient and a generalized inverse Gaussian (GIG) distribution parameter is obtained; performing pre-clustering on the measured values; online adaptive clustering is realized by using an EM algorithm, and unknown outdoor scene noise can be dynamically identified and divided; joint posterior updating is carried out, fixed point iteration is not needed, and therefore an updated measurement noise covariance matrix is obtained; updating the state and outputting the unmanned aerial vehicle state. According to the invention, through precise noise modeling, online clustering and adaptive adjustment of the measurement noise covariance matrix, the positioning precision and system robustness of the unmanned aerial vehicle are significantly improved.

The application particularly relates to a radar main lobe interference suppression method based on direction constraint, which realizes suppression of interference and separation of target echo in a main lobe suppression type interference scene by embedding array manifold vectors into FastICA fixed point iteration and applying direction constraint to the separation vectors after each update so that the separation vectors are always focused on a target angle subspace.

The application provides a non-Gaussian noise suppression method and device with adaptive kernel width, and belongs to the field of inertial base combined navigation algorithm and state estimation. The method solves the problems that the robust filtering method based on fixed kernel width is difficult to adapt to time-varying noise characteristics, and the scheme depending on an optimization algorithm has the problem of insufficient real-time performance. The method comprises the following steps: initializing filter parameters according to a SINS / DVL combined navigation system; performing time updating to obtain a predicted state vector and a predicted state covariance matrix at the current moment; updating a measurement noise covariance matrix through a variational Bayesian method, wherein the measurement noise covariance matrix is modeled as an inverse Wishart distribution; adaptively updating a kernel width parameter according to a filter innovation at the current moment and the measurement noise covariance matrix; and updating a state quantity estimation value and a state covariance matrix at the current moment through a fixed-point iteration algorithm by using the updated kernel width. The method is used in the field of underwater resource exploration.

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 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.

The invention discloses a maximum correlation entropy Kalman filtering method based on a rational kernel function, and belongs to the technical field of signal processing. The method specifically comprises the following steps: 1) constructing a linear system equation and a measurement equation; step 2) selecting a kernel width of a rational quadratic kernel function, and initializing a system state and a covariance; 3) according to a system equation, updating one-step prediction of a state and a covariance; (4) the state value is initialized again at the fixed point iteration starting moment; 5) performing system model deformation according to the initial system and the measurement equation to obtain an error vector after deformation; 6) according to a concept based on a weighting criterion and entropy, defining a cost function by using an error vector; 7) for the cost function, solving an optimal solution of a state estimation value according to a maximum correlation entropy principle; and step 8) estimating the variance of a posterior estimation value. Compared with the existing GSKF, HF and MCKF algorithms, the method provided by the invention has the advantage that the accuracy of state estimation and the robustness of estimation are greatly improved.

The invention provides a robust Kalman filtering method based on STIW distribution, and relates to the technical field of state estimation and filtering. The method comprises the following steps: S1, constructing a state space model, and initializing parameters; step S2, on the basis of the initialized parameters, modeling the process noise as Gaussian-inverse Wishart distribution; s3, modeling the measurement noise as STIW distribution based on the initialized parameters, and generating a three-layer layering model; s4, based on the state space model, executing time updating, predicting state and covariance, and updating noise parameters; step S5, based on the output of the step S4, using a VB method to update posterior distribution through fixed-point iteration; and step S6, repeating the step S5 until a convergence condition or a maximum number of iterations is reached, and outputting a final estimation result. According to the robust Kalman filtering method based on STIW distribution, the precision and robustness of an estimation result in a complex noise environment are improved.

The invention relates to a power distribution network producer and consumer hierarchical collaborative optimization scheduling method and device and a medium, and the method comprises the steps: building a double-layer optimization model facing producers and consumer accessed to a power distribution network; according to the required power reported by the consumer and the load prediction information of each node of the power distribution network, clearing by using an upper-layer power distribution market clearing model, and outputting a clearing electricity price; on the basis of a Lagrange relaxation method, relaxation is carried out on coupling constraints in the lower-layer disappearance producer cluster model, a relaxed Lagrange function is decomposed into a single disappearance producer problem, and a subgradient method is adopted to carry out repeated iteration of demand power and clearing electricity price so as to realize power demand balance of high-yield disappearance producers; and introducing a fixed point theorem to describe a hierarchical decision behavior between the power demanded by the consumer and the clearing electricity price of the power distribution network, and when a fixed point iteration termination condition is satisfied, obtaining a transaction price and transaction power in an equilibrium state. According to the invention, excessive information interaction can be avoided, and the risk of privacy disclosure is reduced.

The invention discloses a transformer area safety boundary division method considering voltage constraint conditions. The transformer area safety boundary division method comprises the following steps: constructing a Zbus linearization power flow model based on sequential iteration of single fixed points; calculating the partial derivative of each node voltage relative to the injection power based on a power flow model to obtain the constraint expression of each node voltage, calculating the balance node voltage based on the PQ node, and determining the influence of the balance node voltage on each PQ node voltage; calculating lower network point power of a coupling point of a distribution network and a main network and a station area power balance safety boundary by using distributed load and distributed photovoltaic prediction data under the same time scale; and with the transformer area node voltage qualification as the target, correcting the transformer area power balance safety boundary based on the obtained node voltage qualification safety boundary, and obtaining the safety operation boundary considering the node voltage qualification. According to the method, the safe operation boundary of the distributed new energy element transformer area and the node voltage safety constraint are considered, and the situation that the node voltage in the transformer area is unqualified due to power balance allocation of the transformer area is avoided.

The invention discloses an equipment parameter estimation method, and particularly relates to a capacitance estimation method and device based on improved variational Bayesian Kalman filtering. The method comprises the following steps: estimating a prior distribution parameter at a moment k based on a posterior distribution parameter at a moment k-1; n times of fixed point iteration operation are executed based on the measurement information of the moment k and the prior distribution parameters of the moment k, and posterior distribution parameters of the moment k are obtained; determining an estimated value of the state vector at the moment k based on the posterior distribution parameter at the moment k; calculating the capacitance of the capacitor based on the estimated value by using a pre-built pre-charging model to obtain an estimated capacitance; wherein the measurement information comprises capacitor voltage, the state vector comprises model parameters of the pre-charging model, and the measurement noise covariance matrix is a covariance matrix of random noise in the measurement process of the measurement information. The method can improve the accuracy of capacitance estimation of the capacitor.

A dynamic control allocation method based on the frequency characteristics of actuators is disclosed, belonging to the field of aircraft control technology. This invention aims to improve the allocation efficiency of multiple actuators. The method includes establishing a dynamic model of multiple actuators, discretizing the model, and setting physical constraints for each actuator; designing a control allocation mapping function based on a control efficiency matrix to describe the control allocation problem of multiple actuators; constructing a dynamic control allocation method based on the frequency characteristics of multiple actuators; constructing a preliminary set of feasible control inputs that satisfy the actuator constraints; designing a dynamic control allocation optimization objective function with a time-varying matrix; and using a fixed-point iterative algorithm to find the optimal solution for dynamic control allocation based on the actuator frequency characteristics. The proposed algorithm is convergent, providing a guarantee for the subsequent design of virtual control laws.