A method, apparatus, equipment, and medium for monitoring the integrity of navigation satellites.

Abstract

This invention discloses a method, apparatus, equipment, and medium for monitoring the integrity of navigation satellites, applicable to the field of navigation technology. It involves acquiring ionospheric error data from monitoring stations using regional grid ionospheric products; constructing a sparse scenario using historical data from reference stations during anomalous periods and their distribution attributes to obtain compensation coefficients; compensating the ionospheric error data with these compensation coefficients to obtain the true error value; and then encapsulating the true error value with an error envelope to obtain integrity parameters; finally, monitoring the navigation satellites based on these integrity parameters to obtain monitoring results. Considering reference stations in remote areas, compensation coefficients are obtained by constructing sparse scenarios and applying these coefficients to reduce the integrity risks that users may face due to the sparseness of reference stations. When there are few reference stations in a region, sparse scenarios can be simulated using data from reference stations in other regions to improve the accuracy of ionospheric integrity assessment and reduce errors in positioning results.

Description

Technical Field

[0001] This invention relates to the field of navigation technology, and in particular to a method, apparatus, equipment and medium for monitoring the integrity of navigation satellites. Background Technology

[0002] With the rapid development of technologies such as autonomous driving and unmanned systems, integrity has become a relatively important performance indicator for high-precision positioning systems, in addition to positioning accuracy and convergence time. The key to integrity lies in the calculation of the protection level on the user side, and the protection level is greatly influenced by the integrity of the server-side data.

[0003] The enhanced product data provided by the Regional Reference Network Enhanced Precise Point Positioning – Real Time Kinematic (PPP-RTK) server includes precise orbital clock errors, biases, regional grid ionospheric data, and regional grid tropospheric data. Among these, the regional grid ionospheric integrity assessment uses reference stations within the region to calculate ionospheric errors, and then obtains conservative integrity parameters through an envelope method. In areas with a high and uniform density of reference stations, observations from these stations can be used to fit the ionospheric characteristics of the entire region. However, in remote areas, the distribution of reference stations may be sparse, leading to significant errors during ionospheric anomalies due to this sparseness. Specifically, during ionospheric anomalies, the ionospheric delay gradient changes drastically within a small range, even occurring within reference stations. Existing assessment methods do not consider the risks posed by the sparseness of reference stations, reducing the accuracy of ionospheric integrity assessments and resulting in significant errors in the obtained positioning results.

[0004] Therefore, improving the accuracy of positioning is a problem that urgently needs to be solved by those skilled in the art. Summary of the Invention

[0005] The purpose of this invention is to provide a method, apparatus, equipment, and medium for monitoring the integrity of navigation satellites, thereby reducing the integrity risks that users may face due to sparse reference stations, improving the accuracy of ionospheric integrity assessment, and reducing errors in positioning results.

[0006] To address the aforementioned technical problems, this invention provides a method for monitoring the integrity of navigation satellites, comprising:

[0007] Obtain ionospheric error data from monitoring stations based on regional grid ionospheric products;

[0008] A sparse scenario is constructed using historical data from reference stations during abnormal periods and the distribution attribute information of the reference stations to obtain compensation coefficients. The sparse scenario refers to the distribution scenario of the monitoring stations and the reference stations.

[0009] The ionospheric error data is compensated according to the compensation coefficient to obtain the true value of the error, and the integrity parameter is obtained by enveloping the true value of the error.

[0010] The monitoring results are obtained by monitoring the navigation satellite based on the integrity parameters.

[0011] Preferably, the step of constructing a sparse scene using historical data from a reference station during an abnormal period and the distribution attribute information of the reference station to obtain a compensation coefficient includes:

[0012] Ionospheric delay data of the monitoring stations corresponding to the abnormal period and the normal period are obtained according to the sparse scenario, wherein there are multiple monitoring stations;

[0013] The compensation coefficient is obtained by processing the ionospheric delay data during the abnormal period and the ionospheric delay data during the normal period.

[0014] Preferably, obtaining the ionospheric delay data of the monitoring station corresponding to the abnormal period and the normal period based on the sparse scene includes:

[0015] Based on the sparse scenario, obtain the first and second delay truth values ​​corresponding to the abnormal period and the normal period of the monitoring station, respectively.

[0016] The product model of the regional grid ionosphere is invoked to input the first delay truth value and the second delay truth value, respectively;

[0017] Obtain the first theoretical delay value and the second theoretical delay value corresponding to the output of the product model;

[0018] The ionospheric delay data for the abnormal period is obtained by subtracting the first true delay value from the first theoretical delay value; the ionospheric delay data for the normal period is obtained by subtracting the second true delay value from the second theoretical delay value.

