Ionized layer integrity parameter calculation method of Big Dipper-based enhancement system

Abstract

The invention discloses an ionosphere integrity parameter calculation method of a Big Dipper-based enhancement system. The method comprises the following steps: acquiring an ionosphere puncture point observation value in a fitting domain around an ionosphere grid point; based on this, an ionosphere integrity parameter is calculated according to a spatial undersampling risk error, a model inconformity risk error, and a quantization error. The method comprises the following steps: based on an ionosphere anomaly detector, analyzing ionosphere environment characteristics of a BDSBAS service area and constructing a historical observation data set; on one hand, the defects of wide data range and insufficient pertinence existing in searching data based on space weather indexes (such as a Kp index, a Dst index and the like) in the prior art are effectively overcome, and on the other hand, corresponding integrity parameter values can be constructed according to matching of specific geographic areas and ionosphere anomaly types, so that the accuracy of data searching is improved. On the premise of ensuring that the system service integrity meets the preset minimum operation performance standard, the system service availability is remarkably improved, and then user positioning is optimized to enhance the experience.

Description

Technical Field

[0001] This application relates to the field of BeiDou satellite navigation technology, and more specifically, to a method for calculating ionospheric integrity parameters of a BeiDou satellite-based augmentation system. Background Technology

[0002] The BeiDou Satellite-Based Augmentation System (BDSBAS) is an important component of the BeiDou Navigation Satellite System. Its working principle is as follows: The data center calculates various correction information such as ephemeris error, satellite clock error, and ionospheric delay based on satellite signal data collected by monitoring stations. Using geostationary satellites as the communication medium, this augmentation information is broadcast to users within the service area in an enhanced message format to improve the system service performance of the basic navigation system in terms of accuracy, integrity, service continuity, and availability.

[0003] As the biggest risk factor affecting the integrity of BDSBAS, the complex and variable ionospheric structure, especially its unpredictable anomalies, can cause ionospheric delay values ​​to change rapidly over relatively short distances, leading to a decrease in ionospheric spatial correlation. From the perspective of system service performance, to effectively address the impact of anomalies in the ionosphere, the common approach is to increase the reasonable safety margin of integrity parameters. However, current BDSBAS ionospheric integrity parameter safety margins are mainly designed based on mid-latitude regions or the entire service area. In reality, the overall characteristics of the BDSBAS service area exhibit a coexistence of mid- and low-latitude ionospheric activity. In practical applications, integrity parameters present two types of problems: First, when designed based on the entire service area, the ionospheric integrity parameter margin for mid-latitude regions is too conservative, affecting service availability performance in actual applications. Second, when designed based on mid-latitude regions, the ionospheric integrity parameter margin for low-latitude regions cannot effectively cover the user's ionospheric delay estimation residuals, leading to integrity risk events. Summary of the Invention

[0004] In view of the above problems, the purpose of this invention is to provide a method for calculating the ionospheric integrity parameter of the BeiDou satellite-based augmentation system, so as to solve the problem that the existing technology has problems such as the ionospheric integrity parameter value cannot effectively encompass the user's ionospheric delay estimation residual, or the envelope is too conservative, which affects the performance of BDSBAS service.

