942 results about "Inertial navigation system" patented technology

The invention discloses a vehicle positioning method and system based on UWB and vehicle constraint, and relates to the field of vehicle positioning, and the method comprises the steps: deploying a UWB reference station in a well mining environment, and enabling a vehicle to be provided with a UWB tag; obtaining distance measurement data of at least two UWB reference stations, and detecting and compensating the distance measurement data to obtain a vehicle UWB position; collecting vehicle-mounted IMU (Inertial Measurement Unit) data, and obtaining a vehicle attitude quaternion through attitude calculation; collecting vehicle wheel speed meter data, and correcting the vehicle wheel speed meter data according to vehicle motion constraint conditions; according to the vehicle UWB position, the vehicle attitude quaternion and the corrected vehicle wheel speed meter data, the vehicle position is obtained through an adaptive Kalman filtering fusion algorithm. Aiming at low positioning precision caused by easy accumulation of errors and drifting of a traditional inertial navigation system in a well mine closed space in the prior art, the invention reduces the influence of a multipath effect in the traditional inertial navigation system on the positioning precision.

The invention relates to the technical field of communication, in particular to an airborne Tiantong phased-array antenna and an application method thereof. According to the method, phased-array antenna beams are driven to perform electronic scanning according to a geometric azimuth angle, beacon signal blind acquisition is performed in a preset airspace in a wide beam mode, and when an initial signal is detected, the mode is immediately switched to a narrow beam scanning mode; dynamically adjusting a phase weighting coefficient to realize pointing adjustment, so that a signal quality parameter is continuously converged in an optimal threshold interval; carrying aircraft maneuvering acceleration feedback is introduced, beam pointing offset is dynamically compensated based on a pre-constructed interference feature library, and signal fluctuation caused by a multipath effect is suppressed; and continuously checking a data validity flag bit of the main inertial navigation system, seamlessly switching to an antenna built-in inertial navigation data stream when a failure threshold is triggered, and activating a rapid recapture program to maintain zero interruption of a communication link. Through dual-source inertial navigation data fusion and intelligent beam control, rapid and accurate tracking of Tiantong satellites in an airborne environment is realized.

The invention relates to the technical field of calibration test, and particularly discloses a strapdown inertial navigation automatic calibration test method of a temperature control biaxial rate position turntable. Comprising the following steps: S1, multi-dimensional error modeling and layered calibration architecture: constructing a comprehensive error model containing a sensor scale factor error, an installation non-orthogonal error, a zero offset error, a cross axis coupling error and a temperature related error; s2, a wide temperature range self-adaptive compensation mechanism: carrying out heat preservation at a preset temperature point and executing turntable positioning and rate movement; s3, performing automatic data processing and parameter estimation; and S4, performing real-time error correction and dynamic verification: performing real-time error correction based on the estimated parameters, and evaluating navigation performance indexes such as position, speed and attitude precision. Through technical innovation, high-precision, automatic and wide-temperature-range calibration of the strapdown inertial navigation system is realized, and the method is suitable for aerospace, intelligent equipment and other scenes with strict navigation precision requirements, and has remarkable engineering application value and market competitiveness.

The invention relates to the technical field of underwater intelligent navigation and integrated navigation, in particular to a submersible integrated navigation multi-source data fusion method based on an elastic random model, which comprises the following steps: preprocessing multi-source navigation data output by an inertial navigation system (INS), a Doppler velocimeter, an ultra-short baseline / long baseline, an altimeter and a depthometer; constructing a state space model containing navigation parameter errors and sensor errors, and screening an optimal sensor combination mode; the method comprises the following steps: constructing an elastic correction parameter model and a dynamic adjustment state and observation noise covariance matrix by combining an elastic PNT framework, performing local filtering on multi-source data by adopting a plurality of parallel elastic self-adaptive robust volume Kalman filters (CKF), outputting a fusion resolving result, and realizing optimal estimation of a global navigation state through a dynamic information distribution strategy. The method has relatively high anti-interference performance, self-adaptive capability and combination switching robustness, and is suitable for navigation and positioning tasks of a high-precision submersible in a complex underwater environment.

