Ionospheric model parameter and differential code bias estimation method and apparatus

Abstract

The application provides an ionosphere model parameter and differential code bias estimation method and device, comprising: reading observation files and ephemeris files for single point positioning, marking complete arc segments according to observation value continuity and cycle slip detection, performing carrier phase smoothing pseudo-range on continuous observation values in the complete arc segments, and calculating ionosphere slant delay; projecting the ionosphere slant delay to ionosphere vertical delay through a projection function; fitting the regional ionosphere through an ionosphere polynomial model; establishing an error equation according to the projected ionosphere vertical delay and the estimated ionosphere vertical delay value obtained through fitting, obtaining initial ionosphere model parameters and differential code bias; and obtaining robust estimation ionosphere model parameter estimation and differential code bias estimation through a robust estimation algorithm. The application improves the detection and control of observation data quality, reduces the interference of abnormal values on the results, and ensures the stability and accuracy of ionosphere estimation.

Description

Technical Field

[0001] This application belongs to the field of occultation detection technology, and in particular relates to a method and apparatus for estimating ionospheric model parameters and differential code bias. Background Technology

[0002] Total electron content (TEC) of the ionosphere is the integral of the ionospheric electron density along the signal propagation path. It is an important physical parameter describing the properties of the ionosphere and also a significant source of error in navigation and positioning. The slant path ionospheric delay can generally be obtained by inverting GNSS observations at different frequencies. The impact of pseudorange hardware delay on TEC is not negligible; the hardware delays of different pseudorange observations are inconsistent, and the deviation between them is called differential code bias (DCB). This can generally be corrected using DCB products provided by major institutions (such as CODE, CAS, and WHU). Single-station ionospheric modeling can describe the ionospheric variation characteristics above the station, which is of great significance for monitoring special ionospheric events in the region. Furthermore, satellite and receiver DCBs can be used as parameters for estimation to describe the characteristics of hardware delay.

[0003] In ionospheric modeling, the hardware delay of pseudorange observations is often overlooked, leading to significant errors in ionospheric delay estimation. Furthermore, many traditional methods rely on externally provided differential code bias (DCB) products, lacking self-estimation capabilities, and DCB products from different sources may differ, affecting accuracy. Additionally, the impact of observation noise and elevation angle on ionospheric delay is not fully considered, limiting the accuracy and universality of the modeling results. Summary of the Invention

[0004] In view of this, this application aims to propose a method and apparatus for estimating ionospheric model parameters and differential code bias, in order to solve at least one of the above-mentioned problems.

[0005] To achieve the above objectives, the technical solution of this application is implemented as follows:

[0006] In a first aspect, this application provides a method for estimating ionospheric model parameters and differential code bias, including:

[0007] Based on the read daily observation files from the observation station and the broadcast ephemeris or precise ephemeris files, the receiver's location is determined by single-point positioning, and abnormal observations are eliminated.

[0008] Epichrony discontinuity is determined based on pseudorange and carrier phase observations, and cycle slip is detected by combining wide-lane ambiguity and ionospheric residuals to mark complete arc segments.

[0009] By performing carrier phase smoothing pseudorange on continuous observations within the complete arc segment, the total electron content of the slant path ionosphere, including differential code bias, is obtained through the smoothed dual-frequency pseudorange.

[0010] The total electron content of the ionosphere along the oblique path is projected to the total electron content of the ionosphere along the vertical path using a projection function, and the regional ionosphere is fitted using an ionospheric polynomial model to obtain an estimate of the total electron content of the ionosphere along the vertical path.

[0011] An error equation is established based on the total electron content of the vertical path ionosphere obtained by projection and the estimated value of the total electron content of the vertical path ionosphere obtained by the ionosphere polynomial model, so as to obtain the initial ionosphere model parameters and differential code bias.

[0012] By adjusting the observation weights using a robust stochastic model constructed based on a robust estimation algorithm, we can obtain the estimated parameters of the ionospheric model and the estimated differential code bias after robust estimation.

