370 results about "Extend kalman filter" patented technology

The invention relates to the field of heart rate monitoring, and discloses a millimeter wave radar breath and heart rate synchronous monitoring method and system, and the method comprises the steps: transmitting a linear frequency modulation continuous wave signal according to a millimeter wave radar, and collecting original echo data reflected by a target region; baseband signal demodulation and phase information extraction are carried out on the original echo data to obtain original phase time sequence data, and the original phase time sequence data comprise thoracic cavity micro-motion features; and according to a Butterworth band-pass filter, preprocessing the original phase time sequence data through human body physiological signal frequency band characteristics to obtain a breathing frequency band signal and a heart rate frequency band signal. According to the method, the apnea event triggering threshold value and the arrhythmia early warning index are updated in real time through Kalman filtering and extended Kalman filtering, so that the monitoring system can dynamically adjust the health parameters, which means that the monitoring system can be optimized in real time and the abnormal health event can be accurately responded in different physiological states.

The invention provides an inertial navigation attitude resolving method and device based on sensor fusion. The method comprises the following steps: carrying out multi-clock domain synchronous calibration processing on a double-antenna GNSS system, an IMU inertial measurement unit and an antenna servo system in a vehicle-mounted communication-in-motion system; according to the time synchronization reference, carrying out quality evaluation and weight distribution processing on the GNSS satellite signal under the satellite communication in motion antenna pointing constraint; and carrying out multi-sensor fusion resolving processing on the inertial attitude of the vehicle body according to the time synchronization reference and the GNSS observation data weight distribution result. According to the method, millisecond-level time synchronization is realized by establishing a robust extended Kalman filtering model of a multi-dimensional extended state vector, GNSS signal quality is optimized by adopting an adaptive weight distribution strategy of antenna pointing constraint, and attitude fusion precision is enhanced by utilizing high-precision angle feedback of an antenna servo system. The problem that a traditional attitude resolving method in a vehicle-mounted communication-in-motion system is not high in precision is solved.

The invention relates to an autonomous energy-saving soaring flight path planning method for a small-sized low-cost aircraft, belongs to the technical field of aircraft trajectory planning, solves the problem of low-cost wind field energy acquisition of the small-sized low-cost aircraft in the prior art, and comprises the following steps: S1, configuring a sensor for the aircraft, and measuring through the sensor to obtain observation parameters; s2, establishing a state vector of the aircraft; s3, establishing an aerodynamic force model, introducing a dynamic equation and a state transition equation, and performing accurate modeling on aerodynamic force; s4, performing multi-source data fusion by adopting extended Kalman filtering, establishing an extended Kalman filter of a nonlinear system, and executing real-time wind vector high-precision sensing; and S5, performing global wind field modeling, estimating a wind field environment, and performing energy-obtaining flight path planning to obtain an optimal energy-obtaining soaring flight path planning scheme.

The invention discloses a self-adaptive battery life calibration method for cloud-side cooperation, and belongs to the crossing field of an energy storage system and cloud-side cooperation calculation. According to the invention, a cloud-edge double-layer collaborative framework is provided; an edge end estimates the health state and the residual life of a battery in real time through a recursive least square extended Kalman filtering model; the error observer calculates a prediction error based on a sliding window, and a dynamic threshold triggers an uploading mechanism; the edge end adopts an auto-encoder to compress original time sequence features into abstract vectors, and the abstract vectors and error statistics are uploaded together; the cloud performs incremental learning by using a deep sequential network, and only finely adjusts tail level parameters of which the gradient sensitivity exceeds a threshold value to generate a correction value; and the correction value is compressed and issued to an edge end, local model parameters are updated through weighted fusion, and a covariance matrix is adjusted. The method realizes high-precision life prediction and dynamic calibration, remarkably reduces the communication load, and is suitable for electric vehicles, power grid energy storage and other scenes.

