11results about How to "Improve State Estimation Accuracy" patented technology

The invention discloses a power distribution network topology identification and state estimation method based on a message passing neural network and online deep learning, and the method comprises the steps: training a neural network through historical measurement data, building a power distribution network topology identification model, combining the obtained topology information with corresponding power grid measurement data, and constructing a graph data set; taking the obtained graph data set as a training set to train a message passing neural network to obtain a power distribution network state estimation model; and finally, inputting the real-time measurement data into a state estimation model of the topology identification model to obtain a real-time state estimation result under the section. According to the method, the power distribution network topology can be accurately identified under limited measurement data, accurate state estimation is provided for a power distribution system, and technical support is provided for subsequent safety assessment and optimized operation.

The invention discloses an Internet of Vehicles driving state monitoring method and system, and the method comprises the steps: 1, collecting the driving state data in real time, and enabling the driving state data to comprise the behavior data of a driver and environment data; step 2, preprocessing the collected multi-source heterogeneous data at a vehicle-mounted edge computing node; step 3, inputting the fused data into a variational auto-encoder, and when the variational auto-encoder extracts the low-dimensional hidden variable representation of the driving state data, deducing hidden variable distribution by optimizing a variational lower bound; 4, inputting the inferred hidden variables into the fractional order differential equation model to obtain a predicted value of the driving state; step 5, federated learning is carried out on a plurality of vehicle-mounted edge nodes, and collaborative optimization of a global model is realized while data privacy is protected; according to the method, distributed collaborative decision-making of multiple agents is realized by solving Nash equilibrium, the adaptability and robustness of the system are improved, and the traffic safety and the driving experience can be remarkably improved.

The invention discloses a phased array radar for unmanned aerial vehicle detection based on multi-target tracking, which comprises a radar antenna array, a signal processing unit, a control unit and a display unit, and is characterized in that the control unit comprises a motion state judgment module; a filtering switching module; a Kalman filtering module; a particle filtering module; and a data association module. The method can achieve the high-precision detection and positioning of an unmanned plane target, has a multi-target tracking capability, and improves the detection efficiency and accuracy of the unmanned plane.

The invention relates to a distributed power distribution network fusion estimation method. The method comprises the following steps: S1, establishing a power distribution network system model; s2, obtaining predicted values and a predicted information matrix of the node i and a neighbor node j thereof; s3, setting an iteration initial step and a filtering estimation value when t times of fixed point iteration is carried out on the ith node; s4, Cholesky decomposition is carried out; s5, calculating a consensus weighted item of the node i, and obtaining an optimal estimation value of a current state by using a fixed point iteration method of a maximum correlation entropy extension information filter; and S6, comparing the current state estimation value with the estimation value of the previous iteration step, if the current state estimation value is smaller than a threshold value, taking the current state estimation value as the state estimation value of the next iteration, and calculating the state estimation error covariance, otherwise, updating the number of iterations, and returning to S5. Compared with the prior art, the method has the advantages of improving the precision of the filter adopted by the power distribution network state estimation method and the like.

This invention discloses an aircraft state estimation method based on optimized operator filtering, relating to the fields of aircraft autonomous navigation, state estimation, and signal processing. The steps are as follows: A discrete state-space model of the aircraft containing state time delay terms is established; an auxiliary model is introduced, and historical state estimates are used to replace unknown time-delay states; an aircraft state estimator is constructed; a state reconstruction error and error covariance matrix are constructed, and the state reconstruction error covariance is further minimized based on the Delta operator to obtain the optimal filtering gain; it is determined whether the observation sequence length requirement is met; if so, the state estimates of the aircraft's position and velocity under time delay and noise interference conditions are output. This invention achieves state estimation of the aircraft's position and velocity under conditions such as time delay and the influence of historical state information on the current state.

The present application belongs to the technical field of aircraft state estimation. The present application provides an aircraft state estimation method combining multi-model interactive filtering and DNN. The disclosed embodiment trains the LSTM network model according to the collected aircraft trajectory set, and the LSTM-KF filter uses the nonlinear function formed by the LSTM network model to replace the original state transfer matrix for state prediction update, and connects the LSTM-KF filter as a filter module to the interactive multi-model filtering framework. This method has interactions between multiple different models, and can adapt well to the complex motion mode of the aircraft, even when the aircraft motion mode jumps, it can still adapt well, and at the same time integrates the results of the deep neural network filter to improve the upper limit of the aircraft state estimation accuracy.

