A method and system for generating integrity support information parameters applicable to BeiDou

Abstract

The present invention proposes a method and system for generating integrity support information parameters applicable to Beidou. After collecting observation data of each orbital satellite of the Beidou system and calculating user distance errors accordingly to obtain an initial sample data set, the initial sample data set is classified by an FCM clustering algorithm to obtain a multi-class sample data set, and the worst-case URE distribution of each tail of the multi-class sample data set is selected to evaluate the broadcast URA of the Beidou system ground monitoring station, and the broadcast URA that best meets the Beidou system satellites is obtained and sent to the user, so that the obtained broadcast URA is more in line with the requirements of the Beidou system integrity monitoring, and then the ISM parameters broadcast by the ground monitoring station meet the Beidou system performance, thereby reducing the integrity risk when using ARAIM.

Description

Technical Field

[0001] The present invention relates to the field of satellite navigation technology, and in particular to a method and system for generating integrity support information parameters applicable to Beidou. Background Art

[0002] In 2008, the Federal Aviation Administration of the United States proposed the concept of Advanced Receiver Autonomous Integrity Monitor (ARAIM) for the first time based on the development status of multi-constellation and multi-frequency satellite navigation systems: supporting the use of dual-frequency signals to eliminate ionospheric errors; supporting the simultaneous use of multiple navigation constellations; utilizing the globally distributed ground monitoring network; and using the Integrity Support Message (ISM) provided by ground monitoring stations to timely adjust the performance parameters of each core constellation, ultimately achieving the vertical navigation performance requirement LPV-200 on a global scale.

[0003] In recent years, with the continuous development and breakthroughs of ARAIM technology, the use of dual-frequency multi-constellation ARAIM still has the following problems: the ISM generation algorithm currently applicable to GPS is not applicable to the Beidou system. This is because the Beidou constellation has significant differences in spatial architecture from other constellations. If the three Beidou satellites in different orbits all use a set of ISM parameters to provide to users, it may lead to greater integrity risks.

[0004] The United States has obtained ISM parameters that meet GPS performance through statistical analysis of GPS navigation data over a long period of time. However, the BeiDou system has a short operating time and lacks long-term stable navigation data to analyze its performance. If the ISM algorithm of GPS is directly used, the BeiDou ISM parameter values ​​may be too optimistic, which will lead to a greater integrity risk. Considering that BeiDou has three different orbits, it is necessary to consider the different sizes of errors in each orbit and other practical factors to generate URE and URA that meet BeiDou performance, and it is necessary to calculate the worst-case URE calculation method from the perspective of integrity, and then evaluate whether the accuracy of the current BeiDou URA broadcast meets BeiDou performance.

[0005] In view of the above problems, it is necessary to provide a method and system for generating integrity support information parameters applicable to the Beidou system, so that satellites in different orbits of the Beidou system can adjust the performance parameters of each core constellation in time through the integrity support messages provided by ground monitoring stations, thereby improving the overall performance of the Beidou system. Summary of the invention

[0006] The purpose of the present invention is to provide a method and system for generating integrity support information parameters suitable for Beidou, so as to solve the problem that the Beidou system has a short operating time and lacks long-term stable navigation data to analyze its performance, resulting in that the performance parameters of each core constellation cannot be adjusted in time by the integrity support messages provided by the ground monitoring station for the different orbital satellites of the Beidou system, and the existing GPS ISM algorithm is not suitable for the satellites on the different orbits of the Beidou system, which easily leads to a greater integrity risk problem.

[0007] To achieve the above object, on the one hand, the present invention provides a method for generating integrity support information parameters applicable to BeiDou, the method comprising the following steps:

[0008] Set an initial sampling time and sampling interval, sample the satellite observation data of each orbit of the Beidou system within the sampling time and according to the sampling interval, and obtain the Beidou satellite observation data set of each orbit;

[0009] Calculate the user distance error URE using the satellite observation data in each orbit satellite observation data set of the Beidou to obtain an initial sample data set;

[0010] Using the FCM clustering algorithm to classify the initial sample data set according to the BeiDou system orbit number, to obtain a multi-class sample data set;

[0011] Selecting sample data at the tail of each category in the multi-category sample data set as the worst-case URE distribution;

[0012] Get the Beidou broadcast URA from the Beidou system ground monitoring station;

[0013] Using the worst case URE distribution to evaluate the Beidou broadcast URA, and obtain an evaluation result;

[0014] If the evaluation result is passed, the Beidou broadcast URA and the classified URE are added to the integrity support information and sent to the user;

[0015] If the evaluation result is failure, the Beidou broadcast URA of the Beidou system ground monitoring station is re-obtained and evaluated.

