493results about "Navigation by terrestrial means" patented technology

The invention provides a tunneling equipment autonomous navigation positioning method and system based on multi-source data, and relates to the technical field of inertial navigation, the method comprises the following steps: connecting multi-source monitoring equipment, obtaining a multi-source data set, carrying out space-time alignment, and establishing a monitoring data network; butting the transparent geologic model, and fusing the transparent geologic model with a monitoring data network to construct a virtual model; fitting the physical structure parameters and the operation state parameters of the tunneling equipment to the virtual model, positioning the tunneling equipment, and performing tunneling path optimization search; performing attitude and inertia analysis in the virtual model based on the planned path to obtain a path attitude strategy; and carrying out operation navigation control on the tunneling equipment. According to the method and the device, the technical problem of high difficulty in long-time navigation and positioning in a complex environment due to lack of autonomous navigation of the tunneling equipment is solved, and accurate positioning of the tunneling equipment in the complex environment is realized through fusion of multi-source monitoring and the virtual model, so that the tunneling efficiency and safety are improved.

The invention discloses a vehicle navigation positioning method and system when a navigation signal is lost, and relates to the technical field of vehicle autonomous localization, and the method comprises the steps: carrying out the tight coupling matching based on a geomagnetic feature map, obtaining the geomagnetic sequence similarity through a sliding window dynamic time warping algorithm, and generating a fusion positioning track; extracting a road network topology connection relation from the high-precision map, constructing a road connection graph Laplacian matrix with a weight, and obtaining a topology constraint correction coefficient through a graph convolutional network in combination with the fusion positioning track; and constructing a joint optimization function based on the topological constraint correction coefficient, and solving an optimal positioning coordinate by adopting an asymmetric Gaussian-Newton iterative algorithm. According to the method, the defects of topology modeling stiffness and non-linear optimization instability of a traditional method are overcome, so that high-precision and high-robustness vehicle continuous positioning is realized in a GNSS signal loss scene.

The invention relates to the technical field of control systems, in particular to an unmanned aerial vehicle flight control multi-sensor attitude confidence calculation method, which comprises the steps of multi-source sensor data acquisition, sensor independent attitude calculation, confidence coefficient dynamic evaluation, multi-modal data fusion, attitude error compensation and optimization and real-time verification and fault-tolerant processing. Compared with a fixed weight distribution scheme in the prior art, the method has the advantages that the attitude calculation precision is limited possibly, and particularly when the sensor data quality is influenced by environmental factors, the scheme adopts a strategy of checking the data quality in real time, judging the environmental state and dynamically distributing the weight; the validity of sensor data is effectively identified by verifying whether the acceleration modulus length is close to the gravitational acceleration or not and whether the magnetic field intensity is stable or not; meanwhile, the weight of each sensor is flexibly adjusted according to different environment states such as static, maneuvering or magnetic interference of the unmanned aerial vehicle, so that the self-adaptive capability and accuracy of attitude calculation are remarkably improved.

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

The invention relates to the technical field of multi-agent cooperation, provides a multi-agent cooperative unmanned aerial vehicle cluster simulation training method, system and device, and a storage medium, and solves the problems of low penetration success rate and poor formation cooperation efficiency of an unmanned aerial vehicle cluster. The method comprises the following steps: collecting pigeon flock biological motion data, cluster trajectory data when an unmanned aerial vehicle cluster executes a historical penetration task, and magnetic field gradient data of a simulation environment; constructing a three-dimensional path library, and calculating geomagnetic course angle deviation; on the basis of the geomagnetic course angle deviation and the three-dimensional path library, a multi-target collaborative path is generated in combination with a swarm intelligent optimization model; generating a flight control instruction based on the multi-target cooperation path in combination with the real-time state data of the unmanned aerial vehicle cluster; and loading the flight control instruction to a simulation model of the unmanned aerial vehicle cluster, and driving the unmanned aerial vehicle cluster to carry out cooperative defense penetration training in a simulation environment. According to the invention, the penetration success rate and formation cooperation efficiency of the unmanned aerial vehicle cluster in a complex electromagnetic environment are improved.

In one embodiment, a method includes accessing a first series of first magnetic values that represents a first magnetic recording that includes a first set of first magnetic measurements. Each of the first magnetic values represents one of the first magnetic measurements. The method includes accessing a second series of second magnetic values that represents a second magnetic recording that includes a second set of second magnetic measurements. Each of the second magnetic values represents one of the second magnetic measurements. The method includes approximately aligning the first and second series with each other; calculating a difference between the first and second series as aligned with each other; and determining a similarity between the first and second series based on the difference.