[0013] Secondly, based on the same inventive concept, this application also provides an ionospheric model parameter and differential code bias estimation device, comprising:

[0014] The file reading module is configured to locate the receiver's position using a single point based on the read daily observation files of the observation station and broadcast ephemeris or precise ephemeris files, and to remove abnormal observation values.

[0015] The cycle slip detection module is configured to determine epoch discontinuities based on pseudorange and carrier phase observations, and to perform cycle slip detection by combining wide-lane ambiguity and ionospheric residuals to mark complete arc segments.

[0016] The pseudorange smoothing module is configured to smooth the pseudorange by carrier phase through continuous observations within the complete arc segment, and obtain the total electron content of the slant path ionosphere, including differential code bias, from the smoothed pseudorange.

[0017] The polynomial fitting module is configured to project the total electron content of the oblique path ionosphere to the total electron content of the vertical path ionosphere through a projection function, and to fit the regional ionosphere through an ionospheric polynomial model to obtain an estimate of the total electron content of the vertical path ionosphere.

[0018] The error establishment module is configured to establish an error equation based on the total electron content of the vertical path ionosphere obtained by projection and the estimated value of the total electron content of the vertical path ionosphere obtained by the ionosphere polynomial model, so as to obtain the initial ionosphere model parameters and differential code bias.

[0019] The robust estimation module is configured to adjust the observation weights through a robust stochastic model constructed based on the robust estimation algorithm to obtain the ionospheric model parameter estimates and differential code bias estimates after robust estimation.

[0020] Thirdly, based on the same inventive concept, this application also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the method described in the first aspect.

[0021] Fourthly, based on the same inventive concept, this application also provides a non-transitory computer-readable storage medium, wherein the non-transitory computer-readable storage medium stores computer instructions for causing the computer to perform the method as described in the first aspect.

[0022] Compared with existing technologies, the ionospheric model parameter and differential code bias estimation method and apparatus described in this application have the following advantages:

[0023] The ionospheric model parameter and differential code bias estimation method described in this application effectively solves the impact of hardware delay in pseudorange observations on ionospheric estimation by jointly estimating ionospheric model parameters and differential code bias. Through carrier phase smoothing pseudorange methods and regional ionospheric polynomial models, the accuracy and robustness of ionospheric modeling are improved. Simultaneously, by introducing robust estimation algorithms and residual checks, the detection and control of observation data quality are further enhanced, reducing the interference of outliers on the results and ensuring the stability and accuracy of ionospheric estimation. Attached Figure Description

[0024] The accompanying drawings, which form part of this application, are used to provide a further understanding of this application. The illustrative embodiments and descriptions of this application are used to explain this application and do not constitute an undue limitation of this application. In the drawings:

[0025] Figure 1 This is a flowchart of an ionospheric model parameter and differential code bias estimation method according to an embodiment of this application;

[0026] Figure 2 This is a schematic diagram of the ionospheric puncture point described in the embodiments of this application;

[0027] Figure 3 This is a schematic diagram of the structure of an ionospheric model parameter and differential code bias estimation device according to an embodiment of this application;

[0028] Figure 4 This is a schematic diagram of the hardware structure of the electronic device described in an embodiment of this application. Detailed Implementation

[0029] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with specific embodiments and the accompanying drawings.

[0030] It should be noted that, unless otherwise defined, the technical or scientific terms used in the embodiments of this application should have the ordinary meaning understood by one of ordinary skill in the art to which this application pertains. The terms "first," "second," and similar terms used in the embodiments of this application do not indicate any order, quantity, or importance, but are merely used to distinguish different components. Terms such as "comprising" or "including" mean that the element or object preceding the word encompasses the elements or objects listed after the word and their equivalents, without excluding other elements or objects. Terms such as "connected" or "linked" are not limited to physical or mechanical connections, but can include electrical connections, whether direct or indirect. Terms such as "upper," "lower," "left," and "right" are only used to indicate relative positional relationships; when the absolute position of the described object changes, the relative positional relationship may also change accordingly.

