390 results about "Inertial navigation system" patented technology

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.

The invention discloses a PPP / INS / vision / LIDAR tight coupling navigation method based on multi-system real-time precision service, and belongs to the technical field of navigation and positioning. In order to solve the problem of insufficient real-time positioning precision and reliability in a complex urban environment, the invention provides a hierarchical fusion architecture: firstly, by using original data of an inertial navigation system, a visual sensor and a laser radar, high-precision and high-frequency local pose estimation is generated through tight coupling factor graph optimization; the accumulative error of inertial navigation is effectively inhibited; then, taking the local attitude as observation information, and performing tight coupling factor graph optimization on the observation information, a precise orbit obtained by PPP enhanced service decoding and a GNSS original observation value after clock correction, namely a pseudo range and a carrier phase; multi-source information is deeply fused on the observation value level, the complementary advantages of the sensors are fully played, and finally continuous and reliable centimeter-level high-precision positioning in the urban complex environment is achieved.

Systems and methods are provided for recognizing and mapping features inside of pipes using Artificial Intelligence (AI). In an exemplary embodiment, a pipe inspection camera system including Inertial Navigation Systems (INS) and other sensor capabilities is inserted into a pipe to collect data that can be provided to a Deep Learning model to build a training set. Training data may include newly collected data and / or historical data. The model may be trained based on collected sets of training data, testing data, and / or user predefined classifiers. The Deep Learning model may use thresholds to determine if a set of data falls within a specific class. Classes may be based on data related to pipe features such as size, shape, material, age, routing or connection features including bends and / or joints, etc. AI data may be processed locally in the pipe inspection camera system, remotely on a mobile device, and / or in the Cloud.

The invention provides a one-key alignment method for heavy-load equipment in multiple scenes, which comprises the following steps of: acquiring a gravity direction component of an equipment body, and establishing a magnetic attitude reference coordinate of the equipment body by combining the reading of a tilt angle sensor and a horizontal reference plane of an optical ranging unit and a calibration point. Capturing the spatial distribution information of the mark points, calculating the offset of the current attitude of the equipment relative to the horizontal reference plane according to the geometric constraint among the mark points, and generating an initial attitude mapping matrix; and reading the angular velocity and the acceleration of the inertial navigation system, determining the deviation between the inertial attitude and the visual attitude, and generating a combined attitude calculation result. The hydraulic cylinder and the rotating table are driven to be synchronously adjusted according to the deviation value between the current attitude of the equipment and the target mounting point, the change trend of the attitude angle is monitored, and suppression feedback is triggered to adjust the fusion weight of the sensor. And calling a micro-motion mechanism to execute a fine pose adjustment instruction, monitoring a pose error in real time, and generating a pose locking report when the pose error is within a tolerance range.

The invention provides an ultra-short baseline calibration method, device and system, and belongs to the technical field of underwater acoustic measurement, and the ultra-short baseline calibration method comprises the following steps: according to an antenna phase center position, a carrier instantaneous attitude, a distance and azimuth information at each observation moment, and installation parameters of an ultra-short baseline acoustic transducer relative to an inertial navigation system, carrying out calibration on the ultra-short baseline; establishing a distance equation between the ultra-short baseline acoustic transducer and the ultra-short baseline acoustic beacon at each observation moment; establishing a residual equation of each distance equation; and solving the residual equation through a nonlinear least square method to obtain installation parameters. According to the method, the installation parameters of the ultra-short baseline acoustic beacon can be efficiently and accurately calibrated, and the problems that beacon laying and recycling operation is complex, cost is high, the operation period is long, efficiency is low, and the beacon is prone to drifting due to the influence of ocean currents are effectively solved.

The invention relates to the technical field of high-precision positioning, and discloses an anti-vibration interference optical fiber inertial navigation and vision fusion positioning system, which comprises a data acquisition module, a first identification module, a positioning compensation module, a second identification module and a positioning correction module, wherein the data acquisition module is used for acquiring inertial navigation data and a positioning image sequence of an aircraft; the first identification module is used for identifying interference fluctuation of the inertial navigation data; the positioning compensation module is used for constructing an attitude constraint curve and performing constraint compensation on the inertial navigation data with interference fluctuation; the second identification module is used for identifying course drift of the inertial navigation data; and the positioning correction module constructs a course reference sequence based on the positioning image sequence, and performs course correction on the inertial navigation data with course drift. According to the invention, the positioning precision of the aircraft under vibration, shaking and complex maneuvering conditions is improved.