[0019] Preferably, the step of processing the ionospheric delay data during the abnormal period and the ionospheric delay data during the normal period to obtain the compensation coefficient includes:

[0020] The maximum ionospheric delay data is selected from the ionospheric delay data of each of the aforementioned anomalous periods;

[0021] The mean ionospheric delay data is obtained by averaging the ionospheric delay data of each of the aforementioned normal periods.

[0022] The compensation coefficient is obtained by dividing the maximum ionospheric delay data by the mean ionospheric delay data.

[0023] Preferably, the construction of a sparse scene using historical data from reference stations during abnormal periods and the distribution attribute information of the reference stations includes:

[0024] Obtain the historical data;

[0025] Based on the historical data, construct the sparse scene corresponding to the same distribution attribute information under the reference station area grid;

[0026] The process of determining the distribution attribute information includes:

[0027] Obtain the coordinate information of each reference station under the regional grid, as well as the length and width values ​​of the regional grid;

[0028] The coordinate information, length value, and width value are processed by a cumulative relative distance function to obtain the distribution attribute information of the regional grid.

[0029] Preferably, the step of enveloping the true error value to obtain the integrity parameter includes:

[0030] The bilateral envelope result is obtained by processing the envelope bias and envelope standard deviation using a bilateral envelope function;

[0031] When the bilateral envelope result satisfies the envelope condition, the protection level is determined based on the relationship between the envelope deviation and the envelope standard deviation to determine the integrity parameter, wherein the envelope condition is obtained through the relationship between the bilateral envelope function and the error true value.

[0032] Preferably, the ionospheric error data of the monitoring station acquiring the regional grid ionospheric product includes:

[0033] Obtain the true ionospheric delay data of the monitoring station;

[0034] The fitting polynomial parameters are obtained by least squares processing based on the true ionospheric delay data.

[0035] The residual data of each monitoring station are obtained based on the fitting polynomial parameters;

[0036] The residual data of each grid point are obtained by interpolation of the residual data of each monitoring station.

[0037] The product model of the regional grid ionosphere is constructed by the fitting polynomial parameters and the residual data of each grid point to obtain theoretical ionospheric delay data.

[0038] The ionospheric error data is obtained by subtracting the true ionospheric delay data from the theoretical ionospheric delay data.

[0039] To address the aforementioned technical problems, the present invention also provides a navigation satellite integrity monitoring device, comprising:

[0040] The acquisition module is used to acquire ionospheric error data from monitoring stations based on regional grid ionospheric products;

[0041] A construction module is used to construct a sparse scene to obtain a compensation coefficient by using historical data of the reference station during an abnormal period and the distribution attribute information of the reference station. The sparse scene is the distribution scene of the monitoring station and the reference station.

[0042] The compensation module is used to compensate the ionospheric error data according to the compensation coefficient to obtain the true value of the error, and to envelop the true value of the error to obtain the integrity parameter;

[0043] The monitoring module is used to monitor the navigation satellite based on the integrity parameters and obtain monitoring results.

[0044] To address the aforementioned technical problems, the present invention also provides a navigation satellite integrity monitoring device, comprising:

[0045] Memory, used to store computer programs;

[0046] A processor is used to implement the steps of the navigation satellite integrity monitoring method as described above when executing the computer program.

[0047] To address the aforementioned technical problems, the present invention also provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the steps of the navigation satellite integrity monitoring method described above.

[0048] This invention provides a method for monitoring the integrity of navigation satellites, comprising: acquiring ionospheric error data of monitoring stations under a regional grid ionospheric product; constructing a sparse scene using historical data of reference stations during anomaly periods and the distribution attribute information of the reference stations to obtain compensation coefficients, wherein the sparse scene is the distribution scene of monitoring stations and reference stations; compensating the ionospheric error data according to the compensation coefficients to obtain the true error value, and enveloping the true error value with an error to obtain an integrity parameter; and monitoring the navigation satellite according to the integrity parameter to obtain the monitoring result. This method considers reference stations in remote areas, obtains compensation coefficients by constructing sparse scenes, and performs compensation based on the compensation coefficients to reduce the integrity risk that users may bear due to the sparse scene of reference stations. When there are few reference stations in the region, sparse scenes can be simulated using reference station data from other areas to improve the accuracy of ionospheric integrity assessment and reduce errors in positioning results.

[0049] In addition, the present invention also provides a navigation satellite integrity monitoring device, equipment and medium, which have the same beneficial effects as the navigation satellite integrity monitoring method described above. Attached Figure Description

[0050] To more clearly illustrate the embodiments of the present invention, the accompanying drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0051] Figure 1 A flowchart illustrating a method for monitoring the integrity of navigation satellites provided in an embodiment of the present invention;

[0052] Figure 2 This is a schematic diagram of a PPP-RTK server-side grid ionospheric product integrity monitoring embodiment of the present invention;

[0053] Figure 3 A schematic diagram of a double-sided envelope provided in an embodiment of the present invention;

[0054] Figure 4 A structural diagram of a navigation satellite integrity monitoring device provided in an embodiment of the present invention;

[0055] Figure 5 This is a structural diagram of a navigation satellite integrity monitoring device provided in an embodiment of the present invention. Detailed Implementation

[0056] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the protection scope of the present invention.