The invention relates to the technical field of intelligent transportation, and particularly discloses a multi-sensor data fusion method based on adaptive Kalman filtering, which is suitable for vehicle trajectory tracking in a tunnel scene. The method comprises the steps of sensor deployment and data acquisition, data preprocessing and anomaly detection, multi-target data association, adaptive filtering fusion and state estimation. By introducing an anomaly detection mechanism and dynamically adjusting a filtering estimation error covariance, the influence of abnormal data such as a multipath effect and electromagnetic interference is effectively suppressed, and the tracking precision and stability of the position, speed and trajectory of the vehicle are improved. The method is remarkably superior to a traditional filtering algorithm in a complex tunnel environment, has the technical advantages of high-precision tracking, high robustness, high adaptability, high real-time performance, comprehensive functions and the like, and can remarkably improve the reliability and performance of a tunnel vehicle monitoring system; the method is suitable for multiple key fields of tunnel safety early warning, accident prevention, traffic flow optimization, emergency response and the like.

The invention relates to the technical field of magnetic detection, in particular to a target positioning method based on geomagnetic anomaly and an axis frequency magnetic field. Comprising the following steps: S1, preparing before entering water; s2, approaching a target; s3, target detection: when no target appears, an observation signal is composed of an environment magnetic field, and the energy value of the part is very small; once the target appears, the energy of the observation signal can be continuously and obviously increased, when the energy value is greater than a preset judgment threshold, the target point is judged as a suspected target point, and if the suspected point is continuously detected for a specified time, the existence of the target is confirmed; s4, combined positioning: under a Kalman filtering framework, enabling the target to be equivalent to a magnetic dipole and an electric dipole, and correcting a position predicted by a target state equation by combining measurement equations of the two models, thereby realizing combined positioning of the target and obtaining more accurate target position information; and S5, verifying and outputting a positioning result. The method has the advantages of improving the positioning precision, enhancing the anti-interference capability and the like.

The invention discloses a geomagnetic diurnal variation data forecasting method, system, equipment, medium and product, and relates to the technical field of geomagnetic navigation, and the method comprises the steps: collecting and preprocessing global geomagnetic station data, carrying out the fast Fourier transform of geomagnetic field diurnal variation data, solving to obtain a trigonometric function coefficient, and carrying out the calculation of the trigonometric function coefficient; according to the method, geomagnetic daily variation data of any region in the world are obtained through geomagnetic daily variation, then an association Legendre coefficient is obtained through solving, an internal source field Gaussian coefficient and an external source field Gaussian coefficient are obtained through solving according to the Gaussian theory, finally daily variation model forward modeling can be carried out according to the internal source field Gaussian coefficient and the external source field Gaussian coefficient, and geomagnetic daily variation data of any region in the world are output. According to the method, coefficient solution and model forward modeling related to spherical harmonic analysis are utilized, the problems that in the prior art, the control range of a virtual terrestrial magnetism daily variation station is limited, and data are prone to being polluted are solved, and the forecasting precision of terrestrial magnetism daily variation data is improved.

In various examples, a technique for generating a path between a current location and a destination waypoint is disclosed, including receiving a route plan associated with a plurality of waypoints representing locations in a physical environment. The technique also includes identifying a search space containing the route plan and identifying a destination waypoint from the plurality of waypoints, wherein the destination waypoint is located in a portion of the search space between a current location of a mobile robot and an end waypoint of the route plan. A path may then be generated between the current location of the mobile robot and the destination waypoint.

The invention relates to a visual feature constrained inertial / geomagnetic positioning method, and belongs to the technical field of navigation positioning. The method has the advantage that the method does not depend on collection, updating and maintenance of a high-precision map. A traditional image matching method usually needs to consume a large amount of time and resources to construct and update an accurate indoor map, but according to the method, by deploying a small number of feature markers with known coordinates in an indoor environment, the map construction and maintenance process can be greatly simplified, and the deployment cost of a system is reduced. Meanwhile, real-time updating of the multi-source sensor is combined, and pose estimation can be dynamically adjusted according to actual movement and environmental changes, so that higher precision and reliability are provided in actual use.

