528 results about "Kaiman filter" patented technology

The invention discloses an unmanned ship multi-agent collaborative obstacle avoidance method and system for complex sea conditions, and relates to the field of unmanned ship multi-agent collaborative obstacle avoidance, and the method comprises the steps: obtaining the real-time data of each unmanned ship and the surrounding environment; generating a candidate obstacle target point cloud cluster list based on a density clustering algorithm; on the basis of a Kalman filter, real-time absolute motion state estimation of the candidate obstacles is obtained, and an obstacle feature list is output; determining a safety radius compensation amount required by autonomous obstacle avoidance of each unmanned ship; obtaining the safe sailing space of each unmanned ship at the current moment; constructing a global synthetic potential field, and generating a group of optimal alternative paths of the unmanned ship from the current position to the target point; on the basis of adopting a consensus binding algorithm, an unmanned ship multi-agent collaborative obstacle avoidance path reaching a consensus is obtained. The method has the advantages that safe, efficient, cooperative and consistent intelligent obstacle avoidance of a multi-unmanned-ship cluster in a real complex marine environment is realized.

The invention discloses a thermal management strategy management method and system based on an energy storage battery system, and relates to the technical field of battery thermal management, and the method comprises the steps: collecting temperature data, carrying out the preprocessing, generating and recording a measurement result and a convective heat transfer coefficient, taking the measurement result and the convective heat transfer coefficient as the input of a Kalman filter, and outputting an optimal estimation result of a battery. The temperature data refer to the environment temperature and the core temperature of the single battery, based on the optimal estimation result of the battery, establishing a fuzzy rule base for iteration and updating, outputting a dynamic temperature difference threshold value, constructing a three-dimensional thermodynamic potential function, calculating the cosine similarity of a cooling driving force vector and a cooling mode, and correcting the cosine similarity to obtain a cooling driving force vector. The mode switching list is generated, the cooling equipment decision mode is generated, starting judgment and optimization of the cooling mode are carried out, and the intelligent level, safety and energy efficiency ratio of thermal management of the energy storage battery system are remarkably improved.

The invention discloses a shielding target tracking method based on dynamic size attenuation perception, and belongs to the technical field of computer vision and target tracking. The method comprises the following steps: continuously tracking a target by using a baseline tracker to obtain state information of the target in each frame; judging whether the target is lost or not according to the state information, if not, performing a conventional tracking mode, and directly outputting a target position through a baseline tracker; and otherwise, if the target is lost, judging whether the target is shielded, and if the target is shielded, performing motion state estimation and trajectory prediction according to the shielding starting moment and a Kalman filter to obtain a target prediction position. According to the invention, the real motion state of the target can be estimated more accurately, and the track prediction precision of the target during shielding is improved, so that the capture probability during target reproduction can be improved.

The invention discloses a multi-target tracking method combining camera motion compensation and pseudo depth estimation, belongs to the field of computer vision, and is suitable for a complex automatic driving road environment. The method comprises the following steps: constructing a training set and a test set; detecting the image by using a deep learning detector and extracting features; a Kalman filter is adopted to correct a motion modeling state vector, and the target position and size prediction precision is improved; solving a homography matrix through feature point matching, performing global camera motion compensation, and reducing camera jitter and displacement interference; target pseudo depth information is calculated, hierarchical cascade matching is carried out, and association performance in dense and shielding scenes is optimized; a three-level cascade strategy is adopted to complete high confidence degree, low confidence degree and residual target matching in sequence; and finally, outputting a tracking result with a detection frame and identity information to obtain a trained model. According to the invention, accurate detection and stable tracking of multi-category targets can be realized in a complex environment, and identity switching is effectively reduced.