[0005] This invention provides a method for calculating ionospheric integrity parameters of the BeiDou satellite-based augmentation system, including: Obtain observations of N ionospheric puncture points within the fitted domain surrounding the ionospheric grid points; Calculate the spatial undersampling risk error term, the model mismatch risk error term, and the quantization error term based on the observation values ​​of N ionospheric puncture points within the fitted domain surrounding the ionospheric grid points. The integrity parameter value is calculated based on the spatial undersampling risk error term, the model mismatch risk error term, and the quantization error term; The ionospheric puncture point distribution index is calculated based on the distribution of puncture points within the fitted radius around the ionospheric grid points. Using the fitting radius around the ionospheric grid points and the ionospheric puncture point distribution index as risk model index indicators, the BeiDou satellite-based augmentation system regional undersampling risk error index table is queried to obtain the space undersampling risk error item. The process of constructing the regional undersampling risk error index table of the BeiDou satellite-based augmentation system includes: Based on the physical characteristics of the ionosphere itself, the service specifications and technical characteristics of the BeiDou satellite-based augmentation system, at least two types of ionospheric anomaly detectors were selected to detect the ionospheric state in historical years, and a database of ionospheric puncture point observations on anomaly dates was established, with several sub-databases configured according to the anomaly type. Based on the aforementioned sub-databases, statistical analysis was conducted on the statistical characteristics of different ionospheric anomaly types within the service area of ​​the BeiDou satellite-based augmentation system; the statistical characteristics of ionospheric anomalies include: anomaly diurnal variation characteristics and seasonal variation characteristics; Based on the ionospheric anomaly distribution characteristics of the BeiDou satellite-based augmentation system service area, in a given service area where a risk model needs to be established, the residuals between the ionospheric delay estimate and the virtual user ionospheric delay measurement are statistically analyzed using a data stripping scheme based on each of the sub-databases. The risk model index indicators are tabulated and stored separately.

[0006] As a preferred technical solution for calculating the ionospheric integrity parameters of the BeiDou satellite-based augmentation system, the method involves using the fitted radius around the ionospheric grid points. The distribution of puncture points within the ionosphere is used to calculate the ionospheric puncture point distribution index (RCM). The calculation formula is as follows: ;in: The distance between the centroid of the ionospheric puncture point and the ionospheric grid point used in the fitting process. The fitted radius around the ionospheric grid points is denoted as .

[0007] As a preferred technical solution for calculating the ionospheric integrity parameters of the BeiDou satellite-based augmentation system, the method of using a data stripping scheme to statistically analyze the residual between the estimated ionospheric delay and the virtual user's measured ionospheric delay includes: Based on the database of ionospheric puncture point observations on the abnormal dates, the N ionospheric puncture point observations observed in one epoch are divided into two groups: a measurement set and a virtual user set for fitting. Based on the first set of measurements, a strategy of deleting some puncture point measurements is adopted to construct undersampling conditions. According to the undersampling conditions, the ionospheric delay estimate of each user in the virtual user set is estimated using a plane fitting algorithm, and the residual between the ionospheric delay estimate and the virtual user ionospheric delay measurement is calculated.

[0008] As a preferred technical solution for calculating the ionospheric integrity parameters of the BeiDou satellite-based augmentation system, the space undersampling risk error term... The calculation formula is: ; in: The maximum residual between the estimated ionospheric delay and the virtual user's measured ionospheric delay over a certain time interval T. Represents a certain time interval T, For virtual user ionospheric delay measurement, For the ionospheric delay estimate, For the delay estimation error of the nth user's measurement, This represents the upper boundary of the Gaussian distribution.

[0009] As a preferred technical solution for calculating the ionospheric integrity parameters of the BeiDou satellite-based augmentation system, the model does not meet the risk error term. The calculation method is as follows: ; in: It is a function of n, and the calculation formula is: , The matrix consists of N plane fitting coefficients to be solved, and the calculation formula is: , Let N×N be the spatial covariance matrix of puncture point measurements. The inverse of , where h is the distance between the measurements of the puncture points. Let be the spatial covariance vector between the puncture point and the grid points. Let be the spatial covariance between two observations that are 0 units apart. An N×3 observation matrix consisting of the puncture point locations. The location vector of the ionospheric grid points. For the N ionospheric puncture point observations within the fitted domain surrounding the ionospheric grid points; The The calculation formula is: ; Where Φ is the integral variable.

[0010] As a preferred technical solution for calculating the ionospheric integrity parameters of the BeiDou satellite-based augmentation system, the quantization error term L is calculated based on the minimum resolution r of the grid ionospheric delay value quantization. The formula for calculating the quantization error term L is as follows: .

[0011] As a preferred technical solution for calculating the ionospheric integrity parameters of the BeiDou satellite-based augmentation system, the method based on the space undersampling risk error term... The model does not meet the risk error term. The quantization error term L is used to calculate the integrity parameter value. ,in: .

