A method and system for generating and broadcasting a high-confidence atmospheric error model product

Abstract

This invention discloses a method for generating and broadcasting a high-reliability atmospheric error model product, comprising: acquiring satellite differential code bias correction, ionospheric model data, tropospheric wet delay, and ionospheric slant path delay including differential code bias; establishing a tropospheric model based on the tropospheric wet delay; estimating the differential code bias of the reference station receiver based on the satellite differential code bias correction and ionospheric model data and removing it from the ionospheric slant path delay to obtain an ionospheric slant path delay without differential code bias; establishing an ionospheric model based on the ionospheric slant path delay; calculating the reliability-related parameters of the tropospheric model and the ionospheric model; combining the model parameters of the tropospheric model and the ionospheric model with the corresponding reliability-related parameters to obtain a high-reliability tropospheric delay model and a high-reliability ionospheric delay model; encoding and broadcasting the model to users so that users can obtain high-precision atmospheric error delay data and its reliability.

Description

Technical Field

[0001] This invention relates to the field of satellite navigation technology, specifically to a method and system for generating and broadcasting a highly reliable atmospheric error model product. Background Technology

[0002] The delay error introduced by Global Navigation Satellite System (GNSS) signals as they traverse the atmosphere in the ionosphere and troposphere is a significant factor reducing positioning accuracy. Therefore, it is necessary to model the ionosphere and troposphere and correct for this error during positioning. Modeling research on ionospheric and tropospheric delay errors is quite advanced. The Global Ionosphere Map (GIM) broadcast by the IGS is the most widely used ionospheric model. It is calculated by eight ionospheric analysis centers using their respective algorithms and models, representing the global Vertical Total Electron Content (VTEC) in a grid format, and is widely used in positioning calculations and space environment research. In addition to the global ionospheric products released by the IGS, regional CORS systems or space environment monitoring agencies also model and publish their regional ionospheric conditions. Tropospheric delay error is another major source of error in positioning, with errors reaching the meter level. Tropospheric delay errors are mainly divided into two parts: tropospheric hydrostatic delay (ZHD) error and tropospheric wet delay (ZWD) error. ZHD changes slowly and exhibits strong regularity, and is typically corrected using empirical models. ZWD errors are estimated through parameters or corrected using product data. Zenithal-hydrostatic models include empirical models such as the Saastamoinen and Hopfield models, as well as parametric models such as the global pressure and temperature model (GPT) and its improved models GPT2, GPT2w, and GPT3, which generally perform well. Evaluating the accuracy of atmospheric model products is a crucial prerequisite for model application. However, accuracy evaluation results are usually in grid format, suitable only for local verification on the server side and not for broadcasting to users over the network to provide accuracy references.

[0003] Existing research on atmospheric error modeling is extensive, encompassing combinations of various data sources, physical model building, and cutting-edge machine learning methods. Through continuous optimization, it can provide users with high-precision products for positioning calculations, work planning, and space environment research. However, providing only atmospheric error models without accuracy information poses a safety hazard to users. Anomalies in the atmosphere, especially within the ionosphere, can cause positioning errors of tens of meters. Therefore, providing users with both model products and accuracy references is crucial for improving positioning reliability and service dependability. Currently, the accuracy assessment products for atmospheric error models are primarily in grid form. This type of product can flexibly and accurately provide accuracy information within the model's coverage area. Users can extend the accuracy information to any location within the range through interpolation. However, due to the large and variable data volume, this type of product cannot be encoded and broadcast over a network, preventing users from obtaining real-time accuracy information from atmospheric error model products. Summary of the Invention

[0004] To overcome the shortcomings of existing technologies that cannot enable users to obtain the accuracy information of atmospheric error model products in real time, this invention provides a method and system for generating and broadcasting highly reliable atmospheric error model products. By constructing highly reliable atmospheric error model products, users can obtain high-precision atmospheric error delay data and its reliability.

[0005] According to one aspect of the present invention, a method for generating and broadcasting a high-confidence atmospheric error model product is provided, comprising: step S1: acquiring satellite differential code bias correction, ionospheric model data, tropospheric wet delay, and ionospheric slant path delay including differential code bias; step S2: establishing a tropospheric model based on the tropospheric wet delay; estimating the differential code bias of a reference station receiver based on the satellite differential code bias correction and ionospheric model data, removing the differential code bias of the reference station receiver from the ionospheric slant path delay to obtain an ionospheric slant path delay without differential code bias, and establishing an ionospheric model based on the ionospheric slant path delay; step S3: calculating the confidence-related parameters of the tropospheric model and the ionospheric model, combining the model parameters of the tropospheric model and the ionospheric model with the corresponding confidence-related parameters to obtain a high-confidence tropospheric delay model and a high-confidence ionospheric delay model, encoding them, and broadcasting them to users.

[0006] Further, step S1 includes: acquiring actual observation data, satellite orbit and clock bias data from broadcast ephemeris, satellite orbit and clock bias corrections, ionospheric model data, and satellite differential code bias corrections; correcting the satellite orbit and clock bias data using the satellite orbit and clock bias corrections; and, based on the actual observation data and the corrected satellite orbit and clock bias data, using a non-differential non-combined precise single-point positioning method to estimate the tropospheric wet delay of each reference station and the ionospheric skew path delay of each satellite including differential code bias in real time, and eliminating gross errors.

