Ionosphere model parameter and differential code deviation estimation method and device

Abstract

The invention provides an ionosphere model parameter and differential code deviation estimation method and device, and the method comprises the steps: reading an observation file and an ephemeris file, carrying out the single-point positioning, marking a complete arc segment according to the observation value continuity and cycle slip detection, carrying out the carrier phase smoothing pseudo-range of the continuous observation values in the complete arc segment, calculating the ionosphere oblique delay, and obtaining the ionosphere model parameter and differential code deviation. Projecting the ionized layer oblique delay to the ionized layer vertical delay through a projection function, fitting the regional ionized layer through an ionized layer polynomial model, and establishing an error equation according to the projected ionized layer vertical delay and the estimated value of the ionized layer vertical delay obtained by fitting to obtain initial ionized layer model parameters and differential code deviation; and obtaining an ionosphere model parameter estimated value and a differential code deviation estimated value after robust estimation through a robust estimation algorithm. According to the invention, the detection and control of the observation data quality are improved, the interference of abnormal values on the result is reduced, and the stability and accuracy of ionosphere estimation are ensured.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of occultation detection, and particularly relates to an ionospheric model parameter and differential code bias estimation method and device. BACKGROUND

[0002] Ionospheric total electron content (TEC) is the integral of ionospheric electron density along the signal propagation path, is an important physical parameter for describing the properties of the ionosphere, and is also an important error source for navigation and positioning. Generally, ionospheric delay on the slant path can be obtained by inversion of GNSS observations at different frequencies. The influence of pseudorange hardware delay on TEC cannot be ignored. The hardware delays of different pseudorange observations are inconsistent, and the bias between them is called differential code bias (DCB). Generally, the DCB products provided by major institutions (such as CODE, CAS, and WHU) can be used for correction. Single-station ionospheric modeling can describe the characteristics of ionospheric changes above the station, is of great significance for monitoring special events of regional ionosphere, and can also estimate satellite and receiver DCB as parameters to describe the characteristics of hardware delay.

[0003] In ionospheric modeling, the hardware delay problem of pseudorange observations is often ignored, resulting in large errors in ionospheric delay estimation. In addition, many traditional methods rely on externally provided differential code bias (DCB) products, lack the ability to estimate themselves, and different sources of DCB products may differ, affecting the accuracy. At the same time, the influence of observation noise and elevation angle on ionospheric delay has not been fully considered, limiting the accuracy and universality of the modeling results. SUMMARY

[0004] Therefore, the present application aims to provide an ionospheric model parameter and differential code bias estimation method and device to solve at least one of the above problems.

[0005] To achieve the above-mentioned purpose, the technical scheme of the present application is as follows:

[0006] In a first aspect, the present application provides an ionospheric model parameter and differential code bias estimation method, comprising:

[0007] reading the observation station single-day observation file and the broadcast ephemeris or precise ephemeris file, positioning the receiver's position by single-point positioning, and eliminating abnormal observation values;

[0008] performing epoch discontinuity judgment according to the pseudorange observation value and the carrier phase observation value, and performing cycle slip detection through wide-lane ambiguity combination and ionospheric residual combination to mark complete arc segments;

[0009] performing carrier phase smoothing pseudorange on the continuous observation values in the complete arc segment, and obtaining slant path ionospheric total electron content including differential code bias through the smoothed dual-frequency pseudorange.

[0010] projecting the slant-path ionospheric total electron content to vertical-path ionospheric total electron content by a projection function, and fitting the regional ionosphere by an ionospheric polynomial model to obtain an estimated value of the vertical-path ionospheric total electron content;

[0011] establishing an error equation according to the projected vertical-path ionospheric total electron content and the estimated value of the vertical-path ionospheric total electron content obtained by the ionospheric polynomial model to obtain the initial ionospheric model parameter and the differential code bias;

[0012] adjusting the observation weight by a robust random model constructed based on a robust estimation algorithm to obtain the ionospheric model parameter estimation value and the differential code bias estimation value after robust estimation.

