457 results about "Extended Kalman filter" patented technology

The present invention relates to an automatic speed regulation and grouting regulation system for construction of a deep mixing pile in a reclamation area, the main steps comprising: first, configuring and installing a multi-modal sensing system (S101) to monitor key parameters, torque, energy consumption, grouting pressure and stratum permeability of a construction site in real time; constructing a data acquisition and fusion module (S102), integrating multi-sensor data, and forming real-time mapping of stratum-construction parameters; establishing a relation model of formation input and formation response through an extended Kalman filter (EKF) and predictive control (MPC) algorithm (S103), and intelligently evaluating formation compactness and grouting quality; on the basis of a feedback closed-loop regulation and control system (S104), the stirring speed, the grouting amount and the construction pressure are adaptively adjusted in real time, and precise control is achieved; through construction data storage and self-learning optimization (S105), the system records all construction data, and through a deep reinforcement learning optimization control algorithm, the long-term construction precision is improved.

The invention relates to the technical field of nondestructive testing, and discloses an ultrasonic nondestructive testing method and system for a welding seam of a steel structure. The method comprises the following steps: adopting a self-adaptive coupling ultrasonic probe to carry out signal acquisition on the surface of a welding seam of a steel structure to obtain original ultrasonic signal data; performing multispectral adaptive wavelet transform processing on the original ultrasonic signal data to obtain an enhanced ultrasonic signal; performing blind source separation on the enhanced ultrasonic signal to obtain a separated weld defect signal; performing triangular constraint average interpolation and adaptive extended Kalman filtering fusion processing on the separated weld defect signals to obtain filtered weld defect feature data; and performing regularization Gaussian field weld contour reconstruction and defect positioning processing based on the filtered weld defect feature data to obtain a weld contour model and defect position data. According to the invention, the stability and reliability of weld defect detection are improved, and high-precision detection of weld internal defects is realized.

The invention discloses a dynamic control method of a lithium battery module intelligent equalization management system, and relates to the field of lithium battery module equalization, and the method comprises the steps: constructing a hierarchical data fusion platform, and generating a battery state data set containing SOC, SOH and an inconsistency evaluation value; a credibility reasoning algorithm is adopted to calculate a single body imbalance credibility factor value to divide an equalization stage, and equalization current is dynamically adjusted; a charge and discharge curve health factor is extracted through an LAB health factor algorithm, SOH is updated online in combination with extended Kalman filtering, and an SOH attenuation rate is fed back to the battery model; in-module equalization is carried out through a bidirectional energy transfer circuit, inter-module equalization is carried out through a ring bus topology, and layered topology linkage control is formed. The method has the advantages that the consistency and the heat management efficiency of the battery pack are improved through the hybrid equalization strategy and the layered topology linkage control, the service life of the battery pack is prolonged, and the overall performance and the safety of the electric vehicle are improved.

The invention relates to the field of low-altitude airspace monitoring, and relates to a low-altitude monitoring system based on multi-source heterogeneous perception fusion and rule engine driving, which comprises a perception access layer used for completing data standardization and protocol adaptation and realizing data stream release through message middleware; the processing calculation layer is used for generating a structured alarm event; the application support layer is used for three-dimensional visual interactive display of target situation data and alarm events, unified access of multiple types of sensors such as radar, photoelectricity, radio frequency, ADS-B and meteorological sensors is realized through a plug-in protocol adapter, and target state estimation and track smoothing are realized by combining space-time registration and extended Kalman filtering. The limitation that an existing system can only process a single data source or simple superposition is overcome, and the target recognition precision and the track continuity are remarkably improved.