The invention discloses a multi-modal measurement data fusion method for an unmanned aerial vehicle. The method comprises the following steps: synchronously acquiring multi-modal measurement data through a plurality of sensors carried on the unmanned aerial vehicle; performing preprocessing including time synchronization and space alignment on the data; based on the environmental reliability evaluation model, dynamically analyzing reliability parameters of each sensor in the current environment, and generating a dynamic weight for each sensor data; and utilizing a fusion module to carry out adaptive weighted fusion on the preprocessed data according to the dynamic weight, and generating a fusion result containing unmanned aerial vehicle global pose estimation and an environment map. The device comprises functional modules corresponding to the steps. The core of the method is that through dynamic reliability evaluation and weight generation, the weight is introduced into the Kalman filter as an observation noise covariance adjustment factor, adaptive optimization of the fusion strategy to the environment is realized, and the pose estimation precision, the system robustness and the long-term operation stability of the unmanned aerial vehicle in a complex scene are remarkably improved.

The invention provides a multi-target tracking method and device based on a large model. The method provided by the invention comprises the following steps: acquiring a current frame image of a video, detecting a target in the current frame image through a detector, and generating a target detection frame and a corresponding confidence score; based on a dynamic equation of a Kalman filter, predicting the position of a track fragment in the current frame of image according to the track fragment of the previous frame of image; when the target detection frame is an effective detection frame, associating the target detection frame with the predicted trajectory fragment based on a feature coordinate matching method, and updating the position of the trajectory fragment through an observation equation after successful association; when the target detection frame is not the effective detection frame, generating a mask fragment of the target based on a mask fragment updating method, associating the mask fragment with the predicted trajectory, and updating the position of the trajectory fragment; and outputting the tracking result of the current frame, repeatedly executing the step of outputting the tracking result of each frame, integrating the tracking results of all single frames, and generating a complete tracking trajectory of all targets in the video.

The invention provides a satellite weak and small target on-orbit observation system and method based on intelligent closed-loop feedback, and the system comprises a detection module which carries out the image enhancement and time sequence consistency enhancement of an infrared image, obtains an enhancement feature, and obtains a candidate target set based on the enhancement feature; the tracking module is used for carrying out target matching in the search area; when the matching succeeds, the candidate position is used as an observation value, and the observation value is input into a Kalman filter to obtain a target state vector; when the matching fails, taking a prediction state of the Kalman filter as a target state vector, expanding a search window by taking a prediction position as a center, and executing re-identification; the control module is used for mapping the target state vector into an attitude error and generating a control instruction by adopting a single-neuron self-adaptive PID (Proportion Integration Differentiation) controller; and the closed-loop scheduling module is used for dynamically adjusting operation parameters of at least one module according to the execution error and the detection confidence coefficient. According to the invention, the continuous tracking precision and attitude control stability of the weak and small target are improved.

The invention discloses an automatic driving carrying equipment scheduling system and method for an industrial robot. The system comprises a plurality of automatic driving carrying devices, a central dispatching device and corresponding communication modules. The system adopts a multi-agent reinforcement learning framework and combines a graph neural network processing environment topological structure to realize dynamic path planning and multi-device collaborative scheduling; integrating an energy consumption prediction mechanism based on a Kalman filter, and bringing energy consumption factors into a task allocation decision; meanwhile, a fault-tolerant management mechanism based on a distributed account book and federated learning is established, and fault detection and rapid recovery are achieved. The technical modules are deeply coupled, and a unified collaborative optimization framework is formed through reward function design, utility function optimization and fault probability calculation of multi-agent reinforcement learning. According to the method, the problems of poor dynamic environment adaptability, isolated decision making of each module, extensive energy consumption management and the like in the prior art are effectively solved, and the overall efficiency, energy efficiency and reliability of a scheduling system are remarkably improved.

The invention discloses an unmanned aerial vehicle tracking method and device based on vision and point cloud fusion and a medium, and relates to the field of unmanned aerial vehicles, and the method comprises the steps: building a total energy function based on each frame of binocular image and each frame of laser radar point cloud, and taking the minimum total energy function as a target, and determining each frame of camera-radar extrinsic parameter matrix; inputting the binocular image sequence into a target detection model to obtain a plurality of target detection results corresponding to each frame of binocular image, and obtaining a bounding box through three-dimensional coarse positioning; projecting the bounding boxes into a radar coordinate system by using a projection expression based on the camera-radar extrinsic parameter matrix to obtain three-dimensional positions of the plurality of bounding boxes in the radar coordinate system; and processing the local point cloud subset by using a bisecting K-means algorithm and a Kalman filter to obtain three-dimensional positions and attitude parameters of all target unmanned aerial vehicles, and completing unmanned aerial vehicle tracking. According to the invention, the detection precision and tracking precision of the unmanned aerial vehicle can be improved in a GNSS limited environment.