The present invention belongs to the field of marine underwater positioning, navigation and timing. Disclosed are an underwater navigation and communication integrated position service system and method. The system comprises a buoy base station, a submerged buoy base station, a seabed base station and a shore-based operation and control unit, and the system utilizes a three-dimensional base station network observation mode combining the buoy base station, the submerged buoy base station and the seabed base station. The position service system and method in the present invention have the capabilities of underwater acoustic navigation and communication, satellite positioning and communication, atomic clock timekeeping, hydrological measurement and responding to instructions of a shore-based operation and control unit, have the functions of autonomous, real-time and online base station position and clock calibration and sound velocity field correction, have the functions of base station position and clock calibration, and sound velocity field correction and control based on the shore-based operation and control unit, and can expand the range and functions of underwater position services, and provide accurate positioning, navigation and timing services for water-surface / underwater users.

The invention provides an indoor and outdoor seamless navigation method and system based on multi-source positioning weight dynamic allocation, and the system comprises a multi-source positioning data collection module which is used for obtaining and preprocessing original data of different positioning sources in real time; the building structure big data engine is used for constructing a three-dimensional space constraint model; the positioning source reliability evaluation module is used for quantifying the credibility of each positioning source in a specific scene; the dynamic weight distribution decision-making module is used for adjusting the weight of the positioning source in real time based on a multi-factor decision-making model; the multi-source data fusion filtering module is used for fusing the heterogeneous positioning data; the indoor and outdoor scene switching control module is used for performing seamless switching triggering and smooth transition on indoor and outdoor scenes; according to the technical scheme, efficient cooperation of multiple positioning sources is realized, and the navigation precision and adaptability are improved.

The invention belongs to the technical field of data processing and analysis, and particularly relates to a geomagnetic and inertial navigation fused glasses guide direction identification and content triggering system, which comprises a sensing compensation component, a calculation storage component, a guide resource component and a display component, the sensing compensation assembly comprises an inertial measurement unit, a geomagnetic sensor, an active magnetic compensation coil and a coil driving module; the calculation storage assembly comprises a control processor and a memory, and the control processor is used for driving the active magnetic compensation coil to obtain a coil compensation geomagnetic vector according to a phase coding zero-returning sequence, executing attitude propagation based on the inertial measurement unit, and executing course updating on the coil compensation geomagnetic vector based on Huber anti-outlier loss to obtain a head attitude. According to the invention, the influence of magnetic disturbance and attitude jitter on direction identification and content triggering can be effectively reduced, and the stability, consistency and practicability of the glasses guide system in a real use scene are improved.

The invention discloses an underground target positioning method based on a rotary permanent magnet type magnetic beacon, and belongs to the field of navigation, guidance and control. According to the invention, the problem of low precision of an existing positioning method caused by attitude errors in an underground positioning scene is solved. The method specifically comprises the following steps of: 1, respectively placing four same rotary permanent magnet type magnetic beacons at four points with known spatial positions, and respectively rotating the four magnetic beacons around a fixed shaft at a set rotating speed; 2, placing a magnetic sensor at an underground target point, and collecting mixed alternating magnetic field data under a sensor coordinate system by using the magnetic sensor; step 3, filtering the acquired mixed alternating magnetic field data by using a designed band-pass filter to obtain an alternating magnetic field vector of each rotating permanent magnet type magnetic beacon at the underground target point; constructing a positioning model according to the alternating magnetic field vector; and 4, obtaining an estimated target point position according to the positioning model. The method can be applied to underground target positioning.

The invention belongs to the technical field of ocean engineering equipment and underwater detection, and particularly discloses a far-field sensing and positioning method for a submarine cable of a cable laying robot based on a multi-modal sensor, electronic equipment and a readable storage medium. The method comprises the following steps: S1, acquiring acoustic, optical, magnetic and electric field original data of the submarine cable by using a multi-mode sensor array; s2, constructing an original data set with time-space synchronization, ensuring that timestamps and space coordinates of acoustics, optics, magnetic force and electric field data are consistent, and forming a standardized four-dimensional tensor data set; s3, performing space-time registration and joint noise reduction processing on the original data to generate standardized sensing data; s4, constructing a dynamic environment fusion model for fusing the real-time sea condition parameters and the prior knowledge base to generate environment constraint conditions; s5, iteratively calculating the position of the cable by adopting a prediction-correction mechanism based on a particle filtering and model prediction control algorithm; and S6, outputting cable positioning coordinates and cable laying robot adaptive path planning parameters.

