50 results about "State covariance" patented technology

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 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 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 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.

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 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 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.

The invention discloses a real-time optimization scheduling method for opposite conflict of long-tunnel single-lane construction vehicles, and the method comprises the steps: extracting a standard deviation corresponding to a state covariance matrix eigenvalue outputted by a UWB / IMU fusion positioning algorithm, and introducing the standard deviation into dynamic travel time prediction and dynamic safety boundary calculation. The interval prediction of the conflict probability and the self-adaptive adjustment of the security boundary along with the change of the positioning precision are realized; establishing a multi-objective optimization dynamic avoidance decision model which integrates multiple rigid constraints of vehicle staggering platform safety, transportation material timeliness, TBM key process progress, heavy-load vehicle starting and stopping and buffer area smoothness and comprehensively considers three objectives of minimization of construction progress influence, minimization of transportation total delay and maximization of load throughput; the problem that tunnel construction requirements cannot be dynamically met due to the fact that current tunnel vehicle scheduling research is limited in positioning precision, rigid in safety boundary, lack of probability-based conflict advanced early warning capability and dependence on fixed rules when facing a GNSS rejection environment is solved.

The invention belongs to the technical field of inertial measurement unit diagnosis, and particularly relates to an inertial measurement unit fault diagnosis method and system. According to the method, the innovation and the attitude difference are combined, and uncertainty weighting based on covariance is adopted, so that recognition of multiple types of faults is more reliable, the adaptive threshold value is automatically adjusted according to the state covariance and the motion change, the sensitivity and robustness can be balanced in static and dynamic scenes, false alarms and omission are reduced, and the fault diagnosis accuracy is improved. According to the method, the zero offset and the gravity component are removed through preprocessing, filtering is carried out, the attitude representation based on quaternion is matched, the estimation stability in a high-dynamic or complex vibration environment is improved, the zero offset of the gyroscope is used as a state quantity for estimation, slow-varying errors can be compensated in real time, and the accuracy of diagnosis and judgment is improved.

The application provides a non-Gaussian noise suppression method and device with adaptive kernel width, and belongs to the field of inertial base combined navigation algorithm and state estimation. The method solves the problems that the robust filtering method based on fixed kernel width is difficult to adapt to time-varying noise characteristics, and the scheme depending on an optimization algorithm has the problem of insufficient real-time performance. The method comprises the following steps: initializing filter parameters according to a SINS / DVL combined navigation system; performing time updating to obtain a predicted state vector and a predicted state covariance matrix at the current moment; updating a measurement noise covariance matrix through a variational Bayesian method, wherein the measurement noise covariance matrix is modeled as an inverse Wishart distribution; adaptively updating a kernel width parameter according to a filter innovation at the current moment and the measurement noise covariance matrix; and updating a state quantity estimation value and a state covariance matrix at the current moment through a fixed-point iteration algorithm by using the updated kernel width. The method is used in the field of underwater resource exploration.

The present application belongs to the field of civil engineering safety monitoring and Internet of Things technology, and provides a deep and large foundation pit safety monitoring method based on cloud edge collaboration and digital twinning, which comprises the following steps: an edge gateway dynamically and adaptively adjusts the sensor sampling frequency and compressed sampling mode according to the foundation pit deformation; an edge computing server performs real-time anomaly detection by using a lightweight deep learning model; an extended Kalman filter is used to fuse multi-source data and dynamically adjust the warning threshold based on the state covariance; a cloud platform constructs a digital twinning model, inverses soil parameters by using a genetic algorithm, and predicts the deformation trend; and the cloud edge collaboration optimizes the sampling strategy and warning parameters in a closed loop; the present application realizes real-time warning of deep and large foundation pit monitoring, greatly reduces network bandwidth consumption, effectively reduces the false alarm rate, significantly prolongs the endurance time of the sensor node, and significantly improves the real-time performance, accuracy and energy efficiency of the monitoring system.

