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

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 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 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.