[0031] The embodiments of this application are described in detail below with reference to the accompanying drawings.

[0032] Please see Figure 1 As shown, this embodiment provides a method for estimating ionospheric model parameters and differential code bias, specifically including the following steps:

[0033] Step S101: Based on the read daily observation files of the observation station (including pseudorange and carrier phase data received by the receiver) and broadcast ephemeris or precise ephemeris files (including satellite position and clock deviation), locate the receiver position using a single point and eliminate abnormal observation values.

[0034] Specifically, in this embodiment, single-point positioning is performed by reading the observation files of the observation station for a single day and the broadcast ephemeris or precise ephemeris files. An equation is established based on the pseudorange observation values ​​read from the observation files. The position of each satellite at the observation time is calculated by reading the ephemeris files. An equation based on pseudorange and satellite position is established. The least squares method is used to fit the observation values ​​of multiple satellites to obtain the optimal solution. The position of the receiver is located by single-point positioning. At the same time, satellites with too small an elevation angle and abnormal observation data are eliminated.

[0035] It should be noted that the calculation method involved in this step is common knowledge in the field, and this step is not the core inventive point of this embodiment, so it will not be described in detail further.

[0036] Step S102: Based on the pseudorange observations and carrier phase observations, determine the epoch discontinuity and perform cycle slip detection through the combination of wide-lane ambiguity and ionospheric residual to mark the complete arc segment.

[0037] Specifically, in this embodiment, this step involves marking complete arc segments based on the continuity of observations and cycle slip detection. Specifically, by determining the epochal discontinuity of pseudorange and carrier phase observations, cycle slip detection is performed using the MW combination (wide-lane ambiguity combination, which calculates a wide-lane ambiguity based on the carrier phase difference between two frequency signals) and the GF combination (ionospheric residual combination, which suppresses ionospheric delay through the difference in the phases of two frequency carriers and detects cycle slips through changes in the ionospheric residual). This allows for obtaining continuous arc segments where a satellite is continuously observed without cycle slips. The MW combination is equivalent to the wide-lane ambiguity and is generally a constant value when cycle slips are not occurring. The GF combination, also known as the ionospheric residual method, exhibits small changes in ionospheric delay over short periods and can be used for cycle slip detection. Therefore, the difference between the MW combination and the GF combination of two consecutive epochs can be used for determination, as shown in the following formula:

[0038] ;

[0039] ;

[0040] ;

[0041] ;

[0042] In the formula, , Indicates the frequencies of two frequency signals; , This represents the pseudorange observation value of the corresponding frequency signal; , Indicates the wavelength of the corresponding frequency signal; , This represents the observed carrier phase value of the corresponding frequency signal; , Indicates the integer ambiguity of the corresponding frequency signal; , Indicates the ionospheric delay of the dual-frequency pseudorange; The threshold representing the MW combination; This represents the threshold for the GF combination.

[0043] Step S103: By smoothing the carrier phase pseudorange of the continuous observations within the complete arc segment, the total electron content of the ionosphere along the oblique path, including the differential code deviation, is obtained from the smoothed pseudorange.

[0044] Specifically, in this embodiment, carrier phase smoothing pseudorange is performed on continuous observations within the complete arc segment, and the total electron content of the ionosphere along the slant path (STEC, also known as ionospheric slant delay) is calculated. Specifically, because carrier phase observations have high accuracy but contain unknown ambiguity parameters, while pseudorange observations are absolute observations of the satellite-to-Earth distance but have lower accuracy, a combination of pseudorange and carrier phase observations from multiple epochs can be used to calculate the smoothed combined ambiguity. Thus, the smoothed pseudo-distance is obtained. STEC is obtained by multiplying the smoothed pseudorange correction DCB (differential code deviation) by the frequency correlation constant.

