200 results about "Navigation error" patented technology

The invention discloses a vehicle-mounted Beidou positioning deviation self-calibration system fused with inertial navigation information, and relates to the technical field of navigation and positioning. The method is used for solving the problems of positioning deviation accumulation and signal failure in a complex environment. The system realizes space-time alignment of inertial navigation and Beidou observation data through timestamp interpolation and coordinate system conversion, and constructs a multi-source synchronous data stream. A difference value between an inertial navigation calculation speed and a Beidou Doppler speed is analyzed based on a vehicle incomplete constraint model, and a weight factor is generated in combination with a confidence threshold to suppress abnormal observation. A geometric matching error is calculated through a high-precision lane map curvature and a vehicle steering angle, and candidate tracks are screened to generate transverse calibration parameters. The noise covariance is dynamically adjusted through a tight coupling filtering algorithm, the mode is switched to a lane constraint calibration mode when a Beidou signal is interrupted, inertial navigation errors are compensated through error boundary constraint, and stable output is achieved. Multi-source data deep fusion and adaptive robust calibration are realized, and system robustness and positioning continuity are improved.

The invention discloses a trajectory optimization method based on a multi-source heterogeneous positioning data fusion algorithm, and relates to the technical field of intelligent navigation and high-precision positioning, multi-modal data are acquired through a multi-modal sensor array, positioning redundancy of scenes such as tunnels and indoor scenes is enhanced, a weight distribution strategy is dynamically adjusted through an Actor-Critic network architecture, and the positioning accuracy is improved. The state space input comprises an environment semantic tag, a historical error sequence and a real-time noise variance, the output action space is continuous weight distribution of each data source, a multi-target reward function optimization strategy is combined, scene adaptability is realized, a local SLAM map, inertial navigation error parameters and a weight distribution strategy are shared in real time based on a V2X protocol, and the real-time performance of the system is improved. According to the method, a single device accumulative error is compensated by using adjacent vehicle data, a terminal locally trains an error compensation model, parameters are uploaded to a cloud end through differential privacy encryption, the cloud end adopts a FedAvg algorithm to aggregate a global model and issue the global model, the error difference between devices is inhibited, and dynamic road network updating and scene differentiation model distribution are supported at the same time.

The invention provides a vehicle cluster tunnel access positioning method based on intelligent cooperation and a vehicle-mounted terminal, relates to the technical field of intelligent driving control, and solves the problems of positioning interruption, inertial navigation error accumulation and insufficient cooperative positioning reliability caused by satellite signal failure of a vehicle cluster in a tunnel scene. The method comprises the following steps: judging whether each vehicle enters or exits from a tunnel area, when part of vehicles enter a tunnel, dynamically selecting a reference node from vehicles with effective satellite signals outside the tunnel, and broadcasting original observation values of the satellite signals; establishing a relative position constraint model of a vehicle cluster, acquiring relative position data of adjacent vehicles in real time among the vehicles, and performing dynamic weighted fusion on observation values, IMU data and relative position data of the vehicles with effective satellite signals by adopting a combined filtering algorithm; the satellite signal failure vehicle performs positioning calculation by combining the broadcast observation value with the IMU data and the relative position constraint model; and pre-capturing satellite signal loop parameters when driving out of the tunnel.

The invention provides an oral implant navigation method and system and a storage medium, after an optical locator is fixedly arranged on an implant mobile phone, three-dimensional data of a rotating shaft of the implant mobile phone, the tail end of the rotating shaft and a spherical drill on an optical three-dimensional coordinate system of the optical locator are obtained, and a registration pit is used as a medium to obtain a three-dimensional coordinate system of the implant mobile phone; and positioning three-dimensional data of the three-dimensional coordinate system in the optical three-dimensional coordinate system, registering the three-dimensional data of the pits on the CT three-dimensional coordinate system to complete registration, calculating the pose of the tip of the drill point and the pose of the ideal path under the positioning three-dimensional coordinate system, and performing real-time navigation on the planted mobile phone according to the difference between the tip of the drill point and the ideal path. An optical positioning instrument is fixedly arranged on the planting mobile phone, and while the planting mobile phone is used for drilling operation, image data of the optical identification points are collected through the optical positioning instrument for real-time navigation; the optical positioning instrument and the planting mobile phone are integrally connected, so that the process of positioning the planting mobile phone in real time can be omitted, and navigation errors are effectively reduced.

