25 results about "Unscented kalman filtering" patented technology

The application relates to an automatic parameter calibration method of a handling stability controller for an electric automobile, which comprises the following steps: constructing a vehicle dynamics model, performing a discretization operation on the vehicle dynamics model by adopting an Euler discretization method to obtain a discrete nonlinear state equation; constructing a control target function; constructing a calibration evaluation function, obtaining a relaxation factor by subtracting an actual calibration evaluation index from an expected calibration evaluation index; designing an updating rule of a calibration parameter; calculating the relaxation factor and a calibration parameter control increment by adopting an unscented Kalman filtering to obtain the calibration parameter control increment; and substituting the calibration parameter control increment into the updating rule of the calibration parameter to obtain a control weight parameter at each moment, so that automatic calibration of the control weight parameter is completed. Compared with the prior art, the application has the advantages of not requiring a large amount of manpower and parameter calibration experience, greatly reducing artificial cost, improving parameter calibration efficiency and the like.

This invention discloses a DDoS attack detection method based on heteroscedastic unscented Kalman filtering. Addressing the issues of limited resources in edge networks and low anomaly detection accuracy and high latency in low signal-to-noise ratio environments, this invention utilizes Sketch for full-scale, unsampled traffic feature extraction in the programmable data plane and reports observations to the control plane through differential processing. In the control plane, a third-order state-space model incorporating instantaneous traffic rate, rate of change, and acceleration is innovatively constructed. An additive heteroscedastic noise model is proposed to adaptively handle Sketch hash collisions and traffic shot noise, and unscented Kalman filtering is employed for state estimation. Simultaneously, an intelligent hybrid control mechanism is introduced to overcome filtering lag, and finally, NIS and CUSUM are combined for dual-modal anomaly detection, outputting the system state. This invention enables high-precision real-time detection of DDoS attacks with extremely low resource overhead, effectively solving the trade-off between measurement and detection in existing technologies.

The application discloses an omnidirectional guiding interaction multi-model positioning method and system for a motorized mother ship to recover an underwater robot and a storage medium, the method obtains the state information and distance data of the mother ship by integrating a water acoustic ranging and a water acoustic communication device, constructs an interaction multi-model singular value decomposition unscented Kalman filtering algorithm fusing a uniform straight line and a uniform bow turning model, and realizes real-time estimation of the position of the mother ship under a mouth-shaped maneuvering track. An event-triggered communication mechanism is designed, information is broadcast only when the turning state of the mother ship changes, so as to reduce the communication load and maintain real-time performance; meanwhile, a Markov transition matrix is updated, the turning model weight is enhanced, and the estimation accuracy and model matching are improved. According to the model probability and the estimation result, a LOS guiding law with an adaptive look-ahead distance is constructed, and the autonomous guiding recovery of the underwater robot to the motorized mother ship is realized. The method provided by the application can realize dynamic estimation and guiding based on the measurement data of a single water acoustic device under the conditions of the mother ship maneuvering and communication limitation.

This invention discloses an indoor positioning method based on LTE signal fingerprint matching and nearest neighbor observations constrained by PDR (Progressive Directional Recognition), belonging to the field of indoor navigation and positioning technology. The method first performs step count detection, step length estimation, and heading estimation based on MEMS sensor output to obtain PDR positioning results. Then, it uses the PDR positioning results and an indoor map to estimate LTE signal propagation model parameters online, quickly constructing a fingerprint database, and obtaining location observations through fingerprint matching. Simultaneously, it utilizes the peak variation pattern of the LTE signal RSRP (Regressive Range Recognition Point) to identify antenna positions using peak detection and threshold constraints, extracting nearest neighbor position, step length, and heading observations. Finally, it employs unscented Kalman filtering to fuse the PDR results and the aforementioned observations in different scenarios. This invention requires no prior data, can adapt to changes in environment and equipment, and combines low cost with high accuracy, making it suitable for indoor pedestrian positioning.

The application relates to the technical field of medical data processing, and discloses a thyroid disease management and health data statistical analysis system. A physiological kinetics model containing a drug metabolism and endogenous secretion mechanism is constructed, discrete detection data are filled in time sequence blanks by using an unscented Kalman filtering algorithm, a continuous physiological panorama conforming to a human mass conservation law and a neural humoral regulation mechanism is generated, a time lag causal tensor construction mechanism with an adaptive causal window is introduced into the system, nonlinear lagging effects of hormone fluctuations on nodule morphological evolution are accurately captured, and deep causal chains of pathological causes and morphological results are restored, finally, an entropy-driven dynamic scheduling module based on information thermodynamics replaces a traditional fixed follow-up mode, a Lyapunov cognitive failure critical point is calculated as an optimal review time by quantizing an uncertainty accumulation process of a disease condition prediction and combining a risk adaptive mechanism.