[0007] Furthermore, based on the actual observation data and the corrected satellite orbit and clock error data, the non-differential non-combined precise single-point positioning method is used to estimate the tropospheric wet delay of each reference station and the ionospheric slant path delay of each satellite including differential code bias in real time, and to remove gross errors. This includes: estimating the tropospheric wet delay of each reference station and the ionospheric slant path delay of each satellite including differential code bias in real time using Kalman filtering based on the corrected satellite orbit and clock error data and the observation equation; calculating the theoretical observation value of each reference station for each satellite based on the estimation results of the tropospheric wet delay of each reference station and the ionospheric slant path delay of each satellite including differential code bias based on the estimation results of the tropospheric wet delay of each reference station and the ionospheric slant path delay of each satellite; calculating the observation residual based on the theoretical observation value of each reference station for each satellite and the actual observation data, and removing the actual observation data whose observation residual exceeds a preset threshold.

[0008] Furthermore, in step S2, when the number of reference stations is no more than ten, a four-parameter tropospheric model is established, and the corresponding formula is:

[0009]

[0010]

[0011] in, For tropospheric wet delay, arrive These are the model parameters for the tropospheric model. , , Let be the latitude, longitude, and elevation of the i-th reference station, respectively. Let be the mean latitude, mean longitude, and mean elevation of each reference station in the region, and dLat, dLon, and dHgt be the differences between the latitude, longitude, and elevation of the i-th reference station and their respective mean latitude, mean longitude, and mean elevation.

[0012] Furthermore, in step S2, when the number of reference stations is greater than ten, a ten-parameter tropospheric model is established, and the corresponding formula is:

[0013]

[0014] in, arrive These are the model parameters for the tropospheric model.

[0015] Further, in step S2, the differential code bias of the reference station receiver is estimated based on the satellite differential code bias correction and ionospheric model data, and the corresponding formula is:

[0016] ,

[0017] in, Let represent the differential code bias of the base station receiver, i be the sequence number of the observed data, and N be the total number of observed data. Let the weights be the values ​​for the i-th observation data. Let i be the time corresponding to the i-th observation. This represents the ionospheric slant path delay, including differential code bias. This refers to the ionospheric oblique path delay error calculated based on ionospheric model data. The satellite differential code offset is calculated based on the satellite differential code offset correction.

[0018] Further, in step S2, an ionospheric model is established based on the ionospheric slant path delay, and the corresponding formula is:

[0019] ,

[0020] ,

[0021] in, This represents the ionospheric slant path delay excluding differential code bias. arrive These are the model parameters for the ionosphere model. , These represent the differences between the latitude and longitude of the satellite puncture point and the mean latitude and mean longitude, respectively. , el represents the mean latitude, mean longitude, and satellite elevation angle of the puncture point observed by all reference stations within the modeling time region for satellite s. , Let represent the latitude and longitude of the i-th puncture point of satellite s, respectively.

[0022] Further, step S3 includes: estimating the confidence-related parameters of the tropospheric model using the Gaussian process method, obtaining the confidence-related parameters of the ionospheric model by calculating the standard deviation of the ionospheric model; combining the confidence-related parameters of the tropospheric model and the model parameters into a high-confidence tropospheric delay model, combining the confidence-related parameters of the ionospheric model and the model parameters into a high-confidence ionospheric delay model, and encoding the high-confidence tropospheric delay model and the high-confidence ionospheric delay model and broadcasting them to users via the network.

[0023] According to one aspect of this invention, a system for generating and broadcasting a high-confidence atmospheric error model product is provided, comprising: a data acquisition module for acquiring satellite differential code bias corrections, ionospheric model data, tropospheric wet delay, and ionospheric slant path delay including differential code bias; a model building module for establishing a tropospheric model based on the tropospheric wet delay; estimating the differential code bias of a reference station receiver based on the satellite differential code bias corrections and ionospheric model data, removing the differential code bias of the reference station receiver from the ionospheric slant path delay to obtain an ionospheric slant path delay without differential code bias, and establishing an ionospheric model based on the ionospheric slant path delay; and a confidence calculation module for calculating confidence-related parameters of the tropospheric model and the ionospheric model, combining the model parameters of the tropospheric model and the ionospheric model with the corresponding confidence-related parameters to obtain a high-confidence tropospheric delay model and a high-confidence ionospheric delay model, encoding them, and broadcasting them to users.

[0024] According to one aspect of the present invention, a non-transitory computer-readable storage medium is provided, the non-transitory computer-readable storage medium storing computer instructions that cause the computer to execute the method for generating and broadcasting a high-confidence atmospheric error model product.

