A method and device for monitoring the integrity of GNSS satellites in a coordinated space-ground manner.

Abstract

This invention provides a GNSS satellite integrity monitoring method and apparatus using a ground-based collaborative system, belonging to the field of high-precision and high-integrity positioning. The method includes: extracting carrier phase residuals from GNSS satellite observation data from ground monitoring stations, and generating integrity support information for GNSS satellite signals based on the carrier phase residuals, including a quality index (QI) and a health index (HI); broadcasting the QI and HI to low-Earth orbit (LEO) satellites, where the LEO satellites refine their orbit calculations based on the received QI and HI to obtain their orbital coordinates; optimizing the configuration of ground monitoring stations and LEO satellites to construct a space-ground integrated monitoring network, using the LEO satellite orbital coordinates as input to regenerate the integrity support information, including the QI and HI, in real time; and broadcasting the integrity support information to ground-based PPP user terminals. This invention effectively balances system performance and deployment costs.

Description

TECHNICAL FIELD

[0001] The application belongs to the field of high-precision and high-integrity positioning, and particularly relates to a GNSS satellite integrity monitoring method and device based on satellite-ground cooperation. BACKGROUND

[0002] Global Navigation Satellite System (GNSS) is widely used in key fields such as positioning, navigation and timing. The existing GNSS integrity monitoring technology mainly relies on a ground monitoring station network, which analyzes the quality, error residuals and related performance indicators of the received signals to identify and determine whether the satellite is in an abnormal state. For example, the SBAS scheme proposed by the International Civil Aviation Organization (ICAO) and the traditional GBAS method both establish a large-scale reference station array on the ground to continuously monitor GNSS signals. Such methods have good performance on land, but have obvious limitations in coverage, real-time performance and deployment cost.

[0003] Specifically, the layout of the ground monitoring station is subject to geographical, climatic and economic factors, and it is difficult to achieve wide coverage of signal monitoring in remote areas such as oceans, deserts and polar regions, resulting in "blind spots" in the global monitoring network, which cannot meet the high timeliness and high coverage requirements of GNSS signal integrity in key scenarios such as long-distance aviation, offshore operations and polar exploration. In addition, due to the geometric relationship between the ground station and the satellite and the observation interval, there is still room for improvement in the sensitivity and response speed of the traditional scheme in anomaly detection.

[0004] Low-orbit satellites have the characteristics of low orbit height, fast relative navigation satellite movement speed, short revisit period, wide ground visibility, etc., and can obtain a large amount of observation data in a short time and cover areas that ground stations cannot reach. These advantages make them an important complementary value in GNSS signal monitoring, especially in terms of improving monitoring frequency, enhancing spatial coverage capability and supporting real-time performance. However, although low-orbit satellites have the natural advantages of wide coverage and high frequency, they have the potential to become a complementary means of navigation system signal monitoring, but in the current technical system, there is still a lack of unified coordination mechanism between ground stations and low-orbit satellites. Existing methods generally focus on the observation capability of a single platform and fail to establish an efficient data fusion and joint discrimination strategy, resulting in low resource utilization efficiency and failing to fully utilize the complementary characteristics of satellite-ground observation. This separate monitoring mode has obvious limitations in the face of global, all-weather high-integrity protection requirements. Therefore, a systematic satellite-ground cooperative monitoring method is needed to realize the organic integration of multi-source observation information to improve the integrity monitoring capability and high-precision service level of the navigation system in different environments and different application scenarios. SUMMARY

[0005] To solve the above technical problems, the application provides a GNSS satellite integrity monitoring method and device in cooperation with satellites and the ground.

[0006] A GNSS satellite integrity monitoring method in cooperation with satellites and the ground comprises the following steps:

[0007] Step S1: Extract carrier phase residuals based on GNSS satellite observation data of ground monitoring stations, and generate integrity support information of GNSS satellite signals based on the carrier phase residuals, including quality indicators QI and health indicators HI;

[0008] Step S2: Broadcast the quality indicators QI and the health indicators HI to low-orbit satellites, and refine orbit calculation based on the received quality indicators QI and the health indicators HI to obtain orbit coordinates of the low-orbit satellites;

[0009] Step S3: Optimize the configuration of the ground monitoring stations and the low-orbit satellites, build a space monitoring station network in cooperation with satellites and the ground, take the orbit coordinates of the low-orbit satellites as input, and then regenerate the integrity support information including the quality indicators QI and the health indicators HI in real time;

[0010] Step S4: Broadcast the integrity support information to ground PPP user terminals, thereby realizing final positioning.