This invention relates to the field of construction safety monitoring technology, and discloses a construction site safety early warning system based on UWB and AI cameras. Through multi-source sensor calibration and synchronization, it collects UWB ranging and video frames, and outputs aligned multi-source data. Based on time-frequency domain features and residual networks, it identifies non-line-of-sight locations and compensates for deviations, combining adaptive unscented Kalman filtering to output worker 3D coordinates. It detects targets and behaviors in video frames, achieving cross-modal binding through trajectory constraints and depth metric learning. It analyzes the BIM model to obtain hazardous zone boundaries, using instance segmentation and point cloud reconstruction for correction, and sets dynamic warning zones based on location covariance. It outputs tiered warnings through three-level risk assessment and attention fusion. Edge nodes trigger alarms locally, and edge-cloud collaboratively synchronizes data to the cloud, outputting records and reports. This invention can achieve multi-modal fusion perception and tiered early warning of worker location, behavior, and hazardous zones in complex environments such as tunnel construction.

This invention provides a method for generating bird-repelling images using dynamic LED simulation of biological movement, relating to the field of airport bird control technology. The method includes: acquiring bird images; determining the escape type based on bird distribution and movement trends in the images; clustering the birds to obtain tracking targets; obtaining the predicted trajectory of the tracking targets based on unscented Kalman filtering; acquiring the reaction parameters of the tracking targets; inputting the reaction parameters into a preset action generation model to obtain a simulated action sequence; obtaining simulated flight speed and action parameters based on the simulated action sequence; calculating the predicted distance between the LED screen and the tracking targets based on the predicted trajectory of the tracking targets; obtaining size adjustment parameters based on the simulated flight speed and predicted distance; and generating bird-repelling images. This method achieves realistic simulation of the natural behavior of birds of prey, with a high degree of intelligence, requiring no manual monitoring. Compared to existing bird-repelling equipment, it has lower operating costs and effectively solves the problems of bird adaptability, high cost, and low efficiency in traditional bird-repelling methods.

The application relates to the technical field of lithium batteries and discloses a lithium battery SOC estimation method and device based on adaptive unscented Kalman filtering, which comprises the following steps: acquiring working state data of a lithium iron phosphate battery; constructing a model according to the working state data to obtain an equivalent circuit model representing the electrochemical characteristics of the battery; processing the working state data by using an electric quantity integration method to obtain an initial estimation value of the battery SOC; identifying key parameters in the equivalent circuit model on line by using a recursive least square algorithm to obtain model parameters updated in real time; constructing a hysteresis model considering the hysteresis characteristics of the open circuit voltage and the SOC of the battery; correcting the corresponding relationship between the open circuit voltage and the SOC in the equivalent circuit model based on the hysteresis model to obtain the corresponding relationship between the corrected open circuit voltage and the SOC; and calculating the real-time updated model parameters, the corresponding relationship between the corrected open circuit voltage and the SOC and the initial estimation value to obtain a final battery SOC estimation result.

This invention discloses a method and system for UAV attitude control based on active disturbance rejection control and unscented Kalman filtering, belonging to the field of UAV attitude control. The system includes a sensor module, an extended state unscented Kalman filter (ESO-UKF), a nonlinear state error feedback controller (NLSEF), a control allocation module, and a parameter adaptation module. The method establishes a UAV dynamic model containing extended disturbance states, uses the ESO-UKF to optimally fuse multi-source sensor data, estimates the attitude state and total disturbance, and combines this with the nonlinear error processing and feedforward disturbance compensation of the NLSEF. The results are then converted into actuator commands by the control allocation module, and the parameter adaptation module dynamically adjusts the controller and filter parameters. This invention integrates the strong disturbance rejection capability of active disturbance rejection control with the optimal estimation characteristics of Kalman filtering, reducing dependence on accurate models, effectively suppressing sensor noise, and enhancing the accuracy, robustness, and dynamic response performance of UAV control in complex environments. It is applicable to multi-rotor and fixed-wing aircraft.

