124 results about "State covariance" patented technology

The invention discloses a UE4-based digital twin model construction system and method, and relates to the technical field of computer vision, and the system comprises a geometric modeling unit which generates three-dimensional model data based on geometric modeling data of a target physical entity, and achieves dynamic LOD rendering level switching through virtualization processing; the real-time data interaction unit is used for analyzing the real-time sensor data flow and generating equipment state data in combination with a transmission operation and maintenance instruction; the simulation engine unit is used for executing rigid body motion and collision simulation based on the equipment state data and outputting a collision event sequence and state transition data by utilizing an event trigger; the machine learning unit is used for training an LSTM (Long Short Term Memory) model based on the collision event sequence and the state transition data, and optimizing state covariance parameters through adaptive Kalman filtering; and the visual interaction unit is used for realizing data synchronization of the command large screen and the mobile terminal through multi-screen collaboration. According to the method, the digital model capable of accurately reflecting physical entity behaviors and characteristics is constructed.

The invention provides a Kalman filtering navigation information fusion method based on intelligent optimization algorithm optimization, and relates to the technical field of integrated navigation, and the method comprises the following steps: obtaining inertial navigation data and auxiliary measurement data of a navigation target at a current moment, obtaining a historical state prediction result and a historical state covariance matrix at a previous moment adjacent to the current moment; based on the inertial navigation data and the auxiliary measurement data, performing adaptive search estimation on the measurement noise matrix of the navigation target to obtain an optimal measurement noise matrix of the navigation target; calculating Kalman gain according to the initial state covariance matrix and the optimal measurement noise matrix; and correcting the initial state prediction result according to the auxiliary measurement data and the Kalman gain to obtain a state prediction result of the navigation target at the current moment and a state covariance matrix corresponding to the state prediction result. The precision of the integrated navigation system can be improved under the condition that no additional sensor is added.

The invention relates to the technical field of new energy battery management, and discloses a sodium ion battery state-of-charge and state-of-health joint estimation method, which comprises the following steps: acquiring a state equation and an observation equation of a sodium ion battery; initializing a state estimation value, a state covariance, a capacity estimation value and a capacity covariance; performing unscented transformation on the state equation to obtain a plurality of key points and weights; a state equation and an observation equation are utilized to spread key points at the # imgabs0 # moment, the state at the # imgabs1 # moment, an observation nonlinear transmission result, a prediction state, an observation estimation value, a corresponding prediction state covariance and a corresponding prediction observation covariance are obtained, Kalman gain at the # imgabs2 # moment is constructed, and an extended Kalman gain matrix is obtained; presetting an innovation length, calculating a historical observation residual error, and constructing a multi-innovation matrix; and based on the extended Kalman gain matrix, the multi-information matrix and the actual observation value at the # imgabs3 # moment, correcting to obtain the sodium ion battery charge state and the battery health state at the # imgabs4 # moment.

The invention provides a sequential invariant extended Kalman filtering method for a navigation positioning task, and belongs to the field of automobile navigation. The problems that efficient fusion of multi-coordinate system observation is difficult to achieve under an IEKF framework, and geometric consistency of covariance transfer cannot be guaranteed are solved. The method comprises the following steps: dividing observed quantities of a sensor according to different coordinate systems of the navigation sensor; constructing a state vector in a Lie group form; modeling the output of the inertial measurement unit; performing state propagation according to a Lie group state equation; calculating propagation of a covariance matrix; establishing a left invariant observation equation and a right invariant observation equation according to the two types of divided matrixes; and synchronously updating state estimation through the left invariant extended Kalman filter and the right invariant extended Kalman filter, and mapping the left invariant covariance matrix and the right invariant covariance matrix by using the common state covariance matrix to complete sequential fusion. The method is mainly used in the automatic driving field.

The invention relates to an information fusion method and device of a multi-source navigation system, computer equipment and a computer program product. The method comprises the following steps: constructing a preset number of sub-models according to sensor systems in the multi-source navigation system, taking the product of the expected credibility and the real-time credibility of the performance of each sensor system as the comprehensive credibility of each sensor system, and determining a state transition probability matrix of the sub-models according to the comprehensive credibility; state prediction updating and measurement updating are carried out on each sub-model, and a system error state and a system state covariance are calculated; and calculating a predicted posterior probability of each sub-model, determining an interactive weight between the sub-models according to the predicted posterior probability and the state transition probability matrix, and correcting and updating the system error state and the system state covariance according to the interactive weight to obtain a final system error state and a final system state covariance. By adopting the method, the robustness of information fusion can be effectively improved.