The invention belongs to the technical field of meteorological observation equipment, and particularly discloses an air-drop self-maintenance meteorological station bracket for observing a freezing ring, which comprises a telescopic piece, a steering mechanism, a supporting rod part and a control part. The telescopic part is provided with a telescopic end, the steering mechanism is connected with the telescopic end and used for driving the telescopic end to rotate so as to adjust the direction of the meteorological monitoring part, the supporting rod part comprises three supporting rod assemblies distributed in the circumferential direction of the telescopic part, and each supporting rod assembly comprises an electric push rod, a first buffer rod piece, a supporting foot and a second buffer rod piece. Based on the first buffer rod piece and the second buffer rod piece, severe impact on the ground is effectively buffered when a meteorological station support is air-dropped, air-dropped placement can be achieved, and the field meteorological observation problem of the area where personnel cannot reach is effectively solved. And the three fulcrums of the bracket body can be independently adjusted by utilizing the electric push rod, so that the problem of inclination or overturning caused by ablation or movement deformation of the underlying surface can be effectively solved.

The invention belongs to the technical field of navigation, and discloses an inertial navigation method and system based on dual modes and a medium, the dual modes comprise a first navigation mode and a second navigation mode, the first navigation mode adopts a first combined navigation mode formed by combining an inertial unit with an ultra-short baseline acoustic positioning unit and a Doppler velocity measurement unit, and the second navigation mode adopts a second combined navigation mode formed by combining an ultra-short baseline acoustic positioning unit and a Doppler velocity measurement unit. The navigation mode II adopts a second combined navigation mode of combining an inertial unit and a geomagnetic measurement unit. The method comprises the following steps: acquiring the depth of the submersible at the current position; and updating the current position of the submersible in real time by adopting the navigation mode I and / or the navigation mode II according to the depth of the submersible at the current position. The method has high positioning accuracy.

The invention provides an indoor local magnetic field modeling method and device based on a magnetic source equivalence principle, and belongs to the technical field of indoor localization, and the method comprises the steps: collecting the data of a three-axis magnetometer in a space range through an array type magnetic sensor, and obtaining the original measurement information of an indoor magnetic field; based on a magnetic source equivalence principle, an indoor magnetic field is modeled into an equivalent magnetic dipole model consisting of a plurality of equivalent magnetic dipoles, and a magnetic field generated by the model is consistent with a real magnetic field in an observation area by setting magnetic moment and position parameters of the magnetic dipoles; according to the measured value of the array type magnetic sensor, an observation model is established in combination with an equivalent magnetic dipole model and used for describing the relation between the measured value and model parameters; through a nonlinear least square optimization method, parameters of the equivalent magnetic dipole model are solved according to the observation model, the equivalent magnetic dipole model of the indoor magnetic field is obtained, and the parameters comprise the magnetic moment and the position of the magnetic dipole. The precision and the reliability of the model are remarkably improved.

The invention provides an inertia / geomagnetic integrated navigation method based on a factor graph, which comprises the following steps of: constructing a geomagnetic graph, matching current geomagnetic information with the geomagnetic graph, and further acquiring positioning information and a geomagnetic matching function of a current navigation object; constructing an inertial measurement error based on an inertial pre-integration result between the two geomagnetic key frames, and constructing a geomagnetic positioning error according to a geomagnetic matching function; obtaining an inertia / geomagnetic error optimization function by combining the inertia measurement error and the geomagnetic positioning error; constructing a map optimization model according to the inertia / geomagnetic error optimization function, and obtaining an optimized geomagnetic state estimation state quantity and an optimized inertia state estimation state quantity through iterative solution; according to the geomagnetic state estimation state quantity and the inertial state estimation state quantity, motion compensation is carried out on inertial navigation data at historical moments, a geomagnetic pose is corrected, and inertial / geomagnetic integrated navigation of a navigation object is realized. According to the invention, inertial / geomagnetic data resolving and positioning in a typical environment can be completed.