[0012] Compared with existing technologies, the beneficial effects of this invention are as follows: Based on an ionospheric anomaly detector, this invention analyzes the ionospheric environmental characteristics of the BDSBAS service area and constructs a historical observation dataset. On the one hand, it effectively overcomes the shortcomings of traditional technologies that rely on space weather indices (such as Kp index, Dst index, etc.) to find data, which have a broad data range and insufficient specificity. On the other hand, it can construct corresponding integrity parameter values ​​for specific geographical areas and ionospheric anomaly types. Under the premise of ensuring that the integrity of the system service meets the preset minimum operating performance standards, it significantly improves the availability of the system service, thereby optimizing user positioning and enhancing the user experience. Attached Figure Description

[0013] Figure 1 The flowchart illustrates the steps of a method for calculating ionospheric integrity parameters of a BeiDou satellite-based augmentation system provided by the present invention. Detailed Implementation

[0014] To better understand the above-mentioned objectives, features, and advantages of the present invention, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments. It should be noted that, unless otherwise specified, the embodiments and features described in these embodiments can be combined with each other.

[0015] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and therefore the scope of protection of the invention is not limited to the specific embodiments disclosed below.

[0016] like Figure 1 As shown, this invention discloses a method for calculating the ionospheric integrity parameters of the BeiDou satellite-based augmentation system, including: Step S1: Obtain the observation values ​​of N ionospheric puncture points within the fitted domain surrounding the ionospheric grid points; Step S2: Calculate the spatial undersampling risk error term, model mismatch risk error term, and quantization error term based on the observation values ​​of N ionospheric puncture points within the fitting domain surrounding the ionospheric grid points. Step S3: Calculate the integrity parameter value based on the spatial undersampling risk error term, the model mismatch risk error term, and the quantization error term.

[0017] Furthermore, the spatial undersampling risk error term is calculated based on the observations of N ionospheric puncture points within the fitted domain surrounding the ionospheric grid points. ,include: Based on the fitted radius around the ionospheric grid points The distribution of puncture points within the ionosphere is used to calculate the ionospheric puncture point distribution index (RCM). The calculation formula is as follows: ;in: The distance between the centroid of the ionospheric puncture point and the ionospheric grid point used in the fitting process. The fitting radius is the area around the ionospheric grid points; the ionospheric puncture point distribution index (RCM) is used to describe the uniformity of the IPP distribution within the fitting domain.

[0018] In practice, it is necessary to estimate the delay value of the ionospheric grid points. First, it is necessary to obtain the observation values ​​of N ionospheric puncture points within the fitting domain around the ionospheric grid point to be ionized, where N has a minimum value of 10; the fitting domain is the fitting radius around the ionospheric grid point. The size of the fitting radius around the ionospheric grid points is related to the number of ionospheric puncture points involved in the fitting, and its value ranges from 800 to 2100 kilometers. By continuously increasing the fitting radius around the ionospheric grid points to increase the number of fitting points, the computability of the delay values ​​of the ionospheric grid points in the edge region can be effectively increased.

[0019] Furthermore, based on the fitted radius around the ionospheric grid points... Using the ionospheric puncture point distribution index (RCM) as the index indicator for the risk model, the spatial undersampling risk error item was obtained by querying the regional undersampling risk error index table of the BeiDou satellite-based augmentation system. .

[0020] Furthermore, the construction process of the BeiDou satellite-based augmentation system regional undersampling risk error index table includes: Based on the physical characteristics of the ionosphere itself, the service specifications and technical characteristics of the BeiDou satellite-based augmentation system, at least two types of ionospheric anomaly detectors were selected to detect the ionospheric state in historical years, and a database of ionospheric puncture point observations on anomaly dates was established, with several sub-databases configured according to the anomaly type. Based on each sub-database, statistical analysis was conducted on the statistical characteristics of different ionospheric anomaly types in the service area of ​​the BeiDou satellite-based augmentation system. The statistical characteristics of ionospheric anomalies include: the diurnal variation characteristics and seasonal variation characteristics of the anomalies. Based on the ionospheric anomaly distribution characteristics of the BeiDou satellite-based augmentation system service area, in a given service area where a risk model needs to be established, the residuals between the ionospheric delay estimate and the virtual user ionospheric delay measurement are statistically analyzed using a data stripping scheme based on each sub-database. According to the risk model index indicators ( The tables for RCM and RCM are stored separately.