[0057] The core of this invention is to provide a method, apparatus, equipment, and medium for monitoring the integrity of navigation satellites, thereby reducing the integrity risks that users may bear due to sparse reference stations, improving the accuracy of ionospheric integrity assessment, and reducing errors in positioning results.

[0058] To enable those skilled in the art to better understand the present invention, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0059] It should be noted that PPP-RTK technology utilizes sparse reference stations deployed nationwide and even globally to generate precise satellite orbits, precise satellite clock errors, satellite differential code delays, uncalibrated satellite phase delays, regional ionospheric grids, and regional tropospheric grid correction products on the server side. Users can achieve centimeter-level positioning in approximately 10 seconds using the correction products provided by the server. Integrity refers to the ability to promptly issue alarms to users when any fault or error exceeds the limits of the navigation system, failing to meet navigation and positioning requirements. This invention uses the integrity monitoring of regional ionospheric grid products to monitor the positioning of navigation satellites.

[0060] Figure 1 A flowchart of a navigation satellite integrity monitoring method provided in an embodiment of the present invention is shown below. Figure 1 As shown, it includes:

[0061] S11: Obtain ionospheric error data from monitoring stations based on regional grid ionospheric products;

[0062] S12: Construct a sparse scenario using historical data from reference stations during abnormal periods and the distribution attribute information of the reference stations to obtain compensation coefficients;

[0063] The sparse scenario refers to the distribution of monitoring stations and reference stations;

[0064] S13: The true value of the error is obtained by compensating the ionospheric error data according to the compensation coefficient, and the integrity parameter is obtained by enveloping the true value of the error.

[0065] S14: Monitoring results are obtained by monitoring navigation satellites based on integrity parameters.

[0066] Understandably, the regional reference station network utilizes observational information, along with precise orbit, clock error, and bias products, to obtain oblique ionospheric delay results for the reference stations. Based on the ionospheric information of the reference stations, it obtains ionospheric error data for monitoring stations under the regional grid point ionospheric product. As one embodiment, obtaining ionospheric error data for monitoring stations under the regional grid ionospheric product includes:

[0067] Obtain true ionospheric delay data from the monitoring station;

[0068] The fitting polynomial parameters are obtained by least squares processing based on the true ionospheric delay data.

[0069] The residual data of each monitoring station were obtained based on the fitting polynomial parameters;

[0070] The residual data of each monitoring station were processed by interpolation to obtain the residual data of each grid point;

[0071] By fitting polynomial parameters and residual data of each grid point, a product model of the regional grid ionosphere is constructed to obtain theoretical ionospheric delay data.

[0072] The ionospheric error data is obtained by subtracting the true ionospheric delay data from the theoretical ionospheric delay data.

[0073] Specifically, the true ionospheric delay data of the monitoring station is obtained, and using a polynomial fitting method, the true ionospheric delay data of each reference station can be expressed by formula (1):

[0074] (1)

[0075] in, The parameters of the polynomial to be fitted are... These represent the differences in latitude and longitude of each reference station relative to the regional center. This represents the number of reference stations within the region. The true ionospheric delay data are obtained from each reference station.

[0076] Formula (1) above can be written in matrix form as shown in formula (2):

[0077] (2)

[0078] By performing least squares processing on the true ionospheric delay data of each ionosphere, the least squares solution of the parameters of the polynomial to be fitted can be obtained, as shown in formula (3):

[0079] (3)

[0080] The residual data of each monitoring station are obtained based on the fitting polynomial parameters, as shown in formula (4):

[0081] (4)

[0082] After obtaining the residue data from each monitoring station, the residual data for each grid point is obtained through inverse distance weighted interpolation, as shown in formula (5):

[0083] (5)

[0084] in, This indicates that a total of [number] exist in this area. Individual grid points, Indicates the first From the grid point to the first The weighting coefficients between the reference stations are shown in formula (6):

[0085] (6)

[0086] in, For the first From the grid point to the first The distance between reference stations.

[0087] Once the polynomial fitting parameters and grid point residual data are obtained, the user can process these parameters to construct a product model of the regional grid ionosphere. The model formula is shown in formula (7):

[0088] (7)

[0089] in, For polynomial components, The residual components can be obtained by interpolating the residuals at grid points, as shown in formula (8):

[0090] (8)

[0091] in, The weights of the grid points involved in the interpolation calculation are obtained by weighting the back distances. For the residual data of the grid points participating in interpolation, To determine the number of grid points to participate in interpolation, the 3-4 closest grid points are generally selected.