The invention discloses a deep and far sea unmanned aerial vehicle monitoring and management platform based on multi-sensor fusion, and relates to the technical field of unmanned aerial vehicle marine monitoring. The intelligent analysis module is used for receiving the fusion data after time-space alignment and the real-time ocean current data, optimizing the moving path of the unmanned aerial vehicle according to the received data, evaluating the resource development possibility, obtaining an analysis result and transmitting the analysis result; an anti-interference communication module; an energy management module; and dynamically adjusting an energy distribution strategy. The module combines the task priority weight and the environment energy consumption weight, optimizes the energy use efficiency, prolongs the endurance time of the unmanned aerial vehicle, and ensures the smooth completion of the task. Particularly in a complex marine environment, navigation errors are corrected in real time through a quantum navigation error compensation model, the positioning precision is improved, and the moving path of the unmanned aerial vehicle is optimized in combination with real-time ocean current data and an ocean flow field cost function.

The invention discloses a coal mine underground navigation control system and device based on big data, and particularly relates to the technical field of underground navigation control. The underground roadway waveguide effect grade is judged by monitoring underground radio signals in real time and recognizing a signal reflection mode by combining wavelet transform with a convolutional neural network; meanwhile, three-dimensional point cloud data of the underground roadway are processed through multi-scale curvature analysis and a DBSCAN clustering algorithm, and division of a structure mutation region is achieved; determining a navigation error level according to the waveguide effect level and a structure mutation result, and when the navigation error level reaches a second level or a third level, analyzing historical navigation trajectory data by using a dynamic time warping algorithm, and evaluating a current navigation path deviation degree; comprehensive analysis is carried out through the deep belief network, the reliability of the navigation control system is evaluated, whether a path re-planning instruction is triggered or not is judged, and the underground navigation precision and safety are improved.

The invention discloses a method and system for calibrating an inertial sensor by a Doppler log, and relates to the technical field of underwater integrated navigation, and the method comprises the following steps: obtaining an IMU pre-integration factor and a DVL speed residual factor according to IMU observation data and DVL observation data; the method comprises the following steps: constructing a factor graph structure model by taking prior information of initial states of a gyroscope and an accelerometer as prior factors, taking the prior factors, an IMU pre-integration factor and a DVL speed residual factor as factor nodes, taking state variables of the gyroscope and the accelerometer as variable nodes and taking a relationship between the factor nodes and the variable nodes as edges; and correcting the state variables by using the factor graph structure model to obtain theoretical outputs of the gyroscope and the accelerometer after inertial navigation error compensation. Observed DVL data are used for carrying out online calibration on IMU errors, an underwater integrated navigation problem is modeled as nonlinear least square optimization, global optimal estimation of all-time state quantities is achieved, and the divergence speed of inertial navigation position errors is restrained.

The invention discloses a satellite navigation decoy detection method based on inertial navigation, and the method comprises the steps: carrying out the validity verification of received satellite navigation data before the alignment of equipment is not completed; once the data is valid, the equipment enters a combined navigation state, and in the state, the judgment threshold values of the satellite navigation position and speed are dynamically set by utilizing the characteristic that inertial navigation errors diverge along with time; position and speed information provided by satellite navigation and inertial navigation is compared in real time, and if the difference value of the two exceeds a preset dynamic threshold value, it is judged that the satellite navigation information may be decoy; once decoy is detected, the device exits the integrated navigation state; according to the invention, satellite navigation decoy detection is realized through a software algorithm, navigation data can be judged and processed autonomously and accurately in a complex and severe navigation environment, and navigation errors caused by signal decoy are prevented.

The invention belongs to the technical field of inertial navigation, relates to a high-precision calibration method for residual drift of a rotation modulation inertial navigation system, and is suitable for a long-endurance flight environment of an aircraft. According to the method, a mathematical model of the longitude and latitude navigation error of the rotary inertial navigation geographical system and the sky-direction residual drift, the north-direction residual drift and the sky-direction mathematical platform error angle of the geographical system is established, and the long-endurance navigation test of eight positions at the azimuth angle interval of 0-360 degrees and 45 degrees is designed; accurate identification of sky-direction and north-direction residual drifts and alignment course errors in different directions is realized. The method has the advantages of high residual drift identification precision, obvious improvement of long-endurance navigation performance, strong autonomy and easy realization.