The invention provides a medical RFQ accelerator low-level signal phase stability control method and system. The method comprises the following steps: constructing a time-frequency energy spectrum feature vector based on wavelet packet transformation; extracting a second disturbance feature based on a lightweight convolutional neural network and an attention mechanism; constructing a phase dynamic trend prediction module based on a long short-term memory network, and obtaining a first prediction phase error; constructing a phase compensation module based on a residual control network to obtain a second phase compensation amount; and outputting a real-time driving control signal based on the extended Kalman filter. According to the method, the time-frequency energy spectrum feature vector based on wavelet packet transformation is constructed, accurate characterization of the multi-scale disturbance features of the low-level signals is achieved, a medical RFQ accelerator phase dynamic compensation system is established in combination with a deep learning network and an extended Kalman filtering algorithm, the control precision and the anti-interference capability of signal phase stability are remarkably improved, and the method is suitable for popularization and application. The method is suitable for a high-precision medical particle accelerator control system.

The invention discloses a multi-base-station AOA cooperative low-altitude target rapid positioning system. The system comprises an AOA measurement base station network, an AOA data preprocessing module, an adaptive weighted intersection positioning module, an extended Kalman filtering state estimation module and a data fusion and system integration module. The system synchronously measures the arrival angle of a target signal through multiple base stations, removes noise in combination with smoothing filtering and an anomaly rejection algorithm, and then solves the initial position of a target by using a self-adaptive weighted intersection algorithm based on measurement quality and geometric distribution. And then, fusing the target motion model and the measurement model by adopting an extended Kalman filtering algorithm to realize dynamic estimation and prediction of the position, the speed and the course. The system can realize high-precision and real-time positioning and continuous tracking of targets such as low-altitude unmanned aerial vehicles, small aircrafts and the like in a complex electromagnetic environment and a sight distance limited scene, and has visual display and regional alarm functions.

The invention relates to an underwater cleaning robot positioning and tracking method. Positioning comprises the steps that data of multiple sensors on an underwater cleaning robot are fused through an extended Kalman filter, and coordinates of the underwater cleaning robot on the surface of a ship body are obtained after sensor measurement noise is filtered through a first-order low-pass filtering algorithm. The tracking method comprises the step of realizing the path tracking of the underwater cleaning robot based on the positioning method in combination with an iterative linear quadratic regulator (ILQR). According to the invention, a multi-sensor fusion scheme of the encoder, the IMU and the depth meter is innovatively provided, and the positioning problem of the underwater cleaning robot in a wall attaching state is solved. And double-plane differential treatment is adopted, so that the working state of the robot on the ship wall or the ship bottom is better fitted, and positioning is more accurate. A target point updating mechanism of geometric distance constraint is provided, the nearest neighbor point of the reference trajectory is adjusted in real time, and the problem of tracking lag caused by updating rate mismatch in a traditional method is solved.

The invention discloses a multi-source navigation data fusion method for an unmanned loader, which comprises the following steps: data acquisition: acquiring environmental perception data of satellite navigation, an inertial measurement unit IMU, a wheel type odometer and a laser radar and camera in real time; performing space-time synchronization preprocessing, realizing multi-source data time synchronization, unifying environment sensing data to a body coordinate system, eliminating abnormal data, and complementing missing data; carrying out dynamic weight calculation, establishing an error model of each sensor, and adjusting a fusion weight by using an error reciprocal exponential weighting method; layering fusion is carried out, a satellite and an IMU are fused through bottom-layer extended Kalman filtering (EKF), a laser radar and a high-precision map are fused through middle-layer ICP, a middle-layer result and a wheel type odometer are integrated through high-layer federated filtering, and high-precision fusion is achieved; and outputting and optimizing a result, outputting navigation data, performing closed-loop optimization, monitoring the health degree of the sensor, and executing redundancy switching when a fault occurs. The system comprises a corresponding processing unit, and a storage medium stores a program for executing the method.