[0092] In formula (8), the weights of the grid points participating in the interpolation calculation corresponding to the residual data of the grid points participating in the interpolation are obtained by selecting the grid points participating in the interpolation based on the residual data of the grid points obtained in formula (5) and resetting the weight coefficients to obtain the residual components.

[0093] The product model constructed by formula (7) is used to obtain the theoretical ionospheric delay data of the ionosphere. The error data of the ionosphere is obtained by subtracting the true ionospheric delay data from the theoretical ionospheric delay data.

[0094] In step S12, a sparse scene is constructed using historical data and distribution attribute information of reference stations during the anomalous period. The compensation coefficient is then obtained from the constructed sparse scene. Based on historical observation data from periods of ionospheric anomalies (historical data), reference stations with the same function value share the same distribution attributes. The sparse scene is simulated using observations from existing, relatively dense regional reference stations or reference stations from other institutions within the region. In other words, the sparse scene is simulated by constructing a reference station distribution with the same cumulative relative distance function attribute as the actual reference station distribution.

[0095] As one embodiment, a sparse scenario is constructed using historical data from reference stations during abnormal periods and the distribution attribute information of the reference stations to obtain compensation coefficients, including:

[0096] Ionospheric delay data of monitoring stations corresponding to abnormal periods and normal periods are obtained based on sparse scenarios, where there are multiple monitoring stations;

[0097] The compensation coefficient is obtained by processing the ionospheric delay data during abnormal periods and the ionospheric delay data during normal periods.

[0098] Since this sparse scenario is a simulation of the sparse scenarios of the reference station and monitoring stations, this embodiment only acquires the ionospheric delay data of the monitoring stations. It can be understood that the sparse scenario is simulated during anomaly periods in historical data, and the ionospheric delay data of the monitoring stations during these anomaly periods can be directly obtained. For the ionospheric delay data of the monitoring stations during normal periods, it needs to be obtained over a specific time period. The compensation coefficient is obtained by comparing the ionospheric delay data of all monitoring stations during anomaly periods with the ionospheric delay data of all monitoring stations during quiescent periods.

[0099] The ionospheric error data is compensated using the obtained compensation coefficients to obtain the error value. After obtaining the ionospheric errors of all monitoring stations within a certain time period that have undergone sparse scenario compensation by the reference station, integrity parameters need to be provided. Since the calculation of the user-end protection level is based on the assumption that all errors follow a Gaussian distribution, but ionospheric errors do not conform to a Gaussian distribution and have a "heavy-tailed" characteristic, considering the conservatism of integrity, an "envelope" method needs to be considered for ionospheric errors. Therefore, the true error value needs to be processed using envelope error processing to obtain the integrity parameters.

[0100] Figure 2 This is a schematic diagram of a PPP-RTK server-side grid ionospheric product integrity monitoring embodiment of the present invention, as shown below. Figure 2 As shown, the regional reference station network uses observations and precise orbital clocks and deviation products to obtain the oblique ionospheric delay of the reference stations. Based on the ionospheric information of the reference stations, the ionospheric products of the regional grid points can be obtained. Based on historical data, the unsampled scenario of the reference stations during the period of ionospheric anomaly can be simulated, and the unsampled error compensation is performed in combination with the distribution attributes of the regional reference stations. Finally, based on the ionospheric results that have been compensated for the unsampled error, the ionospheric error FCDF map is enveloped by the bilateral envelope method to obtain the relevant integrity parameters. As can be seen from the above process, the algorithm mainly includes five parts, namely (1) calculating the regional grid ionospheric products; (2) constructing functions to describe the distribution attributes of the reference stations; (3) simulating the unsampled scenario based on historical data; (4) compensating for the unsampled error; and (5) enveloping the ionospheric error FCDF map by the bilateral envelope to obtain the integrity parameters.

[0101] This invention provides a method for monitoring the integrity of navigation satellites, comprising: acquiring ionospheric error data of monitoring stations under a regional grid ionospheric product; constructing a sparse scene using historical data of reference stations during abnormal periods and the distribution attribute information of the reference stations to obtain compensation coefficients, wherein the sparse scene is the distribution scene of monitoring stations and reference stations; compensating the ionospheric error data according to the compensation coefficients to obtain the true error value, and enveloping the true error value to obtain an integrity parameter; and monitoring the navigation satellite according to the integrity parameter to obtain the monitoring result. This method considers reference stations in remote areas, obtains compensation coefficients by constructing sparse scenes, and performs compensation based on the compensation coefficients to reduce the integrity risk that users may bear due to the sparse scene of reference stations. When there are few reference stations in the region, sparse scenes can be simulated using reference station data from other areas to improve the accuracy of ionospheric integrity assessment and reduce errors in positioning results.

