61results about How to "Improve State Estimation Accuracy" patented technology

The invention relates to the technical field of operation and control of an electric power system and discloses a method for detecting and identifying dynamic bad data of the electric power system. The method comprises the steps of A, reading a current network parameter and a network topology of the electric power system and forming a node admittance matrix and a branch circuit-node incidence matrix; B, establishing an equivalent circuit according to the network topology of the electric power system and configuring measurement functions and a power management unit (PMU) of the electric power system, wherein system measurement includes node voltage amplitude measurement, node current amplitude measurement, node power injection measurement and node trend measurement; C, detecting and identifying the dynamic bad data of the electric power system; D, judging the convergence condition. By means of the method, accidentally arisen bad data from a supervisory control and data acquisition (SCADA) system and in the PMU measurement can be found and eliminated, and accordingly an accurate operation state, voltage amplitude and phase angles of nodes of a power grid are obtained.

To provide a human state estimating device which accurately estimates a human state. The human state estimating device includes: a storage unit (16) which stores reference data (17) in which a human state and a reference profile including a horizontal component of a standard microsaccade in the human state are corresponded to each other, for each of plural personal attribute information; a video obtaining unit (11) which obtains video of a user's eyeball, with an eyeball rotation angle accuracy of 0.05 degrees or higher, and a measuring speed of 120 samples per second or higher; an analyzing unit (12) which generates an actual-measurement profile including the horizontal component of the microsaccade, from fixational eye movement shown in the video; a personal attribute information obtaining unit (13) which obtains the user's personal attribute information; and an analyzing unit which estimates the human state by searching, in the reference data (17), for a reference profile that corresponds to the obtained personal attribute information and is closest to the actual-measurement profile.

The invention relates to a deep-sea vessel dynamic positioning oriented high-precision real-time state estimation method and system. The method includes: separating a high-frequency signal caused by a first-order wave force from a low-frequency vessel motion signal so as to obtain a high-frequency motion component and a low-frequency motion component; in a marginalized particle filter frame, subjecting both the high-frequency motion component the low-frequency motion component to parameter estimation and state estimation; using results of the parameter estimation and state estimation as input of next-cycle parameter estimation and state estimation, and repeating the steps until the end. The deep-sea vessel dynamic positioning oriented high-precision real-time state estimation method and system has the advantages that the difference between high-frequency linear motion and low-frequency linear motion is fully considered, the nose parameter estimation method applicable to both high-frequency and low-frequency motions is provided, the state estimation method based on marginalized particle filter and oriented towards vessel dynamic positioning is therefore provided, and state estimation precision increase and timeliness are balanced.

The invention discloses an underwater robot control system and a dynamic positioning method, and provides a modular control system which can achieve both floating and crawling functions and an underwater robot dynamic positioning method based on an adaptive unscented Kalman filtering state observer and a multivariable and multi-modal fast nonsingular terminal sliding mode controller which can improve the stability of underwater robots. An adaptive unscented Kalman filtering algorithm adopted has a better tracking filtering effect under the influence of factors such as external interference, physical characteristics of acceleration and artificial manipulation, has better robustness for uncertain models, and has high state estimation precision. An underwater robot can arrive at a specified place more quickly and steadily within the thrust range of a propeller, and the thrust compensation can be properly reduced in the area near a target location, so that the underwater robot can make small adjustments to ensure the positioning accuracy.

The invention discloses an underwater robot control system for detection and operation. The underwater robot control system comprises a water surface control system and an underwater control system, the water surface control system is placed on a bank side or a mother ship, and the underwater control system is installed on an underwater detection and operation robot; the water surface control system includes a water surface control panel, a water surface communication transceiver, an umbilical cable, and VR glasses; and the underwater control system includes an underwater communication transceiver, an ARM9 main controller, an ARM-M0 alternate controller, an underwater power supply module, a power supply security detection module, a water leakage detection module, a first low voltage powersupply, a communication module, a second low voltage power supply, a download port, a pan-tilt camera, a sensor module, an underwater lamp, a mechanical hand, and a propeller control module. The underwater robot control system for detection and operation has the advantages of multi-function and modularization, and is convenient to install or disassemble; and the water surface control panel has anemergency stop function and is provided with a wirelessly connected tablet computer for mobile monitoring and positioning.

