216 results about "Troposphere" patented technology

A method for retrieving tropospheric wet delay and atmospheric water vapor content over polar sea ice with TDS-1 satellite grazing angle spaceborne GNSS-R is provided, including: Si, obtaining TDS-1 GNSS-R raw intermediate frequency signal data, VMF3 grid data, GPT3 grid data and ERA5 data; S2, correcting an error of tropospheric wet delay estimation of grazing angle spaceborne GNSS-R; S3, constructing a grazing angle spaceborne GNSS-R tropospheric wet delay estimation model; S4, calculating grazing angle spaceborne GNSS-R ZWD; S5, calculating a Tm value of a target point based on GPT3 model, substituting the Tm value into a conversion factor II, and combining calculated GNSS-R ZWD to obtain a GNSS-R IWV estimated value; and S6, verifying inversion performance of GNSS-R ZWD and integrated water vapor (IWV) by using reference data.

The invention discloses a PPP-RTK time delay processing method and device in conversion from SSR to OSR, and relates to the technical field of global navigation satellite system positioning, and the method comprises the steps: converting orbit, clock error, ionosphere and troposphere delay SSR parameters provided by a server into OSR virtual observation values through geometric distance calculation and projection function mapping; based on a constant velocity model and a static random process, predicting a correction amount of a future moment, and correcting an OSR virtual observation value through state transition matrix compensation time delay; combining server side parameters and user side observation data, recursively generating an error covariance matrix, dynamically adjusting a state prediction weight, and obtaining covariance information through adaptive filtering optimization positioning calculation; and realizing ambiguity rapid fixation and centimeter-level positioning by using the corrected OSR observation value and covariance information. According to the method, real-time centimeter-level positioning in a high-delay scene is realized through a dynamic correction value prediction method and a recursive covariance estimation method.

The invention discloses a troposphere delay model construction method based on a numerical meteorological model and an unmanned aerial vehicle, and the method comprises the steps: obtaining meteorological parameter values in the numerical meteorological model for a target region; a troposphere observation value obtained by the target unmanned aerial vehicle through the airborne GNSS receiver and the airborne multi-source meteorological equipment in the process of moving along the preset path in the target area is obtained; determining first troposphere delay data according to the meteorological parameter values and a corresponding preset oblique troposphere delay model; determining second troposphere delay data by adopting a preset ambiguity fixing algorithm based on the satellite observation value; and according to the first troposphere delay data and the second troposphere delay data, determining troposphere delay data of the target area. According to the method, accurate estimation of troposphere delay in a complex region and a meteorological condition rapid change region is realized, and a reliable troposphere delay model is provided for realizing high-precision monitoring of InSAR (Interferometric Synthetic Aperture Radar) deformation monitoring.

The invention relates to the technical field of satellite navigation and positioning, in particular to a zenith troposphere wet delay calculation method based on deep learning, and the basic steps of segmented ZWD multi-source data fusion based on an improved Transform model are as follows: establishing a ZTD model, establishing a TCA-Transform model, and fusing data. The task of establishing the ZTD model is to calculate the atmospheric refractive index according to meteorological data so as to obtain ZTDPP; the TCA-Transform model establishment comprises the following steps of: improving a Transform model by using a domain self-adaptive method; according to the data fusion, the ZWDPP and the ZWDMODIS are fused by using a TCA-Transform model. And finally, a ZWD product with high temporal-spatial resolution and high precision can be obtained. The generalization ability of an original Transform model is improved, so that the troposphere delay correction ability in a specific area is improved, and a zenith troposphere delay (ZWD) product with high resolution and high precision is obtained.

The invention relates to the technical field of mine environment risks, and discloses a mine environment risk multi-modal analysis and early warning decision-making method, which comprises the following steps of: performing time and space reference alignment on multi-source observation data, constructing a unified spatio-temporal data set, and establishing a troposphere disturbance model to realize observation disturbance coupling. And constructing a physical evolution operator and a rainfall-driven external input operator based on a seepage mechanical relationship, and generating a state evolution mechanism. Semantic direction parameters are generated by mapping semantic query information to a target attention dimension, and a lagging propagation operator is constructed by setting a rainstorm ending moment as a starting point. And determining a non-regular spectrum transition index and a peak lag moment through the non-regularity measurement and the dynamic change rate of each lag propagation operator, and setting a reference threshold value based on the spectrum transition index of a historical non-risk time period. Finally, when the early warning intensity meets the condition, early warning is output, and a query result is generated in combination with the lagging propagation operator and the semantic direction parameters.