The embodiment of the specification discloses a target tracking method of a low-altitude strong maneuvering unmanned aerial vehicle, relates to the technical field of target tracking, and comprises the following steps: acquiring a measurement point set and a state estimation value and a state covariance matrix of a previous moment, so as to obtain a current prediction index corresponding to each strong maneuvering model at a current moment through a plurality of preset strong maneuvering models; 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 intramodel correlation and intermodel correlation through a preset hierarchical correlation strategy, and screening a global optimal measurement corresponding to a current moment and a matching strong maneuvering model; performing Kalman filter updating on the current prediction index of the matching strong maneuvering model according to the global optimal measurement corresponding to the current moment, so as to obtain an updated prediction index of the target at the current moment, and target tracking is realized.

This invention belongs to the field of equipment control technology, specifically relating to a power equipment control method and system based on the Internet of Things (IoT), comprising the following steps: S1, acquiring real-time operating data of nodes of each power device in the IoT, constructing a second-order state tensor representing the operating state of each node; projecting the second-order state tensor onto a low-dimensional subspace defined by basis vectors to obtain a low-dimensional state descriptor, wherein the basis vectors are adaptively updated based on the historical state covariance matrix of all nodes; calculating the Wasserstein distance of the low-dimensional state descriptor sequences between nodes, and generating a weighted transition matrix for parameter aggregation between nodes by combining the communication link quality; S2, if the condition number of the local state covariance matrix of a node exceeds a first threshold, then local optimization is initiated. This invention improves the convergence speed and accuracy of the optimization algorithm, and achieves more stable collaborative control of power equipment groups through a two-level control mechanism combining local optimization and global correction.

The invention discloses a robot observation optimization target positioning method with sensor measurement drift. The method comprises the following steps: acquiring sensor measurement data; for the measurement data, an expansion Kalman filter is facilitated for filtering, current state information and corresponding state covariance information of the robot are obtained, and the state information comprises a measurement deviation angle; a gradient descent algorithm is adopted, the moving direction of the robot is optimized by minimizing a set loss function, so that the robot obtains an optimal observation path for a target when moving in the direction, the loss function is constructed by utilizing the robot state information and the state covariance information, and the loss function is constructed by utilizing the state covariance information. And the loss function reflects the cost value generated when the robot moves towards the positive and negative directions of three components of the X axis, the Y axis and the Z axis. According to the method, the error influence caused by measurement drift due to sensor installation errors is eliminated, and the problem of robot target positioning in a three-dimensional information interference environment is solved.

The invention relates to the technical field of three-dimensional measurement and data processing, and discloses a three-dimensional measurement data fusion device and method based on Beidou positioning and laser scanning. The device comprises a Beidou positioning module, an inertial measurement unit, a laser scanning module comprising a holder scanner, a data fusion processing module, a data transmission module and a data storage and visualization module. The data fusion processing module estimates a state vector of the device and a state covariance matrix representing the uncertainty of the state vector based on a kinematic model; and when the uncertainty of the state vector exceeds a preset threshold value, the module actively generates a control instruction to drive the pan-tilt scanner to execute target scanning to obtain high-value observation data. The obtained high-value observation data passes through an observation noise covariance matrix and is used for updating a state vector, so that closed-loop control capable of rapidly converging uncertainty is formed. According to the invention, the fusion precision, the long-term stability and the reliability of a measurement system are improved through adaptive weighting and an on-line external parameter calibration technology.

The application discloses a pedestrian inertial navigation positioning method based on LSTM zero speed detection and EKF, wherein the LSTM is used to identify the pedestrian state as zero speed or walking state according to IMU data; an error state vector is constructed; an error differential propagation equation is constructed according to the error state vector; a state transition matrix is obtained by discretizing the continuous time state equation; the prior state estimation value and the prior state covariance matrix at the current time are calculated; zero is taken as a virtual absolute observation value; the difference between the actual observation speed and the current predicted speed is taken as the measurement innovation; the Kalman gain is calculated according to the measurement innovation; the error state vector is corrected by using the Kalman gain, and the posterior state covariance matrix is updated; the false positive label noise is eliminated from the source, the pure and high-fidelity observation update signal can be provided for the EKF filter, and thus the inertial navigation drift problem in a complex dynamic scene is greatly inhibited.