The invention discloses an adaptive environment weighted data fusion navigation and positioning method for a manned submersible in a polar region, which belongs to the technical field of ocean navigation and positioning, is used for navigation and positioning of the submersible in the polar region, and comprises the following steps: acquiring data of IMU (Inertial Measurement Unit), acoustics, optics, environment sensors and Doppler sensors, and carrying out de-noising, time synchronization and coordinate conversion; modeling the polar region environment variable, calculating the adaptive weight, and dynamically adjusting the contribution degree of the sensor by adopting an exponential decay function; introducing an environment factor to correct the weight, and performing nonlinear state estimation by adopting an EKF (Extended Kalman Filter); performing error modeling by using historical data, and optimizing a weight adjustment strategy in combination with machine learning; and filtering optimization is performed on the fusion result, and multi-scale correction is performed in combination with prior environment information. Compared with the prior art, the influence of a single sensor error on a final positioning result is reduced, the positioning precision of the submersible in a polar region complex environment is improved, and the stable navigation capability is kept under the condition that sensor data suddenly changes or is lost.

The invention relates to the technical field of data processing, and discloses a VICTS antenna attitude estimation and correction method and system based on satellite pointing feedback, and the method comprises the steps: constructing a tight coupling depth fusion system with extended Kalman filtering as a frame, introducing satellite communication antenna pointing information as a new observation source on the basis of traditional IMU / GNSS fusion, and carrying out the satellite communication antenna attitude estimation and correction. By deducing the Jacobian matrix of the carrier attitude, effective fusion of the observed quantity is realized, so that the attitude estimation precision and robustness of the carrier are reversely corrected and remarkably improved, the three-axis attitude error is estimated and corrected more comprehensively and steadily, and the problem of lack of roll angle observation is fundamentally solved. Observation information of different dimensions is unified into the same state estimation framework through accurate mathematical modeling, and the precision, autonomy and robustness in a complex environment of the system are greatly improved.

The invention relates to the technical field of forklift driving stability control, in particular to a forklift driving stability control system based on ultrasonic and laser composite ranging, which comprises a composite ranging module, a multi-modal data fusion module, a stability prediction and control module, an execution mechanism control module and an online learning system. The composite distance measuring module collects surrounding environment information and corrects motion errors through a dynamic compensation algorithm; the multi-modal data fusion module dynamically adjusts the weight of the sensor based on an environmental interference factor, and realizes space-time calibration in combination with extended Kalman filtering; the stability prediction and control module is fused with the vehicle dynamic characteristics to predict the rollover risk and generate a hierarchical control instruction; the actuating mechanism control module drives actuating components such as an active suspension and an electronic differential mechanism through a layered framework. The method can adapt to complex working conditions, the prediction precision and fault-tolerant capability are improved, safety and efficiency are balanced, the maintenance cost is reduced, and the method is suitable for various forklifts.

The invention particularly relates to a lead-acid battery SOC accurate calculation and lead-acid battery polymorphic anomaly detection and protection method, which comprises the following steps: firstly, collecting and preprocessing multi-dimensional data information of a lead-acid battery, and then respectively calculating the preprocessed data based on a preset ampere-hour integral method and an improved extended Kalman filtering method; meanwhile, the battery operation condition is recognized through a preset condition recognition algorithm, and then dynamic weight fusion is conducted on calculation results of the two methods according to the condition to obtain a first fusion result; and then temperature compensation correction is performed on the result by using a preset temperature compensation model, and finally correction is performed in sequence by using a preset internal resistance-SOC relationship and an aging correction model to obtain a first SOC value. Through multi-dimensional data fusion, dynamic working condition weight distribution, temperature compensation and multi-mode cooperative protection, the SOC estimation error of the lead-acid battery is reduced, the safety and environmental adaptability under complex working conditions are improved, and meanwhile, intelligent interaction and low-power-consumption management are supported.