[0013] In a second aspect, based on the same inventive concept, the application further provides an ionospheric model parameter and differential code bias estimation device, comprising:

[0014] a file reading module configured to determine the position of the receiver by single point positioning according to the read observation station single-day observation file and the broadcast ephemeris or precise ephemeris file, and eliminate abnormal observation values;

[0015] a cycle slip detection module configured to perform epoch discontinuity judgment according to the pseudo-range observation value and the carrier phase observation value, and perform cycle slip detection by wide-lane ambiguity combination and ionospheric residual combination to mark a complete arc segment;

[0016] a pseudo-range smoothing module configured to smooth the pseudo-range by carrier phase smoothing on the continuous observation values in the complete arc segment, and obtain the slant-path ionospheric total electron content including the differential code bias by the smoothed pseudo-range;

[0017] a polynomial fitting module configured to project the slant-path ionospheric total electron content to vertical-path ionospheric total electron content by a projection function, and fit the regional ionosphere by an ionospheric polynomial model to obtain an estimated value of the vertical-path ionospheric total electron content;

[0018] an error establishing module configured to establish an error equation according to the projected vertical-path ionospheric total electron content and the estimated value of the vertical-path ionospheric total electron content obtained by the ionospheric polynomial model to obtain the initial ionospheric model parameter and the differential code bias;

[0019] a robust estimation module configured to adjust the observation weight by a robust random model constructed based on a robust estimation algorithm to obtain the ionospheric model parameter estimation value and the differential code bias estimation value after robust estimation.

[0020] In a third aspect, based on the same inventive concept, the present application also provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and capable of running on the processor, wherein the processor implements the method according to the first aspect when executing the program.

[0021] In a fourth aspect, based on the same inventive concept, the present 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 execute the method according to the first aspect.

[0022] Compared with the prior art, the ionospheric model parameter and differential code bias estimation method and device have the following beneficial effects:

[0023] The ionospheric model parameter and differential code bias estimation method effectively solves the influence of hardware delay in the pseudo-range observation value on the ionospheric estimation, improves the accuracy and robustness of the ionospheric modeling through the carrier phase smoothing pseudo-range method and the regional ionospheric polynomial model, and further improves the detection and control of the observation data quality through the introduction of the robust estimation algorithm and the residual inspection, reduces the interference of outliers on the result, and ensures the stability and accuracy of the ionospheric estimation. BRIEF DESCRIPTION OF DRAWINGS

[0024] The accompanying drawings, which form a part of this application, are included to provide a further understanding of the application and are incorporated in and constitute a part of this application. The embodiments of the present application illustrated in the drawings and their descriptions are used to explain the present application and are not intended to limit the present application. In the drawings:

[0025] Figure 1 A flow chart of the ionospheric model parameter and differential code bias estimation method according to the embodiments of the present application;

[0026] Figure 2 A schematic diagram of the ionospheric piercing point according to the embodiments of the present application;

[0027] Figure 3 A structural schematic diagram of the ionospheric model parameter and differential code bias estimation device according to the embodiments of the present application;

[0028] Figure 4 A hardware structural schematic diagram of the electronic device according to the embodiments of the present application. DETAILED DESCRIPTION

[0029] In order to make the purpose, technical scheme and advantages of the present application clearer, the present application is further described in detail below with reference to the embodiments and the accompanying drawings.

[0030] It should be noted that the technical terms or scientific terms used in the embodiments of the present application should be understood as the general meaning understood by the person skilled in the art in the field to which the present application belongs, unless otherwise defined. The terms "first", "second", and similar terms used in the embodiments of the present application do not represent any order, number or importance, but are only used to distinguish different components. The terms "include" or "contain" and similar terms mean that the elements or objects before the terms cover the elements or objects listed after the terms and their equivalents, and do not exclude other elements or objects. The terms "connect" or "connected" and similar terms are not limited to physical or mechanical connections, but can include electrical connections, whether direct or indirect. The terms "upper", "lower", "left", "right" and the like are only used to represent relative positional relationships, and when the absolute position of the described object changes, the relative positional relationship may also change accordingly.

[0031] The embodiments of the present application will be described in detail below with reference to the accompanying drawings.