[0102] Based on the above embodiments, ionospheric delay data of monitoring stations corresponding to abnormal periods and normal periods are obtained according to sparse scenarios, including:

[0103] Based on the sparse scene, obtain the first and second delay truth values ​​corresponding to the abnormal and normal periods of the monitoring station, respectively.

[0104] Call the product model of the regional grid ionosphere to input the first delay truth value and the second delay truth value respectively;

[0105] Obtain the first and second theoretical delay values ​​corresponding to the output of the product model;

[0106] The difference between the true value of the first delay and the theoretical value of the first delay is used to obtain the ionospheric delay data during the abnormal period; the difference between the true value of the second delay and the theoretical value of the second delay is used to obtain the ionospheric delay data during the normal period.

[0107] Specifically, in a sparse scenario, monitoring stations can be obtained, and the first and second true delay values ​​corresponding to abnormal and normal periods can be obtained according to formula (1). The first and second true delay values ​​are then input into the product model of the grid ionosphere in the value region to obtain the corresponding first and second theoretical delay values.

[0108] The ionospheric delay data for the abnormal period can be obtained by subtracting the true value of the first delay from the theoretical value of the first delay. The ionospheric delay data for the normal period can be obtained by subtracting the true value of the second delay from the theoretical value of the second delay.

[0109] This embodiment provides ionospheric delay data of monitoring stations in two periods based on the constructed sparse scene, which facilitates the subsequent calculation of the corresponding compensation coefficients.

[0110] Based on the above embodiments, as one embodiment, a compensation coefficient is obtained by processing ionospheric delay data during abnormal periods and ionospheric delay data during normal periods, including:

[0111] The maximum ionospheric delay data was selected from the ionospheric delay data during each anomalous period;

[0112] The mean ionospheric delay data is obtained by averaging the ionospheric delay data of each normal period.

[0113] The compensation coefficient is obtained by dividing the maximum ionospheric delay data by the mean ionospheric delay data.

[0114] It is understandable that the ionospheric delay data is determined by the number of monitoring stations, meaning there are multiple ionospheric delay data points. It is necessary to select the maximum ionospheric delay data among the ionospheric delay data during the abnormal period, and at the same time, average the multiple ionospheric delay data during the normal period to obtain the mean ionospheric delay data. The compensation coefficient is obtained by comparing the maximum ionospheric delay data with the mean ionospheric delay data, as shown in formula (9).

[0115] (9)

[0116] in, For error compensation parameters, This contains ionospheric delay data from all monitoring stations during periods of ionospheric anomaly. This contains ionospheric delay data from all monitoring stations during normal ionospheric periods.

[0117] The compensation coefficient provided in this embodiment is obtained by processing ionospheric delay data during abnormal and normal periods. By using two different periods as a reference, the obtained compensation coefficient is more authoritative and fair, making it applicable to error processing in different scenarios.

[0118] Based on the above embodiments, as one example, a sparse scenario is constructed using historical data of reference stations during abnormal periods and the distribution attribute information of the reference stations, including:

[0119] Obtain historical data;

[0120] Construct sparse scenarios corresponding to the same distribution attribute information under the reference station area grid based on historical data;

[0121] The process of determining the distribution attribute information includes:

[0122] Obtain the coordinate information of each reference station under the regional grid, as well as the length and width values ​​of the regional grid;

[0123] The distribution attribute information of the regional grid is obtained by processing the coordinate information, length value, and width value through the cumulative relative distance function.

[0124] Specifically, historical data is acquired by utilizing observational data from historical periods of ionospheric anomalies, and by using observational data from existing relatively dense regional reference stations or observational results from reference stations of other institutions in the region, a sparse scenario of reference station distribution with distribution attribute information under a regional grid that is identical to the actual reference station distribution is constructed.

[0125] Its sparse scenario simulates the distribution characteristics of reference stations and observation stations through reference stations. The distribution pattern of the reference stations to be simulated is that the reference stations are only distributed around the perimeter of the region, the ionospheric delay in the perimeter is small, and the ionospheric delay in the middle region is large.

[0126] Correspondingly, in the process of determining distribution attribute information, for a reference station within a region, its weight (effect) on the surrounding region decreases with increasing distance. For example, within a 10*10 grid, with the region center as the origin, all coordinates are restricted to... Within a given area, a reference station located at the origin exerts a decreasing "effect" on the surrounding area with increasing distance, ranging from a maximum of 1 to a minimum of 0. For a single point within the area, the "effect" it experiences is the sum of the "effects" of all reference stations within that area.