[0025] The above technical solution firstly acquires actual observation data, satellite orbit and clock bias data from broadcast ephemeris, satellite orbit and clock bias corrections, ionospheric model data, and satellite differential code bias corrections. However, the satellite orbit and clock bias data in the broadcast ephemeris have biases, so it is necessary to correct the satellite orbit and clock bias data using satellite orbit and clock bias corrections. Secondly, based on the actual observation data and the corrected satellite orbit and clock bias data, a non-differential, non-combined precise single-point positioning method is used to estimate the tropospheric wet delay of each reference station and the ionospheric slant path delay of each satellite in real time. Thirdly, since the tropospheric wet delay and ionospheric slant path delay are both estimated theoretical values, the deviation between the theoretical and actual values ​​is used to fit the corresponding model parameters, and then the tropospheric model and ionospheric model are constructed based on the model parameters. Furthermore, since the ionospheric slant path delay includes differential code bias, it is necessary to estimate the differential code bias of the base station receiver based on the satellite differential code bias correction and ionospheric model data before constructing the ionospheric model. Then, the differential code bias of the base station receiver is removed from the ionospheric slant path delay to obtain an ionospheric slant path delay without differential code bias. Finally, the reliability-related parameters of the tropospheric model and the ionospheric model are calculated. The model parameters of the tropospheric model and the ionospheric model and the corresponding reliability-related parameters are combined to obtain a high-reliability atmospheric error model product (including a high-reliability tropospheric delay model and a high-reliability ionospheric delay model). After encoding, the product is broadcast to users, who can then obtain high-precision atmospheric error delay data and its reliability based on the high-reliability atmospheric error model product.

[0026] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0027] (1) By integrating multi-source data such as reference station observation data, high-precision satellite orbit and clock error corrections, and ionospheric models, a precise foundation was provided for the subsequent construction of tropospheric and ionospheric models.

[0028] (2) The differential code deviation of the base station receiver is estimated by using the satellite differential code deviation correction and ionospheric model data, so as to effectively eliminate the deviation in the ionospheric slant path delay data and significantly improve the accuracy of the ionospheric model.

[0029] (3) The high-reliability atmospheric error model product generated by the present invention can provide users with real-time high-precision atmospheric error delay data and its reliability with a small network bandwidth usage, thereby improving the reliability of user positioning and atmospheric error model application. Attached Figure Description

[0030] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the accompanying drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the accompanying drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0031] Figure 1 This is a structural block diagram of a system for generating and broadcasting a high-reliability atmospheric error model product, provided in an embodiment of the present invention.

[0032] Figure 2 This is a flowchart illustrating a method for generating and broadcasting a highly reliable atmospheric error model product, as provided in an embodiment of the present invention. Detailed Implementation

[0033] It should be noted that:

[0034] The terms “comprising” and “having”, and any variations thereof, in the specification, claims, and accompanying drawings of this invention are intended to cover a non-exclusive inclusion, such as a process, method, system, product, or apparatus that includes a series of steps or units, not necessarily limited to those explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0035] The block diagrams shown in the accompanying drawings are merely functional entities and do not necessarily correspond to physically independent entities. That is, these functional entities can be implemented in software, in one or more hardware modules or integrated circuits, or in different network and / or processor devices and / or microcontroller devices. The flowcharts shown in the accompanying drawings are merely illustrative and do not necessarily include all content and operations / steps, nor do they necessarily have to be performed in the described order. For example, some operations / steps can be decomposed, while others can be combined or partially combined; therefore, the actual execution order may change depending on the specific circumstances.

[0036] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention. In addition, the technical features of the various embodiments or individual embodiments provided by the present invention can be arbitrarily combined to form new technical solutions. Such combinations are not bound by the order of steps and / or structural composition patterns, but must be based on the ability of those skilled in the art to implement them. When the combination of technical solutions is contradictory or cannot be implemented, it should be considered that such a combination of technical solutions does not exist and is not within the scope of protection claimed by the present invention.

[0037] Please refer to the appendix. Figure 1 and 2 This invention provides a method for generating and broadcasting a high-reliability atmospheric error model product, comprising the following steps:

[0038] Step S1: Obtain satellite differential code bias correction, ionospheric model data, tropospheric wet delay, and ionospheric slant path delay including differential code bias.

[0039] Step S11: Obtain actual observation data, satellite orbit and clock error data from broadcast ephemeris, satellite orbit and clock error corrections, ionospheric model data, and satellite differential code deviation corrections; and use the satellite orbit and clock error corrections to correct the satellite orbit and clock error data.

[0040] In step S11, actual observation data and satellite orbit and clock bias data from the broadcast ephemeris are acquired from the base station. Satellite orbit and clock bias corrections, ionospheric model data, and satellite differential code bias corrections are acquired from the internet. The core component of the base station is a high-precision GNSS receiver, used for long-term, stable reception of satellite signals broadcast to the ground, and for parsing and extracting the broadcast ephemeris and observation data. The broadcast ephemeris is a set of forecast parameters obtained by the satellite based on historical observation data. That is, the satellite orbit and clock bias data in the broadcast ephemeris are actually the forecast satellite orbit and clock bias data. There is a significant error between the forecast satellite orbit and clock bias data and the actual satellite orbit and clock bias data, requiring correction. Observation data includes pseudorange observation data and phase observation data. The satellite orbit and clock bias correction refers to the difference between the actual satellite orbit and clock bias data and the forecast satellite orbit and clock bias data, used to correct errors in the forecast satellite orbit and clock bias data. Ionospheric model data characterizes the spatiotemporal variations of ionospheric electron concentration and is used for modeling corrections and auxiliary estimations of ionospheric delay. Real-time satellite differential code bias correction refers to the compensation amount for systematic bias between pseudorange observations of different frequencies caused by onboard satellite equipment; it is used to eliminate the impact of this bias on ionospheric delay and positioning calculations.