[0011] A GNSS satellite integrity monitoring device in cooperation with satellites and the ground comprises the following modules:

[0012] An integrity support information generation module extracts carrier phase residuals based on GNSS satellite observation data of ground monitoring stations, and generates integrity support information of GNSS satellite signals based on the carrier phase residuals, including quality indicators QI and health indicators HI;

[0013] An orbit coordinate obtaining module broadcasts the quality indicators QI and the health indicators HI to low-orbit satellites, and refines orbit calculation based on the received quality indicators QI and the health indicators HI to obtain orbit coordinates of the low-orbit satellites;

[0014] An optimized integrity support information generation module optimizes the configuration of the ground monitoring stations and the low-orbit satellites, builds a space monitoring station network in cooperation with satellites and the ground, takes the orbit coordinates of the low-orbit satellites as input, and then regenerates the integrity support information including the quality indicators QI and the health indicators HI in real time;

[0015] A positioning module broadcasts the integrity support information to ground PPP user terminals, thereby realizing final positioning.

[0016] An electronic device comprising: one or more processors; memory storing one or more programs, wherein the one or more programs, when executed by the one or more processors, cause the one or more processors to implement the method.

[0017] A computer-readable storage medium having stored thereon executable instructions that, when executed by a processor, cause the processor to implement the method.

[0018] The present application has the following beneficial effects:

[0019] (1) The present application extracts the original phase residual in the multi-system ambiguity fixed solution by the non-combination multi-frequency precise point positioning ambiguity fixed PPP-AR model, avoids the problem of error mixing and information loss in the combination model, and improves the sensitivity and accuracy of integrity information.

[0020] (2) The present application constructs a double-index system including quality index (QI) and health index (HI), and finely represents the satellite signal state from the error statistical characteristics and risk constraint angles, and enhances the identification ability of complex environment anomalies.

[0021] (3) The present application combines the observation ability of ground monitoring stations and low-orbit satellites, and relays and broadcasts information through high-orbit communication satellites, breaks through the limitations of traditional systems in information acquisition and transmission link, and significantly improves the information coverage range and timeliness.

[0022] (4) The present application introduces an optimization algorithm for satellite-ground resource configuration, effectively balances system performance and deployment cost, and provides a feasible scheme for constructing a large-scale, high-reliable and high-precision GNSS integrity guarantee system. BRIEF DESCRIPTION OF DRAWINGS

[0023] Figure 1 The flowchart of the present application;

[0024] Figure 2 The sliding window diagram of the present application. DETAILED DESCRIPTION

[0025] In order to make the purpose, technical scheme and advantages of the present application clearer, the present application is further described in detail below in combination with the drawings and examples. It should be understood that the specific examples described herein are only used to explain the present application and do not limit the present application. In addition, the technical features involved in each embodiment of the present application described below can be combined with each other as long as they do not conflict with each other.

[0026] As shown in Figure 1 The present application provides a satellite-ground cooperative GNSS satellite integrity monitoring method, comprising the following steps:

[0027] S1, extracting carrier phase residuals from GNSS satellite observation data of ground monitoring stations, and generating integrity support information of GNSS satellite signals based on carrier phase residuals, including quality indicators QI and health indicators HI; more specifically, step S1 includes: based on the precise point positioning with ambiguity resolution (PPP-AR) model of non-combination multi-frequency, real-time extraction of ambiguity-fixed carrier phase residuals of each GNSS satellite of GPS, Galileo and BDS-3. The extracted carrier phase residuals are used as statistical samples, and a sliding window method is used to update each group of carrier phase residuals in real time, and two types of integrity support information including quality indicators (QI) and health indicators (HI) are further calculated in real time. Among them, QI is obtained by using the mixed distribution method of Gaussian distribution and Pareto distribution, and HI is obtained by using the method of synchronously controlling integrity risk and continuity risk. Specifically, it includes:

[0028] Select at least 50 ground monitoring stations uniformly distributed in the ground monitoring station network. The selection of ground monitoring stations needs to meet the following four conditions: ensure that the receiver types of ground monitoring stations are consistent; ensure that the observation data quality of ground monitoring stations is high; ensure that ground monitoring stations have accurate known three-dimensional coordinate values; ensure that ground monitoring stations can receive signals of all frequencies of GPS, Galileo and BDS-3 GNSS satellites.

[0029] Using ground monitoring stations to receive real-time observation data streams of GPS, Galileo and BDS-3 GNSS satellites, broadcast ephemeris data streams and real-time satellite corrections broadcast through high-orbit communication satellites. Among them, real-time satellite corrections include the following four types: satellite orbit correction, satellite clock error correction, satellite code bias correction, and satellite phase bias correction.