[0127] Different reference station distributions mean different "effects" on a certain point in the region. This characteristic can be used to construct a function to describe the attributes of different reference station distributions, called the Cumulative Relative Distance Function (CRDF). To facilitate the description of this characteristic, the grid point in the lower left corner of the region is selected as the origin, the east direction is the x-axis, and the north direction is the y-axis. The effect of all stations in the region relative to the coordinate origin is calculated. This can avoid the situation where the attribute values ​​are the same but the actual station distribution is symmetrical due to the positive or negative sign of the coordinates. The effect of each reference station on the coordinate origin can be expressed by formula (10):

[0128] (10)

[0129] in, For the number of stations, These are the coordinates of each station in the regional grid coordinate system. These are the length and width values ​​of the grid area, respectively.

[0130] A sparse scenario is constructed based on distribution attribute information. For example, the real area grid is a 10*10 grid with a total of 16 monitoring stations and 4 reference stations. Based on the coordinate information of the 4 reference stations and the corresponding area grid, 100 CRDF values ​​are constructed. The 100 CRDF values ​​are processed to show that distributions with the same function value have the same attributes. Based on the 100 CRDF values ​​and their corresponding characteristics, 12 monitoring stations and 4 reference stations are simulated. This invention only focuses on the error data of the monitoring stations.

[0131] The process of constructing a sparse scene provided in this embodiment is obtained through historical data and distribution attribute information of reference stations during abnormal periods. It fully considers the different distribution attribute information caused by the different distances and weights between different reference stations, making the constructed sparse scene more realistic.

[0132] Based on the above embodiments, the integrity parameters are obtained by enveloping the true error value with an error envelope, including:

[0133] The bilateral envelope result is obtained by processing the envelope bias and envelope standard deviation using a bilateral envelope function;

[0134] When the bilateral envelope results meet the envelope conditions, the protection level is determined based on the relationship between the envelope deviation and the envelope standard deviation to determine the integrity parameters. The envelope conditions are obtained through the relationship between the bilateral envelope function and the true value of the error.

[0135] It is understandable that a Gaussian distribution is used to conservatively describe the ionospheric error distribution, and a two-sided envelope function (Fold cumulative probability distribution, FCDF) is used for the envelope. The two-sided envelope is to use two Gaussian envelopes to complete the envelope of the original error distribution, and the mathematical expression is shown in formula (11):

[0136] (11)

[0137] in, For envelope bias, The envelope standard deviation, It is a two-sided envelope function. Let be the envelope function of the left half of the image. is the envelope function of the right half of the image.

[0138] and The envelope condition that needs to be met is given in formula (12):

[0139] (12)

[0140] in, The CDF plot represents the true error, which is the true error value in step S11.

[0141] Figure 3 A schematic diagram of a bilateral envelope provided for an embodiment of the present invention, such as... Figure 3 As shown, the outer image is the enveloped image, and the inner image is the actual image. The probability density function (CDF) exists with X as the boundary. Figure 3 The left half shown is obtained based on the CDF process. The CDF is folded in half, and the sum of the left half and the right half is required to be 1. The existence of formula (12) makes the images of the left half and the right half surround the actual image inside through the outer image.

[0142] It is understandable that, in addition to envelope bias and envelope standard deviation, integrity parameters such as fault probability and accuracy amplification factor can also be obtained through the envelope diagram, so as to complete the integrity assessment of the PPP-RTK server area grid ionospheric product. The amplification factor is the compensation coefficient of formula (9).

[0143] The process of obtaining integrity parameters by enveloping the true error values ​​in the embodiments of the present invention provides the conservatism of the ionospheric error distribution based on the fact that all true error values ​​conform to a Gaussian distribution.

[0144] The foregoing has described in detail various embodiments of the navigation satellite integrity monitoring method. Based on this, the present invention also discloses a navigation satellite integrity monitoring device corresponding to the above method. Figure 4 This is a structural diagram of a navigation satellite integrity monitoring device provided in an embodiment of the present invention. Figure 4 As shown, the navigation satellite integrity monitoring device includes:

[0145] Module 11 is used to acquire ionospheric error data of monitoring stations based on regional grid ionospheric products;

[0146] Module 12 is used to construct a sparse scene to obtain compensation coefficients by using historical data of reference stations during abnormal periods and the distribution attribute information of reference stations. The sparse scene is the distribution scene of monitoring stations and reference stations.

[0147] The compensation module 13 is used to compensate the ionospheric error data according to the compensation coefficient to obtain the true value of the error, and to envelop the true value of the error to obtain the integrity parameter.

[0148] The monitoring module 14 is used to monitor navigation satellites based on integrity parameters and obtain monitoring results.

[0149] Since the embodiments of the device part correspond to the embodiments described above, please refer to the embodiments described in the method part for the embodiments of the device part, and will not be repeated here.

[0150] For an introduction to the integrity monitoring device for navigation satellites provided by the present invention, please refer to the above method embodiments. The present invention will not be described in detail here, but it has the same beneficial effects as the above-described integrity monitoring method for navigation satellites.