The application discloses a kind of urban unmanned aerial vehicle detection countermeasure method and system, it is related to unmanned aerial vehicle detection countermeasure technical field.The method steps include: the motion state original data and communication signal original data of unmanned aerial vehicle are collected, the motion state original data is carried out feature extraction, and motion state feature vector is obtained, communication signal original data is carried out joint time-frequency analysis, and communication feature vector is obtained;Motion state feature vector and communication feature vector are input into the BP neural network model based on improved firefly swarm optimization algorithm, and the threat level determination result of unmanned aerial vehicle is output;Based on motion state feature vector, the trajectory prediction of unmanned aerial vehicle is carried out using unscented Kalman filtering algorithm, and the predicted trajectory of unmanned aerial vehicle is obtained, based on the threat level determination result and the predicted trajectory of unmanned aerial vehicle, threat area is divided and threat area type is determined;According to the threat area type, trajectory guidance countermeasure operation is executed to unmanned aerial vehicle.

This invention belongs to the field of target tracking and sensing integration technology, specifically involving an adaptive interactive multi-model unscented Kalman filtering method based on weighted residual energy density. The method comprises four steps: Step 1, constructing a hybrid motion model set and a nonlinear observation model; Step 2, executing parallel filtering of each sub-model based on unscented Kalman filtering; Step 3, independently calculating the weighted residual energy density for each model; and Step 4, online adaptive reconstruction of the model transition probability matrix and fusion output. This invention utilizes unscented Kalman filtering (UKF) to handle strongly nonlinear observations, combined with a dynamic negative feedback mechanism based on weighted residual energy density (WRED), to address the problems of complex flight trajectories of non-cooperative UAVs, the sluggish response of traditional standard interactive multi-model (IMM) algorithms, and susceptibility to misjudgments caused by non-Gaussian noise.

This invention relates to the field of nonlinear system state estimation technology, specifically to a method for estimating the state of high-dimensional nonlinear systems based on tensor unscented Kalman filtering. This invention is applicable to system state estimation methods based on tensor unscented Kalman filtering in high-dimensional nonlinear scenarios such as multi-sensor fusion, autonomous driving, and aerospace. For example, it estimates the state of a target node, such as its position and velocity, to obtain estimated values ​​of the state tensor. This invention optimizes the accuracy, efficiency, and robustness of real-time state estimation in high-dimensional nonlinear environments, filling the performance gap of traditional estimation methods in high-dimensional dynamic data processing, which suffer from "insufficient accuracy, structural destruction, and poor real-time performance." Its application scope widely covers multiple industries with stringent requirements for high-dimensional nonlinear dynamic data processing, possessing strong scenario adaptability and engineering practical value.

The invention discloses an alternating current servo system control method for controlling the stability of an obstacle breaking vehicle body, and the method comprises the steps: constructing an unscented Kalman filter (UKF) load observer based on the UKF, and estimating a load torque value and the rotor angular speed of a permanent magnet synchronous motor in real time through the UKF load observer; feeding back a rotor angular velocity output based on a UKF load observer to an extended state observer (ESO), introducing a load torque value as a feedforward compensation amount into an NLSEF control law, introducing a Newton-Raphson optimization algorithm (NRBO), and performing global optimization on parameters of an active disturbance rejection controller (ADRC) by taking an improved ITAE index as a fitness function; compared with the prior art, the method can improve the tracking precision and the anti-interference performance of the barrier-breaking vehicle under the complex dynamic working condition, and guarantees the stability of the barrier-breaking vehicle body in the advancing process.

This invention relates to the field of robotic automated assembly technology, and discloses a compliant assembly method and system based on force-position hybrid control. The compliant assembly method includes: acquiring multimodal measurement data, unifying and fusing coordinates; performing unscented Kalman filtering for state recursive estimation; identifying actual assembly constraint parameters online, performing orthogonal decomposition of the force-position control subspace and extraction of deviation component projections; establishing an impedance control dynamic model and adjusting impedance parameters, and generating trajectory integrals; calculating force tracking deviations and designing sliding surfaces, solving for position compensation commands, and performing velocity limiting and trajectory generation; performing feature extraction and neural network state recognition, synthesizing force-position trajectories and mapping joint space; establishing a multi-index evaluation system and determining assembly success and handling anomalies. This invention achieves high-precision compliant assembly, effectively controls contact forces, and improves assembly success rate and efficiency.

The application discloses a pose solution method based on complementary filtering and unscented Kalman fusion, and particularly relates to the technical field of waterways, and the specific solution steps are as follows: S1: for a nonlinear system, the system state vector is iteratively updated by using unscented Kalman filtering; the application fuses Mohony complementary filtering and unscented Kalman filtering (UKF) algorithm, replaces the pose angle directly calculated from acceleration and magnetic field intensity information with the pose angle obtained by solving based on the complementary filtering algorithm from the perspective of making the measurement information more accurate, improves the overall pose solution accuracy; meanwhile, the framework of the fusion algorithm is based on the unscented Kalman filtering algorithm, the probability distribution of the nonlinear function is approximated by unscented transformation, the high-order truncation error caused by Taylor expansion of the extended Kalman filtering algorithm is avoided, and the pose solution accuracy for the nonlinear system is improved.