[0041] A network connection is established between the server and the base station. The server receives the encoded data broadcast by the base station via the network. Data transmission methods include TCP and NTRIP. TCP directly establishes a connection with the base station using IP address and port number, while NTRIP establishes a connection using IP address (or website address), mount point, and account password. After connection, the server decodes the base station's observation data and satellite orbit and clock bias data from the broadcast ephemeris. The observation data is used for the Undifferenced and Uncombined Precise Point Positioning (UDUCPPP) calculation in step S12. In addition, the server needs to receive real-time satellite orbit and clock bias corrections, ionospheric model data, and real-time satellite differential code bias corrections from the Internet. These data can be obtained from regional GNSS service systems, international GNSS service organizations, or other commercial service providers. Generally, NTRIP connection is used, and RTCM encoding is employed; however, this is not limited here. After decoding, the server corrects the satellite orbit and clock bias data in the broadcast ephemeris using satellite orbit and clock bias corrections to obtain high-precision real-time satellite orbit and clock bias, which is used for UDUC PPP calculation in step S12. The ionospheric model data and satellite differential code deviation are stored after decoding and will be used in step S2.

[0042] Step S12: Based on actual observation data and corrected real-time satellite orbits and clock errors, the non-differential non-combined precise single-point positioning method is used to estimate the tropospheric wet delay of each reference station and the ionospheric skew path delay of each satellite, including differential code bias, in real time.

[0043] In step S12, the actual observation data includes the actual observation data of each reference station for each satellite. Tropospheric wet delay refers to the signal propagation delay caused by changes in atmospheric water vapor content when the satellite signal passes through the troposphere above the reference station. Ionospheric slant path delay refers to the signal propagation delay caused by the refraction of free electrons in the ionosphere when the satellite signal passes through the upper ionosphere after being emitted from the satellite. The UDUC PPP method is the core method of GNSS precise positioning in this invention. Undifferentiated (UD): Only the observation data of each reference station is processed separately, without cross-station observation difference. Uncombined (UC): The original pseudorange and carrier phase observation values ​​of each satellite frequency are used directly, without frequency combination calculations. Precise point positioning (PPP): Unlike ordinary point positioning, it combines high-precision satellite orbit and clock error corrections, satellite differential code deviation corrections, and other correction data to estimate the tropospheric wet delay of each reference station and the ionospheric slant path delay of each satellite including differential code deviation in real time. It detects and removes gross errors in the estimation results to achieve centimeter / decimeter-level precise positioning.

[0044] Further, step S12 specifically includes the following steps: based on the corrected satellite orbit and clock error data and observation equations, the tropospheric wet delay of each reference station is estimated in real time using Kalman filtering. And the ionospheric slant path delay for each satellite, including differential code bias. The theoretical observation values ​​for each satellite from each base station are calculated using the estimated tropospheric wet delay of each base station and the ionospheric slant path delay including differential code bias for each satellite. Based on the theoretical observation values ​​and actual observation data for each satellite from each base station, the observation residuals are calculated. Actual observation data with observation residuals exceeding a preset threshold are removed. This process is repeated until no actual observation data exceeding the preset threshold is found. It should be noted that the preset threshold can be set according to actual needs and is not limited here.

[0045] Furthermore, the observation equation is:

[0046] (1)

[0047] (2)

[0048] in,

[0049] = (3)

[0050] = (4)

[0051] (5)

[0052] In the formula, c is the speed of light (i.e., 299,792,458.0 m / s), and the subscript is... Represents the frequency of observations. and These represent the pseudorange and carrier observation values ​​received by receiver r at the i-th frequency point of satellite s, respectively. This represents the geometric distance (m) from the satellite to the receiver. and Frequency The hardware delay bias (m) between the receiver and the satellite pseudorange differential. These are the pseudorange hardware delays at the second frequency point in the signal received by receiver r from satellite s, the pseudorange hardware delays at the first frequency point in the signal received by receiver r from satellite s, the pseudorange hardware delays at the second frequency point in satellite s, and the pseudorange hardware delays at the first frequency point in satellite s, respectively. This represents the first-order ionospheric delay at the reference frequency during the tracking process of satellite s by the base station r. The impact of higher-order delays on the positioning results is ignored here. , These are the receiver clock bias and the satellite clock bias (s), respectively. These represent the wet and dry tropospheric delays (m) above the base station, respectively. For the corresponding projection function; Represents frequency The ionospheric delay (m) along the oblique path. , The reference frequency for the ionosphere. Let i be the frequency of frequency point i; Represents frequency Upper wavelength (m / cycle); Represents the observed value Integer ambiguity (week); and Frequency Phase hardware delay deviation (m) between satellite and receiver, This represents the reconstructed carrier ambiguity parameters; and These represent measurement errors (noise and multipath (m)) on pseudorange and phase observations, respectively. Errors not listed in the observation equations are pre-corrected by default.