[0030] A non-combined multi-frequency PPP-AR model was used to extract the multi-frequency carrier phase residuals after fixing the ambiguities of each GNSS satellite (GPS, Galileo, and BDS-3). The GPS satellite bands / frequencies included are: L1 / 1575.42MHz, L2 / 1227.60MHz, and L5 / 1176.45MHz. The Galileo satellite bands / frequencies included are: E1 / 1575.42MHz, E5a / 1176.45MHz, E5b / 1207.140MHz, E5 / 1191.795MHz, and E6 / 1278.75MHz. The BDS-3 satellite bands / frequencies included are: B1I / 1561.098MHz, B3I / 1268.52MHz, B1C / 1575.42MHz, B2a / 1176.45MHz, and B2b / 1207.140MHz.

[0031] For each epoch, a loop is performed to extract and store the carrier phase residuals of all visible satellites observed by each ground monitoring station at each frequency point under a single epoch, so as to generate the quality index QI and health index HI of different satellites at different frequencies. The extraction formula based on the carrier phase residuals of the ground monitoring station is shown in Equation (1):

[0032] (1)

[0033] In the formula, Indicates ground monitoring station Observed satellites In the Carrier phase residual at the frequency point; This indicates that after various error corrections were made in advance, the ground monitoring station... Received satellite In the The carrier phase observations at the frequency point are subject to error corrections including: satellite phase deviation correction, tropospheric delay correction, antenna phase center offset (PCO) correction and antenna phase center variation (PCV) correction, Earth rotation correction, relativistic correction, phase entanglement correction, tidal correction, etc. Indicates ground monitoring station To satellite The geometric distance between them, where, It is the precise three-dimensional coordinates of the pre-known ground monitoring station. It is the satellite whose orbital corrections are used to correct the broadcast ephemeris. Precise orbital coordinates; Represents the speed of light; This indicates the satellite clock error correction number after adjusting the broadcast ephemeris. Precise clock error; and They represent ground monitoring stations. The tropospheric wet delay projection function and the estimated tropospheric wet delay; Indicates the first Ionospheric scale factor at frequency points; Indicates ground monitoring station Observed satellites The ionospheric slant delay after parameter recombination at the first frequency point; and They represent the first Frequency wavelength and ground monitoring station Observed satellites In the Ambiguity after recombining parameters of frequency points.

[0034] The carrier phase residuals generated by equation (1) are used as statistical samples and analyzed based on their statistical characteristics to generate two types of integrity support information: one is the quality index QI, which includes the mean and standard deviation of the residuals corresponding to each frequency point of all satellites; the other is the health index HI, which is used to comprehensively evaluate the health status of each satellite.

[0035] The method for real-time generation of the Quality Index (QI) is explained as follows: Considering the potential for outliers in the sample data, which can significantly affect the data distribution characteristics and make the tails of the data thicker than a normal distribution, a mixed distribution model is used to address this "fat-tailed phenomenon." Since the vast majority of sample data is concentrated in the middle portion, conforming to the assumption of a normal distribution, a normal distribution is used to fit this portion of the data. For the tail data exhibiting the "fat-tailed phenomenon," a Pareto distribution is used for fitting, which can better describe extreme values ​​and abnormally high values.

[0036] First, the data is segmented according to the threshold, and the 99th percentile is selected ( () is used as the threshold. The middle data points will be fitted using a normal distribution, and the tail data points will be fitted using a Pareto distribution. When When, a normal distribution is used to fit the data in the middle part, when At that time, the Pareto distribution was used to fit the tail data.

[0037] Considering the contributions of the normal and Pareto distributions, the final required mean and standard deviation are calculated. The results from both distributions are combined using a weighted average. The formula for calculating the combined mean is shown in equation (2):

[0038] (2)

[0039] In the formula, This is the overall average. and These represent the sample size of the middle part of the data and the sample size of the tail part of the data, respectively. It refers to each sample value in the middle part of the data; It is the mean of the Pareto distribution.

[0040] Expected value of Pareto distribution The calculation formula is shown in equation (3):

[0041] (3)

[0042] In the formula, For shape parameters; This is the scale parameter.

[0043] Scale parameters It is usually the minimum value of the tail data, and the calculation formula is shown in equation (4):

[0044] (4)

[0045] In the formula, It refers to each sample value in the tail data.

[0046] Shape parameters It can be estimated using maximum likelihood estimation, and the calculation formula is shown in equation (5):

[0047] (5)

[0048] Composite Standard Deviation The variance can also be calculated using weighted variance, as shown in equation (6):

[0049] (6)

[0050] In the formula, The meaning of the other parameters is the same as that of equation (2), which is the comprehensive standard deviation.

