Method and system for estimating differential code deviation of inter-station time-varying receiver of reference station network

Abstract

The invention discloses a method for estimating differential code deviation of an inter-station time-varying receiver of a reference station network, which comprises the following steps of: constructing an inter-station differential observation equation by using reference station observation values distributed in a region, calculating an inter-station relative DCB result which changes along with time, and restoring the inter-station relative DCB result to an absolute DCB to obtain an accurate time-varying receiver DCB. According to the method, the precision of the ionosphere modeling result is improved, and the influence of the pseudo-range hardware delay (DCB) of the GNSS receiver on the ionosphere modeling precision is reduced.

Description

Technical Field

[0001] The present invention belongs to the field of GNSS satellite navigation and near-Earth space environment monitoring, and in particular relates to a method and system for estimating differential code deviations of time-varying receivers between stations of a reference station network. Background Art

[0002] Ionospheric delay error is a significant factor in reducing Global Navigation Satellite System (GNSS) positioning accuracy. Therefore, it is necessary to model the ionosphere and use this model to correct for this error in the positioning process. However, because the ionosphere's impact on GNSS signals is frequency-dependent, ionospheric observations acquired through GNSS contain correlated errors introduced by the frequency signal processing hardware circuits at the satellite and receiver ends, known as pseudorange differential code bias (DCB). To establish a high-precision ionospheric model, high-precision satellite DCB and receiver DCB products are required. Researchers have extensively studied DCB estimation, including simultaneous estimation of receiver and satellite DCB when estimating global ionospheric models, direct extraction of receiver and satellite DCB from observations using existing ionospheric products, and statistical modeling based on the temporal and spatial relationships between ionospheric observations and satellite elevation angles. These methods have yielded highly accurate receiver and satellite DCB estimates, and have been continuously providing relevant products and services to users for nearly 20 years. However, the above estimation method is limited by the continuity of satellite tracking and the discrete distribution of station data. It is not effective in regional distributed stations, especially in municipal base station networks. This is mainly reflected in the low accuracy of the receiver DCB estimation results, which further reduces the accuracy of regional ionospheric modeling results and affects GNSS positioning. Summary of the Invention

[0003] In order to improve the accuracy of ionospheric modeling results and reduce the impact of the pseudorange hardware delay (DCB) of the GNSS receiver on the ionospheric modeling accuracy, the present invention provides a method for estimating the time-varying receiver differential code bias between stations in a reference station network. By using the observation values ​​of reference stations distributed in a region to construct the inter-station differential observation equation, the relative DCB results between stations that vary with time are calculated, and then restored to the absolute DCB to obtain accurate time-varying receiver DCB.

[0004] According to one aspect of the present invention, a method for estimating differential code bias of time-varying receivers between stations in a reference station network is provided, comprising: Step 1: Use the receivers in the base station network to perform undifferenced, non-combined precise point positioning solutions, collect ionospheric observations of all satellites from all base stations, and classify them by satellite; Step 2: Calculate the inter-station differential ionospheric observations of the same satellite observations for all base stations in the same epoch, detect gross errors and outliers in the time series of differential ionospheric observations, and remove them; Step 3: Initialize the model by collecting data within a preset time period to obtain the initial value of the inter-station DCB at the receiver end. At the same time, calculate the absolute receiver DCB of each station; Step 4: After obtaining the initial value of the inter-station DCB at the receiver, use the real-time filtering method to estimate the inter-station DCB; Step 5: After obtaining the real-time solution results of the inter-station DCB, the inter-station DCB is restored to the absolute DCB of each station according to the absolute receiver DCB calculated during initialization, which is used for modeling the ionospheric model.

[0005] As a further technical solution, it is assumed that the DCB at the receiver end is time-invariant during the time period described in step 3, and the inter-station DCB during the time period is represented by a parameter.

[0006] As a further technical solution, in step three, the absolute receiver-side DCB of each station is calculated using a conventional estimation method.