The invention provides an unmanned aerial vehicle obstacle avoidance method and system based on inertial navigation, and belongs to the technical field of unmanned aerial vehicle obstacle avoidance systems. The problem that an existing unmanned aerial vehicle is insufficient in obstacle avoidance capacity under the environment that GPS signals are weak, vision is limited or a sensor detection blind area exists is solved. Acquiring broadband high-frequency dynamic response data output by the inertial navigation system of the unmanned aerial vehicle in real time; establishing or acquiring a reference high-frequency dynamic response model when the unmanned aerial vehicle is in a specific flight state and a power system working mode and is not influenced by an external obstacle; the signal is dynamically responded according to high frequency; identifying whether a specific air medium disturbance echo feature formed by disturbance generated by motion of the unmanned aerial vehicle through an external obstacle exists or not; and finally, generating and executing an obstacle avoidance flight instruction according to the estimation information. The method does not depend on a traditional external environment sensor for initial detection, physical interaction is sensed through the INS, and the autonomous obstacle avoidance capacity and safety of the unmanned aerial vehicle in the complex and challenging environment are improved.

The invention relates to the technical field of communication engineering, in particular to a communication optical cable line intelligent inspection fault point rapid positioning method and device, and the method comprises the steps: obtaining optical cable line state data, constructing an intelligent fault detection model, recognizing an abnormal signal through wavelet transform and a threshold determination method, training an LSTM neural network in combination with historical fault data, and pre-judging a fault type. When a fault is detected, an unmanned aerial vehicle inspection unit is triggered, an inspection path is planned based on an improved Dijkstra algorithm according to the fault type and suspected fault area geographic information, an optical cable line is scanned and detected through a laser radar, physical fault features are recognized through a YOLOv5 algorithm, and geographic coordinates of a fault point are calibrated in combination with an inertial navigation system; and fusing sensor data and a detection result, evaluating a fault confidence level by adopting a DS evidence theory, and generating an alarm work order. Therefore, the problems of long positioning time consumption, high omission ratio, difficulty in distinguishing fault types and the like in the prior art are solved.

The invention relates to the technical field of underwater navigation, and discloses a real-time attitude estimation method of an underwater inertial navigation system. The method comprises the steps that the underwater vehicle collects multi-axis angular velocity and linear acceleration data through an inertial measurement unit and uploads the data to a central processing module, and the central processing module conducts kinematics modeling on the data to generate attitude change initial feature vectors; receiving water flow velocity data of a Doppler velocimeter and pressure data of a depth sensor, and performing flow field interference compensation optimization on the attitude change initial feature vector based on the data to generate an anti-interference attitude feature vector; fusing the anti-interference attitude feature vector and a historical attitude estimation result to generate space-time fusion attitude state representation, and then resolving a real-time Euler angle through a nonlinear filter; and matching a corresponding attitude stability control instruction from the navigation strategy library according to the real-time Euler angle, and issuing the attitude stability control instruction to a propeller execution unit. The method can effectively cope with underwater complex environment interference and guarantee stable operation of the underwater vehicle.

The invention provides an intelligent road crack detection method, and belongs to the technical field of electrical digital data processing.The intelligent road crack detection method comprises the steps that firstly, a line structured light scanner is adopted to obtain road surface point cloud data, and attitude compensation processing is conducted in combination with an inertial navigation system; then noise is eliminated through bidirectional moving average filtering, and the depth matrix is converted into an image and segmented; gaussian background modeling is used to screen abnormal points, a connected graph is constructed through a minimum spanning tree algorithm, and pruning is carried out to obtain a crack skeleton; using a feature enhancement function to optimize the crack saliency; and finally, inputting a pre-trained multi-scale pavement damage identification network, and outputting a crack identification result and classification. According to the method, three-dimensional point cloud data, geometric feature analysis and a deep learning technology are fused, the technical problem of poor adaptability of a complex pavement environment is solved, and accurate identification of various crack types is realized.