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

The invention provides a method and a device for performing gas circuit fault detection on a combined power device. The method provided by the invention comprises the following steps: acquiring a measurement parameter at a current moment and a state parameter at a previous moment; inputting the state parameters at the previous moment into a simulation model corresponding to the combined power device to obtain predicted measurement parameters at the previous moment; determining a Kalman gain matrix of the Kalman filter at the current moment according to a pre-constructed state covariance matrix of the Kalman filter at the previous moment, a measurement noise matrix of the Kalman filter at the current moment, a system noise matrix and a combined Jacobian matrix corresponding to the simulation model; the combined Jacobian matrix is obtained based on the combination of the initial Jacobian matrix of the simulation model and the initial Jacobian matrix of the prediction model; and based on a Kalman filter, calculating health parameters according to the health parameters of the previous moment, the measurement parameters of the current moment, the predicted measurement parameters of the previous moment and the Kalman gain matrix of the current moment.

The invention relates to the technical field of medical robots, in particular to a surgical robot and a remote motion center dynamic adjustment method for the surgical robot, and the method comprises the steps: building a kinematics constraint model between a tool axis of a surgical tool and a robot base coordinate system; on the basis of a visual sensor and a hand-eye transformation matrix, a pose measurement value of the remote motion center point under a robot base coordinate system is obtained in real time; fusing a pose state prediction quantity driven by a robot motion instruction and the pose measurement value, and generating an optimal estimation pose and a state covariance matrix of the remote motion center point; according to the state covariance matrix, determining whether the remote motion center point has a large offset; when it is judged that the large-amplitude offset occurs, a safety strategy is triggered to control the surgical robot. According to the technical scheme, dynamic tracking and real-time adjustment of the remote motion center point in the operation are achieved, and the problems that a traditional scheme is insufficient in flexibility and poor in safety are solved.

The invention discloses a method and a system for enhancing the robustness of a multi-source data fusion algorithm, and aims to improve the adaptability to noise and outliers in a multi-sensor data fusion process. According to the method, an adaptive noise mechanism and an outlier detection mechanism are introduced, so that the influence of abnormal observation data is effectively suppressed, and the fusion precision and the system stability are improved. The method comprises the following steps: firstly, a system carries out preliminary state prediction by initializing a target state, a covariance matrix and observation noise; then, the mahalanobis distance of each observation data is calculated based on chi-square test through an outlier detection mechanism, if the observation data deviates greatly, the observation data is judged as an outlier, and the influence of the observation noise matrix on a fusion result is reduced by increasing the observation noise matrix of the observation data; then, an adaptive noise adjustment method is adopted, adaptive factors are calculated in real time according to observation data of each sensor, and observation noise is dynamically adjusted to adapt to changes of the noise level; and finally, state estimation and covariance updating are carried out in combination with fusion algorithms such as Kalman filtering, and data fusion and system feedback are completed. According to the scheme, noise interference and abnormal values in multi-source data can be effectively dealt with, the robustness and the real-time response capability of the system are improved, and the method has wide application prospects.

The invention provides an IMU sensor space-time fusion industrial defect detection system and method based on an SORT algorithm. The method comprises the steps that S1, system initialization and space-time calibration are carried out, a time synchronization model is established, the single pixel size dpixel is calculated, and IMU and camera coordinate system registration is completed; s2, collecting asynchronous data, and obtaining working parameters of a camera and the speed vt of a conveyor belt; s3, constructing an SORT state vector and predicting an IMU enhancement state, detecting defects of a current frame by adopting YOLOV8, and generating a detection set Dt; s4, executing EKF space-time fusion, and predicting and updating a defect state covariance and a position; s5, quantifying the position uncertainty sigma pos, calculating an optimal acquisition period Topt, controlling the line frequency of the camera, and carrying out confidence verification and misjudgment elimination; s6, cloud federated filtering fuses the edge and the cloud state, IMU zero offset is estimated, and cooperation of multiple detection devices and system expansibility enhancement are achieved.