The invention discloses a dynamic positioning method using an underwater detection and operation robot. The method solves the problem of uncertainty caused by a reason that dynamic positioning is disturbed by waves, and in combination with the developed novel underwater detection and operation robot, based on a hydrodynamic digital simulation recognition parameter method, a dynamic model is established for dynamic positioning analysis of six degrees of freedom. The position and the heading of the underwater robot are acquired via a sensor, the state of the underwater robot is estimated in real time by adopting an adaptive unscented Kalman filter algorithm, the thrust is compensated by adopting non-singular terminal sliding mode control to reduce the influence of random such as waves and the like, and a force and torque distribution strategy is designed according to the positioning error. The method has a good dynamic positioning effect, and can quickly adjust the dynamic distribution strategy after disturbance to reduce the negative effect of the disturbance.

The invention provides a design method for PNKF-VB and relates to the field of a filter. According to the method, variational distribution approximate to a posterior probability density function is constructed under a Gaussian assumption condition; iteration approximation of state estimation is realized by taking KL (Kullback-Leibler) divergence as a penalty function; and the PNKF-VB is derived bytaking confidence lower bound maximization as a target function according to a variational Bayesian frame. According to the method, a relatively tight approximation form of the nonlinear state posterior probability density function can be obtained, so the state estimation precision is improved. The difficult solution problem of the integral of the posterior probability density function in a system state estimation process can be transformed into the problem of optimizing ELBO lower bound. The adaptive weighted KL divergence is taken as the penalty function, so the optimization flexibility isimproved and the state estimation precision is improved. The method has very high significance to nonlinear state estimation theory and practical engineering application.

The invention discloses a bionic polarized light assisted autonomous navigation method for an unmanned combat aircraft. The method comprises the following steps: establishing a quaternion-based dynamic model of a BPNS / MIMU / VNS autonomous integrated navigation system according to an MIMU navigation subsystem; establishing a first observation model and a second observation model through the VNS navigation subsystem and the BPNS navigation subsystem respectively; and acquiring navigation information of each navigation subsystem, and performing optimal estimation on the kinetic model, the first observation model and the second observation model by using an improved federated CKF filtering method to obtain final navigation information of the unmanned combat aircraft. According to the method, the BPNS is used for assisting the traditional MIMU and VNS, the robust factor is introduced in the filtering process, the influence of abnormal observation is weakened, and the sampling rate and precision of asynchronous observation processing are effectively improved through asynchronous time registration. Therefore, the method has the advantages of strong autonomy, high precision, good reliability and the like, can make up the defects of the existing autonomous navigation technology, and improves the autonomous accurate navigation capability of the unmanned combat aircraft in a complex battlefield environment.

The invention discloses a vehicle traveling state estimation method based on sparse grid integrating Kalman filtering. Through a selected seven-degree-of-freedom vehicle motion module, on the basis ofan integrating Kalman filtering method combining with a sparse grid theory, multidimensional integration points are selected, time upgrading and measurement upgrading are conducted, and the longitudinal vehicle speed, the lateral vehicle speed and the side slip angle of a vehicle are estimated. The vehicle traveling state parameter estimation method has the characteristic of high precision, and the real-time performance of state parameter estimation can be effectively improved while the state estimation precision is improved.

A method for the atmospheric entry section navigation of a mars lander based on a sampling point inheritance strategy relates to the technical field of navigation guidance and control, in particular to a method for the atmospheric entry section navigation of the mars lander. The invention aims at solving the problem that the precision is low when the status of the lander is estimated according to the conventional filtering method. The method comprises the following steps: in a filtering beginning cycle, providing a sampling point and a sampling point weight according to sampling point generating rules of unscented Kalman filtering; providing a lander status predicted value x' and a lander status variance Px according to a transmitted sampling point and a generated weight; calculating a measurement predicted value y', a variance Py and a covariance Pxy according to unscented Kalman filtering rules; updating measurement and correcting the estimate value of the lander status; estimating x'<+> and a lander status variance Px<+> according to the lander status, correcting the transmitted sampling point Xi and obtaining the sampling point Xi<+> with the corrected value. The method has higher status estimate precision and is suitable for navigation in the atmospheric entry section of the mars lander.