The invention provides a visual navigation method and system based on an improved optical flow method, and relates to the technical field of computer vision and inertial navigation. A collaborative optimization framework of an inertial navigation system and visual feature navigation is constructed, carrier motion state parameters are obtained through dead reckoning of an inertial measurement unit, variance parameters of pose variation are calculated, and the layering depth of a pyramid LK optical flow method is dynamically adjusted based on the variance parameters. When the inertial navigation output variance exceeds a preset threshold value, the number of layers of an image pyramid is increased to cope with large-range motion, and otherwise, calculation levels are reduced to improve real-time performance. A dual-period optical flow / feature matching tracking mechanism is designed, improved sub-pixel-level optical flow tracking is adopted in a short period, the calculation complexity is reduced while the positioning precision is guaranteed, feature matching is switched to in a long period, accumulative errors are eliminated, and a period parameter N is determined by inertial navigation precision. According to the method, the calculation complexity can be reduced, and a better effect can be achieved on a low-calculation-performance platform.

Disclosed in the present invention are a multi-source aviation remote sensing data acquisition apparatus, system and control method, which are applied to the technical field of aerial survey remote sensing. The apparatus comprises: a flight platform and an airborne device disposed on the flight platform. The airborne device comprises: a multi-sensor ultra-compact integrated device, an airborne integrated synchronization control device, and a GNSS receiving antenna. The multi-sensor ultra-compact integrated device and the airborne integrated synchronization control device are connected by means of cables, and the GNSS receiving antenna is disposed on an upper end face of the flight platform. The multi-sensor ultra-compact integrated device is used for acquiring multi-source remote sensing data; and the airborne integrated synchronization control device is used for controlling the operation of the multi-sensor ultra-compact integrated device. The present invention ultra-compactly integrates a LiDAR, a hyperspectral camera, a thermal infrared camera, and a visible-light camera on the basis of a high-precision inertial navigation system, and achieves efficient synchronous acquisition of texture, spectral, temperature, and geometric information by means of using an integrated control subsystem.

The invention discloses a GNSS (Global Navigation Satellite System) / inertial navigation tightly integrated navigation method and system based on robust auto-velocity constraint factor graph optimization, and the method specifically comprises the following steps: extracting pseudo-range and Doppler observation data from original observation information of a GNSS satellite, and obtaining accelerometer and gyroscope data from an INS (Inertial Navigation System); constructing a pseudo-range residual block, a Doppler residual block, an inertial navigation pre-integration residual block and a speed constraint factor, and integrating into a sliding window estimator based on factor graph optimization; according to the size of a set sliding window, adding all residual blocks in the window to form an optimized objective function; performing a first round of factor graph optimization to obtain a preliminary state estimation result, and endowing the observed quantity containing gross error with a relatively low weight; and updating the prior weight matrix, and carrying out second round of optimization to obtain a joint optimal estimation result of a plurality of moment states in the current time window. According to the method, the influence of gross error-containing observation information on weight estimation and state estimation is suppressed, the positioning precision is high, the robustness is strong, and the reliability is high.

The invention provides a water surface target grabbing system and a control method thereof, and relates to the technical field of marine operation equipment. The system comprises an unmanned ship and a cooperative mechanical arm, the unmanned ship and the cooperative mechanical arm share a processor; the unmanned ship supplies power to the cooperative mechanical arm; the unmanned ship is positioned through an inertial navigation system on the unmanned ship and a scene camera and a depth camera on the cooperative mechanical arm; a scene camera, a depth camera and an integrated sensor on the cooperative mechanical arm transmit data to a processor on the unmanned ship through a local area network, the processor calculates and analyzes the data transmitted by the scene camera and the depth camera, and an action instruction is sent to the cooperative mechanical arm through a CAN bus. According to the water surface target grabbing system, automatic grabbing is achieved, and the grabbing precision, stability and adaptability of the system in the dynamic marine environment are improved.

