496 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 water surface target tracking method and system based on multi-physical parameter fusion, and the method comprises the steps: obtaining a physical layer observation parameter of a target through a water surface monitoring radar, and constructing a multi-dimensional nonlinear observation vector; establishing a multi-mode motion model set including constant speed, variable speed and turning, and realizing dynamic switching among motion modes through a Markov chain; adjusting a process noise covariance and an observation noise covariance based on a residual covariance estimation result in the sliding window by adopting an adaptive extended Kalman filtering algorithm; distributing weights according to the measurement variance of each physical parameter, optimizing the Kalman gain through a weighted least square method, and completing the updating and estimation of a target state; dynamic switching of motion modes is achieved through a Markov chain, and when state estimation residual errors of continuous preset times exceed a preset threshold value, model mismatch is judged, and a Markov chain model switching mechanism is triggered; according to the method, the sea condition adaptability, the calculation efficiency and the engineering expandability can be improved.

A temperature control method for a lithium ion power battery of a new energy automobile comprises the steps that a lumped parameter thermal model of the lithium ion power battery is established, parameter identification is conducted on the lumped parameter thermal model through an eagle and African vulture mixed optimization algorithm, and model parameters are obtained; constructing a maximum correlation entropy tracking extended Kalman filter, and inputting the model parameters into the maximum correlation entropy tracking extended Kalman filter; a multi-target grey wolf algorithm improved based on a deep reinforcement learning algorithm is introduced to optimize the state noise covariance and the observation noise covariance of the maximum correlation entropy strong tracking extended Kalman filter, so that the internal temperature of the battery is estimated; and establishing a model prediction controller based on the estimated internal temperature of the battery, improving the model prediction controller, and controlling the internal temperature of the battery through the improved model prediction controller. According to the invention, accurate online estimation and efficient control can be carried out on the internal temperature of the battery.

The invention discloses a vehicle state estimation method based on adaptive strong tracking extended Kalman filtering. The method comprises the following steps: constructing a nonlinear three-degree-of-freedom dynamic model containing a state equation and an observation equation based on an extended Kalman filtering algorithm to describe longitudinal, lateral and yaw states of a vehicle; state variables of the state equation are a side slip angle, a yaw velocity and a longitudinal vehicle speed; constructing a time-varying measurement noise statistical estimator based on a Sage-Husa algorithm to adaptively correct a measurement noise covariance matrix in the extended Kalman filtering algorithm; state prediction is carried out on the vehicle through the state equation, and a state prediction covariance matrix is calculated; and calculating the ratio of the sum of quadratic terms of the information sequence to the trace of the variance matrix of the information sequence based on an extended Kalman filtering algorithm, and judging whether filtering is really divergent or not. According to the method, the state quantity which is difficult to measure in the vehicle driving process is estimated in real time by establishing an adaptive strong tracking extended Kalman filter, and the vehicle state is accurately estimated.

The invention discloses a magnetic field measurement method and system based on a multi-sensor fusion technology, and relates to the technical field of sensor fusion and magnetic field measurement, and the method comprises the steps: deploying a multi-sensor data array, carrying out the adaptive initialization of a bistable SR parameter range, defining an SR system differential equation, and carrying out the iterative optimization through employing an MPA population. Carrying out Hilbert transform edge detection on enhanced signal component data, calculating an array inclination angle, carrying out abbe error and bidirectional projection error compensation, and carrying out metasurface grid coordinate quantization mapping; the collected and cross-scale magnetic field data set is preprocessed and packaged into data cells, quality evaluation and weight distribution are carried out on the data cells, and extended Kalman filtering data fusion is carried out; by introducing a bistable stochastic resonance system and an MPA population optimization algorithm, a weak magnetic field signal is obviously enhanced, and by calculating an array inclination angle and compensating an Abbe error and a bidirectional projection error, the space consistency of a measurement result is improved.

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 relates to a lithium battery state-of-charge and state-of-health joint estimation method, and belongs to the technical field of lithium ion batteries. The method comprises the following steps: performing a cyclic aging experiment on a battery under a cyclic working condition; an open-circuit voltage curve is extracted through data analysis, a first-order equivalent circuit model is selected to construct a circuit, and a state equation is written; carrying out parameter identification by adopting a multi-forgetting factor recursive least square algorithm, and constructing an SOC estimator of an unscented Kalman filter based on a state equation and an observation equation to carry out SOC estimation; constructing an SOH estimation observer based on an extended Kalman filter, and estimating the SOH by using the estimated SOC; carrying out segmented dynamic identification on the open-circuit voltage curve by adopting an average moving method in combination with recursive least squares; and coupling the SOC estimator and the SOH estimator, and realizing SOC and SOH joint online estimation by applying the dynamically identified open-circuit voltage curve.

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 provides a train operation state estimation method and system based on a high-order extended Kalman filter considering noise correlation, and belongs to the technical field of rail transit train operation management. According to the method, correlation analysis between high-order terms is introduced, the filtering precision is remarkably improved, and particularly, excellent performance is shown under a strong nonlinear system; by establishing a multi-mode train state model, the dynamic characteristics of a train in operation modes such as traction, cruising, sliding and braking are comprehensively covered, and a more accurate input basis is provided for a filtering algorithm; by introducing a sliding window error criterion, the filter can quickly switch operation modes according to real-time errors, so that the filter is ensured to keep high adaptability in a complex and changeable operation environment; the comprehensive performance evaluation framework based on the error covariance matrix not only evaluates the mean square error and the mean absolute error, but also quantifies the contribution of the high-order correlation to the precision of the filter, and provides a more scientific and comprehensive evaluation system.