The invention discloses a least square covariance dynamic weighting satellite inertial navigation combined attitude determination method, which belongs to the technical field of combined navigation and mobile carrier attitude determination, is used for satellite inertial navigation combined attitude determination, and comprises the following steps: obtaining and preprocessing original data, constructing a nonlinear observation model, linearizing by using a Gauss-Newton iterative algorithm, and solving; performing time updating of an error state vector and a state covariance based on inertial navigation system mechanical arrangement; and reconstructing a measurement noise covariance matrix of extended Kalman filtering, constructing a measurement information vector, calculating Kalman gain, performing state updating, and outputting an attitude determination result. According to the method, the current epoch least square posterior covariance is introduced for dynamic weighting, so that instantaneous delay-free adaptive fusion is realized, the short baseline noise amplification effect is remarkably inhibited, and the attitude estimation precision, the statistical consistency and the real-time observation quality self-diagnosis capability under the complex sea condition are improved.

The invention provides an adaptive optimization compensation method for dynamic errors of an inertial navigation system. The method comprises the following steps: obtaining original measurement data of a gyroscope and an accelerometer through measurement and navigation calculation of the inertial navigation system; inputting the original measurement data into a self-adaptive unscented Kalman filter, and carrying out self-adaptive unscented Kalman filtering; time is updated, state prediction is carried out, and observation prediction is carried out; calculating an innovation sequence, and setting a sliding window to calculate an innovation covariance estimated value; updating an observation noise covariance matrix according to the theoretical innovation covariance in combination with an innovation covariance estimated value, and updating a process noise covariance matrix according to residual analysis; updating measurement; the gyroscope zero offset estimation value and the accelerometer zero offset estimation value which are output through filtering are used for compensating gyroscope and acceleration original measurement data. According to the method disclosed by the invention, the high-precision online estimation and compensation of the time-varying error of the inertial navigation system are realized by constructing a self-adaptive filtering framework integrating a dynamic model and data driving, and the navigation precision and stability of the system in a high-dynamic environment are improved.

The invention belongs to the technical field of laser wind-finding radars, and discloses a method and a system for correcting an installation angle of an airborne laser wind-finding radar, which utilize data measured by the radar in a flight process and reference data provided by an aircraft inertial navigation system to automatically and accurately calculate an installation angle deviation through a mathematical model and correct the installation angle deviation. The problem that a traditional installation angle calibration method is tedious in process and difficult to revise is solved, and the precision and reliability of wind field measurement are improved.

The invention discloses an anti-interference GNSS / INS (Global Navigation Satellite System / Inertial Navigation System) integrated positioning optimization algorithm for an unmanned aerial vehicle, particularly relates to the technical field of satellite positioning and navigation, and is used for solving the problem that the positioning precision is reduced due to inaccurate interference identification of an existing integrated navigation system in a complex electromagnetic environment. The positioning precision is improved by constructing a multi-dimensional interference characteristic analysis system; the method comprises the following steps: firstly, carrying out subset division on visible satellites, calculating credibility factors, analyzing pseudo-range residual error distribution characteristics to identify interference source types when the credibility is insufficient, and extracting spectrum characteristics of position sequences at the same time; then, based on the matching relationship between the interference mechanism and the spectrum characteristic, allocating an evaluation weight, and generating a reliability index; and finally, adaptively adjusting a measurement noise covariance matrix of the Kalman filter according to the index, and realizing optimal fusion of GNSS observation data and INS sensor data, thereby keeping the positioning precision and stability of the integrated navigation system in a strong interference environment.

The invention provides an integrated navigation method and system based on deep learning assistance, and the method comprises the steps: based on a preset reference coordinate system, obtaining a satellite observation position and a satellite observation speed through a navigation satellite system, obtaining an inertial navigation observation position, an inertial navigation observation speed and an inertial navigation observation attitude through an inertial navigation system, acquiring a first combined navigation solution according to the data; obtaining target point cloud data and solving depth information based on a deep learning auxiliary algorithm, point cloud data collected by a laser radar and a visual image collected by a visual camera, and obtaining an estimated position and an estimated attitude of a motion carrier according to the depth information; obtaining a second combined navigation solution based on the estimated position, the estimated attitude and the first combined navigation solution; and inputting the second combined navigation solution into the inertial navigation system to generate an error to compensate and correct the navigation deviation of the motion carrier so as to complete the combined navigation. According to the invention, the depth information is solved, various navigation results are fused for navigation compensation correction, and the navigation reliability is improved.