[0151] Figure 5 A structural diagram of a navigation satellite integrity monitoring device provided in an embodiment of the present invention is shown below. Figure 5 As shown, the device includes:

[0152] Memory 21 is used to store computer programs;

[0153] Processor 22 is used to implement the steps of a navigation satellite integrity monitoring method when executing a computer program.

[0154] The navigation satellite integrity monitoring device provided in this embodiment may include, but is not limited to, tablet computers, laptop computers, or desktop computers.

[0155] The processor 22 may include one or more processing cores, such as a quad-core processor or an octa-core processor. The processor 22 may be implemented using at least one of the following hardware forms: Digital Signal Processor (DSP), Field-Programmable Gate Array (FPGA), or Programmable Logic Array (PLA). The processor 22 may also include a main processor and a coprocessor. The main processor, also known as the Central Processing Unit (CPU), is used to process data in the wake-up state; the coprocessor is a low-power processor used to process data in the standby state. In some embodiments, the processor 22 may integrate a Graphics Processing Unit (GPU), which is responsible for rendering and drawing the content to be displayed on the screen. In some embodiments, the processor 22 may also include an Artificial Intelligence (AI) processor, which handles computational operations related to machine learning.

[0156] The memory 21 may include one or more computer-readable storage media, which may be non-transitory. The memory 21 may also include high-speed random access memory and non-volatile memory, such as one or more disk storage devices or flash memory devices. In this embodiment, the memory 21 is used to store at least the following computer program 211, which, after being loaded and executed by the processor 22, is capable of implementing the relevant steps of the navigation satellite integrity monitoring method disclosed in any of the foregoing embodiments. In addition, the resources stored in the memory 21 may also include an operating system 212 and data 213, etc., and the storage method may be temporary storage or permanent storage. The operating system 212 may include Windows, Unix, Linux, etc. The data 213 may include, but is not limited to, the data involved in the navigation satellite integrity monitoring method, etc.

[0157] In some embodiments, the navigation satellite integrity monitoring device may further include a display screen 23, an input / output interface 24, a communication interface 25, a power supply 26, and a communication bus 27.

[0158] Those skilled in the field can understand, Figure 5 The structure shown does not constitute a limitation on the integrity monitoring equipment for navigation satellites and may include more or fewer components than shown.

[0159] The processor 22 implements the navigation satellite integrity monitoring method provided in any of the above embodiments by calling instructions stored in the memory 21.

[0160] For an introduction to the integrity monitoring device for navigation satellites provided by the present invention, please refer to the above method embodiments. The present invention will not be described in detail here, but it has the same beneficial effects as the above-described integrity monitoring method for navigation satellites.

[0161] Furthermore, the present invention also provides a computer-readable storage medium storing a computer program, which, when executed by processor 22, implements the steps of the above-described navigation satellite integrity monitoring method.

[0162] It is understood that if the methods in the above embodiments are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and executes all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0163] For an introduction to the computer-readable storage medium provided by the present invention, please refer to the above method embodiments. The present invention will not be described in detail here, but it has the same beneficial effects as the above-described navigation satellite integrity monitoring method.

[0164] The present invention has provided a detailed description of a method, device, equipment, and medium for monitoring the integrity of navigation satellites. The various embodiments in the specification are described in a progressive manner, with each embodiment focusing on its differences from other embodiments. Similar or identical parts between embodiments can be referred to interchangeably. For the devices disclosed in the embodiments, since they correspond to the methods disclosed in the embodiments, the descriptions are relatively simple, and relevant parts can be referred to the method section. It should be noted that those skilled in the art can make various improvements and modifications to the present invention without departing from its principles, and these improvements and modifications also fall within the protection scope of the present invention.

[0165] It should also be noted that, in this specification, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

Claims

1. A method for monitoring the integrity of navigation satellites, characterized in that, include: Obtain ionospheric error data from monitoring stations based on regional grid ionospheric products; A sparse scenario is constructed using historical data from reference stations during abnormal periods and the distribution attribute information of the reference stations to obtain compensation coefficients. The sparse scenario refers to the distribution scenario of the monitoring stations and the reference stations. The sparse scenario is simulated by constructing a reference station distribution with the same cumulative relative distance function attribute as the real reference station distribution. The ionospheric error data is compensated according to the compensation coefficient to obtain the true value of the error, and the integrity parameter is obtained by enveloping the true value of the error. The monitoring results are obtained by monitoring the navigation satellite based on the integrity parameters. Correspondingly, the step of constructing a sparse scene using historical data from a reference station during an anomaly period and the distribution attribute information of the reference station to obtain a compensation coefficient includes: Ionospheric delay data of the monitoring stations corresponding to the abnormal period and the normal period are obtained according to the sparse scenario, wherein there are multiple monitoring stations; The compensation coefficient is obtained by processing the ionospheric delay data during the abnormal period and the ionospheric delay data during the normal period.