The invention discloses a single base station PPP-RTK (point-to-point protocol-real-time kinematic) positioning method and equipment. The method comprises the following steps: firstly, according to acquired single base station observation data, single base station accurate coordinates and ephemeris data, satellite end error corrections and atmospheric delay corrections are respectively obtained based on different resolving models, and the satellite end error corrections comprise satellite clock error corrections and satellite orbit error corrections; the atmospheric delay correction number comprises an ionosphere delay correction number and a troposphere delay correction number of the first frequency; calculating an atmospheric delay difference between the single base station and the user based on an empirical model, and substituting the atmospheric delay difference into the atmospheric delay correction number to obtain an atmospheric delay correction number which can be used for the user; obtaining observation values of users to construct a PPP-RTK observation equation so as to eliminate error terms related to a single base station; and the satellite end error correction number and the atmospheric delay correction number are substituted into a user end PPP-RTK observation equation, so that single base station PPP-RTK positioning can be realized.

The invention belongs to the crossing field of space geodetics, satellite navigation positioning technology and atmospheric science, and discloses a real-time troposphere delay modeling method for enhancing a global forecasting system. The method comprises the following specific steps: firstly, acquiring zenith fluid static delay and zenith wet delay predicted by earth surface GFS, and constructing a regional grid difference time sequence by taking a fusion result of ERA5 data and a GNSS troposphere delay product as a reference; secondly, fitting the difference to form a GFS enhanced core model; meanwhile, a troposphere delay correction model from the grid height to the target site height is constructed based on ERA5 stratified atmosphere data, time sequence modeling is carried out on model coefficients, and finally refined troposphere delay output at different heights is achieved. The method has the advantages that (1) the real-time troposphere model is high in precision; (2) real-time application can keep robust performance without depending on a GNSS network; (3) expansion and deployment are facilitated; and (4) the real-time performance is strong, and seamless integration precision positioning application can be realized.

The invention discloses a troposphere BrO vertical profile inversion method, medium and equipment based on machine learning, and the method comprises the steps: obtaining BrO vertical profile data, inputting the profile data, aerosol characteristic parameters, geometric angle parameters and earth surface characteristic parameters into an atmospheric radiation transmission model, and obtaining BrO DSCD data; the obtained DSCD and the profile data set are preprocessed; training a troposphere BrO vertical distribution inversion model combining a convolutional neural network CNN and a long short-term memory network LSTM by using the preprocessed DSCD and profile data set; and performing QDOAS processing on MAX-DOAS spectral data to be inverted to obtain DSCD data, inputting the preprocessed DSCD data, aerosol parameters and geometric angle parameters into the trained CNN-LSTM troposphere BrO vertical distribution inversion model to perform data inversion, and finally obtaining an inversion result of troposphere BrO vertical distribution. According to the invention, the accuracy and stability of an inversion result can be significantly improved.

The invention discloses a troposphere delay mapping model construction method and system based on machine learning. The method comprises the following steps: acquiring meteorological reanalysis data; calculating the ZTD at the global site or grid and the STD at different azimuth angles and elevation angles based on the meteorological reanalysis data; based on the ZTD and the STD, constructing an asymmetric mapping relation from the ZTD to the oblique path delay; constructing a data set based on meteorological reanalysis data, the ZTD and the asymmetric mapping relation, inputting the data set into a troposphere delay mapping model to train the troposphere delay mapping model, and generating prior troposphere delay for GNSS high-precision positioning through the trained troposphere delay mapping model; wherein the troposphere delay mapping model is a machine learning model. According to the method, the empirical model of the ZTD and the unified asymmetric mapping relation from the ZTD to the STD can be constructed, and the precision and the applicability of the model are remarkably improved.

The invention relates to the technical field of engineering monitoring, in particular to an unmanned aerial vehicle dynamic monitoring and early warning system and method for the whole subway tunnel excavation process, and the system comprises a virtual base station differential positioning module which is used for carrying out the unmanned aerial vehicle dynamic monitoring and early warning according to a carrier phase observation value transmitted by a ground active control target and a satellite signal observation value obtained by an airborne receiving end, and calculating ionosphere residual values and troposphere residual values between the current position of the unmanned aerial vehicle and the three adjacent ground active control targets. According to the method, the pixel gradient vector of the thermal inertia matrix is further calculated, the edge candidate points exceeding the threshold value are extracted, and the edge candidate points are closed in the gradient vertical direction to form the geometric region, so that the fuzzy thermal field anomaly is converted into the quantized graph with the definite physical boundary, and the hidden danger area and the internal gradient mean value are combined for calculation; the risk early warning level can be dynamically generated according to the tunnel design tolerance, so that recognition and hierarchical management and control of hidden geological disasters are realized on the premise of not contacting the ground.