The invention discloses a track irregularity dynamic detection method based on inertial navigation and non-filtering, which belongs to the field of railway track detection, and comprises the following steps: based on an inertial measurement unit, performing global integrated navigation solution by combining zero-speed information, a speed sensor and a GNSS positioning result to obtain a global reference navigation state sequence; point cloud registration and key point extraction are carried out on the two-dimensional point cloud obtained by scanning the surface of the track to be detected, and key point coordinates are obtained; performing mechanical arrangement of the sliding window inertial navigation system by taking the target chord length as the window length to obtain a bogie motion trail; and performing three-dimensional dynamic lever arm position compensation based on the global reference navigation state sequence, the key point coordinates and the bogie motion trail to obtain a track shape trail: calculating track irregularity parameters, and completing track irregularity dynamic detection. According to the method, track irregularity dynamic detection is realized, band-pass filtering processing is not needed, information loss is less, and waveform distortion can be avoided.

The invention relates to the technical field of robot navigation, in particular to a robust visual inertial navigation method for dynamic and static factor decoupling in a high dynamic environment, and the method comprises the following steps: obtaining prior pose information and visual feature data of an inertial measurement unit from a navigation system; the method comprises the following steps: generating a stable confidence factor and a stable inertia factor of a visual feature based on priori pose information and visual feature data of an inertial measurement unit, and generating a double-factor residual error at the same time; based on prior pose information and visual feature data of the inertial measurement unit, effective dynamic features are identified, and a dynamic effect residual error is generated; and in combination with the basic residual error, the two-factor residual error and the dynamic effect residual error of the navigation system, decoupling dynamic and static residual errors and carrying out joint optimization to obtain an optimized navigation track. According to the method, the robustness of the visual inertial navigation system in a complex environment can be improved through dynamic and static factor decoupling and dynamic compensation constraint, and scene specific training is not needed.

The invention relates to an intelligent multi-source navigation method based on inertia and Beidou / visual information fusion, which comprises the following steps: firstly, establishing a multi-source information system of an inertial navigation system, and embedding an IMU (Inertial Measurement Unit) multi-parameter online calibration mechanism based on carrier dynamics in inertial navigation solution; establishing a tight coupling fusion framework taking an INS resolving result as a reference, and constructing tight combination measurement with the original observed quantity of the BDS and constructing re-projection error measurement with the visual feature points by utilizing INS high-frequency pose information; an intelligent adaptive filter based on inertial dynamics error model constraint is adopted, an INS error equation is used as a state prediction model, a Sage-Husa algorithm is introduced to estimate system noise statistical characteristics in real time, estimated error parameters are fed back to INS calculation through closed-loop correction, real-time correction of navigation results is achieved, and the system noise statistical characteristics are estimated in real time. Through deep tight coupling fusion and intelligent adaptive processing, navigation precision and system reliability in a complex environment are effectively improved.

The invention discloses a system for judging a dragging relation between a powered headstock and an unpowered trailer, and relates to the field of systems for judging the dragging relation between the powered headstock and the unpowered trailer, which comprises a powered vehicle-mounted terminal and an unpowered vehicle-mounted terminal, each of the powered vehicle-mounted terminal and the unpowered vehicle-mounted terminal comprises a near-field Bluetooth local area network, an inertial navigation system, a Beidou / GPS positioning system, a 2G / 4G network access system, a platform management system and a solar power supply. According to the invention, through the near-field Bluetooth local area network, the unpowered vehicle-mounted terminal can conveniently send the Bluetooth short-distance networking application to the powered vehicle-mounted terminal through the near-field Bluetooth local area network, so that networking between the unpowered vehicle-mounted terminal and the powered vehicle-mounted terminal in the range is realized; relative dynamic and static states and position error information are determined through a carried inertial navigation system and a Beidou / GPS positioning system, and comparison is carried out according to the dynamic and static states of the unpowered vehicle-mounted terminal and the power vehicle-mounted terminal and the position information of the unpowered vehicle-mounted terminal and the power vehicle-mounted terminal.