The invention relates to a multi-model self-adaptive fusion filtering method of a ship dynamic positioning system and belongs to the technical field of ship dynamic positioning. The method includes the steps: (1), building a ship three-degree-of-freedom low-frequency and high-frequency motion model, and acquiring a filter state formula and a measuring formula; (2), utilizing a differential global positioning system and a platform compass to measure position information and a heading angle, and collecting information in real time; (3), utilizing prior information and posterior information to initialize input of a model-based filter; (4), on the basis of a system model, utilizing a strong tracking filter and a Sage-Husa filter for parallel filtering; (5), subjecting the model to probability updating, and utilizing residual covariance output by the filters to calculate model probability matched with the model; (6), according to the model probability, acquiring fusion output of multi-model state estimation, namely ship position and heading information. The multi-model self-adaptive fusion filtering method has the advantages of strong robustness, high accuracy in Sage-Husa filter state estimation, stable system, high positioning accuracy and the like.

The invention discloses a radar multi-target tracking PHD implementation method, and the method specifically comprises the following steps: S1, calculating the state prediction estimation of each target at the k moment based on the states of a plurality of tracking targets at the k-1 moment; S2, converting the radar measurement data at the moment k into target state likelihood data under a Cartesian coordinate system; S3, performing product hybrid filtering on the Gaussian component predicted in the step 1 and target state likelihood data; S4, calculating Gaussian components and final weightsof all targets in the radar tracking area; S5, abandoning the Gaussian components of which the final weights are smaller than a pruning threshold value, and combining the Gaussian components of whichthe distribution distances are smaller than a combination threshold value; S6, taking the weight sum of all Gaussian components as the number of tracking targets, taking the mean value of the Gaussiancomponents with the weight greater than 0.5 as the target state, and iteratively carrying out the next round of filtering until the tracking is finished; the data fusion process is simple, the anti-clutter performance is excellent, the state estimation precision of the tracking target is high, and the target number estimation is accurate.

The invention discloses a method for estimating the dynamic state of a generator based on an adaptive H infinite cubature Kalman filter, and the method is used for enhancing the robustness of a dynamic state estimator under the condition of model parameter uncertainty, and the accurate estimation of the state of a power system generator is realized. According to the method, based on the H infinitecubature Kalman filter (HCKF), an adaptive H infinite cubature Kalman filter dynamic estimation method is established by introducing an adaptive method, the method can define the influence of systemmodel parameter uncertainty on a state estimation result and avoids the problem that a HCKF method has uncertain upper bound parameters and the selection is difficult. According to the method of the invention, the state estimation precision can be effectively improved, the robustness of a state estimator is enhanced, the realization process is clear, and the method has a high engineering application value.

The invention discloses a parameter optimization method based on power transmission line state estimation. The device is characterized by comprising a base, the method comprises the steps of basic particle swarm parameter optimization, weight coefficient optimization, learning factor optimization, power transmission line state estimation parameter optimization and the like. According to the method, a traditional swarm intelligence parameter optimization method can be overcome; the problem of insufficient local and global search capability balance is solved; particularly, when the search spacerange is relatively large and the search nodes are difficult to traverse, the global and local search capabilities of the balance algorithm are optimized by adopting the nonlinear weight coefficient and the learning factor collaboratively, and the global optimal solution is quickly converged in the evolution process, so that the optimal state estimation parameter is determined, and the state estimation accuracy is improved. The method is scientific and reasonable, and can be suitable for parameter optimization of various state estimations.

The invention discloses an information fusion method and system of a multi-sensor system, a computer device and a medium. A specific embodiment of the method comprises the steps of dividing a signal time length into a plurality of time windows, and obtaining a target sensor of a current time window; dividing the current time window into at least two sub-windows, judging whether the target sensor has a fault in the current time window or not according to the sum of the observation residual statistical property values of the target sensor at all moments in the current time window and a window preset chi-square distribution threshold; if yes, according to the time sequence of the sub-windows, judging the sub-windows where the faults of the preset chi-square distribution threshold values of the corresponding sub-windows are located sequentially according to the sum values in the sub-windows and the time sequence of the sub-windows, and judging the time when the faults occur moment by moment according to the characteristic values of all the moments in the sub-windows and the time preset chi-square distribution threshold values; and obtaining an information fusion result of the target sensor at the end moment of the current time window according to the fault condition of the target sensor in the current time window.