The invention discloses a missile erection filtering method, system and device and a readable storage medium, and relates to the technical field of inertial navigation and the field of initial alignment. Based on the reference inertial unit pitch angle before erection, the reference inertial unit pitch angle at the current moment, the inertial unit angular velocity on the missile and the trajectory launching inclination angle, whether the missile is in any one of the non-target states of the non-erection state, the just erection state, the to-be-erected in-place state and the erection completion state or not is judged; if yes, measurement updating is not carried out in the Kalman filtering calculation process of the current moment, and time updating is carried out based on the state transition matrix of the current moment, the state vector of the previous moment, the mean square error matrix of the previous moment and the process noise variance matrix; and attitude information output by the inertial navigation system at the current moment is used as a missile attitude result for calculation at the next moment. According to the method, shaking between the guided missile and the launching box in the erecting process before launching is considered, and the attitude precision of initial alignment of the guided missile is improved.

The invention discloses a vehicle-mounted laser gas pollution detection and prediction system, which comprises a mobile laser detection platform, a machine prediction system and a dynamic pollution map generation system, and is characterized in that the mobile laser detection platform is carried on a new energy vehicle and is integrated with a laser gas detector, a high-precision positioning module, an inertial navigation system and an environment sensor group; the machine prediction system receives real-time traffic flow data, weather forecast data and topographic data and outputs a pollution diffusion prediction result; the dynamic pollution map generation system comprises a space-time synchronization module, a data fusion module and a pollution event marking module. And the space-time synchronization module binds the gas pollution data with the positioning coordinates and the millisecond timestamp through hardware clock synchronization.

The invention relates to the technical field of vehicle navigation and path planning, and provides a marine inertial navigation path planning method and system, and the method comprises the steps: obtaining environment risk information of a path corresponding to the driving direction of an autonomous underwater vehicle; adjusting a deviation tolerance threshold value corresponding to an inertial navigation system on the autonomous underwater vehicle according to the environmental risk information; acquiring a position calculation deviation of the inertial navigation system; the position calculation deviation is compared with a deviation tolerance threshold value, and triggering of a communication correction action is decided according to the backlog condition of the emergency event data; and when the communication correction action is triggered, planning and executing a communication correction track to realize position correction of the autonomous underwater vehicle. The method has the advantages that the navigation strategy is dynamically adjusted according to the environment risk and the data backlog condition, the communication correction opportunity is intelligently decided, and the autonomous navigation capability and the task adaptability of the autonomous underwater vehicle in the complex marine environment are improved.

The invention relates to the technical field of intelligent driving, and provides a vehicle control decision-making method and device based on the perceptual fusion technology and electronic device.The vehicle control decision-making method based on the perceptual fusion technology comprises the steps that first positioning information of a vehicle is obtained through data fusion of at least two complementary sensors, the complementary sensor comprises an inertial navigation system, a visual odometer and a laser radar odometer or odometer; correcting the first positioning information through a roadside assisted positioning technology to obtain second positioning information; state information of a target vehicle is acquired through a cellular Internet of Vehicles communication technology; fusing the environment sensing data collected by the vehicle-mounted sensor and the roadside equipment of the vehicle and the state information of the target vehicle, and constructing a global environment model; and making a control decision based on the second positioning information and the global environment model to realize safe vehicle distance keeping and collision avoidance. The method improves the perception, decision and control capabilities of the vehicle.