The embodiment of the specification provides a low-altitude aircraft collision risk assessment method and system based on multi-dimensional trajectory prediction, wherein the method comprises the following steps: collecting and fusing multi-dimensional state data of a low-altitude aircraft, generating a disturbance compensation amount of the low-altitude aircraft based on an observed environmental disturbance through a constructed disturbance observation model; adopting an algorithm of fusion of unscented Kalman filtering and extended Kalman filtering, predicting a future trajectory of the low-altitude aircraft based on the fused multi-dimensional state data and the disturbance compensation amount, quantifying prediction uncertainty through a filtering covariance matrix, and obtaining a final trajectory prediction result; performing trajectory information coordination and synchronization among multiple low-altitude aircrafts; and quantifying a collision risk level and outputting an obstacle avoidance suggestion through a multi-index weighted fusion algorithm based on the trajectory prediction result and the trajectory information among the multiple low-altitude aircrafts.

The application discloses a large vehicle rectification method based on double-layer adaptive unscented Kalman filtering, which comprises the following steps: step one, lane line segmentation is performed on video stream data shot by a camera, and a lateral offset of a vehicle relative to a lane center line and a heading deflection angle are calculated; step two, a lateral offset of the vehicle relative to a reference trajectory and heading angle information are calculated based on GNSS satellite positioning signals; and step three, double-layer adaptive unscented Kalman filtering is adopted to fuse visual measurement results and GNSS measurement results. The application can still maintain stable positioning and rectification ability under complex illumination, rain, snow, dust and satellite signal fluctuation environment through a double-layer adaptive mechanism (first layer priori adjustment based on sensor confidence and second layer posteriori correction based on innovation sequence), and can complete driving direction correction without manual intervention based on real-time fusion processing of video stream and GNSS data, so that the safety and operation efficiency of the large vehicle are improved.

This invention proposes a method for observing the internal state of a SOFC (Solar-Fuel Cell) stack. Based on a distributed parametric modeling method, a state evolution model of three physical fields within the stack is established. The system state, measurement input, and measurement output of the observation system are selected, and the system state is divided into a predicted state and a calculated state based on the coupling characteristics between the three physical fields, thereby reducing prediction difficulty and accelerating prediction speed. The predicted state is estimated online using unscented Kalman filtering and the stack internal state evolution model. The calculated state is calculated online based on the coupling relationship of the three physical fields and the predicted state value, and is used to estimate the predicted state at the next time step. This process is repeated iteratively, correcting the predicted state value based on the deviation between the observer and the actual measurement output until the requirements are met. The method provided by this invention can effectively estimate the internal state distribution of the stack, while solving the problems of high computational cost, difficulty in online application, incomplete observation, and susceptibility to interference in existing methods.

This invention relates to the field of lithium battery SOC estimation. A lithium battery SOC estimation method based on strong tracking adaptive unscented Kalman filtering is proposed. This method introduces multiple suboptimal fading factors to strongly track the observation weights at the current moment, and adaptively updates the noise covariance by real-time correction of noise parameters using residuals. The algorithm uses battery charging / discharging current as input, battery output voltage as output, and battery SOC as the state variable. This invention focuses on the management of retired power batteries and innovatively proposes a strong tracking adaptive unscented Kalman filtering algorithm based on the fusion of multiple fading factors. This algorithm overcomes the limitation of traditional unscented Kalman filtering algorithms, which suffer from large estimation errors when noise parameters are unknown, achieving high-precision SOC estimation while simultaneously optimizing battery pack equalization control.

This invention discloses a robot integrated navigation method based on dual-weighted optimized unscented particle filtering. The method first defines the state vector of the mobile robot and establishes a nonlinear state-space model; then, it performs unscented particle filtering (UPF) initialization to obtain an initial particle set and initial weights, and constructs the importance distribution of the particles; next, it uses unscented Kalman filtering to construct the importance distribution for each particle and samples new particles; then, it combines satellite navigation observation information and updates the particle weights through a maximum entropy-Tukey dual-weighting mechanism to obtain normalized particle weights; finally, it performs particle degradation discrimination and determines whether resampling is needed, and finally outputs the robot navigation state estimation result through weighted fusion. This invention can effectively cope with non-Gaussian noise and outlier interference caused by satellite signal obstruction and multipath effects, significantly improving the state estimation accuracy and stability of the integrated navigation system in complex scenarios.