[0051] When fitting a normal distribution, the sample size is an important factor. Generally, a larger sample size results in a better fit and is more conducive to accurately describing the data distribution. The selection of the sample size is addressed using the sliding window approach.

[0052] Using the carrier phase residuals extracted throughout the entire window as samples, a hybrid distribution model is employed to calculate the values ​​at each time step. Corresponding satellite In the mean at frequency points and standard deviation ,like Figure 2 As shown. Assume Figure 2 The length of the sliding window in the middle is ,because to In the initial stage, the quality index (QI) at any point during this period is not included in the calculation. At the start of the time, the first set of QIs is generated; each set of QIs should cover the quality indicators corresponding to all satellites and their respective frequency points.

[0053] The first group of quality indicators (QI) corresponds to At any given time, it is necessary to sequentially cycle through each frequency point of each satellite and store the data. to The carrier phase residuals extracted from all ground monitoring stations within the window are then used to determine the comprehensive mean and comprehensive standard deviation for each frequency point of each satellite according to a mixed distribution model. After the loop is completed, the resulting series of means and standard deviations will serve as the first set of QIs. This process continues, with the nth set of QIs corresponding to... At any time, it needs to be based on to QI is calculated using the phase residuals of all stations within the window.

[0054] The method for generating the health indicator HI in real time is explained as follows: First, a detection threshold needs to be constructed. The construction of the detection threshold will simultaneously consider the user's integrity risk probability and continuity risk probability.

[0055] To ensure that the integrity risk does not exceed the integrity risk requirement, a detection threshold corresponding to the integrity risk needs to be found such that equation (7) is satisfied:

[0056] (7)

[0057] In the formula, yes Time Satellite In the Carrier phase residual at the frequency point; yes Time Satellite In the Integrity risk detection threshold at a specific frequency point; It is a pre-set requirement for integrity risk; yes Total number of satellites at any given time.

[0058] Integrity risk detection threshold The calculation formula is shown in equation (8):

[0059] (8)

[0060] In the formula, It is the critical value of the quantile corresponding to the integrity risk on the normal distribution.

[0061] It can be calculated using equation (9):

[0062] (9)

[0063] In the formula, Indicates As the mean, with It is the inverse function of the normal distribution of standard deviation.

[0064] Similarly, to ensure that the continuity risk does not exceed the continuity risk requirement, it is necessary to find a detection threshold corresponding to the continuity risk such that equation (10) is satisfied:

[0065] (10)

[0066] In the formula, yes Time Satellite In the Continuity risk detection threshold at specific frequency points; This is a pre-defined requirement for continuous risk.

[0067] The formula for calculating the continuity risk detection threshold is shown in equation (11):

[0068] (11)

[0069] In the formula, It is the critical value of the quantile corresponding to the continuous risk normal distribution.

[0070] It can be calculated using equation (12):

[0071] (12)

[0072] To ensure that the system meets both integrity risk requirements and continuity risk requirements, the larger of the thresholds calculated for integrity risk and continuity risk is selected as the final detection threshold, as shown in equation (13):

[0073] (13)

[0074] In the formula, for Time Satellite No. The detection threshold corresponding to the frequency point.

[0075] because The period before this point is the initialization phase; therefore, no quality indicators (QI) or health indicators (HI) are generated during this time. Starting at time 1, a health indicator HI will be generated progressively in each subsequent epoch. Specifically, the carrier phase residual at each time step... With detection threshold The comparison is performed; if the residual exceeds the detection threshold, the satellite is determined to be faulty. At frequency If an anomaly is detected, an alarm needs to be triggered. In this case, the health indicator corresponding to the frequency point is set to 1; otherwise, the health indicator is set to 0, as shown in equation (14).

[0076] (14)

[0077] In the formula, For satellite In the Health indicators at specific frequencies (HI).

[0078] S2. The quality index (QI) and health index (HI) are broadcast to the low-Earth orbit (LEO) satellite. The LEO satellite refines its orbit calculations based on the received QI and HI to obtain its orbital coordinates. This includes: broadcasting the real-time generated QI and HI to the LEO satellite's onboard receiver via a GNSS satellite. Upon receiving this information, the LEO satellite refines the traditional elevation angle weighted stochastic model used in its real-time orbit determination process based on QI and HI, thereby improving orbit determination accuracy and obtaining higher-precision LEO satellite orbital coordinates. Specifically, this includes:

[0079] When determining the orbit of a low-Earth orbit satellite, an elevation angle-weighted stochastic model is usually used, as shown in equation (15):

[0080] (15)

[0081] In the formula, and They are respectively Time Satellite In the Standard deviations of pseudorange and carrier phase observations at frequency points; and These are the pseudorange factor and the carrier phase factor, typically set to values ​​of [value missing]. and ; and The elevation angle model factor is usually taken as... ; for Time Satellite The altitude angle.