[0045] ;

[0046] ;

[0047] ;

[0048] In the formula, This represents the combined ambiguity after smoothing multiple epochs; Indicates the number of smooth epochs; This represents the smoothed pseudorange combination of observations; These represent the satellite and receiver DCB (Differential Code Offset), respectively.

[0049] Step S104: Project the total electron content of the ionosphere along the oblique path to the total electron content of the ionosphere along the vertical path using a projection function, and fit the regional ionosphere using an ionospheric polynomial model to obtain an estimate of the total electron content of the ionosphere along the vertical path.

[0050] Specifically, in this embodiment, the total electron content of the ionosphere along the oblique path is projected to the total electron content of the ionosphere along the vertical path according to the projection function. Based on the assumption of a single-layer ionospheric model, it is assumed that all electrons in the ionosphere are concentrated in a thin layer at a certain height above the Earth's surface, and the puncture point (the intersection of the signal path and the thin layer) is calculated. Figure 2 This involves determining the latitude and longitude of the ionospheric puncture point (a schematic diagram) and the ionospheric projection function MF, and then projecting the oblique path total ionospheric electron content (STEC) onto the vertical path total ionospheric electron content (VTEC, i.e., ionospheric vertical delay). The specific formula is as follows:

[0051] ;

[0052] ;

[0053] In the formula, It is the projection function from VTEC to STEC; Indicates the Earth's radius; Indicates the height of the receiver; This indicates the zenith distance of the satellite at the observatory receiver at the observation time. This indicates the height of the ionospheric thin layer, typically taken as 300~450KM.

[0054] Typically, VTEC uses a spherical harmonic function model to model the global ionosphere. For the ionosphere of a single station, this embodiment uses a polynomial model to model the fit better. The formula for the total electron content of the vertical ionosphere is as follows:

[0055] ;

[0056] ;

[0057] In the formula, VTEC represents the total vertical electron content of the ionosphere at the puncture point excluding the satellite and receiver DCB; Represents the coefficients of the polynomial function model; Indicates the latitude and longitude of the puncture point; The latitude and longitude of the area center are represented; for a single station, this refers to the latitude and longitude of the base station. Indicates solar hour angle correction; These represent the observation time and the center time of the observation period, respectively, in seconds. If a model is performed every 2 hours, the center time of the region is the time at 1 hour.

[0058] Step S105: Based on the total electron content of the vertical path ionosphere obtained by projection and the estimated value of the total electron content of the vertical path ionosphere obtained by the ionosphere polynomial model, establish an error equation to obtain the initial ionosphere model parameters and differential code bias.

[0059] Specifically, in this embodiment, this step involves obtaining the VTEC through simultaneous STEC projection and constructing the VTEC using a polynomial model, and establishing an error equation, where the parameters to be determined are the ionospheric model parameters. satellite differential code deviation Differential code deviation from receiver .

[0060] The hardware delay deviation between satellites and receivers can be considered to be unchanged within a day, so only one set of DCBs needs to be estimated per day. Since the satellite DCB and receiver DCB cannot be separated, the equations are linearly dependent, resulting in a rank deficiency in the coefficient matrix of the normal equations. Therefore, the sum of the satellite DCBs of each system is considered to be 0 (zero mean constraint), thus separating the satellite and receiver DCBs. If there are multiple systems, multiple constraint equations should be established.

[0061] ;

[0062] make , The above equation can be intuitively expressed as the following equation, where the left side of the equation represents the observed values, and the right side is a function of the unknown parameters, thus yielding the following result. The form is , where "[ ]" represents multiple parameters.

[0063]

[0064] .

[0065] Step S106: Adjust the observation weights using a robust stochastic model constructed based on a robust estimation algorithm to obtain the estimated parameters of the ionospheric model and the estimated differential code bias after robust estimation.