The invention relates to an inter-satellite optimization relative navigation method based on phase compensation and micron-order measurement, which comprises the following steps: respectively carrying out orbit numerical recursion according to states xck-1 and xdk-1 of a primary satellite and an auxiliary satellite involved in a space relative state xrk-1 at a k-1 moment, carrying out conversion to obtain a relative state prediction # imgabs0 # at a k moment, constructing a relative kinetic model, and calculating a Jacobian matrix of a relative state equation; constructing a linear Jacobian matrix of the measurement model to correct filtering error covariance, and after real measurement at the moment k is obtained, calculating a fitting residual error and a gain matrix, and calculating state update and a state covariance matrix update value; after the state is updated, the relative position and relative speed information of the primary satellite and the secondary satellite are obtained, further calculation compensation still needs to be carried out due to deviation of the antenna phase center, and finally corrected space relative navigation information is obtained. According to the invention, through on-line estimation and compensation of the phase center, the output precision of the space precision measurement load is obviously improved.

The invention discloses a node time synchronization and distance measurement method and system of a wireless sensor network, which are used for solving the technical problem of poor time synchronization and distance measurement precision caused by the node time synchronization and distance measurement technology of the existing wireless sensor network. The method comprises the following steps: when a passive node in the wireless sensor network receives a first synchronization signal sent by an active node in the wireless sensor network, the passive node determines first observation data according to the first synchronization signal and sends the first observation data to the active node based on a preset communication condition; and the active node screens the first observation data and the second observation data, determines target observation data, and outputs a target filter state vector for realizing time synchronization between the active node and the passive node in combination with the prediction filter state vector, the prediction state covariance matrix and the preset initial observation noise covariance matrix, constructing a node Euclidean distance matrix according to the state vector of the target filter;

The invention discloses a least square covariance dynamic weighting satellite inertial navigation combined attitude determination method, which belongs to the technical field of combined navigation and mobile carrier attitude determination, is used for satellite inertial navigation combined attitude determination, and comprises the following steps: obtaining and preprocessing original data, constructing a nonlinear observation model, linearizing by using a Gauss-Newton iterative algorithm, and solving; performing time updating of an error state vector and a state covariance based on inertial navigation system mechanical arrangement; and reconstructing a measurement noise covariance matrix of extended Kalman filtering, constructing a measurement information vector, calculating Kalman gain, performing state updating, and outputting an attitude determination result. According to the method, the current epoch least square posterior covariance is introduced for dynamic weighting, so that instantaneous delay-free adaptive fusion is realized, the short baseline noise amplification effect is remarkably inhibited, and the attitude estimation precision, the statistical consistency and the real-time observation quality self-diagnosis capability under the complex sea condition are improved.

The invention relates to the technical field of unmanned aerial vehicle flight control and intelligent navigation, in particular to an unmanned aerial vehicle low-altitude obstacle avoidance route planning method and system, and the method comprises the steps: firstly fusing three-dimensional space data and airspace rules, constructing a signed distance field, a capacity field and a state covariance, generating a channel graph, and calculating a door time lattice capacity upper limit; establishing joint trajectory optimization in the channel graph, combining geometric feasibility, dynamics reachable and covariance-based opportunity constraint separation, outputting a nominal trajectory, and controlling input and door crossing time; aligning the door penetrating time with the door time grid flow by a unification method to obtain the door time slot flow and the dual price; airborne control is executed according to a nominal trajectory and a time sequence constraint closed loop, and capacity change and price change are re-optimized and fed back in a time window under a triggering condition. According to the invention, the safety interval, the punctuality rate and the traffic efficiency are improved.

The invention discloses a target tracking method, active radar equipment and a storage medium. The tracking method comprises the following steps: acquiring a radar echo signal; performing target detection on the radar echo signal, and calculating a current state parameter of a target; based on the current state parameter of the target, calculating the next state optimal prediction value of the target by using an extended Kalman filtering algorithm, and updating the state covariance matrix; and controlling the radar orientation according to the optimal predicted value of the next state of the target to realize target tracking. According to the method, the azimuth angle and the pitch angle of the target at the next moment are predicted by using the extended Kalman filtering algorithm with higher precision, and the radar orientation is controlled through the servo turntable according to the prediction result, so that the radar faces the target direction to realize tracking, the tracking performance of the high-speed radar on the high-speed target is effectively improved, and the tracking efficiency is improved. And the high-speed target tracking accuracy is improved.