[0007] As a further technical solution, in step 4, when estimating the inter-station DCB using the real-time filtering method, the inter-station DCB of each epoch is considered to be changing, and the inter-station DCB of the previous epoch is used as a constraint.

[0008] As a further technical solution, in step 4, the Kalman filter method is used to estimate the inter-station differential ionospheric delay as a random walk process, and the only parameter to be estimated is the inter-station DCB.

[0009] As a further technical solution, in step 5, the absolute DCB of receiver b at epoch k is , in is the absolute receiver-side DCB of receiver a at epoch t, is the time-varying inter-station DCB of receivers a and b at epoch k estimated in real time.

[0010] According to one aspect of the present invention, a system for estimating differential code deviations of time-varying receivers between stations in a reference station network is provided, comprising: The first main module is used to perform non-differential non-combined precise point positioning using the receivers in the reference station network, collect ionospheric observations of all satellites from all reference stations, and classify them according to satellites; The second main module is used to calculate the inter-station differential ionospheric observations of the same satellite observations by all base stations in the same epoch, and detect gross errors and outliers in the time series of the differential ionospheric observations and remove them; The third main module is used to initialize the model by collecting data within a preset time period to obtain the initial value of the inter-station DCB at the receiver end, and at the same time, calculate the absolute receiver DCB of each station; The fourth main module is used to estimate the inter-station DCB using a real-time filtering method after obtaining the inter-station DCB initial value at the receiver end; The fifth main module is used to obtain the real-time solution results of the inter-station DCB, and then restore the inter-station DCB to the absolute DCB of each station according to the absolute receiver-side DCB calculated during initialization, which is used for modeling the ionosphere model.

[0011] According to one aspect of the present invention, a GNSS receiver is provided, comprising a memory and a processor, wherein the memory stores program instructions executed by the processor, and the processor calls the program instructions to execute the method for estimating time-varying receiver differential code deviations between stations of a reference station network.

[0012] According to one aspect of the present invention, a non-transitory computer-readable storage medium is provided, wherein the non-transitory computer-readable storage medium stores computer instructions, and the computer instructions enable the computer to execute the method for estimating differential code deviation of time-varying receivers between stations of a reference station network.

[0013] Compared with the prior art, the present invention has the following beneficial effects: 1. This invention uses observations from regionally distributed reference stations to construct inter-station differential observation equations, calculates the time-varying inter-station relative DCB results, and then restores them to absolute DCB to obtain accurate time-varying receiver DCB. This improves the accuracy of ionospheric modeling results and reduces the impact of the pseudorange hardware delay (DCB) of the GNSS receiver on the ionospheric modeling accuracy.

[0014] 2. By using the high-precision inter-station DCB estimation value obtained by the present invention, after DCB stripping of the ionospheric observation value, a more accurate ionospheric TEC observation value without DCB influence can be obtained, thereby improving the accuracy of the ionospheric model. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, a brief introduction will be given below to the drawings used in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0016] Figure 1 A schematic flow chart of a method for estimating differential code deviation of time-varying receivers between reference station networks provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0017] Because satellites spend a long time in the stable, low-temperature environment of space, coupled with precision equipment and sophisticated manufacturing processes, satellite-side DCB is more stable, generally considered stable for a month. However, receiver DCB stability is less stable due to various factors, including temperature fluctuations, device aging, and firmware upgrades. Current research and development has shown that receiver-side DCB varies with ambient temperature, with fluctuations reaching several nanoseconds (ns). On the other hand, satellite-side DCB can be estimated and released using a global network of receivers, while receiver-side errors can only be estimated by base station operators. Therefore, receiver-side DCB has a greater impact on ionospheric modeling accuracy. In particular, unstable DCB between receiver stations used for modeling can reduce the accuracy of a unified regional ionospheric model due to DCB variations between different receivers, seriously impacting its application in GNSS positioning.