[0032] Referring to Figure 1 The embodiments provide an ionospheric model parameter and differential code bias estimation method, and specifically include the following steps:

[0033] Step S101, according to the read observation station single-day observation file (including the pseudo-range and carrier phase data received by the receiver) and the broadcast ephemeris or precise ephemeris file (including the position and clock bias of the satellite), the position of the receiver is positioned by single point positioning, and the abnormal observation value is eliminated.

[0034] Specifically, in the embodiments, the observation file of the observation station for a single day and the broadcast ephemeris or precise ephemeris file are read for single point positioning, an equation is established according to the pseudo-range observation value in the read observation file, the position of each satellite at the observation time is calculated by reading the ephemeris file, an equation based on the pseudo-range and the satellite position is established, and the least square method is used to fit the observation values of multiple satellites to obtain the optimal solution, so as to position the position of the receiver by single point positioning, and eliminate the satellites with too small elevation angles and abnormal observation data.

[0035] It should be noted that the calculation method involved in this step is common knowledge in the art, and this step is not the core of the present application, so it will not be described further.

[0036] Step S102, epoch discontinuity judgment is performed according to the pseudo-range observation value and the carrier phase observation value, and cycle slip detection is performed through wide-lane ambiguity combination and ionospheric residual combination to mark the complete arc segment.

[0037] Specifically, in the embodiment, the step is to mark the complete arc segment according to the observation value continuity and cycle slip detection. Specifically, by performing epoch discontinuity judgment on the pseudo-range and carrier phase observation values, the cycle slip is detected by using MW combination (wide lane ambiguity combination, which is calculated by the carrier phase difference of two frequency signals) and GF combination (ionospheric residual combination, which suppresses ionospheric delay by the difference of two frequency carrier phases and detects cycle slip by the change of ionospheric residual), so as to obtain a continuous arc segment of continuously observing a satellite and without cycle slip; wherein, the MW combination is equivalent to the wide lane ambiguity, which is generally a constant value in the case of no cycle slip; the GF combination is also called ionospheric residual method, which can be used for cycle slip detection because the ionospheric delay changes little in a short time; therefore, the difference between the MW combination and the GF combination of two continuous epochs can be used for judgment, and the specific formula is as follows:

[0038] ;

[0039] ;

[0040] ;

[0041] ;

[0042] In the formula, , denote the frequencies of the two frequency signals; , denote the pseudo-range observation values of the corresponding frequency signals; , denote the wavelengths of the corresponding frequency signals; , denote the carrier phase observation values of the corresponding frequency signals; , denote the integer ambiguity of the corresponding frequency signals; , denote the ionospheric delay of the dual-frequency pseudo-range; denote the threshold value of the MW combination; denote the threshold value of the GF combination.

[0043] In step S103, the carrier phase smoothed pseudo-range is obtained by performing carrier phase smoothing on the continuous observation values in the complete arc segment, and the slant path ionospheric total electron content including differential code bias is obtained by the smoothed pseudo-range.

[0044] Specifically, in the embodiment, carrier phase smoothing pseudo-range is performed on the continuous observation values in the complete arc segment, and the slant path ionospheric total electron content (STEC, i.e. ionospheric slant delay) is calculated. Specifically, because the carrier phase observation value is high in accuracy, but contains unknown ambiguity parameters, while the pseudo-range observation value is an absolute observation of the satellite-ground distance, but is poor in accuracy; therefore, the pseudo-range and carrier phase observation values of multiple epochs can be combined to calculate the smoothed combined ambiguity , so as to obtain the smoothed pseudo-range , the STEC is obtained after the smoothed pseudo-range is corrected for DCB (differential code bias) and multiplied by the frequency-dependent constant.

[0045] ;

[0046] ;

[0047] ;

[0048] In the formula, N is the number of epochs of the smoothed combined ambiguity; n is the number of smoothed epochs; ρn is the smoothed pseudo-range combined observation value; and DCBn and DCBn are the satellite and receiver DCBs respectively.