The invention discloses a multi-parameter coupling intelligent regulation and control method and system in a vanadium ore smelting process, relates to the technical field of non-ferrous metal smelting automatic control, and solves the problems of feeding deviation and resource chain scission caused by slow response of an existing heat balance model to ore impurity fluctuation, online sensing lag and process island. The impurity concentration and the dielectric constant are obtained in real time through an LIBS / XRF combined probe and a microwave dielectric spectrum, rheological disturbance is dynamically corrected through extended Kalman filtering, a dynamic coupling response model is input, a thermodynamic interference factor is calculated, and a leaching and precipitation strategy is generated; performing mother liquor tail gas material flow collaborative optimization in parallel, constructing a multi-dimensional control vector U, performing iterative updating through near-end strategy optimization in a digital twin and issuing the multi-dimensional control vector U to an edge PLC (Programmable Logic Controller) in a distributed manner, and finally correcting a model weight through online XRD (X-Ray Diffraction) feedback; according to the method, the adaptive capacity to complex vanadium ore component disturbance, the response precision of online control and the inter-process resource circulation efficiency are remarkably improved.

The invention relates to the technical field of aerocar mode switching, and discloses a distributed driving aerocar self-adaptive fault-tolerant control method for actuator multimode failure, which comprises the following steps: constructing a unified six-degree-of-freedom dual-mode state space model; residual signals are generated based on extended Kalman filtering and a sliding-mode observer, and fault types and positions are positioned in real time through a lightweight classifier; the method comprises the following steps: extracting residual time-frequency features, identifying hard faults by using a lightweight convolutional neural network, quantifying soft fault degrees through an incremental support vector machine, fusing multi-source information based on a Bayesian network to output fault types, levels and confidence coefficients, and introducing an incremental learning mechanism to realize self-evolution of a diagnosis model; a virtual control instruction is generated by adopting hierarchical sliding mode control, thrust and torque distribution of remaining actuators is optimized based on a dynamic quadratic programming algorithm, control parameters are adjusted online in combination with a Lyapunov adaptive law, aerodynamic interference and model uncertainty are inhibited, attitude stability and trajectory tracking in air-ground mode switching are guaranteed, and the method has the advantages of being high in reliability and high in reliability. And the fault-tolerant performance and the operation safety of the hovercar in the air-ground mode switching process are obviously enhanced.

The embodiment of the invention discloses a multi-sensor fusion anti-degradation SLAM mapping method and system. The method can effectively solve the problem of pose drift of a robot in a mapping process in structure degradation environments such as an indoor long corridor, constructs a globally consistent three-dimensional point cloud map and a robot trajectory, and comprises the following steps: realizing depth coupling of an IMU and a wheel speedometer based on extended Kalman filtering, and generating high-frequency pose prediction; denoising, down-sampling and motion distortion correction are carried out on the 4D laser radar point cloud, and the normal vector and intensity characteristics of the point cloud are extracted; a normal vector and intensity feature enhanced scanning matching algorithm is adopted, and a target function is optimized through a multi-feature weight, so that the matching precision in a degradation scene is improved; loopback detection is realized through candidate key frame screening and geometric registration verification, and a closed-loop constraint is incorporated into a factor graph for global correction; and finally, incrementally updating the global point cloud map and carrying out consistency optimization, and outputting a robust three-dimensional point cloud map and a high-precision robot track.

The invention relates to the technical field of agricultural machinery control, in particular to a multi-variety vegetable harvester dynamic identification and feeding control system based on image processing, which comprises an image acquisition module, an environment sensing module, an image processing module, a data fusion module, a dynamic identification and tracking module and a feeding control module. Through binocular stereo vision and millimeter wave radar fusion, three-dimensional space positioning and dynamic tracking of the brassica oleracea corm are realized. Track prediction is carried out in combination with extended Kalman filtering and a long-short-term memory network, and the clamping path of the mechanical arm is optimized; the clamping force is adaptively adjusted through fuzzy PID control, and accurate feeding of the cabbages is achieved in combination with speed control of the conveying belt. The automatic cabbage harvesting machine improves the automation level of cabbage harvesting, effectively reduces the damage rate, improves the operation stability and harvesting efficiency, and is suitable for intelligent harvesting operation of a large-scale cabbage planting base.