[0018] Based on the aforementioned situation, the present invention discloses a method for estimating time-varying receiver differential code bias (DCB) between stations in a reference station network. This method constructs an inter-station differential observation equation using observations from regionally distributed reference stations, calculates the time-varying relative DCB between stations, and then restores it to the absolute DCB to obtain an accurate time-varying receiver DCB. This method improves the accuracy of ionospheric modeling results and reduces the impact of the pseudorange hardware delay (DCB) of the GNSS receiver on ionospheric modeling accuracy.

[0019] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention. In addition, the technical features in the various embodiments or single embodiments provided by the present invention are arbitrarily combined with each other to form a new technical solution. This combination is not restricted by the sequence of steps and / or structural composition mode, but must be based on the ability of ordinary technicians in this field to implement it. When the combination of technical solutions is contradictory or cannot be implemented, it should be deemed that this combination of technical solutions does not exist and is not within the scope of protection required by the present invention.

[0020] See attached Figure 1 The embodiment of the present invention provides a method for estimating differential code deviation of time-varying receivers between stations in a reference station network, comprising the following steps: Step 1: Use the receivers in the base station network to perform un-differenced and un-combined precise point positioning (UDUC PPP) solutions, collect ionospheric observations of all satellites from all receivers, and classify them by satellite.

[0021] Step 2: Calculate the inter-station differential ionospheric observations of the same satellite observations for all base stations at the same epoch, detect gross errors and outliers in the time series of differential ionospheric observations, and remove them.

[0022] Step 3: Initialize the model by collecting data over a period of time. During this period, the receiver DCB is assumed to be time-invariant. Therefore, the inter-station DCB over this period is represented by a single parameter. At the same time, conventional estimation methods are used to calculate the absolute receiver DCB for each station, allowing for easy conversion of the inter-station DCB to the absolute DCB.

[0023] Step 4: After obtaining the initial value of the inter-station DCB at the receiver, the inter-station DCB is estimated using a real-time filtering method. In this process, the inter-station DCB of each epoch is considered to be changing. At the same time, to ensure the continuity of the inter-station DCB, the inter-station DCB of the previous epoch is used as a constraint.

[0024] Step 5: After obtaining the real-time solution results, the inter-station DCB is restored to the absolute DCB of each station according to the absolute receiver DCB calculated during initialization, which is used for modeling the ionospheric model.

[0025] In the above technical solution, the specific operation method of using the receivers in the reference station network to perform UDUC PPP solution, collect ionospheric observations of all satellites from all receivers, and classify them according to satellites is as follows: During the UDUC PPP solution, the reconstructed observation equation of the receiver r to the satellite s at time t is as follows:

[0026] In the observation equation and Represent pseudorange and carrier observation values ​​respectively, i represents the frequency point, represents the frequency of frequency point i, and are the reconstructed receiver and satellite clock errors, It represents the ratio of frequency point i to the reference frequency point, represents the slant ionospheric delay error, is the ionospheric elimination combination coefficient, and Represent the receiver and satellite DCB respectively, represents the tropospheric delay projection coefficient, T is the tropospheric delay, Represents the floating point ambiguity of frequency point i. Using the Extended Kalman Filter (EKF), all satellite observations of a single station can be used to calculate the high-precision ionospheric observation value including DCB, that is, .

[0027] Solve the ionospheric observation value Then, we classify them according to different satellites and get 、 、 etc. for subsequent calculations.