The invention discloses a low-slow small target detection and trajectory prediction tracking method based on a laser radar, and the method comprises the steps: firstly collecting the point cloud data of the laser radar, and carrying out the preprocessing of spatial modeling and coordinate transformation of the point cloud data of the laser radar; performing significance screening; based on distance partition driving, Pilllar construction and coding are carried out; constructing a deep learning detection network; based on Anchor design and a matching strategy, carrying out size adaptation on a weak target in the air in the fused features; and performing time sequence prediction and observation updating on the target state based on an extended Kalman filter (EKF), and completing low-slow small target detection and trajectory prediction tracking. The method can maintain the high precision advantage of the laser radar, improves the recognition capability of the laser radar on weak-reflection, small-size and irregular-motion targets, has high robustness and environment adaptability, and achieves the stable and precise sensing and continuous tracking of low, slow and small flight targets.

The invention belongs to the technical field of ship management, and provides a combined intelligent ship water diversion system integrating differential positioning, a laser radar and a camera. In the shore end RTK-inertial navigation depth fusion positioning module, extended Kalman filtering fusion is carried out on data acquired by shipborne equipment, so that continuity of accurate positioning is realized; the construction of a color high-precision map is realized through a laser radar-monocular vision color high-precision modeling module, details such as wharf fender materials and signboard characters can be better distinguished, and the problem of shoreline modeling defects is solved; visualization of obstacles is achieved through the multi-scene dynamic obstacle detection and fusion module, meanwhile, through cooperation of multi-source data, the precision and stability of ship positioning are greatly improved, higher precision and reliability are achieved, and the precision of a guide instruction is improved; through cooperation of the 5G private network and the visualization module, the data transmission efficiency is improved, the timeliness is improved, and transmission delay can be avoided.

The invention discloses an Internet of Things equipment real-time early warning method and system based on digital twinning, and relates to the technical field of equipment operation management, and the method comprises the steps: collecting multi-modal original data of Internet of Things equipment, constructing a graph structure, and generating a semantic graph vector; obtaining a digital twinborn model state corresponding to the target equipment, forming an extended state vector by the digital twinborn state, the physical equipment state and the semantic map vector, and inputting the extended state vector into an extended Kalman filter for state fusion to obtain a state estimation result; and performing Monte Carlo simulation according to the state estimation result, generating a plurality of abnormal state samples, calculating a residual mean value between the abnormal samples and the current estimation result, correcting a covariance parameter of the extended Kalman filter, and generating an early warning tag. Fine estimation and risk level early warning of the state of the Internet of Things equipment are realized, and the state fusion precision and the abnormal response timeliness are improved.

The invention relates to the technical field of high-precision positioning, in particular to a mine tight combination positioning method based on environment self-adaption, and aims to solve the problem of efficient positioning of personnel and equipment in a complex and limited space under a mine. The method comprises the step of acquiring a time difference of arrival measurement value through the ultra-wideband positioning tag and the positioning base station. Chi-square testing is carried out based on the innovation statistics of the extended Kalman filtering, and whether the environment is a sight distance environment is judged according to comparison between the innovation statistics and a threshold value. If the innovation statistic is lower than a threshold value, determining that the environment is a sight distance environment, and if the innovation statistic is higher than or equal to the threshold value, determining that the environment is a non-sight-distance environment. For a sight distance environment, high-precision coordinate estimation is obtained by adopting a time difference of arrival positioning algorithm, and a positioning result is optimized through extended Kalman filtering fusion. And activating the inertial measurement unit, constructing an ultra wide band / inertial measurement unit tight combination extended Kalman filtering model for a non-line-of-sight environment, and outputting a positioning result.

The invention relates to the technical field of data processing, in particular to a laser and binocular camera-based position positioning system, which comprises an acquisition unit, a processing unit, a fusion unit, an abnormality judgment unit, an adjustment unit and a correction unit. According to the method, multi-source information such as a laser point cloud matching result, binocular visual odometer output and the number of feature points is subjected to joint modeling in an extended Kalman filter, so that the AGV obtains stable pose estimation in different environments, and the pose estimation accuracy is improved by analyzing the change trend of pose covariance in an adjusted monitoring window. A density threshold value, an information entropy threshold value and a dynamic proportion threshold value are automatically corrected, so that the threshold values can adapt to real working conditions for a long time, and performance degradation caused by a fixed threshold value is avoided; the problems that due to the fact that sensor information quality fluctuation cannot be recognized and processed, positioning precision is lowered, accumulative errors are increased, and positioning loss is prone to occurring in a complex environment are effectively solved.