[0053] Step S2 involves establishing a tropospheric model based on the tropospheric wet delay; estimating the differential code bias of the base station receiver based on the satellite differential code bias correction and ionospheric model data; removing the differential code bias of the base station receiver from the ionospheric slant path delay to obtain the ionospheric slant path delay without differential code bias; and establishing the ionospheric model based on the ionospheric slant path delay. The specific steps of S2 will be further described below:

[0054] Step S21, based on the tropospheric wet delay estimated in step S12 A tropospheric model was established by combining the latitude, longitude, and elevation of the base station.

[0055] Step S211: When the number of reference stations is no more than ten, establish a four-parameter tropospheric model. The corresponding formula is:

[0056] (6)

[0057] (7)

[0058] in, arrive These are the model parameters for the tropospheric model. , , Let be the latitude, longitude, and elevation of the i-th reference station, respectively. Let be the mean latitude, mean longitude, and mean elevation of each reference station in the region, and dLat, dLon, and dHgt be the differences between the latitude, longitude, and elevation of the i-th reference station and their respective mean latitude, mean longitude, and mean elevation.

[0059] Step S212: When the number of reference stations is greater than ten, establish a ten-parameter tropospheric model. The corresponding formula is:

[0060] (8)

[0061] in, arrive These are the model parameters for the tropospheric model; the other parameters are the same as those in formulas (6) and (7).

[0062] Step S213, based on the tropospheric wet delay estimated in step S12 The least squares method is used to estimate the model parameters in the tropospheric model, and then the tropospheric model is obtained based on the model parameters, as follows (taking a four-parameter tropospheric model as an example):

[0063] (9)

[0064] Where A is the model parameter matrix of the tropospheric model, H is the design matrix, Y is the observation vector, and n represents the number of observations involved in the calculation. It should be noted that the number of observations is equal to the number of tropospheric wet delays, with each base station corresponding to one tropospheric wet delay, and each tropospheric wet delay corresponding to one observation.

[0065] Step S22: Based on the real-time satellite differential code bias correction and ionospheric model data obtained in step S11, estimate the differential code bias of the base station receiver. The corresponding formula is:

[0066] (10)

[0067] (11)

[0068] in, The differential code bias of the base station receiver is calculated, where i is the sequence number of the observed data and N is the total number of observed data. Let the weights be the values ​​for the i-th observation data. Let i be the time corresponding to the i-th observation. This refers to the ionospheric slant path delay, including differential code bias, calculated based on the observation data in step S12. This refers to the ionospheric delay error along the corresponding satellite slant path, calculated based on real-time ionospheric model data. The satellite differential code offset is calculated based on real-time satellite differential code offset correction information, in seconds.

[0069] In this embodiment, the differential code deviation of the base station receiver is calculated using observation data from the past 6 hours to the present. Then, the ionospheric slant path delay on the satellite observation path without differential code bias is calculated using the real-time satellite differential code bias correction and the differential code bias estimation result of the base station receiver.

[0070] Step S23: Remove the differential code deviation of the base station receiver from the ionospheric slant path delay in step S12 to obtain the ionospheric slant path delay without differential code deviation, and use the ionospheric slant path delay without differential code deviation to establish the ionospheric model.

[0071] The formula for calculating the ionospheric slant path delay excluding differential code bias is as follows:

[0072] (12)

[0073] in, This represents the ionospheric skew path delay that does not include differential code bias.

[0074] Furthermore, a second-order polynomial is used as the mathematical model for the ionospheric model to fit the corresponding ionospheric model for each satellite:

[0075] (13)

[0076] (14)

[0077] (15)

[0078] in, This represents the ionospheric slant path delay excluding differential code bias. arrive These are the model parameters for the ionosphere model. , These represent the differences between the latitude and longitude of the satellite puncture point and the mean latitude and mean longitude, respectively. , el represents the mean latitude, mean longitude, and satellite elevation angle of the puncture point observed by all reference stations within the modeling time region for satellite s. , Let represent the latitude and longitude of the i-th puncture point of satellite s, respectively.

[0079] Understandably, the ionospheric slant path delay, which does not include differential code bias, is... Substituting into formula (13) Utilizing ionospheric oblique path delay Difference between latitude and longitude of the puncture point ( , The relationship between the elevation angle (el) and the model parameters A1~A7 is fitted, and then the ionospheric model is obtained based on the model parameters A1~A7.

[0080] Step S3: Calculate the confidence-related parameters of the tropospheric model and the ionospheric model, combine the model parameters and corresponding confidence-related parameters of the tropospheric model and the ionospheric model to obtain a high-confidence tropospheric delay model and a high-confidence ionospheric delay model, encode them and broadcast them to the user.

[0081] In step S3, the high-reliability tropospheric delay model and the high-reliability ionospheric delay model are the high-reliability atmospheric error model products. It should be noted that currently, no authoritative organization provides real-time broadcasting services for tropospheric models. While real-time broadcasting services exist for ionospheric models, existing general-purpose products require broadcasting 16*16=256 parameters (because they construct 16th-order, 16th-degree ionospheric models). The ionospheric model constructed by this invention has fewer parameters, thus broadcasting less data than existing general-purpose products. Therefore, the high-reliability atmospheric error model product generated by this invention only requires a small network bandwidth usage to provide users with real-time, high-precision atmospheric error delay data (i.e., tropospheric wet delay, ionospheric slant path delay excluding differential code bias) and its reliability, thereby improving the reliability of user positioning and atmospheric error model applications.