[0066] Specifically, in this embodiment, the ionospheric model parameters and differential code bias (DCB) estimates obtained through the error equation are based on the initial observations and the solution established by the model. However, due to noise, errors, or outliers in the observation data, these errors can affect the final estimation results, especially in actual measurements where observations are often subject to various disturbances (such as weather, equipment failure, signal interference, etc.). To improve the accuracy and robustness of the estimation results, this embodiment introduces a robust estimation algorithm.

[0067] Furthermore, after establishing the functional model of VTEC in the previous step, it is still necessary to determine the stochastic model of the observed values ​​to ensure the accuracy of the results. The stochastic model can be jointly established based on the elevation angle and the noise of the observed values, and the variance of the calculated VTEC values ​​can be obtained through the error propagation law. The variance formula is as follows:

[0068] ;

[0069] In the formula, The variance of the VTEC calculation value is represented; M represents the smoothing factor, which is taken as 600 epochs in this embodiment; 100 refers to the ratio of the accuracy of the pseudorange and the carrier phase. The value z represents the accuracy of the carrier phase; z represents the zenith distance of the receiver to the satellite.

[0070] The initial weights are obtained using the above formula (in this embodiment, the initial weights are obtained from the variance, i.e.) The initial weights are the diagonal elements of matrix P. Based on the standard deviation of the post-hoc residuals (it should be noted that adjustment refers to the process of optimizing the estimation results using mathematical statistics methods (such as least squares) based on existing observation data. In this process, the residual represents the difference between the observed value and the fitted value (estimated value); the post-hoc residual is the difference between the observed data and the fitted value (i.e., the estimated value) after adjustment calculation, reflecting the accuracy of the measurement; the standard deviation is a statistic that measures the magnitude of residual fluctuation, representing the average deviation of the residuals. A smaller standard deviation means smaller residuals and higher consistency between the observed data and the estimated value), robust estimation based on IGGIII is performed. The observed values ​​are divided into normal, reduced-weight, and rejection regions. The initial weights are adjusted to different degrees until the residuals of the robust estimation meet the threshold. Then, the iteration ends, and the least squares solution after the iteration ends is used as the ionospheric model parameter estimate and differential code bias estimate.

[0071] ;

[0072] In the formula, This is called the right of equivalent value; It represents the absolute value of the standardized residual, which is equal to the ratio of the residual to its standard deviation; Generally, a value of 1 to 1.5 is used. Take 2.5~6.

[0073] The final least squares solution can be expressed as:

[0074] .

[0075] Therefore, the initial estimates of the polynomial coefficients and DCB have been obtained using the least squares method. However, these estimates are susceptible to the influence of outlier observations or noise. This embodiment employs a robust estimation algorithm and dynamically adjusts the weights of the observations to reduce the interference of outlier data on the solution, thereby improving the robustness and accuracy of the estimation. Ultimately, after adjustment by robust estimation, the obtained polynomial coefficient and differential code bias estimates will be more reliable and applicable to situations where noise exists in real observations.

[0076] The ionospheric model parameter and differential code bias estimation method described in this embodiment effectively solves the impact of hardware delay in pseudorange observations on ionospheric estimation by jointly estimating ionospheric model parameters and differential code bias. Through carrier phase smoothing pseudorange methods and regional ionospheric polynomial models, the accuracy and robustness of ionospheric modeling are improved. Simultaneously, by introducing robust estimation algorithms and residual checks, the detection and control of observation data quality are further enhanced, reducing the interference of outliers on the results and ensuring the stability and accuracy of ionospheric estimation.

[0077] It should be noted that the above description describes some embodiments of this application. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recorded in the claims can be performed in a different order than that shown in the above embodiments and still achieve the desired result. Furthermore, the processes depicted in the drawings do not necessarily require a specific or sequential order to achieve the desired result. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0078] Based on the same inventive concept, corresponding to the methods of any of the above embodiments, the embodiments of this application also provide an ionospheric model parameter and differential code bias estimation device.