The invention discloses a battery peak power prediction method and system based on multi-modal data coupling mapping, and relates to the technical field of lithium ion power battery management. The method comprises the following steps: acquiring electrochemical parameters and thermophysical parameters of a battery to be detected, and constructing a single-particle thermal coupling model; injecting a plurality of sinusoidal current disturbances with different frequencies into the battery to be detected, synchronously sampling voltage and current, and extracting an impedance real part under each frequency; mapping an impedance real part and the decomposed total DC internal resistance according to a mapping relation among impedance, temperature and aging; performing closed-loop correction by using an extended Kalman filter to obtain a dynamic total internal resistance; and calculating the maximum allowable current according to the dynamic total internal resistance based on the current open-circuit voltage and cut-off voltage constraint, and further outputting the instantaneous peak power of the load end. According to the method, accurate online estimation is carried out on the instantaneous peak power of the battery under the conditions of the full life cycle and the full weather of the vehicle.

The invention provides a ship path tracking and rolling comprehensive control method and device based on a straight-wing propeller, belongs to the technical field of ship motion control, and solves the technical problem that path tracking and rolling control precision and efficiency of a ship are insufficient due to environmental disturbance. Constructing a four-degree-of-freedom ship motion equation, constructing a four-degree-of-freedom ship motion model with interference and measurement noise, reconstructing a ship motion state by adopting a Kalman filter, calculating a path tracking error by utilizing a self-adaptive sight guidance algorithm, and generating an expected course angle; determining a longitudinal thrust, a rolling stabilizing moment and a course control thrust; and the revolution speed and the rotation swing angle of the straight wing propeller are optimized, and the rotation swing angle is recalculated. The method is suitable for the ship provided with the straight-wing propeller, and high-precision path tracking and efficient rolling stabilization are achieved by controlling the revolution speed and the swing angle of the propeller.

The invention relates to a multi-modal data fusion method and device, computer equipment, a computer readable storage medium and a computer program product. The method comprises the following steps: respectively carrying out feature extraction on image input data from at least two modes to obtain an image feature vector of each mode; on the basis of the image feature vector of each mode, Dirichlet distribution parameters corresponding to each mode are generated, the distribution difference between the modes is evaluated, and the Dirichlet distribution parameters are used for representing the evidence intensity of each category in the classification or clustering task; according to the distribution difference, fusing the Dirichlet distribution parameters of each mode to obtain a basic probability distribution result after fusion; and dynamically adjusting the basic probability distribution result by using a Kalman filter, and outputting a final classification or clustering result. By adopting the method, the fusion weight of each modal data can be dynamically adjusted, and the accuracy and reliability of a multi-modal fusion system are improved.

The invention discloses a self-adaptive integrated navigation method based on geometric accuracy factors, which belongs to the technical field of underwater navigation and positioning, is used for underwater navigation and positioning, and comprises the following steps: initializing an inertial navigation system and an integrated navigation filter, and setting a state vector and a noise covariance matrix; predicted navigation parameters of the recursive carrier are mechanically arranged through inertial navigation, and the state transition matrix is used for state prediction; and dynamically adjusting an observation noise covariance matrix according to the geometric precision factor value, further calculating a Kalman gain, performing optimal estimation and correction on a prediction state by fusing acoustic observation information, and finally outputting a high-precision carrier position, speed and attitude. According to the method, the statistical characteristics of observation noise are dynamically remodeled by calculating and feeding back geometric precision factor values in real time, so that the Kalman filter has the capabilities of knowing the own geometric situation and adjusting the trust degree of each information source, and the global optimal navigation precision and reliability are realized under any motion track.