A robotic catheter system and methods for implementing corrective actions during use thereof. A method comprises: providing a catheter that has one or more bendable segments and a catheter tip, inserting the catheter into a bodily lumen and navigating the catheter through the lumen along an insertion trajectory while an actuation unit applies an actuation force to the one or more bendable segments; recording a navigation parameter in relation to the insertion trajectory while the catheter is moved through the lumen, displaying, on a display, a graphical representation of the navigation parameter relative to a threshold value indicative of a location along the insertion trajectory where a navigation error of the catheter can occur or has occurred; and outputting to the display an interactive user-guide showing one or more than one corrective actions that can be taken to correct and / or prevent the navigation error.

The invention discloses a factor graph underwater integrated navigation method based on adaptive windows and factors. The factor graph underwater integrated navigation method is suitable for an underwater integrated navigation system consisting of an inertial measurement unit (IMU), a Doppler velocimeter (DVL) and an ultra-short baseline positioning system (USBL). The method comprises the following steps: 1, constructing DVL and USBL information into factor nodes, constructing IMU pre-integration factors through pre-integration, and constructing a factor graph model; 2, improving smooth estimation of the system through a sliding window, dynamically adjusting the factor node weight by using a weighting function, and correcting a navigation error; and 4, clustering the navigation data residual error sequence by adopting a DBSCAN algorithm, analyzing a clustering result, adjusting the size of a window, and performing marginalization operation on historical information to reserve prior. According to the method, through adaptive weight adjustment of sensor factor nodes and dynamic optimization of a sliding window, the interference of sensor abnormal data in an underwater complex environment is effectively inhibited, the balance between positioning precision and real-time performance is realized, and the method is suitable for the high-precision navigation requirement of an autonomous underwater vehicle.

The invention provides a neural network assisted vehicle adaptive autonomous navigation method. The method comprises the following steps: designing an improved LSTM model for multi-parameter parallel prediction; collecting data of an inertial measurement unit and executing inertial navigation calculation; when satellite navigation is effective, inertia / satellite integrated navigation calculation is executed; training a designed parallel LSTM model based on the combined navigation result; when satellite navigation is invalid, vehicle speed and attitude information are predicted based on a trained machine learning model; and when satellite navigation is invalid, carrying out adaptive integrated navigation by using variational Bayes based on a machine learning model prediction result. According to the method, the problem of insufficient error accumulation precision of vehicle navigation which only depends on inertial navigation when no reference information exists is solved, full-autonomous and high-precision navigation positioning of the vehicle can be realized, and the method has important practical application value.

The invention discloses a construction site intelligent navigation traffic control system, and relates to the field of construction site traffic control, and the system comprises a data collection module which is used for collecting construction site traffic environment data in real time through employing an Internet of Things sensor, an unmanned plane, a high-definition camera and laser radar equipment; comprising vehicle access flow, motion trails of construction equipment, construction area division and road conditions; according to the invention, the map precision can be improved, navigation errors caused by map errors can be effectively avoided, dependence on a high-performance server is reduced, the overall deployment cost is reduced, popularization and application in a construction site environment with limited resources are facilitated, efficient storage and transmission are realized, and the influence of emergency situations on traffic control is effectively reduced; the intelligent level of traffic signal regulation and control can be improved, traffic resource waste or congestion caused by traditional fixed-time-length signals is avoided, the uncertainty of the decision making process is reduced, the traffic accident rate is effectively reduced, and the overall traffic operation efficiency of a construction site is improved.

The invention discloses a hinged wheel type pipeline robot positioning method based on multi-sensor fusion ESKF, which takes the ESKF as a core, predicts the nominal state of an IMU through a double-subsample INS mechanical arrangement method, introduces an adaptive slip detection method based on IMU kinematics consistency constraint to perform reliability verification on multi-encoder observation, and finally determines the position of the hinged wheel type pipeline robot. Speed observation fusion is realized; meanwhile, heading updating of the magnetometer is triggered through magnetic field stability detection, and long-term drift is restrained; a pseudo measurement constraint model is established based on pipeline environment characteristics, and accumulative errors are systematically inhibited; and after feedback compensation is carried out on IMU errors and navigation errors through ESKF measurement updating, position, speed and attitude navigation data are output in real time. And finally, performing global posterior optimization on the real-time filtering trajectory by adopting an RTS reverse smoothing algorithm, thereby effectively solving the problems of low positioning precision and insufficient adaptability of a multi-section hinge wheel type pipeline robot structure in a complex underground pipe network.