This invention discloses a positioning method and a charging method for video inspection equipment used for roadside parking fee collection. The solution of this invention establishes parking space collection points in advance at parking spaces where RTK positioning fails, and saves the auxiliary positioning information collected in advance at the parking space collection points. This information is used to update the reference of the strapdown inertial navigation system of the video inspection vehicle, so that when RTK positioning fails, accurate position information can be obtained from the strapdown inertial navigation system. Regardless of whether RTK positioning is successful or not, accurate positioning of the video inspection equipment can be achieved.

The application provides a collision avoidance radar target detection and identification method based on remote sensing data knowledge assistance, relates to the low-altitude collision avoidance technology field of helicopters / unmanned aerial vehicles, and solves the detection limitation problem of existing collision avoidance radars for small targets such as power lines. The method first constructs a power tower position auxiliary knowledge base based on remote sensing satellite image data, loads a real-time processing database according to the position data of the carrier inertial navigation system during the flight task; after the radar works, the echo signal is received and threshold detection is performed to obtain a list of isolated targets, and then tracking processing and initialization of attribute labels are performed to form a tracking target list; the tracking target list is matched with the real-time processing database, and if the matching is successful, the power tower target is obtained, and then the unmatched target is identified to determine whether it is a power line target, and after Hough detection confirmation, the detection and identification process is completed. The application not only guarantees the detection and identification ability of the radar itself, but also improves the detection and identification ability for power towers / lines.

This invention discloses a method and system for detecting the angle between a tractor and trailer based on a dual-antenna combined inertial navigation system, applicable to vehicle systems. The method includes: acquiring the position difference vector between the main and auxiliary antennas using a dual-antenna GNSS system on the tractor; calculating the first coarse heading angle of the tractor; and then fusing IMU data and filtering it to obtain a high-precision first heading angle. The roll and pitch angles are calculated using accelerometer data from the trailer's IMU, and tilt compensation is performed on the magnetometer data to obtain the second heading angle of the trailer. Furthermore, the heading and lane curvature data from the tractor can be combined to dynamically compensate and correct the trailer's heading online. The difference between the first and second heading angles is calculated in real time to obtain the angle between them. This invention can maintain heading output using IMU and wheel speed data even when GNSS signals fail, and ensure trailer heading accuracy through dynamic compensation, thereby achieving stable and high-precision tractor-trailer angle measurement.

1. Name of the designed product: inertial navigation system. 2. Use of the designed product: equipment for inertial navigation. 3. Design points of the designed product: in shape. 4. Picture or photo that best shows the design points: perspective view.

The invention discloses a take-off and landing guiding method and device for a manned vertical take-off and landing aircraft, and the method comprises the steps: carrying out the automatic division of a flight guiding stage through the entering and exiting logic based on the flight height, the vertical speed and the horizontal speed; automatically activating a first guide mode in a vertical takeoff stage and a vertical landing stage according to a short-distance target point alignment principle, and automatically activating a second guide mode in an acceleration climbing stage, a cruise stage and a deceleration descending stage based on a track deviation and speed guide cooperation principle; respectively configuring special guidance symbols and general flight information of the first guidance mode or the second guidance mode; the guidance available state is combined, a corresponding guidance state notice is generated according to the flight guidance stage, and the guidance available state judgment basis comprises sensor data validity, GPS positioning accuracy not larger than a first preset value and inertial navigation system error smaller than a second preset value; according to the invention, visual and accurate flight guidance information meeting the requirements of different flight stages can be provided for pilots.

The invention discloses a post-processing method and device for AUV positioning data, a medium and a program product. The method comprises the following steps: acquiring a first measuring point sequence and a second measuring point sequence of the AUV; wherein the first measuring point sequence is a positioning data set determined by a strapdown inertial navigation system, and the second measuring point sequence is a positioning data set determined by an ultra-short baseline underwater positioning system; performing jump point deletion on the second measuring point sequence, performing trajectory fitting, and determining a virtual control point based on a fitting trajectory; constructing a measurement section based on two adjacent virtual control points, and matching the measurement section with each first measurement point in the first measurement point sequence according to time; and for each measurement section, correcting the first measurement point in the measurement section according to the virtual control point to obtain a corrected measurement point, and determining a correction track of the AUV based on each corrected measurement point. According to the technical scheme, the correction of the AUV positioning data is realized by constructing the virtual control point, and the precision of the AUV positioning data is improved, so that the accuracy and applicability of an underwater task are met.