The invention discloses a vehicle cooperative positioning method based on robust adaptive high-order volume Kalman filtering, belongs to the technical field of cooperative navigation, is used for processing cooperative navigation data, and comprises the following steps: establishing a system model according to GNSS / UWB tight coupling integration; carrying out nonlinear updating by utilizing a five-order volume rule; a fading factor is derived based on a strong tracking theory, and correction of a prior state covariance is realized; on the basis that a linear regression model approximates a non-linear measurement model, a Huber function is used for reconstructing measurement noise, so that the influence of non-Gaussian noise is inhibited; and optimizing measurement updating of a filtering algorithm according to the corrected priori state covariance and measurement noise. According to the method, good relative positioning performance can be realized under the condition that the system has model errors and non-Gaussian noise interference, and the precision and robustness of vehicle cooperative positioning are improved.

The invention discloses a master node assisted slave node navigation positioning method, relates to a master node assisted slave node navigation method, and belongs to the field of underwater vehicle navigation positioning. The invention aims to solve the problem of poor low-precision node positioning effect caused by the fact that the influence of a node navigation path and a relative navigation path between interactive nodes on the low-precision node positioning precision is not considered when low-precision node positioning error correction is realized by adopting a non-linear filtering algorithm in the prior art. The invention provides an improved navigation algorithm of a low-precision moving node under the assistance of a high-precision node. The method comprises the following steps: screening observation data, adaptively predicting a state covariance matrix in one step through selection of observability so as to improve the requirement of slave node navigation precision, establishing a slave node navigation system model and a process framework under the assistance of a master node, and correcting a self-navigation position state of a low-precision node by adopting an extended Kalman filtering improved algorithm. And the low-precision node positioning precision is improved.

The invention discloses a low-orbit Doppler positioning method and device for dynamically weighting epochs based on an intelligent agent, and belongs to the technical field of satellite navigation positioning. According to the method, an initial position and an initial state covariance matrix of a receiver are determined based on an initial accumulation low-orbit non-cooperative signal of the receiver, an initial point location of a current epoch signal is determined when a new low-orbit non-cooperative signal is received so as to realize time frame synchronization, and a state characteristic of the current epoch signal is determined at least based on the initial point location. The state features at least comprise satellite features, epoch state features, signal state features and positioning state features, determining a weight of a current epoch signal based on the state features using a pre-training agent, and determining a real-time position of the receiver based on the current epoch signal and the weight of the current epoch signal using a recursive algorithm, according to the invention, adaptive empowerment based on the multi-dimensional features of the epoch signal is realized, the accuracy of the determined epoch signal weight is improved, and the positioning precision is improved.

The combination navigation post-processing covariance inheritance iterative filtering method and system provided by the embodiment of the application are based on the same set of extended Kalman filtering engines, covariance inheritance and direction switching control are used to realize iterative operation of multiple independent complete forward and reverse filtering, and finally high-precision navigation trajectories are fused and output. A preset covariance cold start is used to perform first forward filtering, and a state covariance at the end of filtering is obtained. When switching in reverse, the state covariance is used to replace the preset value as an initial covariance, so that the Kalman gain is prevented from being too large and the filter transient is prevented from being unstable. The first forward state covariance is fixed and inherited every time the direction is switched, the filter state keeps the current filtering end epoch value, each filtering is independently and completely operated, the state estimation, the covariance matrix and the state transition matrix of each direction filtering are not called, and the preset iteration number is reached. The state quantity sequence obtained by the last forward and reverse filtering is averaged according to the epoch, and the final trajectory is output.

The invention belongs to the field of multi-agent co-localization, and provides a manifold-null space constraint-based co-localization state estimation method and system, and the method comprises the steps: carrying out the state prediction of multiple agents based on the data of an inertial measurement unit at a current moment, and obtaining a prediction reference state and a prediction error state covariance matrix; performing adaptive non-line-of-sight processing according to the actual distance measurement value and the predicted distance measurement value to obtain a measurement noise covariance matrix; performing consistency monitoring on the prediction error state covariance matrix according to the measurement noise covariance matrix to obtain an expanded prediction error state covariance matrix; performing state updating on each agent to obtain an updated reference state and an updated error state covariance matrix; and performing null-space projection on the global covariance matrix by using a projection operator to obtain an updated global covariance matrix. According to the method, the problem of numerical instability caused by inconsistent covariance propagation and unobservable degree of freedom in a multi-agent system is solved.