The invention discloses a cavity environment real-time monitoring method based on multi-mode perception and a related device. The method comprises the following steps: synchronously acquiring cavity vision, contact force and electromagnetic positioning data; respectively refining the three types of data; extracting YOLO features from the refined visual data, outputting dynamic tactile features from the contact force data by using a mamba model, and refining the positioning data into positioning features through extended Kalman filtering; respectively generating classification results based on the three types of features, fusing through weighted voting, and mapping the visual and tactile features to a unified three-dimensional coordinate for association marking; and finally, generating an environment state decision according to judgment and association results. According to the invention, multi-mode real-time perception and risk early warning of the cavity environment are realized, and the operation safety and efficiency are improved.

The invention relates to the technical field of safety monitoring and early warning, and discloses a global safety monitoring and intelligent early warning system and method for construction site tower crane operation, and the method comprises the steps: deploying a multi-layer sensor network at a plurality of target parts of a construction site tower crane, and collecting the operation state data of the tower crane; carrying out extended Kalman filtering processing on the tower crane operation state data to obtain a tower crane sensor installation angle parameter and a non-integrity constraint lever arm parameter; performing global state estimation based on the tower crane sensor mounting angle parameter and the non-integrity constraint lever arm parameter to obtain a tower crane global state and external disturbance data; inputting the global state of the tower crane and the external disturbance data into a deep neural network, and performing safety boundary calculation to obtain a dynamic safety boundary model of the construction site tower crane; and performing risk measurement calculation based on the dynamic safety boundary model to obtain a safety risk grading early warning instruction, thereby improving the safety and collaboration of construction site tower crane operation.

The invention discloses an unmanned aerial vehicle detection and tracking system based on multi-sensor fusion and a deep learning technology. The unmanned aerial vehicle detection and tracking system comprises a data collection layer, a data processing layer, an extended Kalman filter (EKF) estimation layer and a deep learning layer. The system firstly judges an unmanned aerial vehicle needing to be tracked, then obtains target distance, speed, direction, image information and the like through a data collection layer (a radar, a camera and an RF signal collection device), and the data processing layer completes time synchronization, denoising, data standardization and data enhancement processing. The EKF estimation layer performs real-time estimation on target motion states (position, speed, acceleration and the like), and the deep learning layer can adopt a target detection and classification model method to realize detection, classification and tracking of the unmanned aerial vehicle in combination with EKF output and image features. The system can detect and track a single unmanned aerial vehicle in a target airspace and a key unmanned aerial vehicle of an unmanned aerial vehicle cluster, and is suitable for the field of airspace management and security.

The invention discloses a fusion filtering attitude estimation method based on EKF and Mahony, and the method comprises the steps: firstly obtaining the original data information of an IMU sensor at the current moment, carrying out the normalization of the attitude information of an accelerometer and a magnetometer, carrying out the error calculation, and compensating the error to the angular velocity of a gyroscope through a PI controller, the method comprises the following steps: performing Mahony filtering correction on an angular velocity and a corrected attitude quaternion to obtain an angular velocity subjected to Mahony filtering correction and a corrected attitude quaternion, constructing a state equation of an EKF by using the corrected attitude quaternion and a gyroscope zero offset value, and constructing an observation equation by using data of an accelerometer and a magnetometer so as to obtain an optimally estimated attitude quaternion and a gyroscope zero offset value; and finally, compensating the optimally estimated zero offset value of the gyroscope to the angular velocity correction process in the Mahony filtering process.

The invention discloses a real-time power generation load estimation method and system for photovoltaic power generation, relates to the technical field of photovoltaic power generation load estimation, and aims to solve the problems of state judgment delay and model mismatching and misestimation caused by short-time cloud shadow rapid movement. The method comprises the following steps: collecting power, current, voltage, irradiance and temperature signals through a multi-array sensor, separating a high-frequency characteristic signal from a low-frequency characteristic signal through high-pass / low-pass filtering, and generating a static baseline multi-dimensional characteristic vector; based on the power spectrum entropy, adaptively adjusting a time window and extracting dynamic characteristics; fusing the static and dynamic characteristics to generate discrimination input; performing state judgment by adopting a logistic regression model, and starting extended Kalman filtering online correction when an energy residual exceeds a threshold; and model drift is detected based on CUSUM and Shewhart algorithms, and an incremental training set is automatically constructed to update model parameters. And high-precision and low-delay real-time power generation load estimation and operation and maintenance support under a short-time cloud shadow condition are realized.