The invention discloses a combined navigation method and system of an unmanned surface vehicle. The method comprises the following steps: forming a GNSS (Global Navigation Satellite System) / INS (Inertial Navigation System) integrated navigation system by using a GNSS (Global Navigation Satellite System) and a strapdown inertial navigation system (INS), and establishing a mathematical model of a GNSS / INS integrated navigation system filter; the mathematical model comprises a state equation and a measurement equation; establishing a mathematical model of the influence of the GNSS auxiliary information on the GNSS positioning precision so as to preliminarily adjust the measurement noise in the measurement equation; through an improved Sage-Husa adaptive filtering algorithm, adaptive estimation is carried out on the preliminarily adjusted measurement noise, and a measurement noise estimation final value is obtained; and according to the motion characteristics of the unmanned surface vehicle, adding motion constraint conditions to the measurement equation. The GNSS auxiliary information mathematical model based on multivariate function fitting is established, the influence of the external environment on the positioning precision is objectively reflected, the measurement noise in the filter is dynamically adjusted, and the positioning precision of the integrated navigation system under the interference condition is remarkably improved.

The invention relates to the technical field of flight control, in particular to a hybrid control FPV unmanned aerial vehicle flight control system and method. The method comprises the following steps: acquiring triaxial angular velocity data of the unmanned aerial vehicle in real time by using an inertial navigation system, eliminating high-frequency noise and performing zero offset calibration; constructing a corresponding linear extended state observer to adjust a gain parameter in real time based on an absolute value of an observation error, and filtering tiny noise disturbance through a dead zone function to realize high-precision estimation of the system state and total disturbance of the unmanned aerial vehicle so as to obtain an error and an error change rate; and designing a self-adaptive PID control law to dynamically adjust PID control quantities corresponding to proportion, integral and differential gains, carrying out dynamic weighted stacking according to a flight mode, generating a three-axis control quantity, carrying out flight control on the FPV unmanned aerial vehicle, and meanwhile, realizing real-time visual verification of a control effect corresponding to the flight control. According to the invention, high-precision and high-robustness attitude control can be realized.

The invention relates to a CNN-GRU-MHSA navigation method and a CNN-GRU-MHSA navigation system for improving pseudo measurement noise covariance under GNSS interruption. Comprising the following steps of: combining three-dimensional output of a gyroscope and three-dimensional output of an accelerometer in an IMU (Inertial Measurement Unit) in a GNSS (Global Navigation Satellite System) observation period as well as a three-dimensional speed and a three-dimensional course angle calculated by an INS (Inertial Navigation System) into an input vector, inputting the input vector into a trained CNN-GRU-MHSA (Global Navigation Satellite System) hybrid neural network, and outputting to obtain a pseudo GNSS position increment in the GNSS observation period. And synthesizing the pseudo GNSS position increment and the GNSS position information of the previous epoch to obtain pseudo GNSS position information of the current epoch. And then, adding an accumulated error compensation factor related to time into Sage-husa adaptive filtering to estimate a pseudo measurement noise covariance matrix. And finally, transmitting the pseudo GNSS position information and the pseudo measurement noise covariance matrix into an integrated navigation Kalman filter for resolving so as to correct an INS navigation resolving result.

The invention discloses an improved Kalman filtering ocean current estimation method based on inertial information and DVL speed. The method comprises the following steps: S1, system initialization and data acquisition; s2, performing inertial navigation system (INS) resolving, namely performing mechanical arrangement resolving based on IMU data; s3, constructing a fusion filtering state model and an observation model; s4, adaptive Kalman filtering iteration estimation: after discretizing the state equation and the observation equation, executing filtering iteration according to a period; and S5, state feedback correction and ocean current output. According to the improved Kalman filtering ocean current estimation method based on inertial information and DVL speed, a fusion framework of an augmented state model and adaptive Kalman filtering is constructed, and high-precision ocean current estimation and navigation correction are realized through five key processes.