The invention discloses an inertial navigation device and system based on multi-modal data analysis, and relates to the field of navigation, and the inertial navigation device comprises an IMU measurement unit, an RTC, a PMIC module, a security module, a processor module, a storage module, an interface module, an NPU, a visual sensor and a terminal. According to the invention, a visual sensor and an NPU are added in a traditional inertial navigation system, and a corresponding algorithm is matched, so that the inertial navigation information is corrected by combining real-time visual information with an AI model before the inertial navigation information is output in order to solve the problem that inertial navigation errors are accumulated along with time in the inertial navigation process; and the information output accuracy of the inertial navigation system is improved.

The invention relates to a disassembly-free online self-calibration double-shaft laser gyroscope strapdown inertial navigation implementation method, which designs a modulation working mode based on double-shaft rotation, according to the design, a double-shaft rotating mechanism is additionally arranged on an inertial navigation unit, and the inertial navigation unit respectively rotates around three gyroscope sensitive shafts by utilizing an outer horizontal shaft and an inner azimuth shaft; exciting a navigation error generated by a scale factor error and an installation error of the laser gyroscope; sensitive shafts of the three accelerometers are enabled to point to the sky respectively, and navigation errors generated by scale factor errors, installation errors and zero positions of the accelerometers are excited; and then obtaining the navigation errors by taking external reference information as a benchmark, establishing an error relation model between the navigation errors and parameters needing to be calibrated, and completing estimation of each calibration parameter by adopting a Kalman filtering algorithm, so as to realize disassembly-free online self-calibration of the double-shaft laser gyroscope strapdown inertial navigation.

The invention discloses an inertial navigation error compensation method and system based on gravity assistance, and belongs to the field of navigation. The inertial navigation error compensation method is suitable for long-time indoor inspection of the robot. Firstly, channel state information, odometer data and inertial measurement data of a robot are obtained; calculating an initial attitude through angular velocity integration, and calibrating a pitch angle and a roll angle by using the separated gravity vector; calculating a motion increment based on odometer data, and performing coordinate conversion in combination with the calibration attitude to obtain navigation position information; inputting the channel state information into a neural network to obtain a channel fingerprint, and matching target position information; and finally, calculating the cross covariance between the navigation position and the target position to obtain a state correction value, and compensating the navigation position and course angle error. According to the invention, when inertial navigation error compensation is carried out on the robot for long-time indoor inspection, the compensation accuracy and effect can be improved.

The invention discloses a data chain relative navigation information fusion method and device, and the method comprises the steps: firstly building a relative navigation information fusion state equation according to input information, and then building a relative navigation information fusion measurement equation according to the input information to guarantee a relative space-time reference under a satellite navigation denial condition; based on the relative navigation information fusion state equation and the relative navigation information fusion measurement equation, selecting a corresponding Kalman filter according to the measurement information to carry out relative navigation information fusion so as to obtain a navigation result, and correcting a navigation error state according to the measurement information after a round of state estimation of relative navigation information fusion is completed. And the corrected relative positioning result is reported to the airborne avionics system. Different types of measurement information are fused, and the information is optimally fused by using a Kalman filter, so that the precision and reliability of relative positioning are improved, the measurement information is quickly processed in a real-time environment, and the robustness and stability of the system are kept by correcting a navigation error state.

The invention discloses a calibration and alignment method suitable for a rotary inertial navigation system under a shaking base. The calibration and alignment method comprises the following steps: firstly, designing a new 20-position transposition by utilizing a rotation-stop combination mode; then establishing an IMU error model and a 39-dimensional RINS navigation error model, and then performing coarse alignment by using an optimization-based method to obtain an initial attitude of a carrier; and performing simultaneous calibration and alignment by using an iterative Kalman filtering method based on zero-speed observation, recording and compensating all error parameters after each time of calibration, and taking the state quantity at the moment and the covariance matrix corresponding to the state quantity as initial values of next iteration until the covariance matrix is converged to a threshold value or reaches the number of iterations. Therefore, various errors are compensated in a short time, the high-precision initial attitude is obtained, and the long-term navigation positioning precision of the RINS is ensured.

The invention discloses a vision and inertial navigation integrated navigation method, system and equipment in a weak vision environment and a medium, and belongs to the technical field of navigation, by fusing an inertial measurement unit and landmark focal plane projection data, the navigation precision of the system under the adverse conditions of low texture, dynamic shielding and the like is remarkably improved, and the navigation continuity can be maintained; according to the method, an indirect Kalman filter is used for carrying out feedback correction on initial navigation parameters, noise interference and inertial navigation accumulative errors are effectively inhibited, navigation errors are remarkably reduced for state estimation, and the robustness and adaptability of the system are enhanced; the method does not depend on complete visual resolving, landmark projection coordinates are directly used, dependence on visual information integrity is reduced, and the system can stably operate even if only a small number of landmarks or a single landmark is visible.