[0082] Furthermore, a random sample of data (e.g., 10%) was used to calculate the accuracy of the tropospheric and ionospheric models. For the tropospheric model, the Gaussian Processes method was used to fit the accuracy, and the fitting result was used as the reliability of the tropospheric model. For the ionospheric model, the accuracy of each satellite and the time delay coefficient were used together to form the reliability of the ionospheric model. It should be noted that the data sampled here includes the tropospheric wet delay estimated according to formulas (1) and (2). The ionospheric slant path delay calculated by formula (12) without differential code bias Extracted tropospheric wet delay These data will not participate in the tropospheric and ionospheric modeling process in step S2. For example, in each modeling process, each data point is assigned a random data number. Then, a number N is randomly selected from 0 to 9, and the data points whose last digit is N are identified. These data will not participate in the tropospheric and ionospheric modeling process in step S2, but will be used for the establishment and optimization of the confidence model, denoted as dataset D. The specific steps of S3 will be further described below:

[0083] Step S31: Use the Gaussian process method to estimate the confidence-related parameters of the tropospheric model.

[0084] In step S31, the confidence-related parameters of the tropospheric model refer to the parameters to be estimated. , , The formula corresponding to the Gaussian process method is:

[0085] (16)

[0086] (17)

[0087] (18)

[0088] (19)

[0089] in, Let GP represent the Gaussian process function, m(x) be the mean function of the distribution, and k be the covariance function (or kernel function) of the Gaussian process. and The kernel function output is obtained by calculating different inputs, and the kernel function uses different length scaling exponents corresponding to each dimension. The Matern kernel function is shown in equation (17). Y represents the observed value with additive noise. It is true. For noise, The variance-covariance matrix is... , , For the parameter to be estimated, and l are the kernel scaling and scale estimation parameters, respectively; d is the data dimension, which is set according to the actual data; and r is the data vector difference. and Let be the data point value, N represent the standard normal distribution, and m be the mean function.

[0090] It should be noted that the parameter to be estimated , , The solution process is as follows: First, construct the equations (17) and (18). The covariance matrix K, where each element It is about and The function is then used; next, an observation vector Y is constructed based on the tropospheric wet delay in dataset D; subsequently, a log-marginal likelihood function is established by combining the observation vector Y:

[0091] (20)

[0092] in, Represents the log-marginal likelihood function. Represents the observation vector, Indicates the coordinates of the base station. Represents the set of parameters to be estimated, i.e. ={ }, The number of observed samples, Represents the identity matrix.

[0093] Finally, the conjugate gradient method is used to maximize the logarithmic marginal likelihood function, and the value is adjusted iteratively. , , These three parameters to be estimated are obtained until the logarithmic marginal likelihood function converges. , , The optimal parameter estimate.

[0094] Step S32: Calculate the standard deviation of the ionospheric model to obtain the reliability parameters of the ionospheric model.

[0095] In step S32, the confidence-related parameters of the ionosphere model refer to the parameter confidence level. Ionospheric slant path delay in dataset D that does not include differential code bias The standard deviation of the ionospheric model shown in equation (13) is used to obtain the parameter confidence level. The specific formula is as follows:

[0096] (twenty one)

[0097] STD is the function for calculating the standard deviation of data.

[0098] Step S33: Combine the credibility-related parameters and model parameters of the tropospheric model into a high-credibility tropospheric delay model, encode it, and broadcast it to users via the network.

[0099] In step S33, the model parameters of the tropospheric model (i.e. arrive or arrive ) and credibility-related parameters (i.e. After encoding, the data is broadcast to the user. When needed, the user can calculate the current tropospheric wet delay by combining the model parameters of the tropospheric model with the corresponding tropospheric model in equation (6) or (8), and then use... Calculate the reliability of the tropospheric model, such as at a certain point within the region. The joint distribution equation of the confidence distribution at a given location and that of existing tropospheric models is expressed as:

[0100] (twenty two)

[0101] Where X represents the coordinates of the reference station, and Y represents the tropospheric wet delay corresponding to reference station X. The coordinates of a point within the region. for The theoretical tropospheric wet delay calculated using the tropospheric model corresponding to equation (6) or (8) is given. These are the tropospheric model itself, the tropospheric model and... , With tropospheric models, The variance and covariance of the variable itself are obtained from the joint distribution equation. The credibility follows a Gaussian distribution. The mean of this distribution and variance The calculation is performed using the following formula, and the corresponding formula is:

[0102] (twenty three)

[0103] (twenty four)

[0104] Among them, variance To assess the credibility of the tropospheric model.

[0105] Step S34: Combine the credibility-related parameters and model parameters of the ionospheric model into a high-credibility ionospheric delay model, encode it, and broadcast it to users via the network.