2. The method for monitoring the integrity of navigation satellites according to claim 1, characterized in that, The step of obtaining the ionospheric delay data of the monitoring station corresponding to the abnormal period and the normal period based on the sparse scene includes: Based on the sparse scenario, obtain the first and second delay truth values ​​corresponding to the abnormal period and the normal period of the monitoring station, respectively. The product model of the regional grid ionosphere is invoked to input the first delay truth value and the second delay truth value, respectively; Obtain the first theoretical delay value and the second theoretical delay value corresponding to the output of the product model; The ionospheric delay data for the abnormal period is obtained by subtracting the first true delay value from the first theoretical delay value; the ionospheric delay data for the normal period is obtained by subtracting the second true delay value from the second theoretical delay value.

3. The method for monitoring the integrity of navigation satellites according to claim 2, characterized in that, The process of obtaining the compensation coefficient by processing the ionospheric delay data during the abnormal period and the ionospheric delay data during the normal period includes: The maximum ionospheric delay data is selected from the ionospheric delay data of each of the aforementioned anomalous periods; The mean ionospheric delay data is obtained by averaging the ionospheric delay data of each of the aforementioned normal periods. The compensation coefficient is obtained by dividing the maximum ionospheric delay data by the mean ionospheric delay data.

4. The method for monitoring the integrity of navigation satellites according to any one of claims 1 to 3, characterized in that, The construction of a sparse scenario using historical data from reference stations during abnormal periods and the distribution attribute information of those reference stations includes: Obtain the historical data; Based on the historical data, construct the sparse scene corresponding to the same distribution attribute information under the reference station area grid; The process of determining the distribution attribute information includes: Obtain the coordinate information of each reference station under the regional grid, as well as the length and width values ​​of the regional grid; The coordinate information, length value, and width value are processed by a cumulative relative distance function to obtain the distribution attribute information of the regional grid.

5. The method for monitoring the integrity of navigation satellites according to claim 4, characterized in that, The step of enveloping the true error value to obtain the integrity parameter includes: The bilateral envelope result is obtained by processing the envelope bias and envelope standard deviation using a bilateral envelope function; When the bilateral envelope result satisfies the envelope condition, the protection level is determined based on the relationship between the envelope deviation and the envelope standard deviation to determine the integrity parameter, wherein the envelope condition is obtained through the relationship between the bilateral envelope function and the error true value.

6. The method for monitoring the integrity of navigation satellites according to claim 5, characterized in that, Obtain ionospheric error data from monitoring stations based on regional grid ionospheric products, including: Obtain the true ionospheric delay data of the monitoring station; The fitting polynomial parameters are obtained by least squares processing based on the true ionospheric delay data. The residual data of each monitoring station are obtained based on the fitting polynomial parameters; The residual data of each grid point are obtained by interpolation of the residual data of each monitoring station. The product model of the regional grid ionosphere is constructed by the fitting polynomial parameters and the residual data of each grid point to obtain theoretical ionospheric delay data. The ionospheric error data is obtained by subtracting the true ionospheric delay data from the theoretical ionospheric delay data.

7. A device for monitoring the integrity of navigation satellites, characterized in that, include: The acquisition module is used to acquire ionospheric error data from monitoring stations based on regional grid ionospheric products; A construction module is used to construct a sparse scene to obtain compensation coefficients by using historical data of reference stations during abnormal periods and the distribution attribute information of the reference stations, wherein the sparse scene is the distribution scene of the monitoring stations and the reference stations; the sparse scene is simulated by constructing a reference station distribution with the same cumulative relative distance function attribute as the real reference station distribution. The compensation module is used to compensate the ionospheric error data according to the compensation coefficient to obtain the true value of the error, and to envelop the true value of the error to obtain the integrity parameter; The monitoring module is used to monitor the navigation satellite according to the integrity parameters and obtain monitoring results; Correspondingly, the step of constructing a sparse scene using historical data from a reference station during an anomaly period and the distribution attribute information of the reference station to obtain a compensation coefficient includes: Ionospheric delay data of the monitoring stations corresponding to the abnormal period and the normal period are obtained according to the sparse scenario, wherein there are multiple monitoring stations; The compensation coefficient is obtained by processing the ionospheric delay data during the abnormal period and the ionospheric delay data during the normal period.

8. A device for monitoring the integrity of navigation satellites, characterized in that, include: Memory, used to store computer programs; A processor, configured to implement the steps of the navigation satellite integrity monitoring method as described in any one of claims 1 to 6 when executing the computer program.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, implements the steps of the navigation satellite integrity monitoring method as described in any one of claims 1 to 6.

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