The invention relates to the technical field of robot navigation, and discloses a visual positioning method, self-moving equipment and a storage medium, and the method comprises the steps: obtaining a plurality of frames of environment images containing a time sequence relation; analyzing the multi-frame environment image to obtain an anomaly recognition result in the environment image; extracting effective features based on the multi-frame environment image to obtain a first effective feature set; screening the first effective feature set according to an anomaly recognition result, and removing false features corresponding to the image anomaly to obtain a second effective feature set after the anomaly is removed; and calculating pose information of the self-moving device based on the second effective feature set. According to the technical scheme, the accuracy and robustness of visual positioning are improved, and navigation errors caused by image abnormity are reduced.

The invention discloses a waypoint prediction method fusing instruction landmark features in visual language navigation, and belongs to the crossing field of artificial intelligence and computer vision. The method is realized through the following steps: firstly, extracting landmarks and co-occurrence landmarks thereof from a natural language instruction by utilizing a large language model, and generating a candidate landmark sequence; then, the occurrence probability of the candidate landmarks in actual observation is calculated based on a visual large model CLIP, and noise interference is dynamically corrected and suppressed through a learnable co-occurrence scoring module; secondly, fusing the corrected landmark features with the depth visual features, inputting a double-layer Transform model to model a spatial relationship, generating a waypoint probability heat map, and outputting adjacent waypoints through non-maximum suppression (NMS); and finally, topological mapping is carried out on the predicted waypoints, an optimal path is obtained through cross-modal path planning, and an intelligent agent is guided to execute low-level actions. The method does not need to depend on a predefined environment map or manually annotate data, navigation errors in a continuous environment are remarkably reduced, the generalization ability of unseen scenes is improved, and the method is suitable for open world navigation scenes such as robot navigation and automatic driving.

The invention relates to the technical field of electric power facility monitoring, and discloses an electric power line detection system based on a three-dimensional inertial gyroscope and deep learning, which comprises a data acquisition module, a segmented preprocessing module, a trajectory resolving module, a trajectory correction module, a deviation analysis module and a risk early warning module. According to the application, the electromagnetic shielding and common-mode noise suppression technology is adopted to overcome high-voltage electromagnetic field interference, and the data acquisition quality is ensured; precise adaptation of different line features is realized through segmented adaptive preprocessing; inertial navigation error accumulation is effectively inhibited by utilizing topological constraint and multi-source data fusion; the problem of deviation identification under the small sample condition is solved by combining an attention mechanism and transfer learning; and finally, intelligent risk assessment and early warning are realized through deep reinforcement learning, so that whole-process intelligent processing from original data to risk assessment is realized, and the precision and efficiency of power line detection are improved.

The invention provides an intelligent hospital guide system and method based on AR vision and voice navigation. The intelligent hospital guide system comprises a user interaction layer, a technical support layer, a data and background layer, a third-party service layer and a hardware dependence layer. According to the intelligent hospital guide system based on AR vision and voice navigation, accurate navigation is achieved, a barrier-free road is guided, through the advanced AR vision navigation technology, the APP provides an accurate and errorless navigation path for a patient, the possibility of navigation errors is greatly reduced, virtuality and reality are seamlessly fused, and the intelligent hospital guide system based on AR vision and voice navigation has the advantages of being high in practicability and the like. The patient can walk on a familiar map in a hospital, and each step is full of confidence; the voice navigation function brings unprecedented convenience to the user, and especially for the visual impairment, the old or the users with inconvenience in hands, the service is as an intimate guide to lead the visual impairment, the old or the users with inconvenience in hands to easily shuttle to all corners of a hospital and enjoy barrier-free medical experience.