The invention relates to an FOC control method based on an automobile air conditioner module motor without a Hall sensor, and belongs to the technical field of automobile electronics. The method comprises the following steps: injecting double-frequency voltage pulses into a stator in stages to position a rotor; collecting current through a sampling resistor and filtering to construct a rotor flux linkage distribution model, and presetting rectangular axis current in combination with noise reduction of a miniature acoustic sensor and air speed gear of an air conditioner; an extended Kalman filter observer is adopted to reversely deduce the load torque, and the torque change rate is monitored to trigger instantaneous load impact protection; the voltage space vector amplitude is corrected through voltage feed-forward compensation, a resistor-capacitor series filter circuit is additionally arranged, and phase dislocation pulse width modulation is adopted for resisting interference; and establishing a motor efficiency-rotating speed-load three-dimensional optimization method, starting dynamic flux-weakening control, and estimating the winding temperature through the stator resistance to adjust the flux-weakening depth. Accurate starting and dynamic load adaptation of the Hall-sensor-free motor are achieved, starting out-of-step and noise exceeding are avoided, and stable air volume of an automobile air conditioner and long-term reliable operation of the motor are guaranteed.

The invention belongs to the technical field of cable manufacturing. According to the self-adaptive correction method and system based on cable insulation layer thickness monitoring, the real-time center offset of a cable is determined according to laser array measurement data after time-space synchronization; determining the thickness of the cable according to the X-ray thickness measurement data after time-space synchronization; determining the long-short axis ratio of the ellipse according to the shot cable ellipse contour image after the time-space synchronization; inputting the real-time center offset of the cable, the thickness of the cable and the long-short axis ratio of the ellipse into an extended Kalman filter to obtain fusion thickness, fusion concentricity and fusion ellipticity, and further determining an extrusion die pose compensation vector and a screw rotation speed correction amount; according to the extrusion die pose compensation vector, a corresponding execution mechanism is driven to adjust the extrusion die pose, and according to the screw rotation speed correction, the screw rotation speed is controlled; according to the invention, the problems of measurement one-sidedness, control hysteresis and deviation accumulation are solved, and the continuous and stable control of the geometric parameters of the cable insulation layer is realized.

The invention discloses a degradation scene-oriented multi-residual fusion laser radar positioning method, which comprises the following steps of: firstly, performing state prediction by adopting an iterative extended Kalman filtering framework and an IMU (Inertial Measurement Unit), and constructing three complementary observation models of a global map matching residual, a local point-to-plane geometry residual and a luminosity residual; secondly, designing a degradation sensing mechanism based on a covariance ellipsoid, representing absolute and relative degradation degrees through a condition number and an information entropy respectively, realizing quantitative evaluation of system observability, and dynamically adjusting fusion weights of observation residuals; meanwhile, a self-adaptive weight strategy based on luminosity Jacobi intensity is introduced; and finally, performing anomaly detection through deviation comparison between the IMU predicted pose and the IEKF estimated pose, inhibiting pose jump, and ensuring continuity of a positioning time sequence. The method effectively overcomes the challenges of geometric constraint deficiency, positioning drift accumulation and the like of the LiDAR positioning system in the geometric degradation environment, does not need to adjust parameters for a specific scene, and improves the precision, robustness and real-time performance of global positioning in the degradation environment.