[0079] like Figure 3 As shown, the ionospheric model parameter and differential code bias estimation device includes:

[0080] The file reading module 11 is configured to locate the receiver's position using a single point based on the read daily observation files of the observation station and broadcast ephemeris or precise ephemeris files, and to remove abnormal observation values.

[0081] The cycle slip detection module 12 is configured to determine epoch discontinuities based on pseudorange observations and carrier phase observations, and to perform cycle slip detection by combining wide-lane ambiguity and ionospheric residuals to mark complete arc segments.

[0082] The pseudorange smoothing module 13 is configured to smooth the pseudorange by carrier phase through continuous observations within the complete arc segment, and obtain the total electron content of the slant path ionosphere, including differential code deviation, from the smoothed pseudorange.

[0083] The polynomial fitting module 14 is configured to project the total electron content of the oblique path ionosphere to the total electron content of the vertical path ionosphere through a projection function, and to fit the regional ionosphere through an ionospheric polynomial model to obtain an estimate of the total electron content of the vertical path ionosphere.

[0084] Error establishment module 15 is configured to establish an error equation based on the total electron content of the vertical path ionosphere obtained by projection and the estimated value of the total electron content of the vertical path ionosphere obtained by the ionosphere polynomial model, so as to obtain the initial ionosphere model parameters and differential code bias.

[0085] The robust estimation module 16 is configured to adjust the observation weights through a robust stochastic model constructed based on the robust estimation algorithm to obtain the ionospheric model parameter estimates and differential code bias estimates after robust estimation.

[0086] For ease of description, the above apparatus is described in terms of its functions, divided into various modules. Of course, in implementing the embodiments of this application, the functions of each module can be implemented in one or more software and / or hardware.

[0087] The apparatus of the above embodiments is used to implement the corresponding method in any of the foregoing embodiments and has the beneficial effects of the corresponding method embodiments, which will not be repeated here.

[0088] Based on the same inventive concept, corresponding to the methods of any of the above embodiments, embodiments of this application also provide an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the methods described in any of the above embodiments.

[0089] Figure 4 This embodiment illustrates a more specific hardware structure of an electronic device, which may include a processor 1010, a memory 1020, an input / output interface 1030, a communication interface 1040, and a bus 1050. The processor 1010, memory 1020, input / output interface 1030, and communication interface 1040 are interconnected internally via the bus 1050.

[0090] The processor 1010 can be implemented using a general-purpose CPU (Central Processing Unit), microprocessor, application-specific integrated circuit (ASIC), or one or more integrated circuits, and is used to execute relevant programs to implement the technical solutions provided in the embodiments of this specification.

[0091] The memory 1020 can be implemented in the form of ROM (Read Only Memory), RAM (Random Access Memory), static storage device, dynamic storage device, etc. The memory 1020 can store the operating system and other applications. When the technical solutions provided in the embodiments of this specification are implemented by software or firmware, the relevant program code is stored in the memory 1020 and is called and executed by the processor 1010.

[0092] The input / output interface 1030 is used to connect input / output modules to realize information input and output. The input / output modules can be configured as components in the device (not shown in the figure) or externally connected to the device to provide corresponding functions. Input devices may include keyboards, mice, touch screens, microphones, various sensors, etc., and output devices may include displays, speakers, vibrators, indicator lights, etc.

[0093] The communication interface 1040 is used to connect a communication module (not shown in the figure) to enable communication between this device and other devices. The communication module can communicate via wired means (such as USB, Ethernet cable, etc.) or wireless means (such as mobile network, WIFI, Bluetooth, etc.).

[0094] Bus 1050 includes a pathway for transmitting information between various components of the device, such as processor 1010, memory 1020, input / output interface 1030, and communication interface 1040.