Certain aspects of the present disclosure provide techniques for uncertainty estimation, such as for deep learning (DL)-based object tracking systems. A method generally includes processing, with a deep learning network, an input state associated with an object to predict an output state for the object, wherein the output state comprises a plurality of state estimates for a first time period; and generating a state covariance based at least in part on estimated covariance between at least two state estimates of the plurality of state estimates, wherein the state covariance represents an estimated uncertainty associated with the output state.

The present application provides a target tracking method, device and equipment combined with an uncertain area. The method includes: if the observation information of the target is obtained at the current update moment, the Kalman filter state estimation result is updated according to the obtained observation information; otherwise, through a preset state model, the most recent Kalman filter state estimation result is projected from the corresponding moment to the current update moment to obtain a new state vector and state covariance matrix. The center of the prediction area is determined according to the position coordinates therein, and the major axis length, minor axis length and inclination angle of the prediction area are determined according to the position sub-matrix therein. The preset state model assumes that the target moves in a straight line within a preset time period, and the movement speed is affected by exponential decay and random acceleration perturbation. Through the present application, after the observation information is lost, a prediction area with a smaller area can be given, and the probability that the target is within the prediction area is higher, significantly improving the confidence of the lost target estimation.

This application provides a GNSS, 5G, and IMU fusion positioning method and electronic device. The method includes: constructing an event queue based on the reception times of GNSS observation data, 5G observation data, IMU measurement data, and SSR enhancement information; performing timeliness modeling on the SSR enhancement information to obtain SSR correction packets; generating a parameter set based on a preset encoder and a hybrid expert model; performing asynchronous fusion updates according to the time sequence of the event queue, using IMU measurement data to drive state prediction, to obtain a fused state vector and a state covariance matrix; outputting the position state vector in the fused state vector as the fused positioning result; and outputting the sub-block in the state covariance matrix corresponding to the position state vector as the uncertainty of the fused positioning result. This application can solve the problem that existing methods are unable to maintain positioning accuracy, continuity, robustness, and system stability in the long term.

The invention discloses a visual inertia filtering method based on manifold processing, and belongs to the field of mobile robot positioning, and the method comprises the steps: obtaining updated pose information based on an inertia IMU sensor; performing Lie group construction based on the updated pose information and the position information of the plurality of landmark points to obtain a complete state of the robot containing deviation information at the current moment; on the basis of the Lie group and the deviation information, uncertain representation, a state model and a measurement model of the current moment of UKF-based invariant Kalman filtering are constructed; updating the state model of the next moment based on the state model; updating a state covariance matrix decomposition factor at the next moment based on the uncertainty of state propagation and the process noise covariance matrix; and if the visual observation data of the landmark exists at the next moment, calculating an observation value based on the measurement model and updating the state and the state covariance matrix decomposition factor at the moment by combining the observation value, otherwise, repeating the step of updating the state model and the state covariance matrix decomposition factor at the next moment.

The application relates to the technical field of control, in particular to a kind of excimer lamp cooperative control method based on multi-source data fusion.The method comprises the following steps: obtaining the multi-source sensing data of excimer lamp system, initializing system state vector, state covariance matrix and the inverse Wishart distribution prior parameter of process and measurement noise covariance matrix, obtaining predicted state mean and predicted covariance matrix based on posterior state estimation, calculating innovation, updating process and measurement noise covariance matrix, calculating state-measurement mutual covariance matrix and measurement innovation covariance matrix, determining optimal gihonov regularization parameter and calculating kalman gain, updating state estimation and state covariance matrix, and generating operation instruction of each excimer lamp based on the updated state.The scheme of the application can overcome the limitations of standard unscented kalman filter, ensure the reliability of kalman gain, and thus improve the uniformity and stability of system output light field.

