285 results about "Extend kalman filter" patented technology

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 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 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 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 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 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 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.

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.

The invention discloses an autonomous mobile robot path planning obstacle avoidance method and system, relates to the technical field of autonomous mobile robot path planning and obstacle avoidance, and is used for solving the problems that in a deep mine multi-layer roadway, multiple environmental risks are difficult to quantify, and once a key bottleneck channel is blocked, task failure is easily caused, and the reliability of path planning is insufficient. After an autonomous mobile machine is powered on, sensor calibration parameters are loaded, pose fusion is completed through extended Kalman filtering based on a multi-source odometer, an inertia sensor and a laser vision sensor, and an occupation grid map with static obstacles, dynamic candidates and passable marks is constructed. The method comprises the following steps: constructing a simultaneous multi-cause environmental risk feature vector to obtain an environmental comprehensive risk cost, performing layered screening and comprehensive cost sorting on search nodes in combination with task blocking sensitivity of off-line calculation on a mine laneway traffic map, and then generating a time parameterized speed reference trajectory; the Mecanum wheel omni-directional drive mechanism is guided to pass through the high-risk area along the safe and smooth path.

The invention discloses a heavy-load AGV hydraulic suspension attitude keeping method, and belongs to the technical field of heavy-load AGV control, and the method comprises the steps: obtaining a vehicle-road surface joint state vector through sliding mode-extended Kalman filtering, obtaining a vehicle linearization dynamic matrix and a covariance thereof through a gating circulation unit network, and obtaining a vehicle-road surface joint state vector; and on the basis, a model prediction controller of a variable prediction time domain is used for outputting a target cylinder displacement instruction and a pressure instruction, and the instructions are corrected in real time so as to realize dynamic level control of the vehicle platform. According to the method, a sensing, decision-making and execution closed-loop self-adaptive link is formed through a four-source coupled pavement and centroid estimation link, a double-layer decision-making architecture composed of a gating circulation network and model prediction control, and a hydraulic network tension compensation mechanism based on a graph convolution network; and road surface prior, load distribution and multi-cylinder pressure instructions can be synchronously updated in a millisecond-level time scale, so that the dynamic levelness and the energy utilization rate are remarkably improved.

The invention relates to the technical field of high-precision positioning, in particular to a high-precision six-degree-of-freedom magnetic positioning system and method, and the method comprises the steps: generating a three-dimensional low-frequency magnetic induction field with a preset frequency in a space through a three-axis orthogonal transmitting coil; detecting the three-dimensional low-frequency magnetic induction field through a three-axis orthogonal receiving coil to obtain an induced voltage signal; transmitting the digitized induced voltage signal to a control layer; in the control layer, the position and attitude of the receiver are estimated in real time by using a joint estimation algorithm fusing error state extended Kalman filtering and particle swarm optimization; the error state extended Kalman filtering comprises the steps of constructing a state transition matrix F of a target carrying a receiver, and constructing a process noise covariance matrix Qk and a parameterized measurement noise covariance matrix R of the system through translation acceleration noise sigma a and rotation noise sigma omega. According to the invention, the PSO module is utilized to dynamically and adaptively adjust the key parameters of the ESKF, and high-precision positioning and low-delay response are realized.

The invention belongs to the technical field of automated guided vehicles, and particularly relates to a multi-source positioning fusion and navigation correction method and system for an AGV, and the method comprises the steps: carrying out the pose estimation through the bidirectional coupling of extended Kalman filtering and particle filtering, and the estimation uncertainty is fed back to the extended Kalman filter to dynamically adjust the noise parameter. And further introducing sliding window factor graph optimization to carry out back-end smoothing. In the navigation stage, a dynamic decision maker based on deep reinforcement learning is adopted, and optimal control parameters are generated according to real-time positioning, map semantics and historical performance. The system can also enable the filtering model and the decision strategy to adapt to the current environment through periodic online fine tuning. Through innovatively and deeply fusing a classical state estimation method and a leading-edge machine learning method, the positioning precision, the environment understanding capability and the navigation intelligence of the AGV in a complex dynamic scene are remarkably improved.

Systems and techniques are provided for determining a pose. A process can include obtaining inertial measurement unit (IMU) data from an IMU associated with a device. The IMU data can be used to determine a propagated state associated with a state estimation engine, wherein the propagated state includes an initial orientation estimate corresponding to a pose of the device. The state estimation engine can comprise an Extended Kalman Filter (EKF). A predicted orientation measurement can be generated using a first machine learning network to process the IMU data and the initial orientation estimate included in the propagated state associated with the state estimation engine. An updated state associated with the state estimation engine can be determined based on using the predicted orientation measurement to update the propagated state. A device pose estimate can be determined based on the updated state associated with the state estimation engine.

The invention provides an atmospheric data analysis method based on observability analysis, belongs to the field of aircraft navigation, guidance and control, and aims at solving the problem that the precision of traditional atmospheric data measurement is reduced under the condition of a complex flow field or a sensor fault. In the prior art, due to lack of strict theoretical support, estimation errors and convergence problems may occur in a transonic stage. According to the method, an atmospheric data state space model is established through flight dynamics, and atmospheric data estimation is carried out by adopting an extended Kalman filter based on the model; in the estimation process, observability analysis is executed to verify the reliability of an estimation result; and finally, optimizing the system model according to an analysis result. According to the method, the dependence of an existing method on an empirical model is reduced, the convergence and the stability of an estimation result are ensured, and the adaptability and the robustness of a system in a complex environment are remarkably improved.