[0095] It should be noted that although the above-described device only shows the processor 1010, memory 1020, input / output interface 1030, communication interface 1040, and bus 1050, in specific implementations, the device may also include other components necessary for normal operation. Furthermore, those skilled in the art will understand that the above-described device may only include the components necessary for implementing the embodiments of this specification, and not necessarily all the components shown in the figures.

[0096] The electronic devices described above are used to implement the corresponding methods in any of the foregoing embodiments and have the beneficial effects of the corresponding method embodiments, which will not be repeated here.

[0097] Based on the same inventive concept, corresponding to the methods of any of the above embodiments, this application also provides a non-transitory computer-readable storage medium that stores computer instructions for causing the computer to perform the methods described in any of the above embodiments.

[0098] The computer-readable medium of this embodiment includes permanent and non-permanent, removable and non-removable media, and information storage can be implemented by any method or technology. Information can be computer-readable instructions, data structures, program modules, or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other non-transfer medium that can be used to store information accessible by a computing device.

[0099] The computer instructions stored in the storage medium of the above embodiments are used to cause the computer to perform the method described in any of the above embodiments, and have the beneficial effects of the corresponding method embodiments, which will not be repeated here.

[0100] Those skilled in the art should understand that the discussion of any of the above embodiments is merely exemplary and is not intended to imply that the scope of this application (including the claims) is limited to these examples; within the framework of this application, the technical features of the above embodiments or different embodiments can also be combined, the steps can be implemented in any order, and there are many other variations of different aspects of the embodiments of this application as described above, which are not provided in the details for the sake of brevity.

[0101] Additionally, to simplify the description and discussion, and to avoid obscuring the embodiments of this application, the well-known power / ground connections to integrated circuit (IC) chips and other components may or may not be shown in the provided drawings. Furthermore, the apparatus may be shown in block diagram form to avoid obscuring the embodiments of this application, and this also takes into account the fact that the details of the implementation of these block diagram apparatuses are highly dependent on the platform on which the embodiments of this application will be implemented (i.e., these details should be fully understood by those skilled in the art). While specific details (e.g., circuits) have been set forth to describe exemplary embodiments of this application, it will be apparent to those skilled in the art that the embodiments of this application can be implemented without these specific details or with variations thereof. Therefore, these descriptions should be considered illustrative rather than restrictive.

[0102] Although this application has been described in conjunction with specific embodiments thereof, many substitutions, modifications, and variations of these embodiments will be apparent to those skilled in the art from the foregoing description. For example, other memory architectures (e.g., dynamic RAM (DRAM)) may be used with the embodiments discussed.

[0103] The embodiments of this application are intended to cover all such substitutions, modifications, and variations that fall within the broad scope of the appended claims. Therefore, any omissions, modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the embodiments of this application should be included within the protection scope of this application.

Claims

1. A method for estimating ionospheric model parameters and differential code bias, characterized in that, include: Based on the read daily observation files from the observation station and the broadcast ephemeris or precise ephemeris files, the receiver's location is determined by single-point positioning, and abnormal observations are eliminated. Epichrony discontinuity is determined based on pseudorange and carrier phase observations, and cycle slip is detected by combining wide-lane ambiguity and ionospheric residuals to mark complete arc segments. By performing carrier phase smoothing pseudorange on continuous observations within the complete arc segment, the total electron content of the slant path ionosphere, including differential code bias, is obtained from the smoothed pseudorange. The total electron content of the ionosphere along the oblique path is projected to the total electron content of the ionosphere along the vertical path using a projection function, and the regional ionosphere is fitted using an ionospheric polynomial model to obtain an estimate of the total electron content of the ionosphere along the vertical path. An error equation is established based on the total electron content of the vertical path ionosphere obtained by projection and the estimated value of the total electron content of the vertical path ionosphere obtained by the ionosphere polynomial model, so as to obtain the initial ionosphere model parameters and differential code bias. By adjusting the observation weights using a robust stochastic model constructed based on a robust estimation algorithm, we obtain robust estimation estimates of ionospheric model parameters and differential code bias estimates, including: By establishing a stochastic model of the observed values ​​and obtaining the variance of the total electron content in the vertical path through the error propagation law, initial weights are established based on the variance of the total electron content in the vertical path. Robust estimation is performed based on the standard deviation of the post-hoc residuals. The observed values ​​are divided into normal domain, reduced weight domain, and rejection domain. The initial weights are dynamically adjusted until the residuals of the robust estimation meet the threshold, at which point the iteration stops. The obtained least squares solution is used as the ionospheric model parameter estimate and the differential code bias estimate.