[0021] In detail, according to the risk model index indicators ( The tables for RCM and RCM are stored separately. The value ranges from 800 to 2100 kilometers, and the value range of RCM is from 0 to 1.

[0022] In implementation, ionospheric anomaly detectors include, but are not limited to, selecting at least two of the following: spatial gradient detectors, temporal gradient detectors, or chi-square irregularity detectors, and setting the spatial gradient SG. x1,x2 and time gradient TG Δt The calculation formulas are as follows: ; ; in: as well as These represent the vertical delay values ​​of x1 and x2 at different ionospheric puncture point locations. This represents the distance between different ionospheric puncture points x1 and x2. as well as These represent the vertical delay values ​​at different epochs of ionospheric puncture points. This represents the epoch interval, with a value of 30 seconds.

[0023] The formula for calculating the chi-square irregularity detector is set as follows: ; in, Chi-square irregularity; This is the chi-square statistic; for The threshold for the statistic is calculated based on the degrees of freedom of the observed data (the total number of observed data around the grid point minus 3) and the false alarm rate (10). -3 To determine; Specifically, The calculation formula is: ; in, This represents the vertical ionospheric delay value of the observed values ​​at N ionospheric puncture points within the fitting domain surrounding the ionospheric grid points. Transpose it; An N×3 observation matrix consisting of the locations of ionospheric puncture points. It is the inverse of the spatial covariance matrix among N×N observations.

[0024] Specifically, the ionospheric anomalies affecting BDSBAS are mainly located in low-latitude regions and can be broadly categorized into two types: enhanced electron density anomalies and small-scale irregular variations. Their diurnal variation characteristics are mainly reflected in the different times of day they occur, and they exhibit significant seasonal variations, occurring most frequently in spring and autumn, followed by winter, with the lowest probability of occurrence in summer. Based on the distribution characteristics of ionospheric anomalies in the BDSBAS system service area, a data stripping scheme was adopted for each sub-database to cover the worst-case undersampling conditions, and the residuals between the estimated ionospheric delay and the virtual user ionospheric delay measurements were statistically analyzed. Furthermore, when detecting certain possible anomalous features of the ionosphere, as well as ionospheric grid points in edge regions or near-edge regions, due to the limitations in the number and distribution of ionospheric puncture points, there may be cases where the ionosphere has not been effectively sampled by any ground reference station. As a result, the ionospheric grid point estimation accuracy is poor and there is a risk of spatial and / or temporal undersampling. By constructing an ionospheric undersampling risk model, a safety boundary value can be increased, thereby mitigating the possibility that the user-end ionospheric correction residual cannot be well enveloped due to poor ionospheric sampling, thus meeting the BDSBAS system performance service standards.

[0025] Furthermore, the residuals between the estimated ionospheric delay and the virtual user ionospheric delay measurement are statistically analyzed using a data stripping scheme, including: Based on the database of ionospheric puncture point observations on abnormal dates, the N ionospheric puncture point observations observed in one epoch are divided into two groups: a measurement set and a virtual user set for fitting. Based on the first set of measurements, a strategy of deleting some puncture point measurements is adopted to construct undersampling conditions. According to the undersampling conditions, the ionospheric delay estimate of each user in the virtual user set is estimated using a plane fitting algorithm, and the residual between the ionospheric delay estimate and the virtual user ionospheric delay measurement is calculated.

[0026] In implementation, the data stripping scheme involves dividing the N ionospheric puncture point observations observed in one epoch into two groups based on each sub-database: a measurement set for fitting and a virtual user set. Based on the first group of measurements, a strategy of deleting some puncture point measurements is employed. The specific number of measurements deleted is set by those skilled in the art, using methods such as random deletion. An undersampling condition is artificially constructed. Based on this condition, the ionospheric delay correction for each user in the virtual user set is estimated using a plane fitting algorithm. The residual between the estimated value and the virtual user's ionospheric delay measurement is calculated, thus covering the worst-case risk of ionospheric anomalies existing but not effectively monitored due to undersampling. This risk, also known as undersampling risk, refers to the failure of the ionospheric puncture points participating in the fitting to effectively observe the ionospheric anomaly region, even though the user happens to be located in that region, leading to a significant ionospheric positioning error. Therefore, constructing an ionospheric undersampling risk model can increase the safety boundary value, thereby mitigating the possibility that the user-end ionospheric correction residual cannot be well enveloped due to poor ionospheric sampling, thus meeting the BDSBAS system performance service standards.