The invention belongs to the technical field of underwater positioning navigation, and relates to an integrated navigation system and method based on robust adaptive Kalman filtering, and the system comprises a strapdown inertial navigation system, a Doppler velocimeter, a depth sensor and a data processing module; the method comprises the following steps: constructing a state space model based on an integrated navigation system, and estimating system noise parameters on line in real time by introducing a Sage-Husa adaptive filtering mechanism; performing sequential processing on the multi-dimensional measurement information by adopting a sequential measurement and variance limitation method, and setting upper and lower limits of scalar measurement noise variance; an IGGIII criterion is introduced, and gross errors of measurement information are detected through a residual filter; according to the method, noise and outlier interference can be suppressed in a complex underwater environment, and the positioning precision, robustness and stability of a navigation system are improved.

The invention discloses a forest fire fighting robot path planning method and system based on ground-air cooperation, and the method comprises the steps: carrying out the air scanning of a disaster area through employing an unmanned plane to carry a laser radar and infrared and visible light cameras, and obtaining the space information and fire intensity of a fire point in real time; the unmanned aerial vehicle is in communication connection with the wind power fire extinguishing robot, shares the three-dimensional point cloud model and performs environment proofreading and positioning; an improved A * algorithm is used for path planning, and a path planning mathematical model with the minimum disaster degree as the target is constructed; through fusion of a global satellite navigation system and an inertial navigation system, the working path of the wind power fire extinguishing robot is dynamically updated and planned; after a target fire point is reached, the wind power fire extinguishing robot identifies a fire source, calculates a safe fire extinguishing distance by using a flame detection model result, and dynamically adjusts an air duct angle according to a flame state to perform fire extinguishing operation. Through cooperative work between the unmanned aerial vehicle and the fire extinguishing robot, the efficiency and the intelligent degree of fire extinguishing operation are improved.

The invention provides a double-antenna attitude and orientation method and system based on integrated navigation, and the method comprises the steps: combining an INS (inertial navigation system) and a double-antenna GNSS (global navigation satellite system), and enabling the system to output continuous and stable high-precision carrier attitude information when a GNSS signal is interfered by using the provided double-antenna GNSS / INS integrated navigation method. According to the method, a course updating strategy is provided, the motion state of the device is detected, course information at the first static moment in the dynamic and static conversion process is stored in time, and then Kalman filtering course updating is conducted through the information. Even if no course information is output subsequently, the system can output high-precision attitude information for a long time. According to the method, the advantages of the double-antenna GNSS and the advantages of the INS are combined, the orientation precision and the real-time performance of the system are improved when the GNSS signal is unavailable, and the method has good practical value.

The invention provides a train attitude precision keeping method and system based on inertial quaternion modeling, and belongs to the technical field of train operation positioning. Through a quaternion modeling train attitude feedback compensation model, auxiliary calibration of a gyroscope is realized by using an accelerometer, and calculation of an inertial attitude is realized; converting the acquired wheel speed sensor information into three-dimensional speed under a navigation coordinate system through attitude information resolved by a quaternion modeling train attitude feedback compensation model, and further integrating to obtain a position; and fusing the speed and position information of the wheel speed sensor after attitude conversion with the speed and position information obtained by inertial navigation dead reckoning to realize error correction of the inertial navigation system. According to the method, the problem that the train state sensing precision is degraded due to the fact that a satellite navigation / inertial navigation system positioning model is difficult to overcome inertial device error accumulation when a satellite fails is solved, the train attitude precision is kept, and then error divergence of a combined positioning system during satellite signal denial is effectively restrained.

The invention discloses a ship position correction method aiming at polar region high latitude factors, and relates to the technical field of ship navigation, the specific steps of the preparation method are as follows: S100, multi-source data acquisition: satellite navigation, inertial navigation, radar and AIS data are respectively acquired by means of a satellite navigation receiver, an inertial navigation system, a radar and AIS equipment; according to the invention, through a multi-source data acquisition and fusion technology, the positioning precision and safety of the ship in the polar high-latitude region are significantly improved, and the traditional ship navigation system is often influenced by extreme weather, complex terrain, ice conditions and other factors in the polar environment, resulting in inaccurate positioning and even safety accidents. By integrating various data sources of satellite navigation, inertial navigation, radar, AIS and marine environment monitoring satellites and utilizing a data fusion algorithm, noise and abnormal values are effectively filtered out, and real-time monitoring of the position, the speed, the course and the surrounding environment of the ship is achieved.