The invention discloses a power system multi-region distributed state estimation method. According to the invention, one power system is divided into a plurality of non-overlapped regions; a state estimator is configured in each region; local state estimation is carried out according to measurement data provided by an SCADA system of each state estimator; and by an average consistency algorithm, global information is acquired to carry out system-level state estimation. The estimation process of each state estimator is parallel and is divided into an initialization stage and an iterative computation stage. In the initialization stage, a state estimation initial value is given out, and a region internal measurement vector and a boundary measurement vector are acquired; in the iterative computation stage, an internal measurement update quantity of a state quantity is computed, a boundary measurement update quantity of the state quantity is computed after the global information is acquiredby the average consistency algorithm, a total update quantity of the state quantity is computed according to the internal measurement update quantity and the boundary measurement update quantity, andwhen the total update quantity is smaller than a preset threshold, the iterative computation process is ended. The power system multi-region distributed state estimation method is high in accuracy, high in estimation speed, high in reliability and small in exchange information quantity.

The invention relates to a global optimal particle filtering method and a global optimal particle filter. The problem of particle filter processing nonlinear and non-Gaussian signals is effectively solved. The main technical means is to use the Lamarck genetic natural law to construct a global optimal particle filter comprising: generating an initial particle set; using Unscented Kalman Filter to perform importance sampling on the initial particle set to obtain sampled particles; performing floating-point number encoding for each of the sampled particles to obtain an encoded particle set; setting an initial population; using the initial population as an original trial population to sequentially perform a Lamarck overwriting operation, a real number decoding operation, and an elite retention operation; using the real-number optimal candidate particle as a prediction sample for a next moment, and obtaining a state estimation value of a system. The invention is applicable to machine learning.

The invention discloses a generator dynamic estimation method based on robust cubature Kalman filtering. The generator dynamic estimation method is used for accurately estimating the generator dynamicstate under the condition that model parameters are uncertain. Firstly, a generator dynamic state estimation model is established; secondly, a robust cubature Kalman filter dynamic state estimator considering model parameter uncertainty is designed according to an uncertainty constraint criterion in a robust control theory in combination with cubature Kalman filtering; according to the method, the influence of model uncertainty on the state estimation precision can be inhibited, the dynamic state estimation precision of the generator under the condition that parameters are uncertain is improved, and relatively high robustness is achieved on model parameters. The method not only can effectively solve the problem of accurate estimation of the dynamic state of the generator under the condition of uncertain model parameters, but also is clear in process, simple to implement and convenient for engineering implementation.

The invention provides an underwater small object multi-base fusion tracking method. The method includes a step 1 of resampling an object measurement set Zk of a multi-base system at a tk moment and obtaining a resampling random particle set; through quantization and fusion treatment of distribution of the resampling random particles, obtaining equivalent measurement zk at the tk moment; a step 2 of estimating the small object state at the tk moment through mixed particle filtering, wherein the mixed particles includes predication particles which are random particles at the tk moment obtained through predication with random particles at the tk-1 moment and target volume measurement sampling particles obtained through measurement according to the random sampling according to the equivalent measurement zk and prior probability distribution; and resampling the mixed particles; a step 3 of making k=k+1 and turning to the step 1 until object tracking is finished. By adopting the method provided by the invention, tracking precision of multi-base underwater small objects is improved.