[0049] In step S104, the slant path ionospheric total electron content is projected to the vertical path ionospheric total electron content by a projection function, and the regional ionosphere is fitted by an ionospheric polynomial model to obtain an estimated value of the vertical path ionospheric total electron content.

[0050] Specifically, in the embodiment, the slant path ionospheric total electron content is projected to the vertical path ionospheric total electron content according to a projection function. Based on the ionospheric single-layer model assumption, it is considered that all the electrons of the ionosphere are concentrated in a thin layer at a certain height above the ground, the longitude and latitude of the piercing point (the intersection of the signal path and the thin layer, Figure 2 is a schematic diagram of the ionospheric piercing point) and the ionospheric projection function MF are calculated, and the slant path ionospheric total electron content (STEC) is projected to the vertical path ionospheric total electron content (VTEC, i.e. ionospheric vertical delay). The specific formula is as follows:

[0051] ;

[0052] ;

[0053] In the formula, MF is the projection function of VTEC to STEC; R is the radius of the earth; and h is the height of the ionosphere. ​​​​​​denotes the height of the receiver; denotes the zenith distance of the satellite at the observation station receiver at the time of observation; denotes the height of the ionospheric layer, usually 300-450 km.

[0054] Generally, VTEC uses a spherical harmonic function model to model the global ionosphere, and for a single station regional ionosphere, a polynomial model is used to model and fit the situation better. The formula of the vertical ionosphere total electron content is as follows:

[0055] ;

[0056] ;

[0057] In the formula, VTEC denotes the vertical ionosphere total electron content at the piercing point without satellite and receiver DCB; denotes the coefficient of the polynomial function model; denotes the latitude and longitude of the piercing point; denotes the latitude and longitude of the center of the region, for a single station, it is the latitude and longitude of the base station; denotes the correction of the hour angle of the sun; denote the observation time and the center time of the observation period, respectively, in seconds. If 2h is selected for modeling once, the center time of the region is the time at 1h.

[0058] Step S105, according to the vertical path ionosphere total electron content obtained by projection, and the vertical path ionosphere total electron content estimate value obtained by the ionosphere polynomial model, an error equation is established to obtain the initial ionosphere model parameter and the differential code bias.

[0059] Specifically, in the embodiment, the step is to construct the VTEC by simultaneously projecting the STEC and the VTEC of the polynomial model, and to establish an error equation, wherein the to-be-solved parameters are the ionosphere model parameters and the satellite differential code bias and the receiver differential code bias .

[0060] The satellite and receiver hardware delay bias can be considered as unchanged within a day, so only one set of DCB needs to be estimated for a single day; because the satellite DCB and the receiver DCB cannot be separated, the equation is linearly related, resulting in a rank deficiency of the normal equation coefficient matrix, so the sum of the DCB of each system satellite is considered to be 0 (zero mean constraint), so as to separate the satellite and receiver DCB; if there are multiple systems, multiple constraint equations should be established.

[0061] ;

[0062] Let , 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 weight is the diagonal element of the matrix P), the IGGIII-based robust estimation is performed according to the standard deviation of the posterior residual (it should be noted that adjustment refers to a process of optimizing the estimation result by using a mathematical statistical method (such as the least square method) according to the existing observation data, in which process, the residual represents the difference between the observation value and the fitting value (estimated value); the posterior residual refers to the difference between the observation data and the fitting value (that is, the estimated value) after the adjustment calculation, which reflects the measurement accuracy; the standard deviation is a statistical quantity for measuring the fluctuation of the residual, which represents the average deviation of the residual; a smaller standard deviation means smaller residual and higher consistency between the observation data and the estimated value), the observation value is divided into a normal domain, a weight-reduced domain and a rejected domain, the initial weight is adjusted to different degrees until the residual of the robust estimation satisfies the threshold, and then the iteration is exited, and the least square solution after the iteration is exited is taken as the ionospheric model parameter estimation and the differential code bias estimation.

