578 results about "Extend kalman filter" patented technology

The invention relates to the technical field of power battery packs, and discloses a power battery pack state evaluation method and system, and the method comprises the steps: collecting an operation characteristic data set of a power battery pack under various working conditions, and constructing a hybrid evaluation model; performing parameter configuration on the double-extended Kalman filter to obtain an improved filter; acquiring real-time voltage, current and temperature data, and inputting the real-time voltage, current and temperature data into the improved filter for state calculation to obtain a charge state value and an internal parameter value; inputting the charge state value and the internal parameter value to perform multi-branch adversarial neural network analysis to obtain a fault mode type and a fault severity; according to the method, the multi-factor health index is calculated, particle swarm optimization fitting is carried out on the degradation curve of the power battery pack, the residual service life prediction value is obtained, more accurate residual service life prediction is further achieved, the precision in a wide temperature range is remarkably improved, and the difference between monomers in the power battery pack can be effectively processed.

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 discloses a robot indoor and outdoor navigation switching method and system based on a large language model, and the method comprises the steps: obtaining multi-dimensional data of an environment where a robot is located through employing a multi-mode sensor, and the multi-dimensional data comprise a visual image, a laser radar point cloud, a GPS signal, and IMU data; the preprocessed image is input into a visual encoder, and key visual features are extracted. And the big language model performs reasoning by using the characteristics, and outputs a scene judgment result in combination with an internal scene knowledge base. In order to enhance the accuracy of scene judgment, the change trend of multi-frame image features is analyzed based on a time sliding window. An extended Kalman filter is adopted in an indoor and outdoor transition area to carry out weighted fusion on multi-mode sensor data, a dual-mode parallel operation mechanism is adopted during navigation mode switching, and SLAM and GPS navigation modes are activated at the same time in the indoor and outdoor transition area. And by dynamically adjusting the weights of the SLAM and the GPS, gradually transitioning to a target navigation mode at a data fusion level, so that smooth switching is realized.

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 IMU / GNSS fusion method based on Kalman filtering, computer equipment and a storage medium. The method comprises the following steps: acquiring discrete observation points according to sampling data; obtaining a reference sampling section according to the integrity of the grouping interval, and comparing forward and reverse data obtained by forward recursion and reverse recursion to obtain a recursion boundary; analyzing the Kalman gain matrix to obtain a prediction state value; fusing the IMU data and the GNSS data according to the double integration result and the mapping conversion result to obtain an initial fusion quantity; obtaining a mean square error value according to a test criterion and the change characteristics; processing the corrected recursion boundary through recursion optimization to obtain a corrected fusion amount; analyzing to obtain a state quantity according to the reconstructed correction fusion quantity, the characteristics of the IMU data and the GNSS data and a reconstruction rule; the state quantity is input into the extended Kalman filter, fusion of IMU data and GNSS data is completed through a prediction-update-feedback mechanism, and the positioning precision and reliability of the integrated navigation system are effectively improved.

The invention relates to the technical field of battery state monitoring, and discloses a sightseeing vehicle battery state monitoring method, device and equipment and a storage medium, and the method comprises the steps: collecting the voltage, current and temperature data of a sightseeing vehicle battery, and carrying out the preprocessing, and obtaining a battery operation parameter data set; constructing a battery equivalent circuit model, and performing global and local two-stage optimization to obtain an optimal parameter set of the battery equivalent circuit model; constructing a state equation and an observation equation, and carrying out initialization setting on a covariance matrix of the triple forgetting factor adaptive extended Kalman filter to obtain a battery state observation model; priori state estimation is obtained through time updating operation, posterior state estimation is obtained through measurement updating operation, hierarchical estimation is carried out on the charge state and the polarization voltage of the battery, and the state characteristic quantity of the battery is obtained; according to the method, the reliability and the safety of the battery management system of the sightseeing vehicle are improved.

The invention relates to the technical field of heat dissipation control, and discloses a heat dissipation fan multi-order filtering control method and device, and the method comprises the steps: collecting the operation parameters of a multi-fan heat dissipation system, and carrying out the construction of a dynamic state space model, and obtaining a system operation state equation and a temperature observation equation; inputting the system operation state equation and the temperature observation equation into an expectation maximization sliding window iteration extended Kalman filter to obtain a system state estimation value and a prediction error covariance matrix; performing three-stage series design on the cut-off frequency of the low-pass filter, the passband range of the band-pass filter and the coefficient of the adaptive filter to obtain a system multi-order filtering control model; based on a system multi-order filtering control model, an airflow coupling influence coefficient matrix among multiple fans is calculated, the total energy consumption, the noise level and the heat dissipation efficiency are subjected to optimization solution, a fan control optimization parameter set is obtained, the heat dissipation efficiency is guaranteed while the energy consumption is reduced, and dynamic balance among all control units is achieved.

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.

Provided in the present invention is an interference-factor-based high-safety and stable control method for an onboard magnetically suspended flywheel battery system. The method comprises: in the case of a safe operating condition interference, obtaining the difference between a displacement reference signal and an actual displacement signal, and inputting the difference into a Butterworth-filter-fused fuzzy active disturbance rejection control module, so as to obtain control currents, wherein the control currents comprise a radial control current, a torsion control current and an axial control current; the radial control current and the torsion control current are subjected to Clark inverse transformation, and the axial control current is subjected to power amplification, and the three currents are then compensated for, such that a three-phase control current of a radial coil, a three-phase control current of a torsion coil, and a control current of an axial coil are obtained; the control currents control the corresponding coils, and are also input into a multi-information extended Kalman filtering prediction module, such that the actual displacement signal is output; and in the case of a dangerous operating condition interference, cutting off an output signal from an operating condition recognition and safety determination module. The present invention can improve the stability, robustness and anti-interference capability of a system.

