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CN-121978727-A - Beidou navigation satellite real-time rapid high-precision positioning method based on wind cloud satellite

CN121978727ACN 121978727 ACN121978727 ACN 121978727ACN-121978727-A

Abstract

The invention discloses a real-time rapid high-precision positioning method of Beidou navigation satellite based on a cloud satellite, which comprises the steps of firstly collecting real-time atmospheric precipitation amount and land surface temperature data of the cloud satellite, obtaining a water vapor conversion coefficient by utilizing the land surface temperature data, converting the atmospheric precipitation amount into zenith humidity delay by utilizing the water vapor conversion coefficient, then collecting real-time electron density profile data of the cloud satellite to obtain zenith direction total electron content of a occultation point, obtaining oblique total electron content by adopting a sheard interpolation method and a mapping relation algorithm, further constructing and training TimeMixer a time sequence prediction model, utilizing the trained TimeMixer model to the zenith humidity delay at the current moment, combining with a Beidou receiver position, and preferentially adopting the zenith humidity delay calculated by the cloud satellite by a Beidou receiver to perform PPP positioning calculation. The method obviously improves the performance of the low-cost Beidou receiver and realizes quick and high-precision positioning.

Inventors

  • BAO YUXUAN
  • SU MINGKUN
  • LI ZHAO
  • ZHAI YANRONG
  • Hao Xiaoshu
  • WU CHAO
  • QIAO LEI

Assignees

  • 杭州电子科技大学

Dates

Publication Date
20260505
Application Date
20260408

Claims (13)