The invention provides a polarization / inertia / vision intelligent navigation method based on model error learning, which belongs to the field of navigation and comprises the following steps: modeling a polarization / inertia / vision integrated navigation system, and establishing a system state equation by taking an inertial navigation error and a camera pose error as system state quantities; establishing a system measurement equation based on the information of the polarization sensor and the visual sensor; a neural network learning modeling error is constructed by considering uncertainty modeling errors of a system state equation and a measurement equation caused by system parameter errors and environmental interference, and the precision of a polarization / inertia / vision integrated navigation system model is improved; the multi-state constraint Kalman filtering method of the embedded neural network is established for solving the problems that in an actual environment, sensor noise statistical characteristics are unknown and time-varying due to environmental factors. According to the invention, the navigation precision of the polarization / inertia / vision integrated navigation system in a complex environment can be improved.

The invention relates to the technical field of geomagnetic adaptive navigation, and discloses a vector magnetograph matching method, device and equipment and a medium, and the method comprises the following steps: obtaining an inertial navigation track point set at the current moment; wherein the inertial navigation track point set comprises inertial navigation track coordinate positions of inertial navigation track points and vector magnetic field intensity corresponding to the inertial navigation track points; determining a candidate track point set corresponding to the inertial navigation track points from a preset vector magnetic map based on the vector magnetic field intensity; screening candidate track points in the candidate track point set, and performing image conversion according to a screening result to obtain a target sequence image; and based on the target sequence image and the inertial navigation trajectory coordinate position, generating a predicted trajectory point at the next moment. According to the technical scheme provided by the invention, the real-time performance and the accuracy of position prediction can be improved in an environment for coping with complex inertial navigation errors.

The invention discloses a high-dynamic aircraft inertia / satellite integrated navigation method, relates to the technical field of integrated navigation, and aims to solve the problems that in a high-dynamic environment, a GPS signal of a carrier is extremely easy to lose lock or even lose, navigation errors of an INS (inertial navigation system) are accumulated and increased along with time, and the navigation precision of the carrier is seriously influenced. The position and the speed of the carrier are obtained, and high-precision navigation of the carrier can be realized during the interruption of a GPS signal in a high-dynamic environment. When the GPS signal is normally received, training the neural network by using the GPS signal; when the GPS signal is lost, the trained neural network is used to predict the position and speed of the carrier, and the output of the neural network is used to replace the GPS signal as the input of the Kalman filter. The navigation problem of the carrier when the GPS signal is lost in a high-dynamic environment is effectively solved, the navigation error of the INS is avoided, and the navigation precision of the carrier is improved.

The invention provides a distributed inertia-based relative navigation method based on a data link, and relates to the technical field of navigation. Firstly, a relative navigation error state equation based on speed error reconstruction is adopted, and a specific force term of high-frequency change is eliminated, so that the degree of dependence on inertial navigation data sharing between nodes is reduced; secondly, by designing and calculating an efficient information fusion method, the calculation complexity of the whole method is reduced; and finally, designing equivalent GNSS measurement for rapidly diffusing high-precision absolute navigation information. According to the invention, two core functions of accurate estimation of the relative state of the cluster node and rapid coverage of absolute information can be realized; through a simulation experiment, high-precision group inertial navigation error state propagation can be realized by sharing own inertial navigation data by cluster nodes at second-level intervals.

According to the carrier motion excitation micro inertial navigation error parameter identification and prediction method provided by the invention, micro inertial navigation use data and calibration data are effectively combined, device errors are preferentially compensated according to a use environment and a motion condition, historical data are fully utilized, the micro inertial navigation key error parameter compensation precision is improved, and the micro inertial navigation performance is further improved; meanwhile, the influence of motion excitation on error parameter compensation precision is also introduced, so that the calibration result is prevented from being polluted by data which does not excite errors. Finally, the learning and prediction method for the micro inertial navigation error parameter variation trend can reduce device errors caused by the increase of the service life, and prolongs the service life of micro inertial navigation.

The invention particularly relates to an inertial navigation / radar / satellite navigation three-combination emitter integrated navigation method based on Lie group, which comprises the following steps: responding to a navigation control instruction, and configuring a navigation system of an aircraft to enter an integrated navigation state; constructing a special European group based on the attitude features, the speed features and the position features, and establishing a motion description model based on the description of the special European group; acquiring navigation information of an inertial navigation system, and resolving the navigation information by using an error model under a special European group to obtain a state matrix; obtaining current measurement information; wherein the measurement information comprises satellite navigation subsystem measurement information and / or radar navigation subsystem measurement information; based on the measurement information, establishing an observation vector and a measurement equation of a corresponding subsystem under a special European group; resolving the state matrix, the measurement equation and the observation vector by using a Kalman filter to obtain navigation error estimation data; and performing feedback correction on the navigation error based on the navigation error estimation data.