The invention discloses a multi-modal fusion robot high-intensity magnetic field interference resisting self-adaptive positioning method. The method comprises the following steps that S1, nine-axis magnetic field data are collected through three sets of three-axis magnetometers arranged in an inclined redundancy mode; s2, constructing a Kalman filtering framework based on motion feature dynamic weighting, and performing real-time denoising on the nine-axis magnetic field data; s3, performing course estimation on the denoised magnetometer data and the motion acceleration and inclination angle data of the robot by adopting a correlation fusion algorithm; and S4, under the condition of not depending on a gyroscope, outputting a course angle positioning root mean square error of 0-15 DEG. The method has the beneficial effects that the inclined redundant magnetometer array is adopted, and dynamic weighted Kalman filtering is combined, so that the method can stably work in a strong magnetic field environment; under the condition that a gyroscope is not introduced, the root mean square error of course estimation is 13.64, compared with a traditional non-filtering magnetometer, data are greatly improved, and positioning failure caused by magnetic field distortion is avoided.

The invention relates to a robot advancing method and system based on an inertial sensor, and belongs to the technical field of robot autonomous navigation and inertial guidance control. The method comprises the following steps: acquiring advancing parameter data of a robot, and analyzing a track generation mode in the advancing process of the robot to obtain autonomous advancing track data; constructing a marching analysis model, inputting the robot marching parameter data into the marching analysis model to analyze inertial navigation information, and outputting a robot marching instruction; the stability of the posture state in the advancing process is verified, a robot advancing instruction is optimized through a path optimization protocol, and an instruction optimization set of an advancing path is generated; and constructing an offset compensation mechanism, selecting an instruction optimization parameter according to the advancing path when the advancing path is detected to deviate, re-inputting the instruction optimization parameter into the advancing analysis model, and re-matching a newly selected advancing path.

The invention is suitable for the technical field of positioning and navigation, and provides a two-dimensional geomagnetic matching and positioning method based on multi-feature parameter constraints, compared with a traditional method, the method comprises the following steps: screening out candidate fingerprint sequences meeting constraint conditions in a geomagnetic reference library according to motion feature constraints and geomagnetic field periodic change feature constraints; and performing matching calculation on the real-time geomagnetic sequence and the candidate fingerprint sequence to obtain a candidate fingerprint sequence with the highest similarity, extracting position information in the candidate fingerprint sequence with the highest similarity, and obtaining a positioning result of the motion carrier on the set route after the motion carrier finishes walking on the set route. A complex two-dimensional matching positioning problem is converted into a one-dimensional path matching optimization problem under the constraint of multiple characteristic parameters, the change trend and matching efficiency of a sequence are considered, the positioning precision is improved, more calculation power is saved, and the method is particularly suitable for high-precision autonomous positioning requirements in GNSS rejection scenes such as underground space and indoor environment.

The invention relates to the technical field of non-networked indoor autonomous positioning and navigation, in particular to a smart phone positioning and navigation method and system in a non-networked indoor scene, and the method comprises the steps that a smart phone receives data returned by a Bluetooth AOA positioning base station through point-to-point communication, and an AOA position estimation result is obtained through calculation of a single-base-station AOA fusion positioning model; the smart phone collects real-time data of an IMU and a magnetometer, and user coordinates and course angles are recursively estimated through a pedestrian dead reckoning (PDR) method; according to an AOA base station deployment condition of an actual indoor space, multi-base station centroid positioning, AOA fusion positioning and PDR positioning are automatically switched, so that a position result can effectively cover a whole space scene; the method comprises the following steps: constructing an obtained binary grid map through an indoor map model, carrying out position post-processing based on map matching on positioning coordinates to obtain pedestrian coordinates, and carrying out digitization on a map model to realize map display and finally realize pedestrian position display. The method can be reliably operated in a scene lacking network coverage.

The invention provides a multi-source heterogeneous sensor adaptive fusion positioning and mapping method and device, and relates to the technical field of automatic driving and mobile robots. The method comprises the following steps: for a mobile robot in an indoor environment, acquiring sensor data through a sensor data acquisition module, inputting the sensor data into a front-end data processing module to obtain processed sensor data, and inputting the processed sensor data into an environment scene recognition module to obtain an environment scene type recognition result; and inputting the result into a back-end state estimation and optimization module to obtain a map construction and pose estimation result. Through innovative front-end feature extraction, a rear-end optimization mechanism and a multi-sensor fusion strategy, the problems in the prior art are solved. By introducing a low-cost magnetometer, the robustness of the system in a GPS signal missing or complex electromagnetic environment is enhanced, meanwhile, the calculation efficiency and precision in different environments are improved through a self-adaptive optimization mechanism, and finally, high-precision and high-robustness autonomous positioning and mapping functions are achieved.