[0028] Step 2 calculates the inter-station differential ionospheric observations of the same satellite observations for all base stations in the same epoch, detects gross errors and outliers in the time series of the differential ionospheric observations, and removes them. The specific operation method is as follows: Calculate the receiver r's relative position to a satellite s at the same epoch According to the formula in equation (1), the difference in ionospheric observation values ​​between receivers a and b can be obtained as

[0029] Equation (2) shows that after calculating the difference, the satellite DCB is eliminated, leaving only the inter-station DCB at the receiver end and the inter-station oblique ionospheric delay difference. Considering that the inter-station DCB of the receiver should change relatively smoothly and the ionospheric changes are also continuous, the inter-station ionospheric observation difference error can be obtained through time series analysis. The median absolute deviation (MAD) of the sliding window can effectively detect gross errors in the sequence. The sliding window size here is 1 hour. The data after eliminating gross errors can be used for subsequent inter-station DCB calculations.

[0030] As described in step 3, the model is initialized by collecting data over a period of time. During this period, the receiver DCB is assumed to be time-invariant, so the inter-station DCB during this period is represented by a parameter. At the same time, the absolute receiver DCB of each station is calculated using conventional estimation methods. For ease of use, the inter-station DCB is converted to an absolute DCB. The specific operation is as follows: First, since the distance between the satellite and the ground is about 20,000 kilometers, and the distance between stations does not exceed 100 kilometers, it is assumed that the satellite signals observed by receivers a and b are parallel, so the altitude angle has the same impact on the ionosphere of receivers a and b. Here, only the effect of the distance between the two stations on the inter-station differential ionospheric delay is considered. After statistically setting that the TEC difference is 0.5TECU for every 100km increase in distance, it is assumed that the inter-station differential ionospheric delay follows a zero-mean normal distribution.

[0031] in, The distance between the observation points of satellite s by receivers a and b is expressed in km. When estimating DCB between stations, a station located at the center of the reference station network is used as the reference. , then the parameters to be estimated are 、 Using the above observations and the random model to construct the equation, we can get

[0032] is the residual of the equation, which obeys the distribution of the above formula (3). Using the weighted least squares method, the estimated parameter is obtained for

[0033] The initial DCB value between receiver stations can be obtained by solving the above equation using the reference station network observation data for 8 consecutive hours at night (e.g., 8 pm to 4 am the next day). Then, the receiver absolute DCB is estimated using conventional methods. The ionospheric model product and satellite DCB product within the above nighttime period are selected and the ionospheric observation value is calculated. The ionospheric slant path delay and satellite DCB are directly removed and the average value is calculated to obtain the absolute DCB of the receiver, as follows

[0034] in, represents the slant ionospheric delay between the receiver r and the satellite s at epoch t estimated using the ionospheric model product, represents the DCB of satellite s obtained from the product, and n is the number of observations.

[0035] After obtaining the initial inter-station DCB value at the receiver as described in step 4, the inter-station DCB is estimated using a real-time filtering method. In this process, the inter-station DCB of each epoch is considered to be changing. At the same time, to ensure the continuity of the inter-station DCB, the inter-station DCB of the previous epoch is used as a constraint. The specific implementation method is as follows: After obtaining the initial value of the inter-station DCB of each receiver, the time-varying inter-station DCB can be estimated using real-time data. The Kalman filter method is used to estimate the inter-station differential ionospheric delay as a random walk process. The only parameter to be estimated is the inter-station DCB, and the filter equation is:

[0036] in, The definition is the same as in Equation (4), which is the DCB between receiver stations to be estimated at epoch k, Compared with formula (4), the existing and The format is as follows

[0037] The current time-varying inter-station DCB can be solved in real time through Kalman filtering.

[0038] After obtaining the real-time solution results described in step 5, the inter-station DCB is restored to the absolute DCB of each station based on the absolute receiver DCB calculated during initialization. The specific implementation method for modeling the ionospheric model is as follows: The time-varying inter-station DCB obtained in step 4 can be converted to a time-varying absolute DCB by comparing it with the receiver absolute DCB in step 3.

[0039] in is the absolute DCB of receiver b at epoch k, Same as formula (6), is the time-varying inter-station DCB at epoch k estimated by formula (7). After obtaining the absolute DCB, accurate ionospheric delay observations without the influence of satellite and receiver DCB can be extracted, and then a high-precision ionospheric model can be established.