[0082] from At any given moment, once the onboard receiver of a low-Earth orbit satellite receives two types of integrity support information in real time, it determines the faulty satellite at each moment based on the Health Index (HI). Since each satellite can simultaneously receive signals from different frequencies, as long as a satellite exists... At frequency If HI equals 1, then the satellite Satellites identified as faulty will be removed from the list at that epoch. .

[0083] After removing faulty satellites, the elevation angle weighted stochastic model for low-Earth orbit satellite orbit determination is further refined based on the quality index (QI). Time Satellite In the mean at frequency points Less than In the latter case, the stochastic model still uses the traditional elevation angle-weighted stochastic model. Conversely, it needs to be changed to... Time Satellite In the Standard deviation at frequency point Introduced into the elevation angle weighted stochastic model, as shown in equation (16):

[0084] (16)

[0085] In the formula, and After refining with QI and HI respectively Time Satellite In the Standard deviation of pseudorange observations and standard deviation of carrier phase observations at frequency points; This indicates that the result obtained through equation (6) Time Satellite In the Overall standard deviation at frequency points; This indicates that the result obtained through equation (2) Time Satellite In the The overall average value across frequency points; Indicates satellite In the The 95th percentile of all composite standard deviations within a day at a given frequency point.

[0086] S3. Optimize the configuration of ground monitoring stations and low-Earth orbit (LEO) satellites to construct a space-ground integrated monitoring network. Using the orbital coordinates of the LEO satellites as input, the network regenerates integrity support information, including the Quality Indicator (QI) and Health Indicator (HI), in real time. This includes: optimizing the number of ground monitoring stations and LEO satellites using a particle swarm optimization algorithm, and reconstructing the space monitoring network based on the optimization results. This monitoring network receives observation data from GPS, Galileo, and BDS-3 satellites and extracts carrier phase residuals using the non-combined multi-frequency PPP-AR model from step S1. The residual extraction process for ground monitoring stations is the same as in step S1, while the residual extraction process for LEO satellites requires the LEO precise orbit obtained in step S2 as the true value input. Subsequently, based on the extracted carrier phase residuals from GPS, Galileo, and BDS-3 satellites, the network regenerates two types of integrity support information, including the Quality Indicator (QI) and Health Indicator (HI), in real time using the same method as in step S1. Specifically, this includes:

[0087] The Particle Swarm Optimization (PSO) algorithm first needs to define the optimization objective and constraints, and then use a multi-objective function to represent the system's performance index, as shown in equation (17):

[0088] (17)

[0089] In the formula, , , , These are weighting coefficients, reflecting the importance of each objective in the optimization process; It refers to the particle position, calculated by ground monitoring stations. and low-orbit satellites composition; It is the system's accuracy function, which can be calculated based on observation residuals and satellite orbit accuracy; It is the system's coverage capability, taking into account areas around the world, including polar regions and oceans, which are difficult to cover through ground monitoring stations; It is a real-time requirement of the system, measuring how many low-Earth orbit satellites can meet the needs of real-time updates; It is the resource cost, which measures the construction and operation costs of ground monitoring stations and low-orbit satellites.

[0090] Further constraints need to be set. Coverage must meet global requirements, especially in polar regions and oceans. Accuracy must meet system requirements, such as the maximum permissible values ​​for satellite orbit and clock errors. Real-time requirements necessitate sufficient low-Earth orbit satellites to ensure rapid data updates. Costs must not exceed the budget to avoid wasting redundant facilities.

[0091] Then, a set of particles is initialized. These particles represent different ground monitoring station and low-Earth orbit satellite configurations, and each particle contains two types of information: position and velocity. For position information, the particle's current position represents a possible configuration, typically a combination of the number of ground monitoring stations and the number of low-Earth orbit satellites. For velocity information, the particle's velocity determines the change in the particle's position in the next iteration.

[0092] The particle position and velocity can be randomly generated within a predefined search space, as shown in equation (18):

[0093] (18)

[0094] In the formula, and These refer to the required number of ground monitoring stations and low-orbit satellites, respectively. and This represents the minimum number of ground monitoring stations and low-Earth orbit satellites. and This represents the maximum number of ground monitoring stations and low-orbit satellites.

[0095] In each iteration, the algorithm evaluates the objective function value for each particle. For each particle, its objective function is calculated. Based on this result, the individual optimal position and the group optimal position of the particles are updated.