The invention provides a course information determination method and device, and the method comprises the steps: obtaining geomagnetic data collected by a geomagnetic sensor at a current moment, and obtaining gyroscope data collected by an inertial measurement unit in a vertical direction at the current moment; under the condition that positioning data acquired by a global positioning system is not acquired, or the positioning data is acquired and the positioning data is smaller than or equal to a first threshold value, determining course angle variation of the current moment relative to the target moment based on gyroscope data in the vertical direction of the current moment and a time difference between the current moment and the target moment; the target moment refers to a moment when the positioning data is greater than a first threshold value for the last time before the current moment; obtaining a geomagnetic course angle at the current moment based on the geomagnetic data at the current moment; and obtaining the final course angle at the current moment based on the final course angle at the target moment in the preset storage space, the course angle variable quantity and the geomagnetic course angle at the current moment.

The invention relates to the technical field of navigation and positioning, in particular to a pedestrian dead reckoning optimization method based on improved wave crest detection and CNN-LSTM, comprising the following steps: S1, data acquisition: acquiring pedestrian motion sensor data including acceleration, angular velocity and magnetic field intensity through an accelerometer, a gyroscope and a magnetometer; s2, on the basis of an improved wave crest detection algorithm, real-time monitoring and analysis are carried out on the acceleration signal by setting a dynamic threshold value, and an effective gait cycle is extracted; s3, extracting reference features of the step length from the pedestrian motion sensor data, training a step length prediction model of the CNN-LSTM network, and optimizing the step length prediction model; and S4, obtaining a pedestrian course angle based on quaternion attitude calculation, realizing gait cycle segmentation and step length prediction in combination with an improved wave crest detection algorithm and a CNN-LSTM model, and finally calculating the position of the pedestrian at the next moment through the course angle and the step length so as to complete continuous navigation positioning. The method can achieve the dynamic extraction and fusion of the spatial-temporal features of multiple sensors, improves the gait detection robustness and step length estimation precision in a complex scene, remarkably optimizes the overall positioning performance of a PDR, and solves a problem that the positioning precision is reduced because a signal is affected by sensor noise, behavior diversity and the like.

The invention relates to the technical field of gyroscopes, in particular to a hemispherical resonator gyroscope gradient magnetic field interference error compensation method which comprises the following steps: step 1, acquiring a triaxial magnetic field intensity component of a gyroscope under a carrier coordinate system through a magnetometer; step 2, acquiring a three-axis acceleration measurement value of the gyroscope under the carrier coordinate system through an accelerometer; 3, constructing an extended Kalman filter by taking navigation information as measurement input, and performing fusion calculation on the three-axis magnetic field intensity component in the step 1 and the three-axis acceleration measurement value in the step 2 through the extended Kalman filter to obtain compensation output of the magnetometer; an inertial navigation module is in signal connection with a magnetometer and an accelerometer, a triaxial magnetic field intensity component and a triaxial acceleration measurement value are acquired, then an extended Kalman filter is used for carrying out fusion calculation on the data, and compensation output is used for correcting an attitude angle of a gyroscope, so that the influence of gradient magnetic field interference on gyroscope output is reduced.

The invention provides a mobile phone side shadow matching positioning method and system in a city complex environment, and the method comprises the steps: extracting building boundaries and road region blocks, and carrying out the statistics of satellite SNR distribution characteristics; gNSS observation data and broadcast ephemeris of a mobile phone terminal are obtained, SNR is smoothed through low-pass filtering, and the absolute position of a pedestrian and the position of a satellite are obtained; acquiring data of an accelerometer, a magnetometer and a gyroscope, and acquiring a pedestrian step length and a course angle according to observation data; generating an initial candidate position set according to the initial absolute position of the pedestrian, and updating candidate positions according to the step length and the course angle of the pedestrian; predicting satellite visibility characteristics according to the satellite position and the building boundary; scoring the candidate position and determining the weight according to the SNR distribution characteristics, the satellite visibility, the elevation angle, the azimuth angle and the distance between the candidate position and the absolute position of the pedestrian, and determining the position of the pedestrian according to a weighted average method. According to the method, the dynamic positioning precision of the shadow matching algorithm in the urban complex environment can be remarkably improved.