The embodiment of the invention provides a Beidou water vapor inversion method and system based on a buoy platform. The method is applied to the technical field of data processing, and comprises the following steps: receiving a Beidou dual-frequency signal through a direct antenna to obtain original observation data; the multi-path effect and the platform shake are inhibited by combining buoy motion characteristics obtained by the MEMS sensor to obtain pre-processed data; calculating a troposphere delay estimated value; establishing a delay-water vapor conversion model; suppressing short message interference to obtain effective observation data; and inputting the data into a model to calculate the amount of precipitable water and the relative humidity. According to the method, the key technical problems of sea surface multi-path effect suppression, platform shake compensation, marine atmosphere modeling, short message interference elimination and the like are solved, so that reliable water vapor parameter inversion on the buoy platform is realized.

The provided is a modeling method of tropospheric delay for arid-areas including sandy, gobi and desert regions based on GNSS. The modeling method incudes: obtaining a troposphere zenith hydrostatic delay ZHD and a troposphere zenith wet delay ZWD by utilizing a GPT-3 model, and summing to obtain a troposphere zenith total delay ZTD; inversely calculating the ZTD by utilizing a ground-based GNSS, obtaining a high-precision ZWD inversed by the GNSS by calculating a difference between the ZTD and the troposphere zenith hydrostatic delay ZHD obtained by a Saastamonien model, inversely calculating an atmospheric weighted average temperature T′m by utilizing an ERA5 data set, performing linear correction to obtain a corrected temperature Tmr, and calculating a priori value ZWD0 of the high-precision ZWD; and correcting the ZWD0, and adding the corrected ZWD0 with the ZHD to obtain a corrected value of the high-precision ZTD.

The invention relates to the technical field of satellite navigation and ionosphere detection, in particular to a low-latitude ionosphere disturbance and non-uniform body multi-means collaborative detection and classification method. A multi-network cooperation-multi-source fusion observation mode is adopted, full space-time coverage of low-latitude ionosphere disturbance is achieved, in addition, a data fusion method based on physical constraints is applied, an ionosphere-middle atmosphere-troposphere multi-physics field coupling constraint mechanism is constructed, space-time consistency of data obtained through different observation means is optimized, and the low-latitude ionosphere disturbance is obtained. And finally, key characteristic parameters are extracted by using a multi-scale signal processing method, a disturbance characteristic matrix is constructed in combination with radar observation data (such as drift speed and spectral width), a'three-axis nine-element 'disturbance / non-uniform body classification standard is proposed firstly, then deep learning classification is used, accurate classification of low-latitude ionosphere disturbance is realized, and GNSS signal anomaly recognition capability is improved.

The invention provides a Beidou geological disaster displacement and image video monitoring fusion method and system based on a high-reliability communication link, and the method comprises the steps: collecting displacement, inclination angle and five-way high-definition video in real time, accessing microclimate and wire clamp temperature data, and carrying out the unified packaging through a data gateway; aPN special line transmission is adopted, and centimeter-level RTK differential service is provided for the unmanned aerial vehicle; based on a Beidou VRS virtual reference station and a long baseline monitoring resolving technology, a Beidou ground-based enhanced station network is multiplexed, and a virtual observation value is generated for real-time dynamic RTK resolving; inverting a three-dimensional water vapor field based on oblique path troposphere delay PWV, realizing tower-level rainfall prediction, and generating a risk assessment report by fusing an A I algorithm; superposing a displacement thermodynamic diagram, a real-time video and an early warning label in a BIM + GI digital twinning scene, and supporting 360-degree panoramic stitching and alarm trigger snapshot; and carrying out intelligent early warning decision. According to the invention, through high-precision Beidou monitoring, multi-modal data fusion and intelligent early warning, the real-time performance, precision and automation level of geological disaster monitoring are significantly improved.