The invention discloses a deformation monitoring method and a deformation monitoring device for a concrete tower drum. The deformation monitoring method comprises the following steps: synchronously acquiring wavelength drift and accompanying temperature of a pre-embedded fiber grating sensor and calibration point coordinates of external visual equipment; carrying out temperature compensation on the wavelength drift to calculate the total mechanical strain; accumulated creep is calculated based on concrete rheological characteristics and stripped from total mechanical strain, and effective elastic strain is obtained; fitting the curvature by using the effective elastic strain and reconstructing the theoretical reference displacement of the tower drum; and inputting the theoretical reference displacement and the calibration point coordinates into a Kalman filter, and outputting a final real deformation amount. According to the method, the creep component is accurately stripped, so that false deformation and false alarm in long-term monitoring are eliminated; and meanwhile, the absolute coordinates of the external visual data and the all-weather characteristics of the internal optical fiber data are combined for fusion correction, so that the problems that a traditional integration method is large in accumulative error and visual monitoring is easily interfered by the environment are solved, and high-precision and high-robustness structural deformation monitoring is realized.

The invention relates to a hexapod robot leg impedance control method and device, equipment and a medium, and relates to the technical field of automation control, and the method comprises the steps: training a primary function neural network through domain adversarial invariant learning based on a force-displacement compensation data set; a robot contact force deviation value is calculated, the contact force deviation value is input into the primary function neural network to extract environment invariant features, a Kalman filter is utilized to estimate an adaptive coefficient on line according to the environment invariant features, and a displacement compensation amount is calculated in combination with the environment invariant features and the adaptive coefficient; and the displacement compensation amount is injected into a robot impedance control loop, and leg movement of the hexapod robot is adjusted. According to the method, the environment invariant features are extracted, so that the method can better adapt to various complex terrains, the sensitivity to environment parameters is reduced, and the urgent requirements of the hexapod robot on high robustness, high precision and rapid self-adaptive contact control in actual operation are met.

An unmanned aerial vehicle target detection tracking method based on intelligent image matching comprises the steps that an unmanned aerial vehicle collects images to make a data set, a model file is obtained through twin neural network training, the model file is loaded to detect a video stream, a current frame target position and detection frame information are obtained, whether the frame is a first frame is judged, and if yes, the target is detected; using a target position information result to initialize a kernel filtering algorithm, and constructing a measurement vector; if not, calculating correlation by using a kernel correlation filter, taking the highest response position as a prediction bit, and constructing a measurement vector; calculating a Kalman gain, and inputting the Kalman gain into a Kalman filter to obtain a predicted state estimated value = (); comparing y-axis deviations of the detector and the tracker, and setting a threshold value for judgment; according to the method, target tracking under resource limitation is realized through light-weight twin network detection in combination with kernel filtering and improved Kalman filtering, the problems of high target loss rate and low precision are solved, and the method is suitable for the fields of military reconnaissance, intelligent autonomous attack and the like.

A lithium ion battery thermal management method based on physical perception and entropy cooperation multiple agents comprises the steps that a lithium ion battery electric-thermal coupling model containing data driving compensation is established, and a full-state space system equation describing the dynamic characteristics of a battery is established; constructing a TCN-Transformer hybrid neural network fused with a physical constraint mechanism, and carrying out online identification on parameters in the electric-thermal coupling model by adopting the network to obtain real-time parameters; based on the full-state space system equation and the real-time parameters, constructing a minimum error entropy adaptive extended Kalman filter optimized by an entropy cooperative multi-agent flexible Actor-Critic algorithm, and performing joint estimation on the state of charge of the battery and the temperature of the battery; and based on a joint estimation result, constructing a TD3 deep reinforcement learning control algorithm embedded with a microsecurity layer, and realizing the self-adaptive thermal management direct control of the lithium ion battery under rule guidance through the algorithm. According to the invention, accurate, effective and safe thermal management control of the lithium ion battery is realized.