[0040] The implementation of each embodiment of the present invention is based on programmed processing performed by a device with processor functionality. Therefore, in practical engineering, the technical solutions and functions of each embodiment of the present invention are packaged into various modules. Based on this reality, and in addition to the aforementioned embodiments, an embodiment of the present invention provides a system for estimating differential code bias between time-varying receivers in a reference station network. This system is used to implement the method for estimating differential code bias between time-varying receivers in a reference station network described in the aforementioned method embodiments.

[0041] The system includes: a first main module, which is used to use receivers in the reference station network to perform non-differential non-combined precise single-point positioning solutions, collect ionospheric observations of all reference stations on all satellites, and classify them according to satellites; a second main module, which is used to calculate the inter-station differential ionospheric observations of all base stations on the same satellite in the same epoch, and detect gross errors and outliers in the time series of the differential ionospheric observations and eliminate them; a third main module, which is used to initialize the model by collecting data within a preset time period, obtain the initial inter-station DCB value at the receiver end, and at the same time, calculate the absolute receiver-end DCB of each station; a fourth main module, which is used to estimate the inter-station DCB using a real-time filtering method after obtaining the initial inter-station DCB value at the receiver end; and a fifth main module, which is used to restore the inter-station DCB to the absolute DCB of each station based on the absolute receiver-end DCB calculated during initialization after obtaining the real-time solution result of the inter-station DCB, for modeling the ionospheric model.

[0042] The inter-station time-varying receiver differential code bias estimation system for a reference station network provided by an embodiment of the present invention addresses the problem that existing estimation methods are limited by the continuity of satellite tracking and the discrete distribution of station data, resulting in poor application effect in regionally distributed stations, especially in municipal-level reference station networks. By using the aforementioned modules, the inter-station differential observation equation is constructed by utilizing the observation values ​​of reference stations distributed in the region, and the relative DCB results between stations that change with time are calculated. The results are then restored to the absolute DCB to obtain accurate time-varying receiver DCB.

[0043] It should be noted that the system embodiments provided by the present invention are not only used to implement the methods in the above-mentioned method embodiments, but also used to implement the methods in other method embodiments provided by the present invention. The only difference is the setting of corresponding functional modules. The principles thereof are basically the same as those of the above-mentioned system embodiments provided by the present invention. As long as those skilled in the art refer to the specific technical solutions in other method embodiments on the basis of the above-mentioned system embodiments, obtain corresponding technical means and technical solutions composed of these technical means by combining technical features, and on the premise of ensuring the practicality of the technical solutions, improve the modules in the above-mentioned system embodiments to obtain corresponding system-type embodiments, which are used to implement the methods in other method-type embodiments. For example: Based on the content of the above system embodiment, as a preferred embodiment, the inter-station time-varying receiver differential code deviation estimation system of the reference station network provided in the embodiment of the present invention, the third main module is also used to execute the following instructions: assuming that the DCB at the receiver end is time-invariant during the time period, and the inter-station DCB within the time period is represented by a parameter.

[0044] Based on the content of the above system embodiment, as a preferred embodiment, the inter-station time-varying receiver differential code deviation estimation system of the reference station network provided in the embodiment of the present invention, the third main module is also used to execute the following instructions: use a conventional estimation method to calculate the absolute receiver-end DCB of each station.

[0045] Based on the content of the above-mentioned system embodiment, as a preferred embodiment, the inter-station time-varying receiver differential code deviation estimation system of the reference station network provided in the embodiment of the present invention, the fourth main module is also used to execute the following instructions: in the process of estimating the inter-station DCB using the real-time filtering method, the inter-station DCB of each epoch is considered to be changing, and the inter-station DCB of the previous epoch is used as a constraint.

