A method for determining an ionospheric anomaly monitoring threshold of a beidou satellite-based augmentation system

Abstract

The application discloses a method for determining an ionosphere anomaly monitoring threshold of a Beidou satellite-based augmentation system, comprising the following steps: acquiring ionosphere piercing point vertical delay values and position information thereof; generating an ionosphere data set; acquiring ionosphere piercing point delay values in a fitting domain around a to-be-fitted point in the ionosphere data set, and calculating at least one monitoring statistic; and determining a monitoring statistic threshold according to a MaxEnt threshold algorithm and the monitoring statistic. K The application is directed to the ionosphere environment characteristics of a service area of the Beidou satellite-based augmentation system, the ionosphere state is monitored through ionosphere anomaly monitoring statistics, and the ionosphere anomaly monitoring threshold is re-analyzed and calculated, so that the Beidou satellite-based augmentation system service integrity under ionosphere anomaly conditions can be ensured, and meanwhile, a higher satellite-based augmentation system user service level can be provided under a quiet ionosphere condition.

Description

Technical Field

[0001] This application relates to the field of BeiDou satellite navigation, and more specifically, to a method for determining the ionospheric anomaly monitoring threshold of a BeiDou satellite-based augmentation system. Background Technology

[0002] For life safety applications such as civil aviation navigation, satellite-based augmentation systems can meet the system service performance requirements in four aspects: accuracy, integrity, service continuity, and availability, from the en-route flight phase to the vertical guidance precision approach phase. As one of the important broadcast parameters in the single-frequency augmentation service of satellite-based augmentation systems, the accuracy of the ionospheric delay correction and its integrity parameters will seriously affect its system service performance.

[0003] The ionosphere exhibits good spatiotemporal correlation under normal conditions, but this correlation decreases under abnormal conditions, making the augmentation system susceptible to integrity risks. Furthermore, the service area of ​​the BeiDou satellite-based augmentation system encompasses mid- and low-latitude regions, with varying ionospheric activity levels across these areas. However, current technologies for generating ionospheric delay corrections and integrity parameters suffer from inconsistencies in regional applicability, particularly in the unreasonable setting of ionospheric anomaly monitoring thresholds. This significantly reduces the service availability of the BeiDou satellite-based augmentation system, impacting its overall service performance.

[0004] Therefore, the existing technology has defects and needs to be improved. Summary of the Invention

[0005] In view of the above problems, the purpose of this invention is to provide a method for determining the ionospheric anomaly monitoring threshold of the BeiDou satellite-based augmentation system, so as to solve the problem that the existing ionospheric anomaly monitoring threshold setting cannot effectively monitor the ionospheric activity status in the service area of ​​the BeiDou satellite-based augmentation system, resulting in poor system service availability.

[0006] This invention provides a method for determining the ionospheric anomaly monitoring threshold of the BeiDou satellite-based augmentation system, comprising:

[0007] Acquire observation data within the target area, and preprocess and calculate the observation data to obtain the vertical delay value of the ionospheric puncture point. and its location information; the observation data includes: raw GNSS observation data, navigation message data, and precise ephemeris data;

[0008] Vertical delay value of the ionospheric puncture point Its location information is stored according to a preset storage path and an ionospheric dataset is generated;

[0009] Obtain the delay values ​​of K ionospheric puncture points within the fitting domain around the point to be fitted in the ionospheric dataset, and calculate at least one monitoring statistic based on the delay values ​​of K ionospheric puncture points within the fitting domain around the point to be fitted.

[0010] The threshold for the monitoring statistics is determined based on the MaxEnt threshold algorithm and the monitoring statistics.

[0011] As a preferred technical solution for determining the ionospheric anomaly monitoring threshold of the BeiDou satellite-based augmentation system, the step of determining the monitoring statistic threshold based on the MaxEnt threshold algorithm and the monitoring statistic includes:

[0012] A dataset is constructed based on the monitoring statistics, and the discrete probability distribution vector P of the dataset is obtained.

[0013] Based on the undetermined threshold T * The dataset is divided into two parts: normal monitoring statistics N and abnormal monitoring statistics A.

[0014] Calculate the entropy values ​​corresponding to the normal monitoring statistic N and the abnormal monitoring statistic A, respectively.