[0096] For an individual's optimal position, each particle records a position that minimizes or maximizes the objective function value and considers it its optimal position. For the swarm's optimal position, the position of the best particle in the entire swarm is the position that minimizes or maximizes the global objective function value.

[0097] Finally, the particle velocity and position are updated. The optimal solution is searched by updating the particle velocity and position, where the velocity update equation and position update equation are shown in equation (19):

[0098] (19)

[0099] In the formula, for The individual's optimal position at any given moment; for The optimal position for the group at any given moment; It is a particle At any moment speed; It is a particle At any moment Location; It is the inertial weight, which controls the retained portion of the particle velocity; and It is the acceleration constant, which controls the tracking speed of particles towards their individual and group optimal positions; and It is a random number between [0,1], which increases the randomness of the search.

[0100] By continuously iterating and updating the position and velocity of the particles, the particle swarm will gradually converge to the optimal solution, which is the best configuration balancing accuracy, coverage, real-time performance, and cost. The stopping condition for convergence can typically be based on two situations. One is the maximum number of iterations, where a maximum number of iterations is set, and the algorithm stops after reaching this maximum. The other is objective function convergence, where the algorithm is considered to have converged and optimization stops when the change in the objective function value is very small, or when the objective function value has not improved significantly over several iterations.

[0101] The algorithm described above will output an optimal configuration of ground monitoring stations and low-orbit satellites. This optimal solution can be used to guide the design of satellite navigation monitoring systems, ensuring that resource waste is minimized while meeting the requirements of accuracy, real-time performance, coverage, and cost.

[0102] After obtaining the optimal number of ground monitoring stations and low-Earth orbit satellites, a carrier phase residual extraction formula is constructed based on the low-Earth orbit satellites, as shown in Equation (20).

[0103] (20)

[0104] In the formula, Indicates low-orbit satellites Observed satellites In the Carrier phase residual at the frequency point; This indicates that after various error corrections have been made in advance, the low-orbit satellite... Received satellite In the The carrier phase observations at the frequency point, the error corrections here include: satellite phase deviation correction, PCO and PCV corrections at the satellite end and receiver end, relativistic correction, phase winding correction, etc. Indicates low-orbit satellites To satellite The geometric distance between them, where, These are the precise orbit coordinates of the low-Earth orbit satellite obtained from step S2. It is the satellite whose orbital corrections are used to correct the broadcast ephemeris. Precise orbital coordinates; Represents the speed of light; This indicates the satellite clock error correction number after adjusting the broadcast ephemeris. Precise clock error; Indicates the first Ionospheric scale factor at frequency points; Indicates low-orbit satellites Observed satellites The slant delay of the ionosphere after parameter recombination at the first frequency point; and They represent the first Frequency wavelength and low-Earth orbit satellites Observed satellites In the Ambiguity after frequency point parameter recombination and All of these represent weighting coefficients.

[0105] Based on equations (20) and (1), the carrier phase residuals of GPS, Galileo, and BDS-3 satellites after fixing the orbital coordinates of low-orbit satellites and the coordinates of ground monitoring stations are extracted respectively. In accordance with the process of generating integrity support information based on ground monitoring stations in step S1, the quality index QI and health index HI of different satellites at different frequency points are generated in real time.

[0106] S4. Broadcasting the integrity support information to ground PPP user terminals, including: broadcasting the quality index (QI) and health index (HI) generated by joint monitoring by ground monitoring stations and low-orbit satellites to different ground users via high-orbit communication satellites; ground-based single-frequency, dual-frequency, or triple-frequency or higher Precise Point Positioning (PPP) users refine the PPP stochastic model by receiving integrity support information from GPS, Galileo, and BDS-3 satellites. Specifically, this includes:

[0107] The integrity support information generated in step S3 is broadcast in real time to ground PPP single-frequency or multi-frequency users using high-orbit communication satellites.

[0108] When a PPP user receives an integrity support message, the first step is to use the Health Indicator (HI) to remove faulty satellites. For single-frequency PPP users, when a satellite... At frequency If HI equals 1, then the satellite is directly removed. For dual-band or triple-band PPP users, as long as the satellite... any frequency point If HI equals 1, then the satellite... Remove.

[0109] Further using the quality index QI, a random model of the ground user terminal is constructed according to equation (16) in step S2, thereby ultimately achieving high-precision and high-reliability positioning.

[0110] A satellite integrity monitoring device for satellite-ground coordination includes the following modules:

[0111] The integrity support information generation module extracts carrier phase residuals from GNSS satellite observation data from ground monitoring stations and generates integrity support information for GNSS satellite signals based on the carrier phase residuals, including quality index (QI) and health index (HI).