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 discloses an EPB anti-lock control method and system based on a self-adaptive wheel speed filtering algorithm, and belongs to the field of anti-lock control, and the method comprises the following steps: S1, collecting driving parameters in a vehicle driving process in real time, extracting feature vectors in the driving parameters after preprocessing, inputting the feature vectors into a pre-trained driving condition classification model, and obtaining a driving condition classification model; outputting the current driving condition type; s2, correcting a wheel speed signal based on an extended Kalman filter with determined parameters; s3, calculating a tire pressure deviation ratio based on the corrected wheel speed signal; and S4, when it is detected that the tire pressure deviation ratio exceeds a set threshold value, an EPB anti-lock control system is triggered. By the adoption of the EPB anti-lock control method and system based on the self-adaptive wheel speed filtering algorithm, the wheel speed information can be accurately extracted under the complex road and environment conditions, the calculation precision of the tire pressure deviation ratio is remarkably improved, and therefore a more reliable basis is provided for starting emergency braking.

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 provides an ultrasonic echo signal joint noise reduction method based on improved EKF and wavelet packet cooperation, belongs to the technical field of ultrasonic detection, and solves the problems that the noise reduction effect is poor in a traditional ultrasonic signal noise reduction method and noise reduction and feature retention are difficult to balance in the wavelet packet noise reduction process. Modeling the ultrasonic echo signal and the noise signal to obtain an ultrasonic echo signal with noise; extracting a target ultrasonic echo signal by using an improved extended Kalman filter to obtain an observation equation; predicting and updating on the basis of the observation equation to obtain ultrasonic echo signal estimation; optimizing the noise covariance matrix and the observation noise covariance matrix by adopting a differential evolution algorithm to obtain an optimal parameter, executing extended Kalman filter filtering based on the optimal parameter, and generating an ultrasonic echo signal estimated value after preliminary denoising; and carrying out wavelet packet threshold denoising to obtain a final denoised ultrasonic echo signal.

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 relates to the technical field of optical fiber sensing, in particular to an optical fiber temperature measurement method and system, and the method comprises the steps: injecting a multi-scale dynamic modulation coding laser signal into a to-be-measured optical fiber and a reference optical fiber respectively, collecting two paths of optical signals, and generating frequency domain data containing modulation coding characteristics through fast Fourier transform; constructing a virtual reference channel, fusing pre-stored historical data through digital matched filtering and self-organizing mapping, and realizing stable output of a reference signal; adaptive calibration is performed on data to be measured by using extended Kalman filtering in combination with fuzzy logic, and calibrated phase data accurately reflecting temperature change is output; and finally, outputting high-precision temperature distribution data based on the temperature inversion model and multi-dimensional correlation analysis. The temperature measurement precision and the system robustness are remarkably improved, and the method is suitable for the fields of industrial and environmental monitoring and the like.

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 crane group anti-collision method based on a motion trend, which relates to the technical field of mechanical automation control, and comprises the following steps: deploying multiple sensors on a crane, collecting and integrating multi-modal data, obtaining a nonlinear state vector, constructing an improved extended Kalman filtering model based on the nonlinear state vector, and calculating the anti-collision degree of the crane group based on the improved extended Kalman filtering model. The method comprises the following steps of: deploying multiple types of sensors, performing joint optimization in combination with a deep learning algorithm, acquiring probability distribution of a motion track, distributing roles for each crane based on the probability distribution of the motion track, generating a global path for a navigator by adopting an A * algorithm, and acquiring an initial speed instruction. According to the method, the multi-source heterogeneous data are extracted and integrated into a non-linear state vector, effective fusion of the multi-source heterogeneous data is achieved, meanwhile, an improved extended Kalman filtering model is combined with a deep learning algorithm, probability distribution prediction is conducted on the motion trail of the crane, the collision risk possibly occurring in the future is effectively predicted, avoidance is conducted, and collision prevention of the crane group is achieved.

The invention relates to the technical field of battery management, and discloses a state estimation method, device and equipment for an energy storage battery and a medium, and the method comprises the steps: carrying out the preliminary estimation of the state of the energy storage battery through an extended Kalman filter, and obtaining an initial state value, a terminal voltage error and a Kalman gain at a current moment; taking the terminal voltage error and the Kalman gain at the current moment as input, performing error optimization through a self-adaptive spiral flight sparrow search algorithm, and outputting an error compensation amount to compensate an initial state value; feeding back the compensated state value to an extended Kalman filter containing a recalibration and quit mechanism so as to estimate the state of the energy storage battery at the next moment, repeatedly executing an error optimization step and a state feedback step based on an estimation result until a preset convergence condition is reached, and outputting a state estimation result of the energy storage battery; through an algorithm cooperation and dynamic adjustment mechanism, the precision and robustness of energy storage battery state estimation are significantly improved.

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 present invention relates to the technical field of dynamic measurement of drill tool attitude, and discloses a dynamic measurement method and system for attitude parameters of a rotary steering tool. The method includes: obtaining in real time the three-axis angular rate, the three-axis earth gravity field components, and the three-axis geomagnetic field components of the rotary steering tool; determining a prior estimate value of the attitude angle according to the three-axis angular rate; determining a posteriori observation values of the attitude angle according to the three-axis earth gravity field components and the three-axis geomagnetic field components; obtaining a posteriori optimal attitude angle estimation result by fusing the prior estimate value of the attitude angle and the posteriori observation values of the attitude angle through iterative extended Kalman filtering; and calculating the attitude parameters according to the posteriori optimal attitude angle estimation result. In this application, an iterative extended Kalman filter is used to adaptively fuse the dynamic observables measured by a three-axis gyroscope, a three-axis accelerometer, and a three-axis magnetometer, so as to eliminate the noise interference of external environments such as high rotational speed, strong vibration, and magnetic anomaly faced during the dynamic measurement process, thereby improving the control and measurement accuracy of the rotary steering tool.