[0027] Spatial undersampling risk error term The calculation formula is: ; in: The maximum residual between the estimated ionospheric delay and the virtual user's measured ionospheric delay over a certain time interval T. Represents a certain time interval T, For virtual user ionospheric delay measurement, For the ionospheric delay estimate, For the delay estimation error of the nth user's measurement, This represents the upper boundary of the Gaussian distribution.

[0028] Model mismatch risk error term The calculation method is as follows: ; in, It is a function of n, and the calculation formula is: ;in: For integration variables; Furthermore, The matrix consists of N plane fitting coefficients to be solved, and the calculation formula is: , Let N×N be the spatial covariance matrix of puncture point measurements. The inverse of, h is the distance between the puncture point measurements, The spatial covariance vector between the puncture point and the grid points, This represents the spatial covariance between two observations that are 0 apart. An N×3 observation matrix consisting of the puncture point locations. The location vector of the ionospheric grid points. These are the observations of N ionospheric puncture points within the fitted domain surrounding the ionospheric grid points.

[0029] Furthermore, the ionospheric delay value of grid points in BDSBAS is estimated using a plane fitting technique by utilizing the spatiotemporal correlation of the ionosphere. When an anomaly occurs in the ionosphere, its spatiotemporal correlation will be significantly reduced, causing the preset ionospheric grid point delay estimation model to fail to accurately represent the actual ionospheric delay state of the grid points. This leads to a deviation between the model output and the true state of the ionosphere, i.e., the generation of a model mismatch risk error term. This invention, by calculating the model mismatch risk error term, can construct corresponding integrity parameter values ​​for specific geographical areas and ionospheric anomaly types. Under the premise of ensuring that the integrity of system services meets the preset minimum operating performance standards, it significantly improves the availability of system services, thereby optimizing user positioning and enhancing the user experience.

[0030] Furthermore, based on the minimum resolution r of the quantization of the grid ionospheric delay value, the quantization error term L is calculated. The formula for calculating the quantization error term L is: , where r takes the value 0125.

[0031] Furthermore, based on the spatial undersampling risk error term Model discrepancy risk error term Quantization error term L is used to calculate the integrity parameter value. ,in: .

[0032] Those skilled in the art will understand that all or part of the steps of the above method embodiments can be implemented by hardware related to program instructions. The aforementioned program can be stored in a computer-readable storage medium. When the program is executed, it performs the steps of the above method embodiments. The aforementioned storage medium includes various media capable of storing program code, such as mobile storage devices, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0033] Alternatively, if the integrated units of this invention are implemented as software functional modules and sold or used as independent products, they can also be stored in a computer-readable storage medium. Based on this understanding, the technical solutions of the embodiments of this invention, or the parts that contribute to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the methods described in the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as mobile storage devices, ROM, RAM, magnetic disks, or optical disks.