[0112] The orbit coordinate acquisition module broadcasts the quality index (QI) and health index (HI) to the low-Earth orbit (LEO) satellite. The LEO satellite refines its orbit calculation based on the received QI and HI to obtain its orbit coordinates.

[0113] The optimized integrity support information generation module optimizes the configuration of ground monitoring stations and low-Earth orbit satellites, constructs a space monitoring station network that integrates space and ground, takes the orbital coordinates of low-Earth orbit satellites as input, and then regenerates integrity support information, including quality index (QI) and health index (HI), in real time.

[0114] The positioning module broadcasts the integrity support information to the ground PPP user terminal, thereby achieving the final positioning.

[0115] An electronic device includes: one or more processors; and a memory for storing one or more programs, wherein when the one or more programs are executed by the one or more processors, the one or more processors cause the one or more processors to implement the method.

[0116] A computer-readable storage medium having executable instructions stored thereon, which, when executed by a processor, cause the processor to implement the method described thereon.

[0117] Those skilled in the art will readily understand that the above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for integrity monitoring of GNSS satellites in cooperation with ground stations, characterized in that, The method comprises the following steps: Step S1: extracting carrier phase residuals based on GNSS satellite observation data of ground monitoring stations, and generating integrity support information of GNSS satellite signals based on the carrier phase residuals, including quality indicators QI and health indicators HI; Step S2: broadcasting the quality indicators QI and the health indicators HI to low-orbit satellites, and refining orbit calculation based on the received quality indicators QI and health indicators HI to obtain orbit coordinates of the low-orbit satellites; Step S3: optimizing the configuration of the ground monitoring stations and the low-orbit satellites, constructing a star-ground integrated space monitoring station network, extracting carrier phase residuals of GNSS satellites after fixing the orbit coordinates of the low-orbit satellites and the coordinates of the ground monitoring stations, and generating integrity support information including the quality indicators QI and the health indicators HI in real time according to the process of generating integrity support information based on the ground monitoring stations in step S1; Step S4: broadcasting the integrity support information to ground user terminals, thereby realizing final positioning. 2.The GNSS integrity monitoring method according to claim 1, wherein, In step S1, the extraction formula of the carrier phase residuals is shown in formula (1): （1） wherein denotes the ground monitoring station observed satellite at the carrier phase residual at the denotes the ground monitoring station received satellite at the carrier phase observation at the denotes the geometric distance between the ground monitoring station and the satellite wherein is the precise three-dimensional coordinate of the ground monitoring station known in advance, is the precise orbital coordinate of the satellite after the satellite orbit correction number corrects the broadcast ephemeris; denotes the speed of light; denotes the precise clock error of the satellite after the satellite clock correction number corrects the broadcast ephemeris; and denote the tropospheric wet delay projection function and the estimated tropospheric wet delay of the ground monitoring station respectively; denotes the ionospheric scale factor of the carrier; denotes the ionospheric slant delay of the satellite observed at the first carrier by the ground monitoring station after parameter reorganization; and denote the wavelength of the carrier and the ambiguity of the satellite observed at the carrier by the ground monitoring station after parameter reorganization respectively. The carrier phase residuals generated by formula (1) are taken as statistical samples, and two types of integrity support information, the quality indicators QI and the health indicators HI, are generated.

3. The method according to claim 2, wherein, The real-time generation method of the quality indicators QI is as follows: First, the mixed distribution model is constructed, specifically: according to the threshold, the data is segmented, and the 99% quantile is selected As a threshold, the middle part of the data points will be fitted using a normal distribution, and the tail part of the data points will be fitted using a Pareto distribution, when The normal distribution is used to fit the middle part of the data, and when The Pareto distribution is used to fit the tail data; the results of the two distributions are combined by weighted average to obtain the comprehensive mean and comprehensive standard deviation, and the calculation formula of the comprehensive mean is shown as formula (2): （2） wherein is the overall mean; and denote the sample size of the middle portion data and the sample size of the tail portion data, respectively; is each sample value in the middle portion data; is the mean of the Pareto distribution; Composite Standard Deviation The calculation formula is shown in equation (6): （6） In the formula, is the composite standard deviation; The sample quantity is processed by using a sliding window, the carrier phase residuals extracted within the window are taken as samples, and a mixed distribution model is used to calculate the quality index QI of each time point The corresponding satellite In the first The mean value of the frequency point And the standard deviation The length of the sliding window is The first group of quality indexes QI corresponds to The time point, each frequency point of each satellite is circulated in turn, and the carrier phase residuals extracted from all ground monitoring stations within the window are stored To Then, according to the mixed distribution model, the comprehensive mean value and the comprehensive standard deviation of each frequency point of each satellite are determined, after the circulation is completed, a series of mean values and standard deviations are generated, which will be taken as the first group of quality indexes QI, and so on, the nth group of quality indexes QI will correspond to The time point, and the quality index QI is calculated based on the phase residuals of all stations within the window from To ​ 4. The method according to claim 2, wherein, The real-time generation method for the Health Indicator (HI) is explained as follows: First, construct detection thresholds, including an integrity risk detection threshold and a continuity risk detection threshold; then select the larger value between the detection thresholds calculated from integrity risk and continuity risk as the final detection threshold. ,from Starting from time 1, each epoch will gradually generate a health indicator HI, which will include the carrier phase residual at each time step. With the final detection threshold The comparison is performed; if the phase residual exceeds the detection threshold, the satellite is determined to be faulty. At frequency If an anomaly is detected, the corresponding health indicator for that frequency point is set to 1; otherwise, the health indicator is set to 0.