[0016] Optionally, the user range error URE is calculated using the satellite observation data in the Beidou satellite observation data set in each orbit, specifically including:

[0017] Remove abnormal observation data from Beidou satellite observation data sets; the abnormal observation data include satellite data with lost ephemeris data, unavailable health marks, and clock error failures;

[0018] The average URE value of each satellite observation data is obtained according to the Beidou global average URE calculation formula, and the average URE value of each satellite observation data constitutes the initial sample data set:

[0019]

[0020] Among them, Re is the radial error, Te is the satellite clock error, Ae is the tangential error, Ce is the normal error, rmsURE BDS is the average URE value.

[0021] Optionally, using the FCM clustering algorithm to classify the initial sample data set according to the Beidou system orbit number specifically includes:

[0022] Dividing the initial sample data set into c initial classes according to the number of BeiDou system orbits;

[0023] Determine the FCM objective function J and its constraints:

[0024]

[0025]

[0026] Among them, n is the number of sample data in the initial sample data set, x j is the position of the jth sample data, u ij is the membership degree of the jth sample data to the ith initial class, C i represents the class center of the i-th initial class, and m is the sample mildness factor;

[0027] The Lagrange multiplier method is used to solve the variables of the FCM objective function.

[0028] Optionally, the Lagrange multiplier method is used to solve the variables of the FCM objective function, specifically including:

[0029] Using the Lagrange multiplier method, the constraints are multiplied by the Lagrange coefficients and added to the objective function:

[0030]

[0031] For each of the u ij and C i Take the derivative and set their respective results to 0, and find u respectively ij The iterative formula and C i Iterative formula of ;

[0032] The u ij The iterative formula and the C i The iterative formula is brought into the objective function to find the current objective function J.

[0033] Optionally, after the initial sample data set is classified according to the Beidou system orbit number using the FCM clustering algorithm, before the multi-class sample data set is obtained, the method further includes:

[0034] Expanding the sampling time, sampling the satellite observation data of each orbit of the Beidou system according to the sampling interval within the expanded sampling time, and calculating the URE value of the sampled satellite observation data of each orbit to obtain an expanded sample data set;

[0035] Using the FCM clustering algorithm to classify the expanded sample data set and calculate the class center point;

[0036] Determine whether the difference between the objective function J1 of the expanded sample data set and the objective function J of the initial sample data set is less than a preset threshold,

[0037] If yes, then adding the expanded sample data set to the initial sample data set to obtain a multi-class sample data set;

[0038] If not, a criterion function is set to continue to expand the sampling time of the expanded sample data set, and recalculate the class center point of the expanded sample data set until the criterion function converges, and the expanded sample data set is added to the initial sample data set to obtain a multi-class sample data set;

[0039] The criterion function is:

[0040]

[0041] Among them, i is the number of classes in the initial sample data set, C i is the center point of the initial sample data set, C sat,k is the center point of the new data class, the number is n, dist(C sat,k ,C i ) 2 is the distance from the center point of the expanded sample data set class to the center point of the initial sample data set class.

[0042] Optionally, obtaining the BeiDou broadcast URA of the BeiDou system ground monitoring station specifically includes:

[0043] The BeiDou broadcast URAI value is obtained from the broadcast data of the BeiDou system ground monitoring station;

[0044] The Beidou broadcast URAI value is converted to the Beidou broadcast URA value according to the conversion rule of URAI and URA; the conversion rule of URAI and URA is:

[0045] When 0≤N<6,

[0046] When 6≤N<15, URA=2 N-2 ;

[0047] When N = 15, it means that the satellite orbit is maneuvering or there is no accurate prediction;

[0048] When N=1, 3, and 5, the URA is rounded to 2.8, 5.7, and 11.3, respectively.

[0049] Optionally, evaluating the Beidou broadcast URA by using the worst case URE distribution specifically includes:

[0050] Calculating the worst-case URA distribution according to the worst-case URE distribution;

[0051] The worst-case URA distribution is compared with the Beidou broadcast URA to determine whether the Beidou broadcast URA can encompass the worst-case URA with a preset probability; if so, the evaluation result is passed; if not, the evaluation result is failed.