The invention discloses a millimeter-wave radar fine-grained human body pose sensing method based on multi-dimensional feature extraction, and belongs to the field of computer vision and radar sensing, and the method comprises the steps: managing a millimeter-wave radar sensor through an equipment access module to generate a 4D point cloud; the target tracking module performs multi-target tracking on the 4D point cloud, predicts a target position by using a Kalman filter, divides point cloud affiliation through a gate function, creates a new target by using DBSCAN clustering, eliminates continuous targets without point cloud affiliation, and obtains a target center position; the human body pose detection module converts point clouds to a local coordinate system for normalization based on a target center position and 4D point clouds, fuses continuous multi-frame point clouds, and inputs a deep learning network of PointNet and U-Net improved based on an attention mechanism to output human body pose joint point coordinates. According to the invention, the human body pose detection precision in a limited data mode is improved, and a human body health monitoring function available to a home environment is realized.

The invention discloses a sensing target motion compensation method, system and device based on high-precision positioning information and a medium, and mainly relates to the technical field of target motion compensation. The method is used for solving the problems that an existing motion compensation method mainly depends on chassis information of a vehicle, a wheel speed meter depended by the existing motion compensation method is prone to being affected by external factors, and accumulated drifting generated by purely depending on integral calculation of pose changes cannot meet the long-period and high-precision sensing requirements of an intelligent driving system. Comprising the steps of calculating a relative motion transformation matrix from a data acquisition moment to a data processing moment, and further calculating coordinates of a sensing target at the data processing moment; inputting the covariance matrix and the moment state at the data acquisition moment into a Kalman filter to obtain a predicted moment state and a predicted covariance matrix at the data processing moment; and calculating the moment state of the sensing target after data processing moment compensation by using the coordinates of the sensing target at the data processing moment, the predicted moment state at the data processing moment and the predicted covariance matrix.

The invention discloses an adaptive Kalman filtering charging pile electric energy metering temperature compensation method and system, and the method comprises the steps: carrying out the filtering of observation vector data through a Kalman filter, and carrying out the prediction and updating, so as to obtain compensated voltage and current; performing temperature compensation on the process noise covariance and the observation noise covariance; updating the process noise covariance and the observation noise covariance after innovation optimization at the current moment according to a temperature sensitive weight factor; calculating a process noise covariance and an observation noise covariance after maximum mission estimation optimization; and weighting the process noise covariance and the observation noise covariance by adopting a progressive smoothing factor so as to update the process noise covariance and the observation noise covariance at the next moment. According to the method, high-precision real-time electric energy metering in the charging process is achieved by dynamically modeling the influence of temperature on charging equipment and optimizing filter parameters, and the strict requirements of electric vehicle charging facilities for electric energy metering precision and reliability are met.

The invention discloses an indoor positioning method and system based on 5G channel characteristics and pedestrian dead reckoning, and belongs to the technical field of indoor positioning. The method comprises the following steps: firstly, converting input complex 5G channel impulse response (CIR) data into a two-dimensional characteristic matrix; inputting the two-dimensional feature matrix into a deep convolutional neural network to perform feature transformation and obtain a 5G positioning position track, and then performing time sequence smooth optimization on the 5G positioning position track by using a Kalman filter to obtain an optimized 5G position track; according to the invention, a deep convolutional network is adopted to fully extract amplitude-phase composite features of 5G channel impact response CIR, and machine learning step length measurement and calculation of multi-dimensional gait features are combined to accurately position an indoor position track, so that the utilization efficiency of positioning data is improved; and intelligent complementation can be carried out between the optimized 5G position trajectory and the dead reckoning trajectory, and the indoor positioning precision and positioning stability in a complex indoor environment are improved.

The invention discloses a multi-source autonomous navigation system and method with embedded carrier dynamic characteristics, and the method comprises the steps: building a carrier dynamic model in a dynamic modeling stage; in a filter prediction stage, a Kalman filter driven by dynamics is established, a carrier execution mechanism control signal is periodically read as filter input, and a state vector and a covariance matrix are predicted in each sampling period; a filter updating stage: calculating information of each sensor and a carrier maneuvering strength index, constructing a dynamics consistency judgment quantity, adaptively adjusting an observation variance matrix, updating a filtering state according to observation information of the sensor, and realizing multi-source information fusion; and a fault detection and isolation stage: constructing a multi-source consistency index, and judging the health state of the navigation system. According to the invention, the problem that the performance of a traditional navigation system is reduced when a carrier platform violently moves or is disturbed by external force of the environment is solved, and robust high-precision navigation under GNSS denial, sensor failure or complex dynamic conditions is realized.