[0106] In step S34, the model parameters of the ionospheric model for each satellite in step S2 are compared with the confidence level of the parameters for each satellite calculated in step S3. After being combined and encoded into binary data, the data is broadcast to the user via the network. When needed, the user can use the model parameters of the ionospheric model and the corresponding ionospheric model in equation (13) to calculate the current ionospheric skew path delay without differential code deviation. Then, the user can use equation (21) to obtain the required ionospheric model credibility.

[0107] The reliability calculation for the ionospheric model of each satellite is performed in two parts: parameter reliability and temporal reliability.

[0108] (25)

[0109] in, To assess the credibility of the ionospheric model, For parameter confidence, For time credibility, time credibility Calculated using the following formula:

[0110] (26)

[0111] Where t is the time difference between the current epoch and the model establishment epoch, in seconds.

[0112] In this embodiment, since the changes in the ionosphere are more rapid and intense than those in the troposphere, to reduce the bandwidth consumption of user data transmission, the ionospheric model is updated every 2 minutes and continuously broadcast to the user, while the tropospheric model is updated every 10 minutes and continuously broadcast to the user. Understandably, the specific broadcast times of the ionospheric and tropospheric models can be adjusted according to actual needs and are not limited here.

[0113] Based on the same technical concept as the aforementioned embodiments, the present invention also provides a system for generating and broadcasting a high-reliability atmospheric error model product, comprising: a data acquisition module, used to acquire satellite differential code bias corrections, ionospheric model data, tropospheric wet delay, and ionospheric slant path delay including differential code bias; a model construction module, used to build a tropospheric model based on the tropospheric wet delay; estimate the differential code bias of the reference station receiver based on the satellite differential code bias corrections and ionospheric model data, remove the differential code bias of the reference station receiver from the ionospheric slant path delay to obtain an ionospheric slant path delay without differential code bias, and build an ionospheric model based on the ionospheric slant path delay; and a reliability calculation module, used to calculate the reliability-related parameters of the tropospheric model and the ionospheric model, combine the model parameters of the tropospheric model and the ionospheric model with the corresponding reliability-related parameters to obtain a high-reliability tropospheric delay model and a high-reliability ionospheric delay model, and then encode and broadcast them to users.

[0114] Based on the same technical concept as the foregoing embodiments, the present invention also provides a non-transitory computer-readable storage medium storing computer instructions that cause the computer to execute the method for generating and broadcasting a high-reliability atmospheric error model product.

[0115] In summary, the above embodiments involve: First, acquiring actual observation data, satellite orbit and clock bias data from broadcast ephemeris, satellite orbit and clock bias corrections, ionospheric model data, and satellite differential code bias corrections. However, the satellite orbit and clock bias data in the broadcast ephemeris contains biases, thus requiring correction using the satellite orbit and clock bias corrections. Second, based on the actual observation data and the corrected satellite orbit and clock bias data, using a non-differential, non-combined precise single-point positioning method, the tropospheric wet delay of each reference station and the ionospheric slant path delay of each satellite are estimated in real time. Third, since the tropospheric wet delay and ionospheric slant path delay are both estimated theoretical values, the deviation between the theoretical and actual values ​​is used to fit the corresponding model parameters. Then, based on these model parameters, the tropospheric model and the ionospheric model are constructed. Furthermore, since the ionospheric slant path delay includes differential code bias, it is necessary to estimate the differential code bias of the base station receiver based on the satellite differential code bias correction and ionospheric model data before constructing the ionospheric model. Then, the differential code bias of the base station receiver is removed from the ionospheric slant path delay to obtain an ionospheric slant path delay without differential code bias. Finally, the reliability-related parameters of the tropospheric model and the ionospheric model are calculated. The model parameters of the tropospheric model and the ionospheric model and the corresponding reliability-related parameters are combined to obtain a high-reliability atmospheric error model product (including a high-reliability tropospheric delay model and a high-reliability ionospheric delay model). After encoding, the product is broadcast to users, who can then obtain high-precision atmospheric error delay data and its reliability based on the high-reliability atmospheric error model product.

[0116] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features therein; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the technical solutions of the embodiments of the present invention.

Claims

1. A method for generating and broadcasting a high-reliability atmospheric error model product, characterized in that, include: Step S1: Obtain satellite differential code bias correction, ionospheric model data, tropospheric wet delay, and ionospheric slant path delay including differential code bias; Step S2: Establish a tropospheric model based on the tropospheric wet delay; estimate the differential code deviation of the base station receiver based on the satellite differential code deviation correction and ionospheric model data, remove the differential code deviation of the base station receiver from the ionospheric slant path delay to obtain an ionospheric slant path delay that does not contain differential code deviation, and establish an ionospheric model based on the ionospheric slant path delay; Step S3: Calculate the credibility-related parameters of the tropospheric model and the ionospheric model, combine the model parameters and corresponding credibility-related parameters of the tropospheric model and the ionospheric model to obtain a high-credibility tropospheric delay model and a high-credibility ionospheric delay model, encode them and broadcast them to the user.