2. The method according to claim 1, characterized in that: The receiver position is obtained by reading pseudorange observations from the observation file and satellite positions from the ephemeris file, and then calculating the position using the least squares method.

3. The method according to claim 1, characterized in that: The pseudorange and carrier phase observations from multiple epochs are smoothed using the phase smoothing pseudorange method to obtain the smoothed pseudorange combination observations. The differential code bias is corrected by combining the smoothed pseudorange observations and multiplying them with the frequency coefficients to obtain the total electron content of the ionosphere along the oblique path.

4. The method according to claim 1, characterized in that: Based on the established ionospheric single-layer model, the latitude and longitude of the puncture point and the projection function are determined, and the total electron content of the ionosphere along the oblique path is projected to the total electron content along the vertical path through the projection function.

5. The method according to claim 1, characterized in that, The variance formula is: ； In the formula, This represents the variance of the total electron content along the vertical path. Indicates the frequency of the observed values. Represents the projection function. Represents the smoothing factor. Indicates the precision of the carrier phase. This indicates the zenith distance between the receiver and the satellite.

6. A device for estimating ionospheric model parameters and differential code bias, characterized in that, include: The file reading module is configured to locate the receiver's position using a single point based on the read daily observation files of the observation station and broadcast ephemeris or precise ephemeris files, and to remove abnormal observation values. The cycle slip detection module is configured to determine epoch discontinuities based on pseudorange and carrier phase observations, and to perform cycle slip detection by combining wide-lane ambiguity and ionospheric residuals to mark complete arc segments. The pseudorange smoothing module is configured to smooth the pseudorange by carrier phase through continuous observations within the complete arc segment, and obtain the total electron content of the slant path ionosphere, including differential code bias, from the smoothed pseudorange. The polynomial fitting module is configured to project the total electron content of the oblique path ionosphere to the total electron content of the vertical path ionosphere through a projection function, and to fit the regional ionosphere through an ionospheric polynomial model to obtain an estimate of the total electron content of the vertical path ionosphere. The error establishment module is configured to establish an error equation based on the total electron content of the vertical path ionosphere obtained by projection and the estimated value of the total electron content of the vertical path ionosphere obtained by the ionosphere polynomial model, so as to obtain the initial ionosphere model parameters and differential code bias. The robust estimation module is configured to adjust the observation weights using a robust stochastic model constructed based on the robust estimation algorithm to obtain robustly estimated ionospheric model parameter estimates and differential code bias estimates, including: By establishing a stochastic model of the observed values ​​and obtaining the variance of the total electron content in the vertical path through the error propagation law, initial weights are established based on the variance of the total electron content in the vertical path. Robust estimation is performed based on the standard deviation of the post-hoc residuals. The observed values ​​are divided into normal domain, reduced weight domain, and rejection domain. The initial weights are dynamically adjusted until the residuals of the robust estimation meet the threshold, at which point the iteration stops. The obtained least squares solution is used as the ionospheric model parameter estimate and the differential code bias estimate.

7. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the method as described in any one of claims 1-5.

8. A non-transitory computer-readable storage medium, characterized in that, in, The non-transitory computer-readable storage medium stores computer instructions for causing a computer to perform the method described in any one of claims 1-5.

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