The application provides an iterative calibration method, device, equipment and medium for an optical fiber inertial navigation system, which comprises the following steps: collecting sample data of the optical fiber inertial navigation system according to a preset calibration path arrangement scheme; establishing a state error model and a measurement model according to the sample data; calibrating parameters in the error model to obtain first error parameters and a first residual error by using the error model; compensating and correcting device error parameters in the first error parameters, and calibrating the first residual error to obtain second error parameters and a second residual error; and repeatedly performing the compensation and correction and the calibration until the residual error converges to a stable value. The iterative calibration method for the optical fiber inertial navigation system provided in the application iterates once by using system-level calibration data, minimizes the final calibration result error, performs the system-level calibration only once, simplifies the calibration process, and greatly saves the calibration time of the optical fiber inertial navigation system.

The application discloses a complex scene-oriented inertial navigation system auxiliary positioning method and system, and belongs to the technical field of navigation and positioning. The technical solution points are as follows: signals collected by an inertial measurement unit at the current time are acquired, the inertial measurement unit is located in an object to be positioned; whether the signals are interfered by electromagnetic interference is determined according to a preset convolutional neural network model; if yes, the signals are projected into a space of a preset dimension for adaptive filtering processing to obtain updated signals; and the predicted position of the object to be positioned is determined according to the updated signals. The original signals are filtered in the application, the electromagnetic interference can be effectively relieved, the relatively accurate signals can be recovered, the position of the object to be positioned can be determined based on the signals, and the rank of the covariance matrix is reduced through the projection mode in the application, the calculation amount is reduced, and the good filtering performance is maintained.

The invention provides a dual-core hemispherical inertial navigation voltage sensitivity coefficient online identification and self-calibration method, which comprises the following steps: respectively constructing equivalent virtual angular rate excitation under electrostatic driving for each hemispherical resonator gyroscope, and determining a driving voltage for calibration; excitation is applied to the two coaxial hemispherical resonator gyroscopes according to a preset rule, and the output angular velocity and the standing wave azimuth angle are collected; establishing a difference observation model; establishing a Kalman filtering equation based on the difference observation model, wherein the Kalman filtering equation comprises an observation matrix, observed quantity and state quantity; kalman filtering is carried out on angular velocities and standing wave azimuth angles output by the two coaxial hemispherical resonator gyroscopes based on the established Kalman filtering equation, and a voltage sensitivity coefficient and a zero offset coefficient are solved. And writing the calculated voltage sensitivity coefficient and zero offset coefficient into a calibration register of the corresponding hemispherical resonator gyroscope. According to the technical scheme, the technical problem that in the prior art, a single gyroscope is prone to external interference, and consequently calibration precision is poor is solved.

The utility model belongs to an inertial navigation system, and particularly relates to an inertial navigation system antenna feeder circuit, which comprises an inertial navigation system and at least two antenna feeder circuits adopting differential signal transmission, and each antenna feeder circuit comprises a feed power supply, an anti-surge module, an anti-backflow module, an isolation module and an anti-static module, the output end of the inertial navigation system is connected with the input end of the feed power supply, the output end of the feed power supply is sequentially connected with the anti-surge module, the anti-backflow module, the isolation module and the anti-static module, and a multi-stage protection link is generated; the multi-stage protection link is integrated on an independent daughter board and is externally connected with a main control board through a metalized connector; the anti-surge module is used for inhibiting voltage impact; the anti-backflow module is used for preventing current from flowing reversely; the isolation module is used for noise isolation; the anti-static module is used for releasing electrostatic charges. The antenna feeder circuit of the inertial navigation system can solve the problem that the antenna feeder circuit of the inertial navigation system in the prior art is weak in protection against power supply surge and electrostatic interference.