The invention discloses a five-dimensional motion vector real-time construction and calibration method based on multi-sensor fusion, and relates to the technical field of multi-sensor information fusion and dynamic state estimation, and the method comprises the steps: collecting and preprocessing motion carrier data, obtaining a preprocessing data set and a feature data set, inputting the feature data set into a long short-term memory network model, and obtaining a multi-sensor fusion model; and outputting a sensor error offset prediction vector to the extended Kalman filtering model, outputting a preliminary five-dimensional motion vector, and performing consistency verification and correction on the preliminary five-dimensional motion vector through a kinematics constraint equation to obtain a corrected five-dimensional motion vector so as to drive a virtual model corresponding to a motion carrier to perform synchronous position and attitude updating. According to the method, the sensor error is predicted through the long-short-term memory network model, and the dynamic motion model constraint module is additionally arranged to perform physical constraint correction, so that the problems of inaccurate error compensation and lack of physical authenticity of the calculation result under the dynamic working condition are solved, and the construction precision and reliability of the five-dimensional motion vector are improved.

The invention relates to the technical field of intelligent control and information security crossing, in particular to an anti-error data injection Kalman filtering method based on neural network enhancement, which comprises the following steps: firstly, constructing a neural network auxiliary correction module for identifying and correcting an extended Kalman filtering intermediate variable affected by an error data injection attack; secondly, designing a hybrid filtering architecture, and combining the learning ability of a neural network with the theoretical advantages of traditional Kalman filtering; and finally, realizing adaptive optimization of model parameters through a mixed training mechanism. Accurate state estimation can be achieved only through limited prior information, and meanwhile the calculation efficiency and interpretability of a traditional Kalman filter are kept. And the anti-interference capability of the system in a non-Gaussian noise environment is effectively improved. The application of the method on an induction motor model is obviously better than that of a traditional anti-error data injection scheme, and breakthrough progress is achieved in the aspects of estimation precision and robustness.

The invention relates to the technical field of wind power generation, in particular to a wind turbine generator deformation vibration monitoring device based on millimeter wave radar. The core function module comprises a benchmark sensing radar, a blade monitoring radar, a reference beacon, an inertial measurement unit, a data synchronization and acquisition unit and a signal processing unit; a reference beacon fixed on a substrate is detected through a reference sensing radar, an absolute motion vector A of the reference sensing radar is solved, then the absolute motion vector A is converted into an absolute motion vector B of a blade monitoring radar, then data C measured by an IMU (Inertial Measurement Unit) is converted into data D of the blade monitoring radar in a coordinate system of the blade monitoring radar, and the blade monitoring radar can be used for monitoring the blade. The absolute motion vector E of the blade monitoring radar is fused and extracted through an extended Kalman filter, and is finally used for compensating original data of blade measurement by the blade monitoring radar, so that the influence of cabin motion on deformation vibration monitoring is eliminated.

The invention relates to the technical field of ship navigation safety, in particular to a real-time ship trajectory tracking and early warning method, which comprises the steps of multi-source data acquisition and preprocessing, trajectory prediction model construction, collision risk assessment, early warning triggering and the like. By integrating AIS, radar and meteorological data, accurate trajectory prediction is realized by adopting Kalman filtering and extended Kalman filtering algorithms, and the method adapts to a complex environment based on dynamic weight factors and fuzzy logic control. Meanwhile, a multi-level early warning mechanism and redundant communication design are provided, and timely transmission of early warning information is ensured. The collision risk assessment model is optimized by using the deep neural network, and the early warning precision is improved. The real-time performance, accuracy and dynamic adaptability of ship trajectory tracking and early warning can be improved, and the efficient and safe requirements of modern shipping are met.

The invention discloses a multi-source information fusion-based tunneling equipment vision-assisted pose detection system and method, and relates to the technical field of exploration and engineering surveying, and the system comprises a binocular vision module, a strapdown inertial navigation module, a laser orientation instrument module and a data fusion processing module. The binocular vision module solves three-dimensional coordinates through stereoscopic vision; the strapdown inertial navigation module provides high-frequency dynamic attitude data; the laser orientation instrument modules are triangularly arranged to provide a global absolute position reference; and the data fusion processing module adopts an extended Kalman filtering dynamic fusion algorithm and combines dynamic weight adjustment to fuse multi-source data. The method comprises the steps of data acquisition, preprocessing and time-space synchronization, fusion calculation and pose information output. Through the multi-source collaborative fusion and dynamic environment adaptation technology, the detection precision and stability are improved, the automatic tracking and remote monitoring requirements of the tunneling equipment are met, and intelligent upgrading of coal mine tunneling is promoted.