The invention relates to the technical field of positioning. The deep learning assisted satellite positioning rapid resolving method comprises the following steps: acquiring and preprocessing satellite observation data, local environment characteristic data and receiver state data to generate multi-source fusion input data; performing dynamic error prediction processing through a locally deployed deep learning model to generate a positioning error correction amount; the positioning error correction is fused to a precise single-point positioning calculation process for iterative optimization processing, and positioning coordinates meeting the requirement for regional surveying and mapping precision are generated; a local historical observation data set is combined to trigger an online learning mechanism to perform model parameter updating processing, and a self-adaptive optimization model is generated so as to dynamically adapt to ionosphere / troposphere disturbance in a local complex environment, improve the resolving convergence efficiency in a multi-path effect scene and improve the real-time performance of the multi-path effect scene. And through multi-source data coupling modeling, the technical effects of error correction and accurate adaptation of a real scene are realized.

The invention discloses a foundation space ionosphere and troposphere integrated joint detection method and device, and belongs to the technical field of satellite navigation, and the method comprises the steps: detecting the vertical total electron content of an ionosphere: extracting the inclined total electron content of the ionosphere at an ionosphere puncture point, and converting the inclined total electron content into the vertical total electron content of the ionosphere; detecting a foundation Beidou ionosphere disturbance index; and detecting an ionosphere phase scintillation index, a troposphere zenith total delay, a troposphere precipitable amount and an ionosphere amplitude scintillation index, and outputting ground-based Beidou space ionosphere and troposphere integrated joint detection parameters. The method effectively improves the reliability, robustness and meteorological perception capability of a navigation positioning system in a complex atmospheric environment, and is especially suitable for special environmental conditions such as severe convection weather, typhoon landing, violent disturbance of an ionized layer and the like.

The invention belongs to the technical field of flight test, and discloses a cockpit vision field evaluation test flight method suitable for a troposphere manned airship, which comprises the following steps: selecting an airship test condition; the airship climbs to a preset field height and keeps level flight; a preset flight test action is completed; and commenting by a flight tester, and filling in a cockpit vision field commenting card. According to the method, the blank of the flight test method for evaluating the visual field of the cockpit of the troposphere manned airship with the vector propulsion system is filled, and the flight test efficiency and the flight test quality of the manned airship are improved.

The invention relates to the field of communication signal processing, in particular to an air defense and disaster prevention early warning alarm real-time control system based on Beidou third-generation communication, which adopts a Daubechies wavelet family to perform five-layer discrete wavelet decomposition and decomposes the total delay of satellite signals into different scale coefficients. By accurately controlling the boundary scale, the system can effectively separate the ionosphere error from the troposphere error. And by inserting a zero value between filter coefficients, information loss caused by down-sampling operation is avoided. And aiming at different atmospheric error characteristics, the system adaptively selects an optimal wavelet basis function. A stable correction effect is kept under extreme weather conditions, and an efficient and practical air defense and disaster prevention real-time early warning control method is constructed.

The embodiment of the invention discloses a real-time GNSS troposphere delay calculation method and device, equipment and a medium. The method comprises the following steps: acquiring an hour-by-hour forecast atmosphere data set; performing numerical integration based on the forecast atmosphere data set, and determining troposphere delay on each air pressure layer of the target grid point under the target time; obtaining a troposphere delay calculation formula according to the troposphere delay of the target grid point on each air pressure layer under the target time; and determining a target troposphere delay calculation formula of the site grid point under the prediction time according to the position information of the target GNSS site, and calculating the troposphere delay of the target GNSS site according to the parameters and the target troposphere delay calculation formula. By predicting the future atmosphere data hour by hour and calculating the troposphere delay based on the predicted atmosphere data, on one hand, the calculation precision of the delay amount is improved by improving the time resolution, and on the other hand, the troposphere delay of the GNSS site can be corrected in real time by calculating the delay amount according to the predicted atmosphere data set.

The invention provides a time-space high-frequency troposphere wet delay modeling method, and relates to the technical field of communication, and the method comprises the steps: obtaining a data set; carrying out normalization and sampling processing on the obtained data set; training a random forest model RF based on a sampling result; and acquiring real-time zenith troposphere moisture delay ZWD correction data by using the trained random forest model RF, and completing modeling of space-time high-frequency troposphere moisture delay. According to the method, the problems that a traditional correction model depends on actually measured meteorological data and is low in precision, low in regional resolution and weak in time-varying capability are solved.