The invention is suitable for the technical field of permanent magnet synchronous motors, and provides a permanent magnet synchronous motor current prediction control method based on parameter identification, which comprises the following steps: constructing a permanent magnet synchronous motor current prediction control model; performing parameter sensitivity analysis based on the constructed current prediction control model; and according to a parameter sensitivity analysis result of the current prediction control model, carrying out online identification on parameters of the permanent magnet synchronous motor by adopting improved extended Kalman filtering, sending the identified parameters to the current prediction controller in real time, and updating the parameters in the current prediction controller. And the current error of the current prediction control of the permanent magnet synchronous motor during parameter mismatch is reduced. According to the permanent magnet synchronous motor current prediction control method based on improved extended Kalman filtering parameter identification provided by the invention, the identification precision of extended Kalman filtering on motor parameters is improved, so that the current prediction control performance of the permanent magnet synchronous motor is effectively improved under the condition that the motor parameters are mismatched.

The invention provides a three-dimensional tracking method applied to a single-photon laser radar to detect a far-field dynamic unmanned aerial vehicle point target, and belongs to the technical field of laser radar detection and target tracking. The problems that in the prior art, under the long-distance condition, the target imaging size is smaller than one pixel, and a detection and tracking algorithm based on shape, texture or edge features fails are solved. The method comprises the following steps: constructing a six-dimensional state vector containing a three-dimensional position and a three-dimensional speed of a target; predicting a state vector according to a state transfer function containing air resistance and turning acceleration; and an extended Kalman filtering framework is utilized to update the predicted state and obtain state estimation of the target, and the extended Kalman filtering framework comprises a self-adaptive process noise adjustment mechanism based on innovation feedback and is used for adjusting a process noise covariance matrix on line. The method is mainly used in the field of detection and multi-dimensional tracking of airspace moving targets.

The invention discloses an underground combined positioning method based on UWB and IMU, and the method comprises the following steps: arranging a UWB base station and a motion capture system in a positioning area, obtaining the distance between the base station and a label through UWB distance measurement, collecting IMU speed data through the movement of a person, recording the position of the label on the person through the motion capture system, and constructing a data set; constructing a positioning model based on the data set and the bidirectional long-short-term memory neural network; positioning the personnel through the positioning model to obtain a predicted positioning result; and fusing the predicted positioning result and IMU data through extended Kalman filtering to obtain a final positioning result. According to the invention, the accurate position of the underground coal mine personnel can be accurately positioned.

The invention discloses a rigid-flexible integrated aerial contact type operation robot and a control method. The robot comprises a full-drive type unmanned aerial vehicle platform, a single-degree-of-freedom omnidirectional rotation rigid mechanism and a single-section soft arm. The control method comprises the steps that a coordinate system is constructed, and a forward kinematics model of the air contact type operation robot system is established; constructing an inverse kinematics model of the single-section soft arm; designing a self-adaptive inverse kinematics control algorithm based on reinforcement learning on the basis of the inverse kinematics model of the single-section soft arm; according to the method, an all-drive unmanned aerial vehicle platform dynamic model is established, a nonlinear model prediction control method based on an extended Kalman filter estimator is designed, lateral force input of the all-drive unmanned aerial vehicle platform can be restrained, and accurate tracking of the all-drive unmanned aerial vehicle platform under the condition of additional disturbance is guaranteed. Therefore, flexible control in the air in a limited environment is realized by using a motion control method and a soft arm inverse kinematics control algorithm of the all-wheel-driven unmanned aerial vehicle platform.