The invention discloses an unmanned aerial vehicle MEMS inertial navigation dynamic calibration method and device based on biaxial rotation inertial navigation. The method specifically comprises the steps that an unmanned aerial vehicle MEMS inertial navigation dynamic calibration system based on double-shaft rotation inertial navigation is constructed, and a calibration computer sends a transposition control instruction to a double-shaft eighteen-position rotation inertial navigation system; the calibration computer receives position and speed information from the differential GNSS navigation system and the double-shaft eighteen-position rotary inertial navigation system, calculates a measurement error of the double-shaft eighteen-position rotary inertial navigation system and an error of MEMS strapdown inertial navigation of the unmanned aerial vehicle, and performs real-time calibration on the double-shaft rotary inertial navigation system; and carrying out dynamic error compensation on the MEMS strapdown inertial navigation system of the unmanned aerial vehicle by utilizing a calibration result of the double-shaft rotating inertial navigation system. The method improves the positioning navigation capability of the unmanned aerial vehicle in the satellite rejection environment, saves the time cost, does not need to modify the unmanned aerial vehicle, can be popularized to other types of unmanned equipment, and is high in adaptability.

The present application provides a satellite navigation and atmospheric fusion positioning method and system based on an inertial navigation system, which relates to the field of navigation. The method includes: first, obtaining multi-source navigation raw data, including inertial navigation data, satellite navigation data, and atmospheric information data; extracting features through deep learning to obtain multi-modal feature data; evaluating the quality of the satellite navigation data to determine whether the satellite navigation signal is valid. When the satellite navigation signal is valid, a federated filtering algorithm is used for integrated navigation solution, and an atmospheric information error model is established; when the satellite navigation signal is invalid, the atmospheric information is compensated using this error model and fused with the inertial navigation data to achieve high-precision navigation and ensure the flight safety of the unmanned aerial vehicle in complex environments.

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.

The invention provides an acousto-optic fusion guiding system and method for AUV underwater dynamic docking, and belongs to the technical field of autonomous underwater robot docking, and a first inertial navigation system and a second inertial navigation system are respectively used for measuring absolute positions, postures and speeds of an AUV and a docking station; the USBL array, the USBL beacon, the multi-beam foresight imaging sonar and the camera are used for measuring the measurement information of the AUV in different guiding stages; and the combined filtering module is used for carrying out filtering fusion by adopting a federated Kalman filter and a maneuvering observer based on the relative motion state at the initial moment and the actual measurement information of the AUV acquired by the USBL array, the multi-beam forward-looking imaging sonar and the camera in real time, so as to acquire the global state estimation information of the AUV. Compared with a traditional estimation method, the method is higher in motion state estimation precision, better in robustness and more suitable for underwater dynamic docking.

The invention discloses a self-adaptive integrated navigation method based on geometric accuracy factors, which belongs to the technical field of underwater navigation and positioning, is used for underwater navigation and positioning, and comprises the following steps: initializing an inertial navigation system and an integrated navigation filter, and setting a state vector and a noise covariance matrix; predicted navigation parameters of the recursive carrier are mechanically arranged through inertial navigation, and the state transition matrix is used for state prediction; and dynamically adjusting an observation noise covariance matrix according to the geometric precision factor value, further calculating a Kalman gain, performing optimal estimation and correction on a prediction state by fusing acoustic observation information, and finally outputting a high-precision carrier position, speed and attitude. According to the method, the statistical characteristics of observation noise are dynamically remodeled by calculating and feeding back geometric precision factor values in real time, so that the Kalman filter has the capabilities of knowing the own geometric situation and adjusting the trust degree of each information source, and the global optimal navigation precision and reliability are realized under any motion track.