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 seamless positioning method based on a single base station UWB / GNSS-RTK, which is suitable for a large storage environment. According to the method, UWB and GNSS-RTK data of a single base station are collected and combined, a fusion positioning model is established, and a weighted filtering extended Kalman filter (WEKF) is adopted to realize high-precision positioning and seamless switching inside and outside a warehouse. According to the characteristics of the internal and external environments of the warehouse, fusion algorithm parameters and a switching strategy are optimized: high-precision positioning which is wide in coverage and simple and convenient to deploy is realized in the warehouse by utilizing a single base station UWB fusion residual GNSS-RTK signal, and centimeter-level precision is provided outside the warehouse by depending on the GNSS-RTK technology. By monitoring and correcting positioning errors in real time, the stability and reliability of the system are ensured, and finally a high-precision positioning result is output. The method effectively fuses the positioning information inside and outside the warehouse, reduces the complexity of deployment of multiple base stations, has the characteristics of high precision, low cost and high stability, and can be widely applied to the scenes of material management, vehicle scheduling, automatic equipment navigation and the like.

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 provides a lithium ion power battery external thermal resistance and internal and external temperature joint estimation method comprising the following steps: building a battery equivalent thermal model according to lithium ion power battery thermal characteristics; identifying the thermal parameters by adopting a self-adaptive particle swarm optimization algorithm based on genetic algorithm optimization; establishing a high-gain observer, and initializing the high-gain observer by taking the external thermal resistance in the identified thermal parameters as an initial value to complete real-time estimation of the external thermal resistance; constructing a self-adaptive strong tracking extended Kalman filter, and inputting the identified thermal parameters and the real-time estimated external thermal resistance into the self-adaptive strong tracking extended Kalman filter; and introducing a multi-strategy improved Tianying optimization algorithm to perform optimization processing on the process noise covariance and the observation noise covariance of the adaptive strong tracking extended Kalman filter to obtain an optimized filter, and performing real-time estimation on the internal and external temperatures of the lithium ion power battery through the optimized filter.

The invention discloses a racing car racing control method based on Gaussian process regression enhancement model predictive control, and belongs to the field of unmanned driving system trajectory planning and tracking control. The method mainly comprises the following steps: firstly, constructing a racing car racing random optimal control problem model comprising a state equation, a measurement equation and probability constraints, and secondly, respectively modeling system uncertainty and measurement links by adopting a Gaussian process regression model; the method comprises the following steps: firstly, designing prudent model predictive control of racing car racing and an extended Kalman filter GP-EKF based on Gaussian process regression to realize high-precision state estimation, finally, fusing the GP-EKF and the prudent model predictive control, constructing learning model predictive control GP-EKF-LMPC based on GP-EKF enhancement, and feeding back posterior state estimation to an optimization problem, so as to realize high-precision state estimation. And solving an optimal control instruction of racing car racing in real time. Experiments show that the method can significantly improve the control precision and robustness in a complex scene.

The invention relates to a vehicle lithium ion power battery charge state estimation method based on an improved extended Kalman filtering algorithm, and belongs to the technical field of lithium ion batteries. The method comprises the following steps: firstly, establishing a second-order RC equivalent circuit model of the lithium ion battery, and carrying out linearization processing by utilizing Taylor expansion; and then, searching an optimal noise covariance matrix by adopting a sunflower optimization algorithm, and applying the optimal noise covariance matrix to charge state online estimation of an extended Kalman filtering algorithm. In order to ensure that the extended Kalman filter can be in contact with sufficient state parameter information, a proportional integral differential control method is introduced to correct a Kalman gain matrix, so that accurate and stable estimation of the state of charge of the lithium ion battery is realized. According to the method, a voltage accumulative error judgment and charge state initial correction method is adopted, so that the precision of charge state estimation is improved. Compared with a traditional ampere-hour integral method and an extended Kalman filtering algorithm based on sunflower optimization, the method has higher charge state estimation precision.

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 discloses a wireless SLAM (Simultaneous Localization and Mapping) method based on multi-base station information fusion, which is characterized in that user positioning and environment mapping are respectively carried out by utilizing different characteristics of line-of-sight (LoS) and non-line-of-sight (NLoS) channel parameters. Based on an extended Kalman filter (EKF) framework, multi-base-station LoS path angle parameters are fused to predict a user position. In the aspect of environment mapping, a virtual reference point is used for replacing a virtual anchor point to model a reflecting surface, so that consistent environment description is formed among different base stations, and a unified map fusion framework is constructed for different scenes. According to the method, the user position predicted by fusing the EKF with the LoS path channel parameter is used as the priori of the PHD-SLAM particle filter, so that the particle efficiency is effectively improved. Finally, through the method, fusion perception of LoS and NLoS information of multiple base stations is realized, the perception performance is effectively improved, and the calculation burden is remarkably reduced.

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.