The invention discloses an atmospheric turbulence phase screen generation method based on layered physical constraints, and belongs to the crossing field of optical communication and artificial intelligence. According to the method, a diffusion probability model is combined with layered atmospheric physical constraints, independent physical constraint loss functions are established for a boundary layer, a troposphere and a stratosphere of the atmosphere, and it is ensured that a generated phase screen conforms to turbulent flow statistical characteristics of all layers through power spectrum density constraints and structure function constraints; enhancing feature learning of each layer of phase screen by adopting a physically guided attention mechanism; and designing a time-dependent constraint weight strategy, and adaptively adjusting the physical constraint strength in the diffusion denoising process. The method solves the technical problems that the vertical layering difference is ignored, the physical consistency is insufficient, and the generation quality depends on a large amount of labeled data in the existing method, is particularly suitable for high-fidelity atmospheric turbulence modeling of a satellite laser communication link, and can also be widely applied to the fields of free space optical communication system simulation, adaptive optical system design and the like.

The invention provides a troposphere delay correction method and system, electronic equipment and a storage medium, and the method comprises the steps: extracting troposphere delay of a reference station through a non-difference combination observation value method according to the obtained real-time reference station observation data, broadcast ephemeris, ultra-fast orbit and clock correction, carrying out the adjustment of observation values of a plurality of satellites, and carrying out the adjustment of the observation values of a plurality of satellites; the zenith troposphere delay of the base station is obtained; the zenith troposphere level of the base station after normalization processing is interpolated to the position of the moving station through an inverse distance weighted interpolation method, zenith troposphere delay of the moving station is obtained according to a pre-constructed elevation normalization model, and the elevation normalization model is constructed based on ERA5 horizontal atmospheric layer meteorological data. According to the method, the adaptability of the model to complex terrains can be improved, so that the model precision is improved, the precision single-point positioning convergence time of the moving station is effectively shortened, and the precision single-point positioning precision is improved.

The application discloses a kind of based on GNSS's dam vertical displacement monitoring reference network automation observation method.It includes the following steps, step one: point site reconnaissance, instrument and observation pier selection;Step two: GNSS data preprocessing and scheme optimization;In GNSS vertical displacement automation monitoring, determine the optimal observation period, repair frequent small cycle jump;Step three: baseline solution and network adjustment method;In GNSS vertical displacement automation monitoring, troposphere delay correction, thermal expansion effect correction these models are applied to baseline solution, introduce prior height difference information in network adjustment, improve the accuracy and reliability of baseline solution.The application has the advantages of improving the vertical direction of GNSS baseline solution precision.The application also discloses a kind of based on GNSS's dam vertical displacement monitoring reference network automation observation platform.

A method of forecasting a maximum wind intensity associated with tropical cyclones, the method includes identifying a three-dimensional (3-D) field of ocean temperature and velocity, identifying a two-dimensional (2-D) field of sea surface temperature, tropopause temperature, surface level winds, incoming total solar radiation, and outgoing longwave radiation, determining a set of heat fluxes associated with ocean heat, and generating a 2-D map of the maximum potential intensity (MPI) based on (i) the set of heat fluxes and (ii) the first and second sets of data. The method may include training a machine learning model based on the first and second sets of data or the 2-D map of the MPI, and performing, based on the trained machine learning model and the 2-D map of the MPI, a mitigating activity corresponding to anticipated effects associated with the determined upper bound for tropical cyclone wind speed at a geographical location.

The invention relates to the technical field of satellite navigation meteorological remote sensing, and discloses a water vapor ionized layer snow depth integrated detection method and system. The method comprises the steps that direct and reflected signals of a Beidou satellite are received through fusion antenna array differentiation, carrier phase and pseudo-range observation values are obtained through carrier tracking loop synchronous demodulation, the total atmospheric rainfall amount and the total ionosphere electron content are extracted through a troposphere and ionosphere combined calculation algorithm, the snow depth is inverted through reflection path geometric difference, and the snow depth is calculated. And carrying out space-time synchronization constraint processing on the extended Kalman filter to realize multi-parameter integrated detection. The problems of low efficiency, insufficient precision and lack of cooperative processing caused by independent observation of water vapor, ionized layer and accumulated snow parameters in the prior art are solved, and the observation efficiency and the measurement precision of multi-parameter integrated detection are improved.