The invention belongs to the technical field of rigid body posture estimation, and discloses a rigid body posture estimation method, system and device based on generalized correlation entropy geometric filtering, and the method comprises the steps: obtaining target motion information, and building a discrete nonlinear kinematics model; the discrete nonlinear kinematics model is initialized; sigma points of a posterior state covariance matrix and a process noise covariance matrix are generated respectively, and the sigma points are propagated and updated through manifold fast unscented transformation; obtaining a prediction state mean value, a priori state covariance matrix and a square root of the priori state covariance matrix; updating the central parameters based on the information of the historical moments; calculating a pseudo measurement matrix and updating the gain; correcting the state estimation by using the gain; judging whether convergence occurs or not, and if not, continuing iteration; otherwise, outputting a posterior state estimation value as a final rigid body posture estimation result at the moment. According to the method, the problem that the robustness and precision of rigid body posture estimation in a non-zero mean value and non-Gaussian noise environment are reduced is effectively solved.

The invention discloses a battery discharge rate and temperature rise prediction method, and belongs to the technical field of battery prediction. Comprising the following steps: acquiring polarization resistance Rx, polarization capacitance Cx, open-circuit voltage VOC and internal resistance Rs of a battery, building an equivalent circuit model, optimizing the equivalent circuit model by using a relation curve L to obtain an optimized model, and calculating the polarization resistance Rx, the polarization capacitance Cx, the open-circuit voltage VOC and the internal resistance Rs of the battery. Solving and iterating the open-circuit voltage VOC corresponding to the discrete temperature by using a square root of a state covariance matrix of a square root volume Kalman filtering algorithm, introducing a compensation model, optimizing an optimization model by using the compensation model to obtain a pre-estimation model, calculating a theoretical temperature rise value of the pre-estimated discharge depth in combination with the pre-estimation model, and calculating the temperature rise value of the pre-estimated discharge depth in combination with a current cell temperature rise deviation. The current environment temperature is used for predicting the temperature of the discharge tail end. According to the invention, the parameters of the prediction model are dynamically adjusted according to the temperature change of the actual operation environment of the battery, and the prediction accuracy of the discharge end temperature of the battery is improved.

This paper proposes a method and device for adaptively modeling the uncertainty of LiDAR point, line, and surface features. This method constructs a maintainable parameterized local subgraph by parametrically extracting and tracking plane / line features. The uncertainty of the point cloud and parameterized plane / line features in the local subgraph is then modeled, thereby adaptively determining the observation covariance. By rationally modeling LiDAR variance, this method enables better integration with other sensors, effectively improving GNSS / INS / LiDAR positioning performance in various complex urban environments and addressing the inconsistency of LiDAR Inertial Odometry (LIO) state covariance.

The invention discloses a cross-water-area bridge deformation solving method based on Beidou No.3 / GNSS inversion, and particularly relates to the technical field of bridge monitoring. The method comprises the following steps: establishing an observation equation by combining all satellites based on satellites which can be observed by a bridge structure monitoring station; solving the observation equation by using a matrix method to obtain a prediction state vector of the deformation quantity of the bridge structure and a covariance matrix of the state vector; in consideration of the influence of gross error of a Beidou No.3 / GNSS observation value, a self-adaptive factor is provided to correct a predicted value of a state vector; obtaining a vector K and an updated state vector covariance; introducing a robust factor M to determine the weight of each type of observation values in consideration of gross errors in the Beidou No.3 / GNSS carrier phase observation values; updating the matrix K and the state covariance matrix by using the corrected measurement covariance matrix R; and calculating the updated state vector. According to the technical scheme, the problem that a conventional Beidou / GNSS data processing method is low in precision is solved, and the precision of bridge deformation calculation is improved.

The embodiment of the invention discloses a target tracking method for a low-altitude strong-maneuvering unmanned aerial vehicle, and relates to the technical field of target tracking, and the method comprises the steps: obtaining a measurement point set, and a state estimation value and a state covariance matrix of a previous moment, so as to obtain a state estimation value of the previous moment through a plurality of preset strong-maneuvering models; obtaining a current prediction index of the current moment corresponding to each strong maneuvering model; determining an elliptical correlation gate corresponding to each current prediction index, so as to determine a candidate measurement point set falling into the elliptical correlation gate according to the measurement point set; performing intra-model association and inter-model association through a preset hierarchical association strategy, and screening a global optimal measurement corresponding to the current moment and a corresponding matching strong maneuvering model; and according to the global optimal measurement corresponding to the current moment, performing Kalman filtering updating on the current prediction index of the matched strong maneuvering model to obtain an updated prediction index of the target at the current moment so as to realize target tracking.