The invention discloses a Beidou_SINS compact combined navigation system and method based on a multi-core DSP. The system comprises a micro inertial measurement unit used for measuring the rotation angular velocity and accelerated velocity of a carrier and the magnetic field intensity information of an environment; a Beidou satellite navigation receiver used for measuring the position, pseudo-range and pseudo-range rate information of a satellite and the initial velocity and position information of the carrier; a multi-core DSP unit used for collecting the data of the micro inertial measurement unit and the data of the Beidou satellite navigation receiver, for analyzing the data, selecting the satellite, correcting the measured range error and performing SINS resolving, estimating the posture information of an SINS, fusing the pseudo-range and pseudo-range rate data of a Beidou system and the SINS, estimating and compensating an SINS position error and velocity error, and then outputting navigation data with high accuracy and high refresh rate. According to the Beidou_SINS compact combined navigation system and method based on the multi-core DSP provided by the invention, the datafrom multiple sensors can be fused, and the data processing can be performed on the multi-core DSP unit, so that the compact combined navigation with high precision, dynamics and relatively high anti-interference performance is achieved.

The invention relates to a variable-structure multi-model target tracking algorithm based on a road information base and target road constraint characteristics. A road information base is constructed,the constraint ability of a road network-based road geographic environment and the adaptive ability of different types of targets to different road geographic environments are used as priori information to assist ground target tracking, the state estimation accuracy of ground target tracking is improved through full utilization of priori knowledge, and the target tracking loss rate is reduced. The method has practical value in the aspect of ground target tracking problems.

The invention provides a maneuvering target tracking algorithm based on a road network. According to the algorithm, a tracking algorithm model adaptive strategy realized through road information is given by aid of a prior road information base, and a variable-structure multi-model method is adopted to realize ground multi-maneuvering-target tracking. In this way, state estimation precision of maneuvering target tracking can be improved, and the target missing rate is lowered; meanwhile, computing burden brought by multiple models adopted in a fixed multi-model algorithm is avoided, and operating time is greatly shortened; and the scheme has practical value in terms of ground target tracking.

The invention discloses a distributed information weighted coherent state filtering method for a sensor network, belonging to sensor information fusion technology, which relates to a distributed nonlinear state estimation problem of the sensor network. Firstly, a square root volume rule is used to obtain the prediction information vector and the square root information matrix of each node to the target state. Then, each node updates local state information by weighted local prediction information and observation information vector, updates local square root information matrix by weighted localprediction information and observation information matrix, and combines matrix triangular decomposition. Finally, the information consistency of the whole network is achieved by using the weighted consistency of the neighboring nodes, and the estimation of the target state is completed. This method improves the convergence rate of the state estimation in the network, enhances the numerical stability of the algorithm, and improves the accuracy of the state estimation of the target under the condition of limited energy.

The invention relates to a power distribution network state estimating method based on a fuzzy theory, which belongs to the field of electric power system scheduling automation. According to the power distribution network state estimating method, a set with higher measuring precision is used as a fuzzy set A. A parameter value beta in a Cauchy membership function is determined according to the fuzzy set A. The same-kind measured values and flow values of different branches or different nodes, which are measured by a same measuring device, are introduced into a Cauchy distribution function for calculating a function value. Through analysis and comparison, error function values which exceed a normal error limit are eliminated. An average value of the function values which satisfy the requirement is calculated as a weighting value for measurement of this kind. According to the power distribution network state estimating method of the invention, different weighting values can be set for different measured values which are measured by one measuring device, and effect of different kinds of measurements for state estimating precision is considered. Convenient algorithm realization and high estimating precision are realized. Furthermore when certain measurements are added or eliminated or the power distribution network state estimating method is applied in the system with different number of nodes, no program change is required, and high practicability is obtained.

The invention relates to a power distribution network affine state estimation method. Photovoltaic power generation and wind power generation-containing distributed power supply output uncertainty andload fluctuation uncertainty intervals are converted to an affine number form to serve as node injection uncertainty power; SCADA data are aligned with the muPMU data in consideration of time delay,and the SCADA data are predicted by using an adaptive coefficient exponential smoothing method and are used as supplementary pseudo measurement data to be fused with the muPMU data; and an affine number model with the SCADA system and the node injection uncertainty power is used for upper-layer state estimation, the affine number result of the upper-layer state estimation is combined with the aligned muPMU real-time measurement to form a power distribution network lower-layer state estimation model of linear hybrid measurement, the directly solved affine number result is converted into an interval number to obtain a state estimation result narrower than the interval state estimation, and the result is no longer conservative. According to the method, the uncertainty of the output of the distributed power supply is fully considered in the power distribution network, and the state of the power distribution network is described by replacing a determined value with a narrower interval stateestimation result.