A model predictive lane centering (MPLC) control system with reference switching and disturbance rejection includes: an MPLC application having control logic that utilizes model predictive control (MPC) for selectively tracking static and dynamic references, adapts MPC weights and constraints for trajectory tracking when the system switches between static and dynamic references. A Kalman filter estimates a lateral force disturbance, a yaw moment disturbance acting upon a vehicle, and a measurement bias corrupting a measured yaw rate. The system detects anomalies in MPC reference trajectory and adjusts actuator constraints in response to detected anomalies. The system actively and continuously adjusts actuator outputs, causing the vehicle to track and follow a current lane center, and on receiving a lane change command, smoothly changes lanes using vehicle actuators to alter position and alter a vehicle trajectory to enter an adjacent lane before returning to tracking and following a center of the adjacent lane.

An example device for detecting objects includes a processing system configured to receive values from one or more sensors of a vehicle; calculate a normalized innovation squared (NIS) value using the values from the one or more sensors and a predicted state formed by an object tracking unit of the vehicle; determine weight values to be used to weight the values from the one or more sensors and the predicted state according to a comparison of the NIS value to one or more thresholds; and apply the weight values to the values from the one or more sensors and the predicted state to determine an updated state of positions of objects near the vehicle. The device may determine the weight values using a Kalman Filter or Covariance Intersection, based on a comparison of the NIS value to the thresholds.

The invention discloses an IMU (Inertial Measurement Unit) drift error calibration method for long-term attitude measurement of an industrial robot. The IMU drift error calibration method comprises the following steps: deducing a pose matrix between a robot base coordinate system and a navigation coordinate system; deriving a pose matrix between a robot tail end coordinate system and a sensor body coordinate system; kinematics modeling is conducted on the industrial robot, and motion parameters are obtained; deriving a homogeneous transformation matrix between two adjacent joints according to the kinematic parameters; acquiring actual joint corner information when the robot runs and actual information measured by a tail end IMU when the robot runs; wherein the actual information of the IMU comprises actual angular velocity information and actual attitude information; the collected actual joint rotation angle information is substituted into the homogeneous transformation matrix, theoretical attitude information of the tail end of the robot is obtained, and the theoretical attitude information is converted into a navigation coordinate system; converting the theoretical attitude information converted into the navigation coordinate system into an Euler angle; designing a Kalman filter; according to a Kalman filter, data fusion of theoretical attitude information and actual attitude information is completed, and calibration is achieved. According to the method, joint angle information, the industrial robot end theoretical attitude obtained through forward kinematics calculation of the kinematics model and actual attitude data obtained through IMU measurement are fused, and accumulated error calibration formed in long-term attitude measurement of the IMU is achieved.

The invention provides an FSO multi-sensor fusion method based on unscented Kalman filtering and an RBF neural network, and the method comprises the steps: carrying out the unscented Kalman filtering time updating, and carrying out the measurement of a noise covariance adjustment mechanism and an event triggering mechanism based on light intensity self-adaption; the unscented Kalman filter measures and adjusts confidence weights of the infrared camera and the four-quadrant detector according to the channel state provided by the avalanche photodiode, and processes data output by the infrared camera and the four-quadrant detector according to the confidence weights; then, the posterior state estimation of the unscented Kalman filter at the current moment is output, and a position component is extracted and transmitted. Normalized light intensity reflecting channel quality is used as a key state to carry out synchronous estimation, an RBF neural network is used to carry out dynamic modeling and compensation on nonlinear errors of a four-quadrant detector, and a self-adaptive unscented Kalman filtering framework with a line learning capability is constructed. And finally, a virtual sensor output with high update rate, high precision and high sensitivity is generated.