[0052] Corresponding to the aforementioned integrity support information parameter generation method, the present invention also provides an integrity support information parameter generation system applicable to Beidou, and the system executes the aforementioned integrity support information parameter generation method when run by a processor.

[0053] According to the specific embodiments provided by the present invention, the present invention discloses the following technical effects:

[0054] The present invention provides a method and system for generating integrity support information parameters applicable to Beidou. Firstly, after collecting data sets of each orbital satellite of the Beidou system and calculating user distance errors accordingly to obtain an initial sample data set, the initial sample data set is classified by an FCM clustering algorithm to obtain a multi-class sample data set, and then the worst-case URE distribution of each tail of the multi-class sample data set is selected to evaluate the broadcast URA of the Beidou system ground monitoring station, so as to obtain the broadcast URA that best meets the broadcast URA of each satellite of the Beidou system and send it to the user; in the present application, a fuzzy clustering algorithm is introduced to classify the user distance error URE data set of each orbital satellite of the Beidou system; the worst-case URE distribution is selected to evaluate the Beidou broadcast URA, which is conducive to setting a more conservative threshold in the ARAIM algorithm, which is more in line with the requirements of integrity monitoring, and thus makes the Beidou ISM parameters meet the Beidou system performance. Different orbital satellites of the Beidou system can timely adjust the performance parameters of each core constellation through the integrity support telegram provided by the ground monitoring station, thereby reducing the integrity risk when using ARAIM. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0056] Figure 1 A flowchart of a method for generating integrity support information parameters applicable to BeiDou provided in Embodiment 1 of the present invention;

[0057] Figure 2 This is a flow chart of step A2 in the method provided in Example 1 of the present invention;

[0058] Figure 3 A flowchart of classifying an initial sample data set in step A3 of the method provided in embodiment 1 of the present invention;

[0059] Figure 4 This is a flow chart of step A3 after classification and before obtaining a multi-class sample data set in the method provided in embodiment 1 of the present invention;

[0060] Figure 5 A structural diagram of an integrity support information parameter generation system suitable for Beidou provided in Example 2 of the present invention.

[0061] Symbol explanation:

[0062] 1: Satellite observation data acquisition module; 2: Satellite observation data set processing module; 2-1: Abnormal data removal unit; 2-2: URE calculation unit; 3: FCM classification module; 4: Data set expansion module; 4-1: Sampling time expansion unit; 4-2: Classification comparison unit; 5: Beidou broadcast URA acquisition module; 5-1: URAI acquisition unit; 5-2: URA conversion unit; 6: URA evaluation module; 7: Information sending module. DETAILED DESCRIPTION

[0063] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0064] The purpose of the present invention is to provide a method and system for generating integrity support information parameters suitable for Beidou, so as to solve the problem that the Beidou system has a short operating time and lacks long-term stable navigation data to analyze its performance, while the existing GPS ISM algorithm is not suitable for the Beidou system, which easily leads to a greater integrity risk.

[0065] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the present invention is further described in detail below with reference to the accompanying drawings and specific embodiments.

[0066] Embodiment 1:

[0067] like Figure 1 As shown in the flowchart, this embodiment provides a method for generating integrity support information parameters applicable to Beidou, and the method comprises the following steps:

[0068] S1. Set the initial sampling time and sampling interval, sample the satellite observation data of each orbit of the Beidou system according to the sampling interval within the sampling time to obtain the Beidou satellite observation data set of each orbit; take 15 minutes as the sampling interval, and select 10 days of satellite data as the Beidou satellite observation data set of each orbit;

[0069] S2. Calculate the user distance error URE using the satellite observation data in each orbit satellite observation data set of the Beidou to obtain an initial sample data set;

[0070] In specific implementation, Figure 2 In the flowchart shown, step S2 specifically includes:

[0071] S21, removing abnormal observation data from the Beidou satellite observation data sets; the abnormal observation data includes satellite data with lost ephemeris data, unavailable health marks, and clock error failures;

[0072] S22. Obtain the average URE value of each satellite observation data according to the Beidou global average URE calculation formula, and form the initial sample data set with the average URE value of each satellite observation data:

[0073]

[0074] Among them, Re is the radial error, Te is the satellite clock error, Ae is the tangential error, Ce is the normal error, rmsURE BDS is the average URE value.