  1. 1. A real-time rapid high-precision positioning method of Beidou navigation satellite based on a cloud satellite is characterized by comprising the following steps: step 1, acquiring real-time atmospheric precipitation amount and land surface temperature data of a cloud satellite, obtaining a water vapor conversion coefficient by utilizing the land surface temperature data, converting the atmospheric precipitation amount into zenith moisture delay by the water vapor conversion coefficient, and broadcasting the zenith moisture delay to a Beidou receiver as a known parameter for PPP positioning calculation; Step 2, acquiring real-time electron density profile data of a cloud satellite to obtain total electron content in the zenith direction of a occultation point, obtaining oblique total electron content by adopting a shepherd interpolation method and a mapping relation algorithm, and broadcasting the oblique total electron content to a Beidou receiver as an initial convergence value for PPP positioning calculation; step 3, respectively acquiring ERA5 historical air pressure layer data and real-time air pressure layer data of the last seven days of ERA5, calculating historical zenith humidity delay by using a direct integration method, and constructing a zenith humidity delay sample library and a prediction sample set by combining longitude, latitude and time information; Step 4, constructing TimeMixer a time sequence prediction model, and training TimeMixer the time sequence prediction model by using a zenith humidity delay sample library, obtaining the zenith humidity delay at the current moment in a rolling prediction mode by using a prediction sample set through a trained TimeMixer model, combining the Beidou receiver position, and adopting a bilinear interpolation method to couple with the predicted zenith humidity delay of the elevation correction calculation receiver position; And 5, preferentially adopting zenith wet delay calculated by the wind-cloud satellite by the Beidou receiver to perform PPP positioning calculation, switching to zenith wet delay predicted by ERA5 to perform calculation when wind-cloud satellite data are lost, simultaneously combining the inclined total electron content to complete PPP positioning calculation, recording convergence time of PPP positioning, and storing and outputting a positioning result.
  2. 2. The method according to claim 1, wherein in step 1, the water vapor conversion coefficient is obtained by calculating an atmospheric weighted average temperature using land temperature data through Bevis model, and calculating the water vapor conversion coefficient by combining the atmospheric weighted average temperature.
  3. 3. The method of claim 1, wherein the Bevis model is an atmospheric weighted average temperature equal to a model coefficient a plus a model coefficient b multiplied by a land temperature, wherein model coefficient a takes a value of 70.2 and model coefficient b takes a value of 0.72.
  4. 4. The method of claim 3, wherein in step 1, the water vapor conversion factor is calculated by multiplying the water vapor conversion factor by the liquid water density to the power of 6 minus 10 times the water vapor gas constant and then multiplying the sum of the atmospheric refractive constant and the atmospheric refractive constant divided by the atmospheric weighted average temperature, wherein the liquid water density is kg/, the water vapor gas constant is 461.495J/(kgK), the atmospheric refractive constant is 16.48K/hPa, and the atmospheric refractive constant is 377600/hPa.
  5. 5. The method according to claim 1, wherein in the step 2, the shepiad interpolation calculates total zenith direction electron content of the receiver position, which is the sum of products of normalized weight functions of all the star-covered points in the influence radius and total zenith direction electron content of the corresponding star-covered points, wherein the normalized weight functions are the sum of the basis weight functions of all the star-covered points in the influence radius divided by the basis weight functions of the individual star-covered points, the basis weight functions are the difference of the local influence radius of the earth minus the distance between the receiver and the star-covered points divided by the product of the local influence radius of the earth and the distance between the receiver and the star-covered points, and the local influence radius of the earth is obtained by taking the square again.
  6. 6. The method of claim 1, wherein in step 2, the total zenith-direction electron content is mapped to a total bias-electron content equal to the total zenith-direction electron content multiplied by 1, divided by 1, and divided by the sum of the earth radius plus the lowest ionosphere layer height plus the ionosphere layer height from the bottom layer, and multiplied by the square root of the satellite's cosine relative to the receiver altitude angle, wherein the ionosphere lowest layer height takes the value of 50 km and the ionosphere layer height takes the empirical value of 300-450 km.
  7. 7. The method according to claim 1, wherein in step 3, the collected ERA5 historical barometric pressure layer data and the real-time barometric pressure layer data of the ERA5 last seven days each include barometric pressure, vapor pressure, specific humidity.
  8. 8. The method of claim 7, wherein in step 3, the direct integration method calculates zenith moisture delay as the sum of the product of the zenith moisture delay equal to the negative 6 th power of 10 times the atmospheric moisture refractive index of each layer and the height of the corresponding layer, wherein the atmospheric moisture refractive index is equal to the atmospheric refractive constant multiplied by the water vapor pressure divided by Liu Biao temperature, plus the atmospheric refractive constant multiplied by the water vapor pressure divided by the square of the land surface temperature, the water vapor pressure is equal to the specific moisture multiplied by the air pressure divided by 0.622, and the atmospheric refractive constant takes the value of 64.79K/hPa.
  9. 9. The method of claim 1, wherein in step 4, the TimeMixer time series prediction model includes a PDM past decomposable mixing module and a FMM future multi-predictor mixing module, the PDM module decomposing and mixing seasonal and trending components for the multi-scale time series, the FMM module integrating a plurality of predictors to achieve short-term prediction of zenith wet delay.
  10. 10. The method according to claim 1, wherein in the step 4, the rolling prediction mode is that an input time sequence including continuous N time nodes is input as a model, zenith wet delay of subsequent H time nodes is predicted, when new time node data is input, the input time sequence and the predicted time sequence are both stepped towards a new data direction by one time node, and zenith wet delay of the new subsequent H time nodes is predicted based on the updated input time sequence.
  11. 11. The method of claim 1, wherein in the step 4, the process of coupling elevation correction by bilinear interpolation is that firstly, the earth height of the receiver is converted into the potential height matched with ERA5 data through an EGM008 model, zenith wet delay of the horizontal position of the receiver is calculated through bilinear interpolation, and then elevation correction is carried out through an elevation attenuation correction factor to obtain the predicted zenith wet delay of the position of the receiver, wherein the elevation attenuation correction factor is the square of the potential difference between the negative measuring station and grid center position of a natural constant e divided by the global average water vapor elevation, and the global average water vapor elevation takes a value of 2500 m.
  12. 12. The method according to claim 1, wherein in the step 4, when bilinear interpolation is performed, the initial coordinates of the receiver preferably use the m-level coarse positioning result output by the SPS standard positioning service module in the receiver, and when the satellite number is insufficient or the satellite is started for the first time, the historical positioning coordinates, the external auxiliary positioning device coordinates or the preset coordinates stored in the receiver are used as initial values.
  13. 13. The method according to claim 1, wherein in the step 1, a zenith wet delay value closest to the position of the beidou receiver is broadcasted to the beidou receiver by using a proximity rule.