[0046] Based on the content of the above system embodiment, as a preferred embodiment, the inter-station time-varying receiver differential code bias estimation system of the reference station network provided in the embodiment of the present invention, the fourth main module is also used to execute the following instructions: using the Kalman filtering method, the inter-station differential ionospheric delay is estimated as a random walk process, and the only parameter to be estimated is the inter-station DCB.

[0047] Based on the content of the above system embodiment, as a preferred embodiment, the inter-station time-varying receiver differential code bias estimation system of the reference station network provided in the embodiment of the present invention, the fifth main module is further used to execute the following instruction: the absolute DCB of the receiver b at epoch k is , in is the absolute receiver-side DCB of receiver a at epoch t, is the time-varying inter-station DCB of receivers a and b at epoch k estimated in real time.

[0048] Based on the same inventive concept as the aforementioned embodiment, an embodiment of the present invention further provides a GNSS receiver, comprising a memory and a processor, wherein the memory stores program instructions executed by the processor, and the processor calls the program instructions to execute the inter-station time-varying receiver differential code deviation estimation method of the reference station network.

[0049] In an embodiment of the present invention, the memory may be a non-volatile memory, such as a hard disk drive (HDD) or a solid-state drive (SSD), or a volatile memory (volatile memory), such as a random-access memory (RAM). The memory is any other medium that can be used to carry or store desired program code in the form of instructions or data structures and can be accessed by a computer, but is not limited thereto. The memory in an embodiment of the present invention may also be a circuit or any other device that can implement a storage function, for storing program instructions and / or data.

[0050] In the embodiments of the present invention, the processor may be a general-purpose processor, a digital signal processor, an application-specific integrated circuit, a field-programmable gate array or other programmable logic device, a discrete gate or transistor logic device, or a discrete hardware component, and may implement or execute the methods, steps, and logic block diagrams disclosed in the embodiments of the present invention. A general-purpose processor may be a microprocessor or any conventional processor. The steps of the methods disclosed in the embodiments of the present invention may be directly implemented and executed by a hardware processor, or by a combination of hardware and software modules within the processor.

[0051] Based on the same inventive concept as the aforementioned embodiment, an embodiment of the present invention further provides a non-transitory computer-readable storage medium storing computer instructions, wherein the computer instructions cause the computer to execute the method for estimating differential code bias of time-varying receivers between stations in a reference station network, including: Step 1: Use the receivers in the base station network to perform non-differential non-combined precise point positioning solutions, collect ionospheric observations of all base stations for all satellites, and classify them according to satellites.

[0052] Step 2: Calculate the inter-station differential ionospheric observations of the same satellite observations by all base stations in the same epoch, detect gross errors and outliers in the time series of the differential ionospheric observations, and remove them.

[0053] Step 3: Initialize the model by collecting data over a period of time. During this period, the receiver DCB is assumed to be time-invariant. Therefore, the inter-station DCB over this period is represented by a single parameter. At the same time, conventional estimation methods are used to calculate the absolute receiver DCB for each station, allowing for easy conversion of the inter-station DCB to the absolute DCB.

[0054] Step 4: After obtaining the initial value of the inter-station DCB at the receiver, the inter-station DCB is estimated using a real-time filtering method. In this process, the inter-station DCB of each epoch is considered to be changing. At the same time, to ensure the continuity of the inter-station DCB, the inter-station DCB of the previous epoch is used as a constraint.

[0055] Step 5: After obtaining the real-time solution results, the inter-station DCB is restored to the absolute DCB of each station according to the absolute receiver DCB calculated during initialization, which is used for modeling the ionospheric model.

[0056] In summary, the present invention discloses a method for obtaining accurate time-varying receiver DCB by constructing inter-station differential observation equations using observations from regionally distributed reference stations, calculating the time-varying inter-station relative DCB, and then restoring the DCB to the absolute DCB. This method improves the accuracy of ionospheric modeling results and reduces the impact of the GNSS receiver's pseudorange hardware delay (DCB) on ionospheric modeling accuracy.