This invention belongs to the field of power system technology and provides a method and system for state estimation of distribution systems that considers measurement communication reliability. First, a stochastic state estimation model for distribution systems considering measurement loss is established, introducing a series of random 0-1 variables to represent the random loss of measurements. To calculate the values ​​of each random variable, a method for calculating measurement communication reliability considering the operating state of communication equipment is proposed. A depth-first search is used to find the communication path between the measurement and the estimator, and the reliability of each path is calculated based on the current operating time of the equipment. Then, all paths are orthogonalized, and the reliability of all orthogonal paths is summed to obtain the measurement communication reliability. Finally, an extended Kalman filter algorithm for the established stochastic state estimation model is derived based on projective theory. By incorporating the communication reliability of each measurement into the estimation process, the reasonable allocation of measurement weights is achieved, improving the accuracy of state estimation.

The invention discloses a dexterous hand force-position coordination control method based on multi-sensor fusion, and relates to the technical field of robot intelligent control, and the method comprises the steps: mapping uncertainty entries in a unified interaction state estimation package into boundary contraction amounts, writing the boundary contraction amounts into a plurality of types of boundaries, executing look-ahead feasibility cutting, and generating a contraction constraint control configuration package; combining a contraction constraint control configuration packet with a unified interaction state estimation packet, constructing a plugging force displacement curve monitoring record, registering an energy suppression mark, executing slow amplitude limiting and compensation recalculation at the same time, and generating a force-position coordination instruction packet; and driving the dexterous hand according to the force-position coordination instruction packet, collecting a multi-modal driving receipt, and binding by combining the channel weight descending mark and the registered energy suppression mark to generate a force-position coordination execution result set. According to the method, credible weighted fusion of the multi-source sensing data is realized, and the state estimation precision is improved.

The application discloses a traffic state estimator design method and device for a multi-agent system, comprising: dividing a highway network into a plurality of cells according to preset positions, taking traffic flow density as a traffic state variable, establishing a traffic flow model of the highway network, and determining an output matrix of a traffic sensor; according to observability or detectability of the highway network system composed of a highway network system matrix and an output matrix of each type of traffic sensor, at least one state observer and at least one Kalman filter are constructed; all vehicle density estimation values obtained based on the state observer and the Kalman filter are respectively subjected to weighted summation fusion calculation to obtain first and second total vehicle density estimation values; and the first and second total vehicle density estimation values are subjected to weighted summation fusion calculation to obtain a final vehicle density estimation value. The application improves the state estimation precision of mixed traffic flow and improves the utilization efficiency of different types of sensors.

This invention relates to the field of airborne geophysical exploration, and more particularly to an intelligent monitoring and predictive guidance method for helicopter-towed pods. This method involves arranging multiple GPS arrays on the flexible structure of the pod, fusing airborne GPS, radar altimeter, attitude sensor, video, and meteorological data to construct a pod oscillation model incorporating structural modes and identifying deformation parameters online. A multi-model adaptive fusion framework is established, employing extended Kalman filtering and a Bayesian weight update mechanism to achieve state estimation. The spatial wind field gradient is inverted and injected into the forced pendulum dynamics equation to predict the trajectory, calculating the ground contact risk index to trigger graded alarms. Pilot cognitive load is assessed based on eye-tracking and joystick spectral analysis, dynamically simplifying the guidance interface. Lateral / longitudinal oscillation patterns are identified in the body coordinate system, generating differentiated control commands. This invention achieves intelligent monitoring and predictive guidance of the pod's state, improving flight safety and data quality.

The invention discloses a mu PMU optimization layout method, system and device for a micro-grid and a medium, and belongs to the technical field of power systems. According to the method, a power grid data acquisition model, a layout generation model, a layout updating model and a layout evaluation model are constructed, a target function with the maximum comprehensive association information degree as a target is established, and mu PMU initial layout position information is obtained; on the basis of the observability and the mutual information amount, updating the initial layout position information of the mu PMU to obtain optimized layout position information of the mu PMU; and then on the basis of the state estimation precision of the micro-power distribution grid, the optimal layout configuration information of the mu PMU is determined, and the optimal layout of the mu PMU of the micro-power distribution grid is completed, so that the installation number and the installation position of the mu PMU can be comprehensively considered, the configuration number of the mu PMU is effectively reduced, the layout cost of the mu PMU is reduced, the maximum economic benefit is realized, and the popularization and the use of the mu PMU configuration method are facilitated. And meanwhile, the state estimation precision of the active power distribution network (ADN) can be effectively improved by a mu PMU optimal configuration method considering the node association information similarity.