The invention relates to an agricultural robot, and discloses an animal husbandry pushing robot autonomous navigation method based on a laser radar, which comprises a microsecond-level multi-sensor time synchronization architecture based on an FPGA (Field Programmable Gate Array); in a navigation layer, seamless switching between the GNSS and the laser SLAM is realized by constructing a virtual fusion observation value by utilizing extended Kalman filtering and a self-adaptive dynamic weight algorithm; in the sensing layer, an RGB-I multi-mode model fused with laser reflectivity and a biological micro-motion detection technology are adopted, and feed and biological obstacles are accurately distinguished; and in the execution layer, flexible self-adaptive material pushing is achieved in combination with material groove edge fitting and force-position hybrid control, and the navigation precision, the operation quality and the safety of the material pushing robot in the all-weather and unstructured pasture environment are effectively improved.

The invention discloses a vehicle-mounted voice identification and positioning intelligent early warning system and method, and relates to the technical field of vehicle-mounted intelligent early warning. The system comprises a sound detector module which collects an audio signal of a surrounding environment in real time, and a sound recognition module which analyzes the audio signal through a deep learning model and recognizes a sound source type. And the calibration module performs space-time calibration to ensure data synchronization and alignment of the sensors. The positioning module combines time difference of arrival (TDOA) and a sound intensity attenuation model, and optimizes sound source positioning through particle swarm optimization (PSO) and extended Kalman filter (EKF) technologies. The risk assessment module assesses the collision risk based on the sound source position, the shielding state and the object type, gives an alarm to a driver in time through the early warning module, and automatically triggers a deceleration instruction. And the self-learning module dynamically adjusts the early warning sensitivity and the noise compensation coefficient through a federated learning framework updating model. The environmental adaptability of the vehicle-mounted early warning system is improved, and the vehicle-mounted early warning system has remarkable safety improvement and wide application prospects.

The invention discloses an offshore wind power prediction method and device based on multi-model collaborative fusion. The method comprises the steps that historical data and original complex environment data of offshore wind power are acquired; the method comprises the following steps: decomposing multivariate data by utilizing multivariate joint mode decomposition, decomposing a signal into a plurality of mode component IMFS with different frequencies, and introducing mutual information entropy to divide a mode component data set into high-frequency components and low-frequency components; through generalized S transformation, obtaining a time-frequency diagram feature of each modal component; a VTransform prediction model and a TiDE prediction model are established to predict a high-frequency component and a low-frequency component respectively, and an improved Newton-Raphson algorithm INRBO is used to optimize model hyper-parameters; and carrying out weighted fusion on prediction results of the two models by utilizing an INRBO algorithm, and carrying out error correction on weighted results by adopting an extended Kalman filter (EKF) model to obtain a final prediction result. According to the method, the precision of offshore wind power prediction can be improved, and the power grid dispatching and the operation of the offshore wind power plant are optimized.

The invention relates to the technical field of remote monitoring, and discloses a high-precision holder control method and system. Comprising the steps of collecting a target scene image, performing denoising, distortion correction and resolution adjustment, obtaining a target pixel coordinate and confidence through a lightweight target detection model, and calling a corresponding image enhancement model to perform preprocessing when an abnormal environment is detected; transmitting a part of detection results as key frames to an edge end based on a frame extraction strategy, caching the rest of the detection results locally, and switching to a local tracking mode and reducing control frequency when network delay exceeds a threshold value; fusing visual detection data and inertial measurement data, and predicting a target position through extended Kalman filtering; carrying out sub-pixel-level refinement on the target position, and calculating a three-dimensional orientation in combination with a participated holder attitude in a camera; and adjusting the attitude of the holder in real time based on a prediction result and an anti-saturation integral separation PID controller.

Exemplary system, method and computer-accessible medium for selecting at least one location of (i) at least one receiver or transceiver or (ii) at least one transmitter or transceiver can be provided. For example, it is possible to facilitate a receipt, from the at least one transmitter or transceiver, of a plurality of signals by the receiver(s) or transceiver(s). Each of the signals has a multipath component. Then, it is possible to determine time of flight (ToF) information and angle of arrival (AoA) information of the multipath components present in the signals. Further, it is possible to determine one or more possible locations of (i) the receiver(s) or transceiver(s) or (ii) the transmitter(s) or transceiver(s) based on the ToF information, the AoA information, and a model of physical surroundings. The location(s) of (i) the receiver(s) or transceiver(s), or (ii) the transmitter(s) or transceiver(s) can be selected based on the one or more possible locations and an extended Kalman filter (“EKF”).