[0075] S3, using the FCM clustering algorithm to classify the initial sample data set according to the Beidou system orbit number to obtain a multi-class sample data set;

[0076] S4, selecting sample data at the tail of each category in the multi-category sample data set as the worst case URE distribution;

[0077] S5. Obtain the Beidou broadcast URA from the Beidou system ground monitoring station;

[0078] S6. Evaluate the Beidou broadcast URA using the worst case URE distribution to obtain an evaluation result;

[0079] S7. If the evaluation result is passed, the Beidou broadcast URA and the classified URE are added to the integrity support information and sent to the user;

[0080] If the evaluation result is failure, the Beidou broadcast URA of the Beidou system ground monitoring station is re-obtained and evaluated.

[0081] like Figure 3 As shown, step S3, before obtaining the multi-class sample data set, classifies the initial sample data set according to the Beidou system orbit number using the FCM clustering algorithm, specifically including:

[0082] S31, dividing the initial sample data set into c initial classes according to the BeiDou system orbit number; dividing it into C initial classes using the average URE value obtained from the initial sample data set and the BeiDou system orbit number sat,1 , C sat,2 , C sat,3 , three initial categories; if these data are divided into C sat,i class, corresponding to i classes with the center point C i ;

[0083] S32, determine the FCM objective function J and its constraints:

[0084]

[0085]

[0086] Each sample j belongs to a class C sat,i The membership degree is u ij According to the normalization principle of FCM, the sum of the membership of a data set is always equal to 1; where n is the number of sample data in the initial sample data set, x j is the position of the jth sample data, u ij is the membership degree of the jth sample data to the ith initial class, C i represents the class center of the i-th initial class, and m is the sample gentleness factor; since each component is calculated as a square, in the present invention, setting the sample gentleness factor m=2 is most suitable for the data sample characteristics;

[0087] S33. Use Lagrange multiplier method to solve the variables of FCM objective function:

[0088] Using the Lagrange multiplier method, the constraints are multiplied by the Lagrange coefficients and added to the objective function:

[0089]

[0090] For each of the u ij and C i Take the derivative and set their respective results to 0, and find u respectively ij The iterative formula and C i Iterative formula of ;

[0091] J vs u ij The derivative of and setting it equal to 0 is:

[0092]

[0093] Simplifying the above formula, we can get

[0094]

[0095] Substituting the constraints into the j :

[0096]

[0097] Continuing to simplify (replace i with k), we get:

[0098]

[0099] Finally, we get u ij The iteration formula is:

[0100]

[0101] In the above formula, the numerator is the distance of this point to the center of a certain class, and the denominator is the sum of the distances of this point to the centers of all classes. The division between the two represents the weight of the distance from this point to the center of a certain class to the sum of the distances from this point to the centers of all classes. The smaller the numerator is, the closer it is to this class, and the larger the overall score is, that is, the corresponding u ij The larger it is, the more the sample data belongs to this class;

[0102] Similarly, for C i Taking the derivative and setting the result to 0 gives:

[0103]

[0104] Simplify and find C iThe iteration formula is:

[0105]

[0106] The FCM class center point iteration adopts a weighted average method, class C sat,i After determination, first sum the membership U of all points to the class, and then for each point, the membership divided by this sum is the proportion, multiplied by x j This is the contribution value of this point to this class i.

[0107] S34, the u ij The iterative formula and the C i The iterative formula is brought into the objective function to find the current objective function J.

[0108] In some implementations, in order to evaluate the rationality of classifying the sample data set, the generation method further includes:

[0109] The information entropy of the expanded sample data set is calculated according to the information entropy formula; the information entropy formula is:

[0110]

[0111] Among them, P K represents the proportion of the K-th category samples in the expanded sample data set;

[0112] The information gain obtained by classifying the expanded sample data set is calculated according to the information gain formula; the information gain formula is:

[0113]

[0114] The center point of each class in the expanded sample data set X is defined as the discrete attribute a of Beidou URE. Using a to divide the expanded sample data set X will generate n branch points, where the vth branch point is recorded as X. v , contains all the values ​​a in attribute a in the expanded sample data set X v Samples of

[0115] According to the information gain result, the rationality of clustering the sample data set is judged; if the information gain is greater than the information gain of clustering based on the center points of other classes, it means that the performance obtained by clustering based on the center point of the current class is more in line with the actual URE of Beidou.