Description

Beidou navigation satellite real-time rapid high-precision positioning method based on wind cloud satellite Technical Field The invention relates to the technical field of global satellite navigation meteorology, in particular to a real-time rapid high-precision positioning method for Beidou navigation satellites based on wind and cloud satellites. Background The Beidou navigation satellite real-time PPP (Precise Point Positioning, precise single point positioning) technology is taken as an important development direction of the satellite navigation positioning field, and the high-precision positioning of the centimeter to millimeter level can be realized by only utilizing a single receiver. With the overall establishment of the Beidou No. three global satellite navigation system, the Beidou system has the capability of providing global high-precision positioning service. The PPP technology is used as one of core technologies in the satellite navigation field, can realize centimeter-to-decimeter positioning accuracy at any global position, does not need to depend on a ground reference station network, and has wide application prospect. However, the conventional PPP technology faces a key bottleneck problem of long convergence time in the positioning process in practical application, which severely limits its popularization in real-time dynamic application scenarios. In the PPP positioning model, ZTD (Zenith Tropospheric Delay ) and ionospheric delay are key sources of error affecting positioning accuracy and convergence speed, since satellite signals are subject to tropospheric and ionospheric effects when traversing the atmosphere, thereby producing delays. In a general troposphere model, ZTD is divided into ZWD (Zenith Wet Delay) and ZHD (Zenith Hydrostatic Delay, zenith troposphere dry Delay), wherein ZHD has good correlation with parameters such as receiver elevation, atmospheric pressure and receiver longitude and latitude, and the ZWD is precisely calculated through a saastamonen (saastamonen) model, and the ZWD is greatly influenced by meteorological factors and is complex to calculate and estimate. In the ionosphere delay model, STEC (Slant Total Electron Content, oblique total electron content) is a physical carrier of ionosphere delay, and is linearly related to parameters such as receiver clock difference, carrier phase integer ambiguity and the like in an observation file, so that the parameters are difficult to directly separate. Conventional methods typically estimate these parameters in real time during the positioning process as an unknown quantity, which greatly increases the dimension of the parameter space and prolongs the convergence time. Therefore, improving the convergence rate of real-time PPP is an important research direction in global satellite navigation meteorology. Currently, three main techniques for accelerating PPP convergence are generally used. The first is a multisystem fusion positioning technology, which combines GPS, GLONASS, galileo and other multisystem navigation system data, improves satellite geometric distribution by increasing the number of visible satellites, thereby improving reliability and convergence speed of positioning calculation, but signal system, coordinate frame and time system difference among different satellite systems can introduce extra errors, and the system deviation calibration precision of a low-cost receiver is inferior to that of a large-scale receiver, and the second is to adopt external atmosphere constraint, and the method adopts parameters which are originally required to be estimated in PPP calculation, particularly ZWD and ionosphere delay as known quantity or strong constraint items to reduce the number of unknown parameters and accelerate the convergence process by introducing priori troposphere and ionosphere delay products. However, such methods generally use IGS (International GNSS SERVICE ) data, and the low spatial resolution of IGS products can result in a significant decrease in the accuracy of the calculated available values, which cannot meet the real-time positioning requirements. The third type is an accurate modeling method based on meteorological data, which utilizes the physical relationship between meteorological observation data and tropospheric delay to reduce tropospheric parameters to be estimated in PPP calculation and accelerate the convergence process by establishing an accurate meteorological-tropospheric model. The core is to accurately model the ZTD by using meteorological data provided by a ground meteorological station, a radiosonde and the like, and particularly to a ZWD part with larger variation. However, the method is severely dependent on ground weather station data, and has limited spatial resolution and limited timeliness in some areas where the ground weather stations are sparsely distributed. The Fengyun satellite No. three (FY-3) is taken as a second generation polar orbit meteorological satelli