[0071] ;

[0072] In the formula, the weight of the observation value is denoted as w i, the residual of the observation value is denoted as r i, the standard deviation of the residual is denoted as s, the threshold is denoted as t, and the standardization residual is denoted as z i. The weight is called the equivalent weight. The absolute value of the standardization residual is equal to the ratio of the residual to the standard deviation of the residual. Generally, the threshold t is 1-1.5. The threshold t is 2.5-6.

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

[0074] .

[0075] Therefore, the initial estimation of the polynomial coefficient and the DCB has been obtained by the least square method, but the estimation is easily affected by abnormal observation or noise, the robust estimation algorithm is adopted in this embodiment, and the weight of the observation value is dynamically adjusted to reduce the interference of abnormal data on the solution, thereby improving the robustness and accuracy of the estimation. Finally, the polynomial coefficient and the differential code bias estimation obtained after the adjustment of the robust estimation are more reliable and suitable for the case that noise exists in the real observation.

[0076] The ionospheric model parameter and differential code bias estimation method described in this embodiment effectively solves the influence of the hardware delay in the pseudo-range observation value on the ionospheric estimation by jointly estimating the ionospheric model parameter and the differential code bias, improves the precision and robustness of the ionospheric modeling by the carrier phase smoothing pseudo-range method and the regional ionospheric polynomial model, and further improves the detection and control of the observation data quality by introducing the robust estimation algorithm and the residual test, reduces the interference of abnormal values on the result, and ensures the stability and accuracy of the ionospheric estimation.

[0077] It is to be understood that the foregoing description is directed to embodiments of the application. Various embodiments fall within the scope of the following claims. In some cases, the actions or steps recited in the claims can be performed in a different order and still accomplish the desired result. In addition, the processes depicted in the figures do not necessarily require the particular order shown, or sequential order to achieve the desired results. In certain implementations, multitasking and parallel processing can be advantageous.

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

[0079] As shown in Figure 3 The ionospheric model parameter and differential code bias estimation device comprises:

[0080] The file reading module 11 is configured to determine the position of the receiver by single point positioning according to the read observation station single-day observation file and the broadcast ephemeris or precise ephemeris file, and eliminate abnormal observation values;

[0081] The cycle slip detection module 12 is configured to perform epoch discontinuity judgment according to the pseudo-range observation value and the carrier phase observation value, and perform cycle slip detection through wide-lane ambiguity combination and ionospheric residual combination to mark a complete arc segment;

[0082] The pseudo-range smoothing module 13 is configured to smooth the pseudo-range through carrier phase smoothing of continuous observation values in the complete arc segment, and obtain the slant path ionospheric total electron content including the differential code bias through the smoothed pseudo-range;

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

[0084] The error establishing module 15 is configured to establish an error equation according to the projected vertical path ionospheric total electron content and the estimated value of the vertical path ionospheric total electron content obtained through the ionospheric polynomial model to obtain the initial ionospheric model parameter and the differential code bias;

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

[0086] For ease of description, the above apparatus is described in various modules in terms of functions respectively. Of course, in the implementation of the embodiments of the present 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 of any of the above embodiments, and has the beneficial effects of the corresponding method embodiments, which are not repeated here.

[0088] Based on the same inventive concept, corresponding to the method of any of the above embodiments, the embodiments of the present application also provide an electronic device, comprising a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the processor implements the method according to any of the above embodiments when executing the program.

[0089] Figure 4 A more specific hardware structure schematic diagram of an electronic device provided by the present embodiment is shown, which can include a processor 1010, a memory 1020, an input / output interface 1030, a communication interface 1040 and a bus 1050. The processor 1010, the memory 1020, the input / output interface 1030 and the communication interface 1040 are connected to each other in the device through the bus 1050.

[0090] The processor 1010 can be implemented in the form of a general-purpose CPU (Central Processing Unit), a microprocessor, an application-specific integrated circuit (ASIC), or one or more integrated circuits, etc., for executing related programs to implement the technical solutions provided by the present embodiment.

[0091] The memory 1020 can be implemented in the form of a ROM (Read Only Memory), a RAM (Random Access Memory), a static storage device, a dynamic storage device, etc. The memory 1020 can store an operating system and other application programs, and when the technical solutions provided by the present embodiment are implemented by software or firmware, the related program codes are saved in the memory 1020 and executed by the processor 1010.