[0116] To evaluate the URE value that is more consistent with the performance of the BeiDou system, it is necessary to collect satellite observation data for a longer period of time, such as Figure 4As shown, after the initial sample data set is classified according to the Beidou system orbit number using the FCM clustering algorithm, and before the multi-class sample data set is obtained, the method further includes:

[0117] S35, expanding the sampling time, sampling the satellite observation data of each orbit of the Beidou system according to the sampling interval within the expanded sampling time, and calculating the URE value of the sampled satellite observation data of each orbit to obtain an expanded sample data set;

[0118] S36, using the FCM clustering algorithm to classify the expanded sample data set and calculate the class center point;

[0119] S37: Determine whether the difference between the objective function J1 of the expanded sample data set and the objective function J of the initial sample data set is less than a preset threshold.

[0120] S38, if yes, then add the expanded sample data set to the initial sample data set to obtain a multi-class sample data set;

[0121] If not, a criterion function is set to continue to expand the sampling time of the expanded sample data set, and recalculate the class center point of the expanded sample data set until the criterion function converges, and the expanded sample data set is added to the initial sample data set to obtain a multi-class sample data set;

[0122] The criterion function is:

[0123]

[0124] Among them, i is the number of classes in the initial sample data set, C i is the center point of the initial sample data set, C sat,k is the center point of the new data class, the number is n, dist(C sat,k ,C i ) 2 The distance from the center point of the expanded sample data set class to the center point of the initial sample data set class;

[0125] The convergence of the criterion function specifically means that the criterion function is smaller than a preset convergence threshold, that is, the center point of the expanded sample data set class and the center point of the initial sample data set class no longer have obvious changes.

[0126] After obtaining multiple sample data sets, you need to first obtain the broadcast information sent by the BeiDou system ground monitoring station. The BeiDou broadcast URA of the BeiDou system ground monitoring station includes:

[0127] The BeiDou broadcast URAI value is obtained from the broadcast data of the BeiDou system ground monitoring station;

[0128] The Beidou broadcast URAI value is converted to the Beidou broadcast URA value according to the conversion rule of URAI and URA; the conversion rule of URAI and URA is:

[0129] When 0≤N<6,

[0130] When 6≤N<15, URA=2 N-2 ;

[0131] When N = 15, it means that the satellite orbit is maneuvering or there is no accurate prediction;

[0132] When N=1, 3, and 5, the URA is rounded to 2.8, 5.7, and 11.3, respectively.

[0133] The BeiDou broadcast URA is a relatively conservative 1σ estimate of the standard deviation of the corresponding URE distribution. It is generally required to envelop the URE standard deviation with a probability of 68% or to envelop 2 times the standard deviation of the URE with a probability of 95%. URA is mainly used to monitor various integrity indicators. In the present invention, the tail 5% data samples in each class are selected as the worst-case URE value distribution of the cluster with 95% as the quantile. Selecting the worst-case URE to evaluate the BeiDou broadcast URA is conducive to setting a more conservative threshold in the ARAIM algorithm, which is more in line with the requirements of integrity monitoring.

[0134] In order to make the acquired URA more conservative and more in line with the requirements of integrity monitoring, in this embodiment, using the worst case URE distribution to evaluate the Beidou broadcast URA specifically includes:

[0135] Calculating the worst-case URA distribution according to the worst-case URE distribution;

[0136] Compare the worst-case URA distribution with the Beidou broadcast URA to determine whether the Beidou broadcast URA can cover the worst-case URA with a preset probability; if so, the evaluation result is passed; if not, the evaluation result is failed;

[0137] Compare the URA calculated by the worst-case URE with the Beidou broadcast URA. If the Beidou broadcast URA can envelop the URA calculated by the worst-case URE with a probability of 68%, the ground station can include it in the ISM and broadcast it to the user; if not, the ground monitoring station should re-broadcast the Beidou broadcast URA, and should re-evaluate to obtain a URA value that is more in line with Beidou performance and broadcast it to the user.