[0057] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or replace some or all of the technical features therein with equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the technical solutions of the embodiments of the present invention.

Claims

1. A method for estimating differential code bias of time-varying receivers between reference stations in a network of reference stations, characterized in that: include: Step 1: Use the receivers in the base station network to perform undifferenced, non-combined precise point positioning solutions, collect ionospheric observations of all satellites from all base stations, and classify them by satellite; Step 2: Calculate the inter-station differential ionospheric observations of the same satellite observations for all base stations in the same epoch, detect gross errors and outliers in the time series of differential ionospheric observations, and remove them; Step 3: Initialize the model by collecting data within a preset time period to obtain the initial value of the inter-station DCB at the receiver end. At the same time, calculate the absolute receiver DCB of each station; Step 4: After obtaining the initial value of the inter-station DCB at the receiver, use the real-time filtering method to estimate the inter-station DCB; Step 5: After obtaining the real-time solution results of the inter-station DCB, the inter-station DCB is restored to the absolute DCB of each station according to the absolute receiver DCB calculated during initialization, which is used for modeling the ionospheric model.

2. The method for estimating differential code bias of time-varying receivers between stations in a reference station network according to claim 1, wherein: It is assumed that the DCB at the receiver is time-invariant during the time period described in step 3, and the inter-station DCB during the time period is represented by a parameter.

3. The method for estimating differential code bias of time-varying receivers between stations in a reference station network according to claim 1, wherein: In step 3, the absolute receiver-side DCB of each station is calculated using conventional estimation methods.

4. The method for estimating differential code deviation of time-varying receivers between stations in a reference station network according to claim 1, wherein: In step 4, when estimating the inter-station DCB using the real-time filtering method, the inter-station DCB of each epoch is considered to be changing, and the inter-station DCB of the previous epoch is used as a constraint.

5. The method for estimating differential code bias of time-varying receivers between stations in a reference station network according to claim 4, characterized in that: In step 4, the Kalman filter method is used to estimate the inter-station differential ionospheric delay as a random walk process, and the only parameter to be estimated is the inter-station DCB.

6. The method for estimating differential code bias of time-varying receivers between stations in a reference station network according to claim 1, wherein: In step 5, the absolute DCB of receiver b at epoch k is ， in is the absolute receiver-side DCB of receiver a at epoch t, is the time-varying inter-station DCB of receivers a and b at epoch k estimated in real time.

7. A time-varying receiver differential code bias estimation system for a reference station network, characterized in that: include: The first main module is used to perform non-differential non-combined precise point positioning using the receivers in the reference station network, collect ionospheric observations of all satellites from all reference stations, and classify them according to satellites; The second main module is used to calculate the inter-station differential ionospheric observations of the same satellite observations by all base stations in the same epoch, and detect gross errors and outliers in the time series of the differential ionospheric observations and remove them; The third main module is used to initialize the model by collecting data within a preset time period to obtain the initial value of the inter-station DCB at the receiver end, and at the same time, calculate the absolute receiver DCB of each station; The fourth main module is used to estimate the inter-station DCB using a real-time filtering method after obtaining the inter-station DCB initial value at the receiver end; The fifth main module is used to obtain the real-time solution results of the inter-station DCB, and then restore the inter-station DCB to the absolute DCB of each station according to the absolute receiver-side DCB calculated during initialization, which is used for modeling the ionosphere model.

8. A GNSS receiver, characterized in that: The invention comprises a memory and a processor, wherein the memory stores program instructions executed by the processor, and the processor calls the program instructions to execute the inter-station time-varying receiver differential code deviation estimation method of the reference station network according to any one of claims 1 to 6.

9. A non-transitory computer-readable storage medium, characterized in that The non-transitory computer-readable storage medium stores computer instructions, and the computer instructions enable the computer to execute the inter-station time-varying receiver differential code bias estimation method for a reference station network according to any one of claims 1 to 7.

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