The application provides a kind of tubing leak detection method and system based on maximum entropy unscented Kalman filtering under coding and decoding mechanism, belong to tubing leak detection field.To solve the problem that the existing oil pipeline leak detection method lacks in detection and positioning accuracy of leakage point due to nonlinear characteristics, non-gaussian noise and limited transmission channel.The application establishes the dynamic model of oil pipeline system, introduces dynamic coding and decoding mechanism to construct recursive filter structure;Using unscented transformation to get priori estimation and error covariance;Then calculate the upper bound matrix of estimation error covariance by matrix inequality, introduce the maximum correlation entropy index to calculate the estimation error and gain matrix;Finally, by setting virtual leakage point to estimate leakage, to determine whether the pipeline leaks and locate the leakage point.Simulation results show that the application can effectively cope with real-time and reliability challenges under complex working conditions, and provides an efficient state estimation method for oil pipeline leak detection.

This invention discloses a dynamic calibration method for fluid pressure sensors based on real-time temperature compensation. The invention constructs a thermo-mechanical coupled digital twin model of the measured fluid pipeline and the sensor. It employs laser-induced fluorescence transient full-field temperature measurement technology to acquire a two-dimensional transient temperature field, simultaneously acquiring high-frequency pressure signals and contact temperature signals from the sensitive core. Through adaptive unscented Kalman filtering using graph convolution and deep reinforcement learning, it achieves time axis alignment and noise filtering for multi-source data with different sampling rates. Real-time closed-loop compensation is performed via hardware-in-the-loop, and feedback is used to correct the parameters of the digital twin model. This invention solves the problems of inaccurate reference in traditional single-point temperature measurement compensation, time misalignment of multi-source data, and asynchronous dynamic compensation, improving the measurement accuracy and calibration reliability of fluid pressure sensors under transient phase change conditions.

The application discloses a UWB positioning method and system based on SLSQP and Newton iteration method, which firstly constructs a UWB ranging positioning model based on a ranging intersection principle, then solves an initial position by using a sequential least squares programming algorithm, and re-groups and switches to a new base station group according to prior map information, so as to reduce multi-region positioning errors, improve positioning continuity, then constructs a'sub' ranging positioning model by using the new base station group, and re-calculates coordinates by using a Newton iteration method, so as to improve system positioning accuracy, and finally processes the coordinates by using an unscented Kalman filtering algorithm, so as to improve the stability of positioning results. The positioning system comprises a network cable, a POE switch, an upper computer, a router and a plurality of UWB base stations and UWB tags, and data required by the positioning method is collected by the system. The UWB positioning method can effectively inhibit the positioning errors of UWB in a complex indoor scene, and improve the accuracy and stability of the positioning system.

This invention discloses a trajectory tracking control method and system based on UKF parameter estimation and ILC command optimization. Addressing the problems of low trajectory tracking accuracy and slow convergence caused by unknown models, time-varying parameters, and measurement noise in non-repeating time-varying systems, this invention uses unscented Kalman filtering to perform real-time filtering and parameter identification on the operating state data, estimating the system's time-varying parameters and the filtered state. Based on the filtered state, the tracking error is calculated, and the transfer matrix in the iterative learning control is dynamically updated using the estimated time-varying parameters to iteratively optimize the control commands. Simultaneously, the weight matrix and iteration step size are dynamically adjusted according to the rate of change of the time-varying parameters to generate the final control commands and drive the actuator. This invention deeply integrates UKF and ILC, achieving closed-loop linkage between parameter estimation and command optimization. It effectively suppresses measurement noise, adapts to non-repeating changes in system parameters in real time, and significantly improves the accuracy, convergence speed, and robustness of trajectory tracking.

This invention discloses a U2U beam tracking method and apparatus for ISAC-enabled UAVs under jitter. The method constructs a jitter model, a communication signal model, and a sensing signal model for the UAV, forming an ISAC-enabled U2U communication system model under jitter. Combining this with beamformer characteristics, an optimization problem is constructed. Through interactive multi-model fusion and unscented Kalman filtering, one-step and two-step prediction angles are obtained. Based on the one-step prediction angle, a first sub-problem is constructed, and the optimal half-beamwidths along the Ψ and Θ axes are obtained, adaptively controlling the transmit beam. The UAV-U, using the two-step prediction angle as a priori, constructs and solves a second sub-problem to obtain the optimal jitter compensation angle, optimizing the receive beam pointing. Based on the controlled transmit beam and optimized receive beam, the UAV-B transmits ISAC signals to the UAV-U, achieving beam tracking and data transmission in the U2U communication system under jitter. This invention solves the problems of insufficient tracking accuracy and poor robustness in existing technologies.