2. The method for generating and broadcasting a high-reliability atmospheric error model product as described in claim 1, characterized in that, Step S1 includes: Acquire actual observation data, satellite orbit and clock error data from broadcast ephemeris, satellite orbit and clock error corrections, ionospheric model data, and satellite differential code bias corrections, and use the satellite orbit and clock error corrections to correct the satellite orbit and clock error data; Based on the actual observation data and the corrected satellite orbit and clock error data, the non-differential non-combined precise single-point positioning method is used to estimate the tropospheric wet delay of each reference station and the ionospheric skew path delay of each satellite, including differential code bias, in real time, and gross errors are eliminated.

3. The method for generating and broadcasting a high-reliability atmospheric error model product as described in claim 2, characterized in that, Based on the actual observation data and the corrected satellite orbit and clock bias data, the non-differential, non-combined precise single-point positioning method is used to estimate the tropospheric wet delay of each reference station and the ionospheric skew path delay of each satellite, including differential code bias, in real time, and to eliminate gross errors, including: Based on the corrected satellite orbit and clock error data and observation equations, the tropospheric wet delay of each reference station and the ionospheric skew path delay of each satellite, including differential code bias, are estimated in real time using Kalman filtering. The theoretical observations of each base station for each satellite are calculated based on the estimated tropospheric wet delay of each base station and the ionospheric slant path delay of each satellite, which includes differential code bias. The observation residuals are calculated based on the theoretical and actual observation data of each satellite from each reference station, and the actual observation data with observation residuals exceeding a preset threshold are removed.

4. The method for generating and broadcasting a high-reliability atmospheric error model product as described in claim 1, characterized in that, In step S2, when the number of reference stations is no more than ten, a four-parameter tropospheric model is established, and the corresponding formula is: in, For tropospheric wet delay, arrive These are the model parameters for the tropospheric model. , , Let be the latitude, longitude, and elevation of the i-th reference station, respectively. Let be the mean latitude, mean longitude, and mean elevation of each reference station in the region, and dLat, dLon, and dHgt be the differences between the latitude, longitude, and elevation of the i-th reference station and their respective mean latitude, mean longitude, and mean elevation.

5. The method for generating and broadcasting a high-reliability atmospheric error model product as described in claim 4, characterized in that, In step S2, when the number of reference stations is greater than ten, a ten-parameter tropospheric model is established, and the corresponding formula is: in, arrive These are the model parameters for the tropospheric model.

6. The method for generating and broadcasting a high-reliability atmospheric error model product as described in claim 1, characterized in that, In step S2, the differential code bias of the reference station receiver is estimated based on the satellite differential code bias correction and ionospheric model data. The corresponding formula is: ， in, Let represent the differential code bias of the base station receiver, i be the sequence number of the observed data, and N be the total number of observed data. Let the weights be the values ​​for the i-th observation data. This represents the ionospheric slant path delay, including differential code bias. This refers to the ionospheric slant path delay error calculated based on ionospheric model data. The satellite differential code offset is calculated based on the satellite differential code offset correction.

7. The method for generating and broadcasting a high-reliability atmospheric error model product as described in claim 1, characterized in that, In step S2, an ionospheric model is established based on the ionospheric slant path delay, and the corresponding formula is: ， ， in, This represents the ionospheric skew path delay excluding differential code bias. arrive These are the model parameters for the ionosphere model. , These represent the differences between the latitude and longitude of the satellite puncture point and the mean latitude and mean longitude, respectively. , el represents the mean latitude, mean longitude, and satellite elevation angle of the puncture point observed by all reference stations within the modeling time region for satellite s. , Let represent the latitude and longitude of the i-th puncture point of satellite s, respectively.

8. The method for generating and broadcasting a high-reliability atmospheric error model product as described in claim 1, characterized in that, Step S3 includes: The Gaussian process method is used to estimate the confidence-related parameters of the tropospheric model, and the confidence-related parameters of the ionospheric model are obtained by calculating the standard deviation of the ionospheric model. The credibility-related parameters and model parameters of the tropospheric model are combined into a high-credibility tropospheric delay model, and the credibility-related parameters and model parameters of the ionospheric model are combined into a high-credibility ionospheric delay model. The high-credibility tropospheric delay model and the high-credibility ionospheric delay model are encoded and broadcast to users through the network.

9. A system for generating and broadcasting a high-reliability atmospheric error model product, characterized in that, include: The data acquisition module is used to acquire satellite differential code bias corrections, ionospheric model data, tropospheric wet delay, and ionospheric slant path delay including differential code bias. The model building module is used to establish a tropospheric model based on the tropospheric wet delay; estimate the differential code deviation of the reference station receiver based on the satellite differential code deviation correction and ionospheric model data, remove the differential code deviation of the reference station receiver from the ionospheric slant path delay to obtain an ionospheric slant path delay that does not contain differential code deviation, and establish an ionospheric model based on the ionospheric slant path delay; The credibility calculation module is used to calculate the credibility-related parameters of the tropospheric model and the ionospheric model, combine the model parameters of the tropospheric model and the ionospheric model with the corresponding credibility-related parameters to obtain a high-credibility tropospheric delay model and a high-credibility ionospheric delay model, and then encode and broadcast them to the user.

10. A non-transitory computer-readable storage medium, characterized in that, The non-transitory computer-readable storage medium stores computer instructions that cause the computer to execute a method for generating and broadcasting a high-reliability atmospheric error model product as described in any one of claims 1 to 8.

Images