The invention discloses a UWB-IMU fusion indoor positioning method based on a space-time diagram and adaptive Kalman filtering. The method comprises the steps that a trained space-time diagram attention model is acquired, the space-time diagram attention model models a positioning problem into a dynamic graph structure between an unmanned aerial vehicle and an anchor point, a synchronous UWB measurement sequence and an IMU measurement sequence serve as input, time and space features of graph nodes are captured through a graph attention mechanism, and the dynamic graph structure is obtained; outputting a quality evaluation result of the UWB measurement, wherein the quality evaluation result comprises non-line-of-sight deviation and uncertainty of the UWB measurement; and embedding the time-space diagram attention model into an updating process of an error state Kalman filter (ESKF), and performing dynamic and adaptive updating according to the quality evaluation result to obtain a positioning result. According to the invention, the problem of poor positioning precision and robustness in a complex dynamic NLOS environment is solved, and real-time, accurate and centimeter-level three-position indoor positioning is realized.

The invention provides an adaptive optimization compensation method for dynamic errors of an inertial navigation system. The method comprises the following steps: obtaining original measurement data of a gyroscope and an accelerometer through measurement and navigation calculation of the inertial navigation system; inputting the original measurement data into a self-adaptive unscented Kalman filter, and carrying out self-adaptive unscented Kalman filtering; time is updated, state prediction is carried out, and observation prediction is carried out; calculating an innovation sequence, and setting a sliding window to calculate an innovation covariance estimated value; updating an observation noise covariance matrix according to the theoretical innovation covariance in combination with an innovation covariance estimated value, and updating a process noise covariance matrix according to residual analysis; updating measurement; the gyroscope zero offset estimation value and the accelerometer zero offset estimation value which are output through filtering are used for compensating gyroscope and acceleration original measurement data. According to the method disclosed by the invention, the high-precision online estimation and compensation of the time-varying error of the inertial navigation system are realized by constructing a self-adaptive filtering framework integrating a dynamic model and data driving, and the navigation precision and stability of the system in a high-dynamic environment are improved.

The invention discloses an anti-interference GNSS / INS (Global Navigation Satellite System / Inertial Navigation System) integrated positioning optimization algorithm for an unmanned aerial vehicle, particularly relates to the technical field of satellite positioning and navigation, and is used for solving the problem that the positioning precision is reduced due to inaccurate interference identification of an existing integrated navigation system in a complex electromagnetic environment. The positioning precision is improved by constructing a multi-dimensional interference characteristic analysis system; the method comprises the following steps: firstly, carrying out subset division on visible satellites, calculating credibility factors, analyzing pseudo-range residual error distribution characteristics to identify interference source types when the credibility is insufficient, and extracting spectrum characteristics of position sequences at the same time; then, based on the matching relationship between the interference mechanism and the spectrum characteristic, allocating an evaluation weight, and generating a reliability index; and finally, adaptively adjusting a measurement noise covariance matrix of the Kalman filter according to the index, and realizing optimal fusion of GNSS observation data and INS sensor data, thereby keeping the positioning precision and stability of the integrated navigation system in a strong interference environment.

The invention discloses an underwater robot global positioning and trajectory tracking method based on a hull NURBS curved surface. The method comprises the following steps: firstly, constructing a hull NURBS curved surface model, and extracting differential geometric parameters including a curved surface basis vector, a curved surface normal vector, a curved surface first basic form matrix and a Cristomide symbol; then, on the basis of the differential geometry theory, according to the hull NURBS curved surface model, a continuous time nonlinear kinematics model is constructed; and finally, constructing an extended Kalman filter based on the continuous time nonlinear kinematics model. Based on an extended Kalman filter, fusing the multi-modal sensing data to perform global positioning, and obtaining a posteriori estimation state vector at the current moment; and performing curved surface trajectory tracking based on nonlinear model predictive control according to the posteriori estimation state vector at the current moment. The problem that the trajectory tracking precision is low or fails due to the fact that the robot is prone to accumulative drifting in positioning and cannot achieve global positioning in underwater, curved-surface, GPS-free and external-vision-free denial environments is solved.