[0138] The integrity support information parameter generation method applicable to Beidou proposed in this embodiment collects the observation data of each orbital satellite of the Beidou system and calculates the user distance error accordingly to obtain an initial sample data set, then flexibly classifies the initial sample data set through the FCM clustering algorithm, selects the worst-case URE distribution of each tail after classification to evaluate the broadcast URA of the Beidou system ground monitoring station, and obtains the broadcast URA that best meets the Beidou system satellites and sends it to the user; by introducing a fuzzy clustering algorithm, the user distance error URE data set of each orbital satellite of the Beidou system is classified and the worst-case URE distribution is selected to evaluate the Beidou broadcast URA, which is conducive to setting a more conservative threshold in the ARAIM algorithm and better meets the requirements of integrity monitoring.

[0139] Embodiment 2:

[0140] Corresponding to Example 1, the present invention also provides an integrity support information parameter generation system applicable to Beidou.

[0141] In addition, the method of embodiment 1 of the present invention can also be used by Figure 5 The architecture of the integrity support information parameter generation system applicable to BeiDou is shown in FIG. Figure 5 As shown, the integrity support information parameter generation system may include a satellite observation data acquisition module 1, a satellite observation data set processing module 2, an FCM classification module 3, a data set expansion module 4, a BeiDou broadcast URA acquisition module 5, a URA evaluation module 6 and an information sending module 7; some modules may also have sub-units for realizing their functions, for example, the satellite observation data set processing module 2 may also include an abnormal data removal unit 2-1 and a URE calculation unit 2-2; the data set expansion module 4 may also include a sampling time expansion unit 4-1 and a classification comparison unit 4-2; the BeiDou broadcast URA acquisition module 5 may also include a URAI acquisition unit 5-1 and a URA conversion unit 5-2. Of course, Figure 5 The architecture shown is only exemplary and can be omitted according to actual needs when implementing different functions. Figure 5 One or at least two components of the system shown.

[0142] The program part of the technology can be considered as a "product" or "manufactured product" in the form of executable code and / or related data, which is participated in or realized by computer-readable media. Tangible and permanent storage media can include any memory or storage used by any computer, processor, or similar device or related module. For example, various semiconductor memories, tape drives, disk drives or any similar devices that can provide storage functions for software.

[0143] All or part of the software may sometimes communicate over a network, such as the Internet or other communications network. Such communications can load software from one computer device or processor to another. For example: from a server or host computer of a video target detection device to a hardware platform of a computer environment, or other computer environment that implements the system, or a system with similar functions related to providing information required for target detection. Therefore, another medium capable of transmitting software elements can also be used as a physical connection between local devices, such as light waves, radio waves, electromagnetic waves, etc., transmitted through cables, optical cables or air. Physical media used to carry carriers, such as cables, wireless connections or optical cables and the like, can also be considered as media that carry software. As used herein, unless limited to tangible "storage" media, other terms referring to computer or machine "readable media" refer to media involved in the process of the processor executing any instructions.

[0144] The present invention uses specific examples to illustrate the principles and implementation methods of the present invention. The above examples are only used to help understand the method and core ideas of the present invention. Those skilled in the art should understand that the modules or steps of the present invention can be implemented by a general-purpose computer device. Alternatively, they can be implemented by a program code executable by a computing device, so that they can be stored in a storage device and executed by the computing device, or they can be made into integrated circuit modules, or multiple modules or steps can be made into a single integrated circuit module. The present invention is not limited to any specific combination of hardware and software.

[0145] Meanwhile, for those skilled in the art, according to the concept of the present invention, there will be changes in the specific implementation and application scope. In summary, the content of this specification should not be understood as limiting the present invention.