Claims

1. A method for calculating ionospheric integrity parameters of the BeiDou satellite-based augmentation system, characterized in that, include: Obtain observations of N ionospheric puncture points within the fitted domain surrounding the ionospheric grid points; Calculate the spatial undersampling risk error term, the model mismatch risk error term, and the quantization error term based on the observation values ​​of N ionospheric puncture points within the fitted domain surrounding the ionospheric grid points. The integrity parameter value is calculated based on the spatial undersampling risk error term, the model mismatch risk error term, and the quantization error term; The ionospheric puncture point distribution index is calculated based on the distribution of puncture points within the fitted radius around the ionospheric grid points. Using the fitting radius around the ionospheric grid points and the ionospheric puncture point distribution index as risk model index indicators, the BeiDou satellite-based augmentation system regional undersampling risk error index table is queried to obtain the space undersampling risk error item. The process of constructing the regional undersampling risk error index table of the BeiDou satellite-based augmentation system includes: Based on the physical characteristics of the ionosphere itself, the service specifications and technical characteristics of the BeiDou satellite-based augmentation system, at least two types of ionospheric anomaly detectors were selected to detect the ionospheric state in historical years, and a database of ionospheric puncture point observations on anomaly dates was established, with several sub-databases configured according to the anomaly type. Based on the aforementioned sub-databases, statistical analysis was conducted on the statistical characteristics of different ionospheric anomaly types within the service area of ​​the BeiDou satellite-based augmentation system; the statistical characteristics of ionospheric anomalies include: anomaly diurnal variation characteristics and seasonal variation characteristics; Based on the ionospheric anomaly distribution characteristics of the BeiDou satellite-based augmentation system service area, in a given service area where a risk model needs to be established, the residuals between the ionospheric delay estimate and the virtual user ionospheric delay measurement are statistically analyzed using a data stripping scheme based on each of the sub-databases. The risk model index indicators are tabulated and stored separately.

2. The method for calculating the ionospheric integrity parameters of the BeiDou satellite-based augmentation system according to claim 1, characterized in that, The fitted radius around the ionospheric grid points The distribution of puncture points within the ionosphere is used to calculate the ionospheric puncture point distribution index (RCM). The calculation formula is as follows: ;in: The distance between the centroid of the ionospheric puncture point and the ionospheric grid point used in the fitting process. The fitted radius around the ionospheric grid points is denoted as .

3. The method for calculating the ionospheric integrity parameters of the BeiDou satellite-based augmentation system according to claim 1, characterized in that, The residual between the estimated ionospheric delay and the virtual user ionospheric delay measurement using the data preemption scheme includes: Based on the database of ionospheric puncture point observations on the abnormal dates, the N ionospheric puncture point observations observed in one epoch are divided into two groups: a measurement set and a virtual user set for fitting. Based on the first set of measurements, a strategy of deleting some puncture point measurements is adopted to construct undersampling conditions. According to the undersampling conditions, the ionospheric delay estimate of each user in the virtual user set is estimated using a plane fitting algorithm, and the residual between the ionospheric delay estimate and the virtual user ionospheric delay measurement is calculated.

4. The method for calculating the ionospheric integrity parameters of the BeiDou satellite-based augmentation system according to claim 2, characterized in that, The spatial undersampling risk error term The calculation formula is: ； in: The maximum residual between the estimated ionospheric delay and the virtual user's measured ionospheric delay over a certain time interval T. Represents a certain time interval T, For virtual user ionospheric delay measurement, For ionospheric delay estimates, For the delay estimation error of the nth user's measurement, This represents the upper boundary of the Gaussian distribution.

5. The method for calculating the ionospheric integrity parameters of the BeiDou satellite-based augmentation system according to claim 1, characterized in that, The model does not meet the risk error term. The calculation method is as follows: ； in: It is a function of n. The matrix consists of N plane fitting coefficients to be solved, and the calculation formula is: , Let N×N be the spatial covariance matrix of puncture point measurements. The inverse of , where h is the distance between the measurements of the puncture points. Let be the spatial covariance vector between the puncture point and the grid points. Let be the spatial covariance between two observations that are 0 apart. An N×3 observation matrix consisting of the puncture point locations. The location vector of the ionospheric grid points. For N ionospheric puncture point observations within the fitted domain surrounding the ionospheric grid points; The The calculation formula is: ; Where Φ is the integral variable.

6. The method for calculating the ionospheric integrity parameters of the BeiDou satellite-based augmentation system according to claim 1, characterized in that, Based on the minimum resolution r of the quantization of the grid ionospheric delay value, the quantization error term L is calculated. The formula for calculating the quantization error term L is: .

7. The method for calculating the ionospheric integrity parameters of the BeiDou satellite-based augmentation system according to claim 1, characterized in that, The spatial undersampling risk error term The model does not meet the risk error term. The quantization error term L is used to calculate the integrity parameter value. ,in: .

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