The invention discloses an ultra wide band (UWB) robust positioning method for a non-line-of-sight (NLOS) interference environment, and belongs to the technical field of wireless positioning. According to the method, firstly, all three-base-station combinations are generated through a permutation and combination method, the initial position of each group of base stations is solved through a trilateral positioning method, and a multi-dimensional coarse positioning point cloud is formed; then grouping the coarse positioning points according to the resolving participation condition of the base stations, acquiring representative position points of the base stations by adopting a central position estimation algorithm, and constructing a base station space distribution model; a mean vector and a covariance matrix of the point cloud are calculated, after a pseudo-inverse matrix is solved through SVD decomposition, the mahalanobis distance from each representative point to the distribution center is calculated, a dynamic threshold value is set according to the 95% confidence coefficient of chi-square distribution, a base station with the distance exceeding the threshold value is judged as an NLOS abnormal node, and data of the base station is abandoned; and finally, constructing a dual-stage anomaly suppression mechanism: screening reliable base stations based on geometric consistency at a measurement stage, dynamically adjusting the Kalman gain through an exponential gain constraint factor at a residual stage, constructing a dynamic noise model by fusing a geometric precision factor, and realizing position calculation by adopting improved extended Kalman filtering. According to the method, the positioning error can be effectively reduced in the NLOS interference environment, and a centimeter-level reliable positioning solution is provided for the fields of industrial Internet of Things or indoor positioning and the like.

The invention discloses an ocean engineering structure extended Kalman filter parameter estimation method based on attitude angle virtual observation, and relates to the technical field of ocean engineering, and the method comprises the following steps: S1, initializing a system process noise covariance matrix and an observation noise covariance matrix, and obtaining an extended Kalman filter attitude angle estimation value; s2, establishing a high-precision angular velocity-attitude angle dynamic conversion model, generating attitude angle virtual observed quantity and using the attitude angle virtual observed quantity for online evaluation of a Kalman filtering result; s3, setting a multi-dimensional performance evaluation index system, and quantitatively evaluating the amplitude error and trend stability of the extended Kalman filter estimated attitude angle; s4, adjusting scale factors of a system process noise covariance matrix and an observation noise covariance matrix by using a gradient descent algorithm based on the attitude angle virtual observation quantity and the error measurement of the multi-dimensional performance evaluation index, and carrying out real-time iteration on the scale factors of the system process noise covariance matrix and the observation noise covariance matrix; the problem that attitude parameters cannot be accurately measured and predicted in real time in the prior art is solved.

The invention relates to the technical field of vehicle-mounted integrated navigation, in particular to a double-clock synchronization IMU error correction method and system fused with 5G communication. The method comprises the following steps: acquiring a GNSS main clock signal, an auxiliary clock signal and a 5G communication clock signal, and establishing a unified time reference system; constructing a transverse speed prediction model to predict a transverse movement track in the high-speed lane changing process of the vehicle, and combining the transverse movement track with IMU inertial measurement data to calculate a transverse offset residual error of the vehicle; inputting the lateral offset residual error, the lane center line offset, the steering wheel angle data and the yaw velocity data of the vehicle into the improved error risk prediction neural network model for joint processing; and correcting the vehicle navigation state data in real time by using an extended Kalman filter. According to the invention, the 5G communication and GNSS double clock sources are fused in the vehicle high-speed lane changing scene, and high-precision IMU error correction of vehicle lane changing navigation state correction is carried out.