Claims

1. A method for generating integrity support information parameters applicable to BeiDou, characterized in that: The method comprises: Set an initial sampling time and sampling interval, sample the satellite observation data of each orbit of the Beidou system within the sampling time and according to the sampling interval, and obtain the Beidou satellite observation data set of each orbit; Calculate the user distance error URE using the satellite observation data in each orbit satellite observation data set of the Beidou to obtain an initial sample data set; Using the FCM clustering algorithm to classify the initial sample data set according to the BeiDou system orbit number, to obtain a multi-class sample data set; Selecting sample data at the tail of each category in the multi-category sample data set as the worst-case URE distribution; Get the Beidou broadcast URA from the Beidou system ground monitoring station; Using the worst case URE distribution to evaluate the Beidou broadcast URA, and obtain an evaluation result; If the evaluation result is passed, the Beidou broadcast URA and the classified URE are added to the integrity support information and sent to the user; If the evaluation result is failure, the Beidou broadcast URA of the Beidou system ground monitoring station is re-obtained and evaluated; The acquisition of the Beidou broadcast URA of the Beidou system ground monitoring station specifically includes: The BeiDou broadcast URAI value is obtained from the broadcast data of the BeiDou system ground monitoring station; The Beidou broadcast URAI value is converted to the Beidou broadcast URA value according to the conversion rule of URAI and URA; the conversion rule of URAI and URA is: When 0≤N<6, When 6≤N<15, URA=2 N-2 ; When N = 15, it means that the satellite orbit is maneuvering or there is no accurate prediction; When N = 1, 3, and 5, the URA is 2.8, 5.7, and 11.3 after rounding; The evaluating of the Beidou broadcast URA by using the worst case URE distribution specifically includes: Calculating the worst-case URA distribution according to the worst-case URE distribution; The worst-case URA distribution is compared with the Beidou broadcast URA to determine whether the Beidou broadcast URA can encompass the worst-case URA with a preset probability; if so, the evaluation result is passed; if not, the evaluation result is failed.

2. The method for generating integrity support information parameters according to claim 1, characterized in that: The method of calculating the user range error URE using the satellite observation data in the Beidou satellite observation data set in each orbit specifically includes: Remove abnormal observation data from Beidou satellite observation data sets; the abnormal observation data include satellite data with lost ephemeris data, unavailable health marks, and clock error failures; The average URE value of each satellite observation data is obtained according to the Beidou global average URE calculation formula, and the average URE value of each satellite observation data constitutes the initial sample data set: Among them, Re is the radial error, Te is the satellite clock error, Ae is the tangential error, Ce is the normal error, rmsURE BDS is the average URE value.

3. The method for generating integrity support information parameters according to claim 1, characterized in that: The method of using the FCM clustering algorithm to classify the initial sample data set according to the Beidou system orbit number specifically includes: Dividing the initial sample data set into c initial classes according to the number of BeiDou system orbits; Determine the FCM objective function J and its constraints: Among them, n is the number of sample data in the initial sample data set, x j is the position of the jth sample data, u ij is the membership degree of the jth sample data to the ith initial class, C i represents the class center of the i-th initial class, and m is the sample mildness factor; The Lagrange multiplier method is used to solve the variables of the FCM objective function.

4. The method for generating integrity support information parameters according to claim 3, characterized in that: The use of the Lagrange multiplier method to solve the FCM objective function variables specifically includes: Using the Lagrange multiplier method, the constraints are multiplied by the Lagrange coefficients and added to the objective function: For each of the u ij and C i Take the derivative and set their respective results to 0, and find u respectively ij The iterative formula and C i Iterative formula of ; The u ij The iterative formula and the C i The iterative formula is brought into the objective function to find the current objective function J.

5. The method for generating integrity support information parameters according to claim 1, characterized in that: After the FCM clustering algorithm is used to classify the initial sample data set according to the Beidou system orbit number, before the multi-class sample data set is obtained, the method further includes: Expanding the sampling time, sampling the satellite observation data of each orbit of the Beidou system according to the sampling interval within the expanded sampling time, and calculating the URE value of the sampled satellite observation data of each orbit to obtain an expanded sample data set; Using the FCM clustering algorithm to classify the expanded sample data set and calculate the class center point; Determine whether the difference between the objective function J1 of the expanded sample data set and the objective function J of the initial sample data set is less than a preset threshold; If yes, then adding the expanded sample data set to the initial sample data set to obtain the multi-class sample data set; If not, a criterion function is set to continue to expand the sampling time of the expanded sample data set, and recalculate the class center point of the expanded sample data set; Determining whether the criterion function converges, and if the criterion function converges, adding the expanded sample data set to the initial sample data set to obtain the multi-class sample data set; If the criterion function does not converge, then continue to expand the sampling time of the expanded sample data set, and recalculate the class center point of the expanded sample data set until the criterion function converges; The criterion function is: Among them, i is the number of classes in the initial sample data set, C i is the center point of the initial sample data set, C sat,k is the center point of the new data class, the number is n, dist(C sat,k ,C i ) 2 is the distance from the center point of the expanded sample data set class to the center point of the initial sample data set class.

6. A system for generating integrity support information parameters for BeiDou, characterized in that: When the system is run by a processor, the system executes the integrity support information parameter generation method as described in any one of claims 1 to 5.

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