5. The method according to claim 1, wherein, Step S2 specifically comprises: When determining the orbit of low-Earth orbit satellites, an elevation angle-weighted stochastic model is used, from... From the moment the satellites in low Earth orbit receive two types of integrity support information in real time, they determine the faulty satellites at each moment based on the health index HI and remove them directly at the epoch. After removing faulty satellites, the elevation angle weighted stochastic model for low-Earth orbit satellite orbit determination is further refined based on the quality index (QI). Time Satellite In the mean at frequency points Less than When using an elevation angle-weighted stochastic model, use the model for elevation angle weighting; otherwise, use... Time Satellite In the Standard deviation at frequency point Introduced into the height angle weighted stochastic model, Indicates satellite In the The 95th percentile of all composite standard deviations within a day at a given frequency point.

6. The method according to claim 1, wherein, Step S3 specifically comprises: using a particle swarm optimization algorithm to optimize the configuration of the ground monitoring stations and the low-orbit satellites, the particle swarm optimization algorithm first defines optimization objectives and constraint conditions, then initializes a group of particles, the particles represent different configurations of the ground monitoring stations and the low-orbit satellites, each particle contains two types of information, position and velocity, for the position information, the current position of the particle represents a possible configuration, for the velocity information, the velocity of the particle determines the change of the position of the particle in the next iteration; the particle position and velocity are randomly generated in a pre-defined search space, and finally the particle velocity and position are updated, the optimal solution is searched by updating the particle velocity and position, and an optimal configuration of the ground monitoring stations and the low-orbit satellites is output.

7. The method according to claim 1, wherein, S4 specifically comprises: When the user receives the integrity support information, first, the health index HI is used to eliminate the faulty satellite. If it is a single-frequency user, when the HI of the satellite at the frequency point is equal to 1, the satellite is directly eliminated. If it is a dual-frequency or triple-frequency user, as long as the HI of the satellite at any frequency point is equal to 1, the satellite is eliminated. Further, the quality index QI is used to construct a random model of the ground user end, so as to finally realize high-precision and high-trust positioning. 8.A satellite-ground cooperative GNSS satellite integrity monitoring apparatus, characterized in that, The method comprises the following modules: An integrity support information generation module for extracting carrier phase residuals based on GNSS satellite observation data of ground monitoring stations, and generating integrity support information of GNSS satellite signals based on the carrier phase residuals, including quality indicators QI and health indicators HI; An orbit coordinate obtaining module for broadcasting the quality indicators QI and the health indicators HI to low-orbit satellites, and refining orbit calculation based on the received quality indicators QI and health indicators HI to obtain orbit coordinates of the low-orbit satellites; An optimized integrity support information generation module for optimizing the configuration of the ground monitoring stations and the low-orbit satellites, constructing a star-ground integrated space monitoring station network, extracting carrier phase residuals of GNSS satellites after fixing the orbit coordinates of the low-orbit satellites and the coordinates of the ground monitoring stations, and generating integrity support information including the quality indicators QI and the health indicators HI in real time according to the process of generating integrity support information based on the ground monitoring stations in step S1; The positioning module broadcasts the integrity support information to a ground PPP user terminal, thereby realizing final positioning.

9. An electronic device, comprising: comprising: one or more processors; a memory storing one or more programs, wherein the one or more programs, when executed by the one or more processors, cause the one or more processors to carry out the method of any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, a processor configured to implement a method recited in any one of claims 1 to 7. a processor configured to implement a method recited in any one of claims 1 to 7.

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