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 landing positioning method and system based on fusion of vision and inertial navigation. The method comprises the following steps: collecting vision information of a current landing area; the visual information of the front landing area is mapped to an aligned grid of a map coordinate system through homography transformation, correlation matching is conducted on the visual information and a map through a frequency domain FFT algorithm, a matching result of rough matching is obtained, and rough matching is completed; the method comprises the following steps of: mapping an area subjected to rough matching and positioning to a grid aligned with a map coordinate system by utilizing homography transformation, performing spatial domain multi-window division on a larger range of images around the area subjected to rough matching and positioning, selecting a landmark area in each sub-window by applying an interest operator, performing spatial correlation matching on the landmark area and a map by utilizing a spatial correlation method, and performing spatial correlation matching on the landmark area and the map by utilizing a spatial correlation method. Finishing fine matching; inputting all matched observation results into an extended Kalman filter, and carrying out joint estimation on the matched observation results and the observation results of the inertial measurement unit; the method has the advantages that the robustness, the precision and the engineering applicability of an existing navigation positioning method are improved.

The invention relates to a TDCP enhanced visual inertial odometer and a navigation positioning method thereof, the method aims at multi-sensor fusion positioning, uses an extended Kalman filter (EKF) to integrate visual features, IMU data and TDCP observation values, improves local pose estimation precision, and suppresses VIO error accumulation through absolute scale and course constraint provided by TDCP; meanwhile, the local estimation result and the global position of the GNSS are optimized and fused through a global pose map, and high-precision pose estimation which is globally consistent and within a global range is achieved. According to the method, optimization can be completed only through intermittent global positions, the problem of positioning failure of a traditional scheme in a GNSS challenge environment is solved, the problems of cycle slip accumulation and gross error interference when TDCP is independently used are effectively solved, and the method is suitable for automatic driving, unmanned aerial vehicles and other scenes with high real-time performance requirements.

The invention provides a vehicle-mounted radar target course angle filtering tracking method and system. The method comprises the steps that S1, the radial speed of clustered point cloud is converted into the absolute radial speed relative to the ground; s2, when a new track is created, an independent course angle extended Kalman filtering tracking module is synchronously initialized; s3, setting a projection direction based on a course angle estimated value output by the module, and completing data association between the point cloud and the track and track state updating through projection; and S4, extracting a position variable quantity based on the updated track state, forming an observed quantity by combining the absolute radial speed of the associated point cloud, inputting the observed quantity to a course angle module for extended Kalman filtering updating, outputting an updated course angle estimated value, and feeding back the updated course angle estimated value to the step S3 of the next frame. According to the invention, by constructing a cooperative closed loop of course angle estimation and data association, the stability of course angle estimation and the overall tracking robustness are significantly improved.

The invention discloses a single crystal blade directional solidification process real-time closed-loop optimization method and casting equipment, and the method comprises the steps: S1, constructing a multi-physical field numerical simulation model to simulate a simulation temperature field in a directional solidification process; s2, arranging a temperature sensor at a key part of the actually poured single crystal blade to obtain an actually measured temperature field; s3, inputting the simulated temperature field and the actually measured temperature field into an extended Kalman filter to update model parameters of the multi-physical field numerical simulation model; s4, extracting solidification process characteristic parameters from the corrected multi-physical field numerical simulation model, inputting the solidification process characteristic parameters into a pre-trained machine learning model, and predicting a mixed crystal tendency probability; s5, optimizing process parameters of directional solidification by adopting a self-adaptive genetic algorithm by taking miscellaneous crystal tendency probability minimization as a target function; and S6, the optimized technological parameter set is issued to an execution mechanism, the directional solidification process is controlled to achieve closed-loop adjustment, and the steps S2 to S6 are executed circularly.

The invention discloses an extended Kalman filtering method based on dynamic self-adaption residual error enhancement, and relates to the field of optimization algorithm error accumulation methods, and the extended Kalman filtering method based on dynamic self-adaption residual error enhancement significantly improves the sensing precision and reliability of a system through cooperative processing of multi-source heterogeneous sensing information, and improves the accuracy and reliability of the system. A sliding window covariance estimator is introduced, time-varying statistical characteristics of IMU angular velocity noise and GPR echo noise are identified in real time, a dual-threshold residual monitoring mechanism is designed, Jacobian matrix update frequency is dynamically adjusted to balance linearization errors and calculation loads, a robust correction term based on a Mahalanobis distance is established, and the robustness of the algorithm is improved. And abnormal observation interference caused by intermittent failure of the sensor is effectively inhibited.