The invention discloses a Kalman filtering passenger vehicle road adhesion coefficient estimation method based on LTDM. The method comprises the following steps of obtaining vehicle-mounted multi-source data and performing time synchronization; dividing the vehicle-mounted multi-source data into short time window data and long time window data; according to the long-time window data, utilizing a retention-innovation gating recursion mechanism to generate a memory state; generating a context vector for reflecting a matching result of the recent excitation in a long-term experience library according to the short time window data and the memory state; performing positive definite parameterization mapping on the context vector to generate a process noise covariance and an observation noise covariance of Kalman filtering; under a time-varying system model framework, predicting a state including speed and an adhesion coefficient and a state covariance by using a time-varying process noise covariance and a time-varying observation noise covariance; and generating road adhesion coefficient estimation according to the read-out operator and the state. According to the method, the time sequence modeling capability and the time-varying noise adaptive capability are combined, and the real-time performance and smoothness of road adhesion coefficient estimation can be considered.

This invention belongs to the field of inertial measurement unit (IMU) diagnostic technology, specifically relating to an IMU fault diagnosis method and system. This invention combines innovation with attitude differences and employs uncertainty weighting based on covariance, making the identification of multiple types of faults more reliable. The adaptive threshold is automatically adjusted based on state covariance and motion changes, balancing sensitivity and robustness in static and dynamic scenarios, reducing false alarms and omissions. Preprocessing removes zero bias and gravity components and filters the data. Combined with quaternion-based attitude representation, it improves estimation stability in highly dynamic or complex vibration environments. Using gyroscope zero bias as a state variable for estimation allows for real-time compensation of slowly varying errors and improves diagnostic accuracy.

The invention discloses a target state determination method and device, and relates to the technical field of automatic driving. A specific embodiment of the method comprises the steps of obtaining a detection result of a detector on a first type of motion features of a target at the current moment, wherein the detection result comprises a detection state value at the current moment and a detection score representing detection reliability; adjusting an initial value of the measurement noise data according to the detection score to obtain a current value of the measurement noise data; obtaining a state optimal estimation value and a state covariance optimal estimation value of the second type of motion features of the target at the previous moment, and determining a Kalman coefficient at the current moment according to the state covariance optimal estimation value and the current value of the measurement noise data; and determining the optimal state estimation value of the second type of motion features of the target at the current moment by using the optimal state estimation value, the Kalman coefficient at the current moment and the detection state value at the current moment. According to the embodiment, the calculation accuracy of the Kalman filtering algorithm can be improved.

This invention discloses a method for predicting the trajectory of an extraterrestrial surface probe: For the actual state vector x... k Perform a traceless transformation to obtain the state vector set σ k ; will σ k Input a nonlinear system and obtain the predicted state vector set σ k+1 ; for σ k+1 The elements in the matrix are weighted and summed to obtain the predicted state vector based on σ. k+1 The invention involves obtaining the predicted state covariance, updating the predicted state vector, updating the predicted state covariance, performing singular value decomposition on the updated predicted state covariance, obtaining the predicted feature diagonal matrix, normalizing the predicted condition number set based on the calculated predicted condition number, obtaining the normalized predicted condition number set, processing the predicted state covariance set to obtain the regional normalized predicted state covariance set, and adaptively adjusting the state prediction error set to obtain the adjusted predicted state covariance set. This process is repeated to obtain the moving predicted trajectory. This invention can improve prediction accuracy and prediction stability.