This invention discloses a motion risk assessment system and method for autonomous vehicles based on multi-mode trajectory prediction, comprising four sensors, a time synchronization module, a verification module, a local anomaly voting and fault detection counter, a trajectory correction and feature enhancement module, and an LSTM network. The four sensors include an INS (Inertial Navigation System) combined inertial navigation sensor, an inertial navigation IMU (Inertial Measurement Unit) sensor, a drive-by-wire chassis sensor, and a visual recognition sensor. The INS combined inertial navigation sensor includes GPS and an IMU. The verification module includes a χ² (X-ray) sensor. 2 The system includes checks for consistency in motion patterns and residual consistency; the LSTM network comprises an LSTM encoder, a classification decoder, and an LSTM decoder. This invention proposes a complete "detection-evaluation-decision" closed-loop solution. By constructing a multi-mode prediction network incorporating fault characteristics, not only is fault identification achieved, but the impact of faults on driving safety can also be accurately assessed, providing comprehensive safety decision-making support for autonomous driving systems.

The invention belongs to the technical field of navigation, and discloses a navigation positioning method based on multi-source data fusion, which comprises the following steps: an error equation of an inertial navigation system is calculated, and a system state equation is the error equation of the inertial navigation system and mainly comprises attitude, speed, position and inertial device error equations; and calculating an inertia / TACAN state equation. Filtering state fusion estimation is calculated, an inertial navigation system serves as a reference system, other satellite navigation systems and the inertial navigation system form a sub-filter, state estimation is conducted through Kalman filtering, and then a filtering result is sent to a main filter for information fusion. Through the steps, the influence of a single navigation system on the airborne navigation system is effectively reduced, and the reliability of the navigation system in a complex electromagnetic environment is improved.

The invention provides an intelligent sensing robot system for a driving face, and belongs to the field of underground robots. The system comprises a sliding rail fixed to the top of a tunneling roadway where the tunneling machine is located, a liftable hanging rail robot is installed on the sliding rail, fixed targets are further arranged on the sliding rail at intervals, and a visual sensor, a laser radar, an event camera and an inertial measurement unit are carried on the hanging rail robot; infrared targets are installed on the two sides of a machine body of the heading machine, a strapdown inertial navigation system is further installed on the heading machine, the heading machine and the rail hanging robot interact according to a two-way interactive cooperative sensing mechanism, and continuous positioning of the heading machine is achieved. And three-dimensional perception real-time digital construction of the tunneling roadway is realized by fusing multi-source sensor data of the rail-mounted robot. The problems of insufficient sensing stability, poor algorithm real-time performance and the like in the prior art are solved, and accurate and stable sensing of the position and attitude of the heading machine body can be realized.

The application discloses a heading information determination method, which comprises the following steps: utilizing a rotation mechanism built in a strapdown inertial navigation system, and according to a preset rotation sequence of a fixed period, performing rotation adjustment on a prism normal of an inertial measurement unit of the strapdown inertial navigation system; utilizing a real-time prism normal of the inertial measurement unit to correct a posture matrix collected by the strapdown inertial navigation system and a speed collected by an odometer; and taking the corrected posture matrix and the corrected speed as heading information. Thus, the rotation modulation of a gyroscope and an accelerometer can be realized by utilizing the rotation mechanism built in the strapdown inertial navigation system, a long-time heading keeping method of a vehicle-mounted inertial / odometer based on the rotation modulation is realized, the constant error source except for the axial (rotation axis direction) error can be automatically offset, the long-time heading precision of the system is improved, and the heading information precision is improved.

This invention provides a spatiotemporal registration method, apparatus, system, and storage medium, belonging to the field of navigation and positioning. The method includes: calculating a priori error state estimate by estimating the error data and sampling time; calculating the priori error state covariance by setting the error value and the priori error state estimate; constructing a measurement using sampling time, Bluetooth sensor coordinates, Bluetooth sensor velocity, inertial navigation system velocity, inertial navigation system coordinates, and accelerometer measurements; and calculating a posterior error state estimate by estimating the priori error state estimate and the priori error state covariance based on the measurement. This invention simplifies the prediction and correction stages of the extended Kalman filter, greatly reduces the impact of IMU zero bias on positioning performance, achieves real-time spatiotemporal registration, and meets the requirements of good real-time performance, strong practicality, and ease of implementation, thus satisfying current needs.