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

The invention discloses a nano-scale displacement real-time compensation measurement method and system based on double-frequency laser interference, and belongs to the technical field of precision measurement. In order to solve the problems that three error sources of environmental drift, periodic nonlinear error and dynamic vibration are mutually coupled and have high real-time requirements in dual-frequency laser interference measurement, a multi-source error unified state space model including measured displacement, an air refractive index parameter, a nonlinear error parameter and a vibration disturbance quantity is constructed. A high-dimensional state vector is divided into a displacement-refractive index group and a nonlinear parameter group by adopting an extended Kalman filter of a grouping updating strategy, the displacement-refractive index group and the nonlinear parameter group are alternately updated in an assembly line parallel mode, nonlinear parameters are identified in real time in combination with an online recursive least square algorithm, and the state of the filter is corrected through a state coupling injection mechanism. Real-time compensation with the end-to-end delay smaller than 80 microseconds is achieved, the displacement measurement residual error is controlled within + / -0.3 nm under the composite disturbance condition, and the method is suitable for precise assembly of intelligent glasses optical assemblies.

The invention discloses an industrial mechanical arm tail end displacement high-precision detection method based on photoelectric interference, and relates to the technical field of precision measurement and detection, and the method comprises the steps: S1, collecting a photoelectric interference signal, and carrying out down-conversion filtering to obtain a complex envelope signal; s2, calculating an initial displacement observation value based on a complex envelope signal accumulation phase; s3, collecting equipment information to construct a sensing data set, and generating a multi-source error state vector; s4, eliminating errors by using extended Kalman filtering to generate a purification displacement value; and S5, the air refractive index is calculated according to the real-time environment parameters, the purification displacement value is compensated by combining the reference air refractive index, and a corrected displacement value is generated. Through multi-source sensor fusion, Kalman filtering error estimation and air refractive index compensation, high-precision, stable and robust measurement of the tail end displacement of the industrial mechanical arm is realized.

The invention discloses a brushless motor rotor angle determination method and device, equipment and a storage medium. The method comprises the following steps: synchronously acquiring original angle data of a Hall sensor, an incremental encoder and a back electromotive force observation module; respectively determining confidence coefficients of the Hall sensor, the incremental encoder and the back electromotive force observation module according to the rotating speed of the rotor, and performing weighted fusion on the original angle data of the Hall sensor, the incremental encoder and the back electromotive force observation module to obtain an initial fusion angle; based on the motor temperature, the motor phase current and the rotor speed, calculating a temperature drift compensation angle through a segmented temperature drift coupling model, and compensating the initial fusion angle according to the temperature drift compensation angle to obtain a temperature drift compensated angle; and inputting the temperature drift compensated angle into an extended Kalman filter for state estimation and filtering to obtain a rotor angle estimation value at the current moment. By means of the method, high-precision real-time calculation of the rotor angle within the wide temperature range and the full rotating speed range can be achieved.

The invention relates to the technical field of satellite navigation meteorological remote sensing, and discloses a water vapor ionized layer snow depth integrated detection method and system. The method comprises the steps that direct and reflected signals of a Beidou satellite are received through fusion antenna array differentiation, carrier phase and pseudo-range observation values are obtained through carrier tracking loop synchronous demodulation, the total atmospheric rainfall amount and the total ionosphere electron content are extracted through a troposphere and ionosphere combined calculation algorithm, the snow depth is inverted through reflection path geometric difference, and the snow depth is calculated. And carrying out space-time synchronization constraint processing on the extended Kalman filter to realize multi-parameter integrated detection. The problems of low efficiency, insufficient precision and lack of cooperative processing caused by independent observation of water vapor, ionized layer and accumulated snow parameters in the prior art are solved, and the observation efficiency and the measurement precision of multi-parameter integrated detection are improved.

The invention discloses a fast beam tracking method and system for a high-dynamic group, and belongs to the technical field of wireless communication. The method comprises the steps that a base station obtains initial angle information of a terminal through a spectrum estimation method; the two auxiliary wave beam pairs are used for sending signals, the terminal measures the receiving power and calculates the power ratio, and an actual physical angle is obtained through inverse solution; taking the angle as an observed quantity of an extended Kalman filter, and realizing dynamic beam direction estimation through state prediction and correction; monitoring the received power in real time to perform beam misalignment judgment, and triggering reinitialization if the received power is misaligned; and inputting an angle estimation residual error output by the extended Kalman filter into the long short-term memory network, and performing residual error modeling and prediction compensation on an estimation result. The system comprises corresponding modules for implementing the steps. The method combines the auxiliary beam pair, the extended Kalman filter and the long short-term memory network, improves the beam tracking precision and robustness, and is suitable for high-speed moving scenes such as an unmanned aerial vehicle group.