The application relates to the field of satellite positioning technology and discloses a high-precision positioning method for a road inspection carrier, which comprises the following steps: acquiring a visible satellite carrier noise variation gradient; in response to the variation gradient crossing a signal degradation threshold, locking the process noise variance parameter and the integer ambiguity parameter of a corresponding satellite branch in a Kalman filter; extracting Doppler frequency shift observation value integration during degradation to acquire a phase offset compensation amount, and correcting the locked integer ambiguity parameter; in response to signal recovery, using the remaining satellites in a fixed solution state to construct a geometric topological constraint equation, checking the corrected integer ambiguity parameter, and restarting positioning calculation; and through the construction of a state maintenance mechanism based on an observation domain topological inhibition, the application prevents degraded signal pollution of a state covariance matrix, enables the system to have a hot start capability at the moment of line-of-sight recovery, and solves the convergence lag and trajectory drift problems caused by ambiguity re-search.

The constant-temperature and constant-humidity air conditioner abnormity monitoring method comprises the steps that different working conditions are divided, and a reference covariant matrix and a vibration quantity coefficient threshold value of the different working conditions are established; calculating a correlation coefficient of the current and the vibration quantity in real time, and establishing a dynamic covariant matrix; according to the dynamic covariant matrix and a reference covariant matrix corresponding to the current working condition, calculating a covariant deviation degree, and if the covariant deviation degree is greater than a set covariant deviation degree threshold value or a correlation coefficient of the current and the vibration quantity is greater than a corresponding vibration quantity coefficient threshold value, judging that the equipment state is abnormal; judging whether an environment state is abnormal or not according to the workshop environment temperature and humidity at the current moment; inputting the workshop environment temperature and humidity in a set period into an isolated forest algorithm to output an unsupervised abnormal score; and air conditioner abnormity grading judgment is conducted. According to the invention, the probability of missing report and false report is greatly reduced, and more comprehensive and reliable abnormity diagnosis is realized.

The application relates to the technical field of state estimation methods of integrated navigation systems, and discloses an adaptive filtering method and device for integrated navigation, which comprises the following steps: obtaining sensor measurement data at a current time; obtaining a predicted state at the current time; constructing a differential feature sequence at a historical time; inputting the differential feature sequence into a hybrid neural network respectively to output a first feature vector and a second feature vector; performing physical constraint parameterization reconstruction based on Cholesky decomposition on the first feature vector and the second feature vector respectively to generate a predicted state covariance matrix and a measurement noise covariance matrix; obtaining Kalman gain at the current time by using a standard analytical formula of Kalman gain; and performing state updating to obtain a posterior state estimation at the current time, so that the technical bottleneck faced in an embedded application scene with a known measurement model, a fixed structure and a low dimension can be solved.

The application relates to a kind of non-gaussian noise under the orbit determination method of spatial non-cooperative maneuvering spacecraft, including obtaining the state prediction value and measurement prediction value of target at current time, construct based on adaptive robust attenuation memory filter;Minimize cost function to obtain modified measurement noise covariance matrix, introduce adaptive fading factor to monitor the residual change in filter in real time, detect whether residual exists impulse maneuver or measurement outlier;According to the deviation degree of standardized measurement residual compared with adjustment parameter and adaptive fading factor, calculate discrimination index, and discriminate maneuver and measurement outlier according to discrimination index;Covariance matrix is corrected by discrimination index;Calculate the Kalman gain matrix at current time, and obtain the state and state covariance estimation value of target according to the measurement information at current time, until the end of task;Make up the existing method under non-gaussian noise filter tracking performance decline, prone to maneuver false detection and other problems.

This invention discloses a robust target tracking method using a dual-entropy fusion multi-random matrix maneuvering extension. The method employs an interactive multi-model fusion architecture. In the fusion step, a cost function is established using the target motion state estimate of the previous time-instance sub-model as the independent variable, based on the correlation entropy criterion. Maximizing this cost function yields the fused target state estimate. Using relative entropy as the criterion, the sum of information gains of each sub-model regarding the target motion state covariance and target morphological distribution parameters is minimized. Then, a correlation entropy cost function is established using the filtered target motion state estimate of the current time-instance sub-model as the independent variable. Maximizing this cost function achieves target motion state estimation fusion. Simultaneously, the total information gain of each sub-model regarding the target motion state covariance and target morphological distribution parameters is minimized again to obtain a weighted estimate, ultimately yielding a robust joint estimate of the target motion state and morphology at the current time.