The invention discloses an SLR troposphere delay modeling method based on ray tracing, and the method comprises the steps: obtaining basic meteorological parameters which are defined on a multi-layer air pressure surface, and have a corresponding value at each horizontal grid node; respectively carrying out vertical interpolation, high altitude continuation and refractive index calculation based on the obtained basic meteorological parameters to obtain a refractive index field defined on the three-dimensional grid; and calculating integral delay along the observation path based on the refractive index field in combination with an SLR ray tracing algorithm. Specifically, a numerical weather model (NWM) is combined with a ray tracing technology to carry out three-dimensional modeling on an atmospheric refractive index field so as to realize delay correction with higher precision.

The embodiment of the invention provides a Beidou water vapor ionosphere detection system calibration method, device and equipment, and is applied to the technical field of detection calibration. The method comprises the following steps: performing double-frequency combination observation according to observation values of any two frequency bands in Beidou multi-frequency observation data, and calculating residual information after first-order delay elimination of an ionized layer; performing optimization combination on the residual information based on a preset combination optimization rule to generate high-order residual information; calculating the moisture delay of the troposphere according to the meteorological parameters; performing filtering correction on the high-order residual error information and the wet delay based on a preset dynamic filtering model to obtain high-order residual error correction information and corrected wet delay; based on a preset navigation positioning equation, static term delay is calculated according to the corrected wet delay; the high-order residual error correction information is used as ionosphere delay, the correction wet delay and the static term delay are used as zenith troposphere delay, and the Beidou water vapor ionosphere detection system is calibrated. In conclusion, the precision of the Beidou system in water vapor and ionosphere detection can be improved.

The invention provides a weather forecasting method based on big Beidou three-dimensional water vapor and weather model cooperative driving, and the method comprises the steps: carrying out the point-by-point registration of the obtained prediction data of a GraphCast model and ERA5 measured data, obtaining residual field data, taking the residual field data as the input of a deep learning residual correction network, and obtaining predicted residual field data; a terrain adaptive algorithm is adopted, predicted ERA5 meteorological parameters corresponding to the predicted residual field data are converted into a three-dimensional grid structure under height coordinates, and zenith troposphere delay is determined through an integration method; based on the dynamic correction term, converting the zenith troposphere delay into a precipitable water amount through a space-time inversion network; inputting the predicted ERA5 meteorological parameters and the rainfall data into a parameter efficient adapter and a big ancient meteorological model to obtain meteorological forecast factors output by the big ancient meteorological model; through the above technical scheme, real-time positioning enhancement and gradual improvement of meteorological element forecasting capability can be realized.

The invention discloses a Beidou / GNSS differential service fault analysis method and system, and relates to satellite positioning. The method comprises the following steps: carrying out pseudo-range single-point positioning on Beidou / GNSS observation data to obtain a general position; reporting the approximate position to a Beidou / GNSS differential service platform to request Beidou / GNSS differential data of the current position; carrying out observation data quality analysis on the Beidou / GNSS observation data, wherein observation data quality analysis indexes comprise an integrity rate, a satellite number, a carrier-to-noise ratio, multiple paths, pseudo-range noise, carrier noise, a cycle slip ratio and a PDOP value; differential data quality analysis is carried out on the Beidou / GNSS differential data; rTK calculation is carried out on the Beidou / GNSS observation data and the Beidou / GNSS differential data; calculating a troposphere residual error and an ionosphere residual error; and performing fault detection according to the data. Aiming at the problem that Beidou / GNSS differential service fault positioning precision is low in the prior art, accurate identification and positioning of differential service faults are realized.

A method, device, equipment and medium for independent modeling of troposphere anisotropy of dry and wet convection layers, the method comprising: constructing a troposphere anisotropy model, which adopts a tilted plane horizontal gradient model to respectively describe dry delay anisotropy and wet delay anisotropy; deriving oblique dry delay through a numerical meteorological model, calculating a dry horizontal gradient parameter through the oblique dry delay, and correcting the dry delay anisotropy by using the dry horizontal gradient parameter; taking a wet horizontal gradient parameter as an unknown parameter to be estimated into an observation equation and performing dynamic estimation, and correcting the wet delay anisotropy by using the estimated wet horizontal gradient parameter. The present application adopts an asymmetric processing strategy of dry delay anisotropy model correction and wet delay anisotropy parameter estimation, which not only avoids the ill-conditioned problem of observation equation caused by simultaneous estimation of dry and wet gradients, but also utilizes the high accuracy of the numerical meteorological model in describing the dry delay anisotropy, so that the positioning stability and accuracy are improved at the same time.