The invention provides a robust fault detection method of an inertial navigation-satellite integrated navigation system. The method comprises the following steps: carrying out optimization estimationon a constructed strapdown inertial navigation system / global navigation satellite system integrated navigation system by adopting a volume Kalman filter algorithm based on singular value decomposition; introducing error detection filtering based on a framework into a volume Kalman filter algorithm based on singular value decomposition, carrying out linearization processing on a constructed integrated navigation system, and calculating a judgment threshold of a sensor for fault detection during integrated navigation of the integrated navigation system in an error detection process; comparing the relation between the fault and the judgment threshold value, and if the fault is small, improving the state estimation precision of the volume fault detection filter through a self-adaptive algorithm based on a measurement residual error and a scale factor; and if the fault is large, adopting robust batch processing H-infinity filtering to guarantee the stability at the moment. According to theinvention, the robustness of fault detection can be well ensured.

The invention discloses a Martian atmosphere entrance interruption allowing estimation method based on multiple models, relates to the Martian atmosphere entrance interruption allowing estimation method, and belongs to the technical field of deep space exploration. The method includes the implementation steps of (1) building a Martian atmosphere entrance section dynamics equation; (2) building the Martian atmosphere entrance section wireless measurement model and the wireless measurement interruption model so as to be used for modifying the detector position and speed information in the step (3); (3) resolving the detector state in cooperation with the wireless model and the interruption model in the step (2) and a filtering algorithm, and estimating the detector state. By means of the Martian atmosphere entrance interruption allowing estimation method, the problem of estimation divergence of the detector state under wireless measurement interruption can be effectively solved, and the reliability and the state estimation accuracy of an autonomous navigation system of a detector on the Martian atmosphere entrance section are improved. By means of the Martian atmosphere entrance interruption allowing estimation method, technical supports and references can be provided for adopting the wireless navigation scheme design on the atmosphere entrance section in Martian landing detection in future.

The invention discloses a power distribution network dynamic state estimation method and system based on a square root cubature Kalman filter. The power distribution network dynamic state estimation method comprises the following steps: S1, acquiring measurement data at a certain moment k; S2, performing state prediction according to the measurement data at the moment k, and predicting the state at the moment k + 1 by using a holt two-parameter exponential smoothing method to obtain a state prediction value at the moment k + 1; S3, correcting the state prediction value at the k + 1 moment by using the measurement data to obtain a state estimation value at the k + 1 moment; S4, updating filtering parameters to prepare for next iteration; carrying out the next iteration, returning to the step S1, obtaining measurement data at the moment k + 1, and executing the steps S2-S4 to realize state estimation at the moment k + 2. According to the invention, dynamic state estimation can be carried out by using the collected power distribution network measurement data to obtain more accurate operation state information of the power distribution network, and measurement errors caused by measurement equipment are reduced. Compared with a standard volume Kalman filtering dynamic state estimation algorithm, the algorithm is stronger in robustness and higher in state estimation precision.

The invention relates to the technical field of electric power system operation and control and discloses an electric power system dynamic state estimation method based on a synchronous phase-angle measuring device. According to the technical scheme, the method comprises the steps of A, reading current network parameters of an electric power system and a network topological structure of the electric power system, and therefore forming a node admittance matrix and a branch-node incidence matrix; B, establishing an equivalent circuit according to the network topological structure of the electric power system, and configuring a measurement function of the electric power system and a PMU of the electric power system, wherein the measurement of the system comprises node voltage amplitude measurement, node current amplitude measurement, node power injection measurement and node load flow measurement; C, conducting dynamic state estimation on the system on the basis of extended Kalman filtering; D, judging conditions of convergence. According to the electric power system dynamic state estimation method based on the synchronous phase-angle measuring device, due to the introduction of the PMU, real-time and accurate measurement information such as the voltage and the phase angel can be supplied to the system, the higher measurement redundancy rate of the system is obtained, and therefore the precision of the state estimation is improved. The electric power system dynamic state estimation method based on the synchronous phase-angle measuring device has the advantages of being good in robustness, high in state estimation precision and good in convergence performance.