The invention relates to the technical field of positioning navigation, in particular to a vehicle-mounted IMU (inertial measurement unit) data compensation method for intelligent driving inertial navigation, which comprises the following steps: acquiring various attitude data of a vehicle-mounted IMU through a rotating platform, acquiring a reference quantity, establishing a target function, and acquiring an offline calibrated rotating error matrix and a scale factor matrix by adopting an optimization algorithm; the method comprises the following steps: acquiring operation original measurement in a vehicle operation process, defining a system state vector, analyzing a Taylor expansion approximation relation between a cross multiplication matrix corresponding to an error small angle and a rotation error matrix, and constructing a discrete time state equation and an observation equation based on an IMU kinematics model; acquiring an error small angle after each update by adopting extended Kalman filtering; and carrying out real-time compensation on the vehicle-mounted IMU data in combination with the operation original measurement at each moment. The invention aims to improve the precision and robustness of inertial navigation and meet the requirement of intelligent driving for continuous and stable positioning.

The invention discloses an underway real-time GNSS acoustic underwater positioning method, which belongs to the technical field of underwater positioning, is used for acoustic underwater positioning, and comprises the following steps: constructing an extended Kalman filtering solution model, constructing a constraint equation by taking coordinate prior information of a seabed transponder as a constraint, and solving an initial state parameter and an initial covariance matrix; and processing ill-conditioned problems in the initial state parameter and initial covariance matrix solving process by adopting a Tikhonov regularization method, substituting the initial state parameter and an initial covariance matrix value into an extended Kalman filtering solution model, and carrying out real-time GNSS acoustic underwater positioning on the submarine transponder. The real-time positioning method for the GNSS-acoustic seabed transponder solves the possible ill-conditioned problem in the solving process, has good performance of short convergence time and high precision, can improve the real-time positioning efficiency of the GNSS-acoustic seabed transponder, and is expected to provide powerful technical support for seabed plate movement, geological disasters, disaster early warning and seabed geodetic datum updating.

The invention provides an intelligent networked vehicle intrusion detection method based on a time sequence and vehicle dynamics, belongs to the technical field of intelligent networked vehicle information security, and fuses a deep learning time sequence prediction model, a vehicle dynamics model and expert experience. A high-level vehicle state of the automatic driving system is predicted through a deep learning model, and a physical state is estimated by combining vehicle dynamics and expert experience to form comprehensive state estimation. And fusing the real-time measurement value by using extended Kalman filtering to obtain optimal state estimation. The precise intrusion detection is realized by calculating the residual error of the optimal estimation and the real-time measurement value, constructing a historical residual error data set, learning normal residual error distribution by adopting a clustering method, and defining a hyper-sphere boundary envelope normal sample. According to the method, the vehicle state estimation precision, the abnormal behavior detection accuracy and the system safety are remarkably improved, and a technical support is provided for safety protection of the intelligent networked vehicle.

The invention discloses an online self-calibration filtering method for inertial navigation, and relates to the technical field of inertial navigation, and the method comprises the following steps: obtaining multi-modal data, and establishing an inertial device error mathematical model and an inertial navigation system error mathematical model; carrying out recursive estimation on the state variables by the extended Kalman filtering model to obtain error parameters of the inertial device and the navigation system; constructing a fuzzy adaptive model based on the error parameters, generating an observation noise correction factor through a fuzzy logic rule, and correcting an observation noise covariance matrix in the extended Kalman filtering model in real time; carrying out online estimation and compensation on error parameters of the inertial navigation system by utilizing an extended Kalman filtering model, and outputting compensation parameters containing zero offset and scale factors in real time; correcting the original data based on the compensation parameters, injecting the original data into the navigation solution, and updating the fuzzy adaptive model according to the compensated error data.