[0092] The input / output interface 1030 is configured to connect an input / output module to realize information input and output. The input / output module can be configured in the device as a component (not shown in the figure) or externally connected to the device to provide corresponding functions. The input device can include a keyboard, a mouse, a touch screen, a microphone, various sensors, etc., and the output device can include a display, a speaker, a vibrator, an indicator light, etc.

[0093] The communication interface 1040 is configured to connect a communication module (not shown in the figure) to realize communication interaction between the device and other devices. The communication module can realize communication through a wired manner (such as a USB, a network cable, etc.) or through a wireless manner (such as a mobile network, WIFI, Bluetooth, etc.).

[0094] The bus 1050 includes a channel to transmit information between various components (such as the processor 1010, the memory 1020, the input / output interface 1030, and the communication interface 1040) of the device.

[0095] It should be noted that although the above device only shows the processor 1010, the memory 1020, the input / output interface 1030, the communication interface 1040, and the bus 1050, in the specific implementation process, the device can also include other components necessary for normal operation. In addition, those skilled in the art can understand that the above device can also only include components necessary for implementing the embodiments of the present specification, and does not have to include all the components shown in the figure.

[0096] The electronic device of the above embodiments is used to realize the corresponding method in any of the above embodiments, and has the beneficial effects of the corresponding method embodiments, which are not described here again.

[0097] Based on the same inventive concept, corresponding to the method of any of the above embodiments, the present application also provides a non-transitory computer readable storage medium storing computer instructions for causing the computer to execute the method of any of the above embodiments.

[0098] The computer readable media of the present embodiments includes permanent and non-permanent, removable and non-removable media, which can be implemented by any method or technology to store information. The 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 technology, compact disc read-only memory (CD-ROM), digital versatile disc (DVD) or other optical storage, magnetic cassette, magnetic tape disk storage or other magnetic storage device, or any other non-transmission medium that can be used to store information accessible by a computing device.

[0099] The storage medium of the above embodiments stores computer instructions for causing the computer to execute the method as described in any of the above embodiments, and has the beneficial effects of the corresponding method embodiments, which are not described here again.

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

[0101] In addition, in order to simplify the description and discussion, and so as not to make the embodiments of the present application difficult to understand, the well-known power / ground connections of integrated circuit (IC) chips and other components can or can not be shown in the provided drawings. In addition, the devices can be shown in the form of block diagrams in order to avoid making the embodiments of the present application difficult to understand, and this also takes into account the fact that the details of the implementation of these block diagram devices are highly dependent on the platform to be implemented the embodiments of the present application (i.e. these details should be fully within the understanding of those skilled in the art). Where specific details (e.g. circuits) are set forth in order to describe the exemplary embodiments of the present application, it will be apparent to those skilled in the art that the embodiments of the present application can be implemented without these specific details or with variations on these specific details. Therefore, these descriptions should be considered illustrative rather than limiting.

[0102] While the present application has been described in connection with certain embodiments thereof, many modifications, substitutions, changes, and of forms will be apparent to those of ordinary skill in the art from the foregoing description. For example, other memory architectures (e.g., dynamic RAM (DRAM)) can use the embodiments discussed.

[0103] Embodiments of the present application are intended to cover all such alterations, modifications, and variations as they can come within the scope of the appended claims. Accordingly, although specific embodiments have been furthered in connection with the present application, any omission, substitution, or change, in principle and in form, made to the present application should be included in the scope of the present 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 can obtain the estimated parameters of the ionospheric model and the estimated differential code bias after robust estimation.

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: By establishing a stochastic model of the observed values ​​and obtaining the variance of the total electron content along the vertical path using the error propagation law, the variance formula is as follows: ； 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. The method according to claim 5, characterized in that: Initial weights are established based on the variance of the total electron content of the vertical path. Robust estimation is performed based on the standard deviation of the post-hoc residuals. The observations are divided into normal, reduced-weight, and rejection domains. 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. 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 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.

8. 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 1-6.

9. 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 1-6.

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