[0015] Calculate the total entropy value of the normal monitoring statistic N and the abnormal monitoring statistic A according to the Shannon entropy definition. ;

[0016] According to the total entropy value Take the largest T * Let T be the optimal threshold and denoted as the final monitoring threshold. In response to the fact that the number of monitoring statistics is not one, the final monitoring threshold corresponding to each monitoring statistic is calculated according to the definition of Shannon entropy.

[0017] As a preferred technical solution for determining the ionospheric anomaly monitoring threshold in the BeiDou satellite-based augmentation system, the expression for the discrete probability distribution vector P is:

[0018] ;

[0019] in: Let L be the probability of the occurrence of the monitoring statistic of the i-th order of magnitude. limit To monitor the lower boundary of the statistic, U limit To monitor the upper boundary of the statistic, This defines the interval between the lower and upper boundaries.

[0020] As a preferred technical solution for determining the ionospheric anomaly monitoring threshold in the BeiDou satellite-based augmentation system, the method based on the undetermined threshold T... * The dataset is divided into two parts: normal monitoring statistics N and abnormal monitoring statistics A. The expression for normal monitoring statistics N is:

[0021] ;

[0022] in: The cumulative probability of the normal monitoring statistic N is calculated based on the undetermined threshold T*.

[0023] The calculation formula is: .

[0024] As a preferred technical solution for determining the ionospheric anomaly monitoring threshold in the BeiDou satellite-based augmentation system, the expression for the anomaly monitoring statistic A is:

[0025] ;

[0026] in: The cumulative probability of the anomaly monitoring statistic A is calculated based on the undetermined threshold T*.

[0027] The calculation formula is: .

[0028] As a preferred technical solution for determining the ionospheric anomaly monitoring threshold of the BeiDou satellite-based augmentation system, the data set is divided according to an undetermined threshold T*, wherein the value range of the undetermined threshold T* is: .

[0029] As a preferred technical solution for determining the ionospheric anomaly monitoring threshold of the BeiDou satellite-based augmentation system, the step of calculating the entropy values ​​corresponding to the normal monitoring statistic N and the anomaly monitoring statistic A respectively includes:

[0030] The entropy value of the normal monitoring statistic N The calculation formula is:

[0031] ;

[0032] The entropy value of the anomaly monitoring statistic A The calculation formula is:

[0033] .

[0034] As a preferred technical solution for determining the ionospheric anomaly monitoring threshold of the BeiDou satellite-based augmentation system, the total entropy value The calculation formula is: ;

[0035] in: The entropy value of the normal monitoring statistic N, Let A be the entropy value of the anomaly monitoring statistic A.

[0036] As a preferred technical solution for determining the ionospheric anomaly monitoring threshold of the BeiDou satellite-based augmentation system, the method based on the total entropy value... The maximum value of T* is taken as the optimal threshold, denoted as T, and the calculation formula is: .

[0037] As a preferred technical solution for determining the ionospheric anomaly monitoring threshold in the BeiDou satellite-based augmentation system, the observation data is preprocessed and calculated. The preprocessing process for the raw GNSS observation data includes: sequentially performing cycle slip detection, carrier smoothing pseudorange, and satellite and receiver DCB value estimation and correction on the raw GNSS observation data; and calculating the ionospheric delay parameter based on the preprocessed data and navigation message to obtain the vertical delay value of the ionospheric puncture point in the target area. and its location information.

[0038] Compared with the prior art, the beneficial effects of the present invention are that, based on measured data, the present invention performs regional compliance verification and optimization of the monitoring threshold in the monitor for the ionospheric activity characteristics of the BeiDou satellite-based augmentation system service area. This can ensure the integrity of system service under abnormal ionospheric conditions, while also providing a high level of SBAS user service under calm ionospheric conditions. Attached Figure Description

[0039] Figure 1 The flowchart illustrates a method for determining the ionospheric anomaly monitoring threshold of the BeiDou satellite-based augmentation system provided by this invention. Detailed Implementation

[0040] To better understand the above-mentioned objectives, features, and advantages of the present invention, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments. It should be noted that, unless otherwise specified, the embodiments and features described in these embodiments can be combined with each other.

[0041] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and therefore the scope of protection of the invention is not limited to the specific embodiments disclosed below.

[0042] like Figure 1 As shown, this invention discloses a method for determining the ionospheric anomaly monitoring threshold of the BeiDou satellite-based augmentation system, comprising:

[0043] Step S1: Acquire observation data within the target area, and preprocess and calculate the observation data to obtain the vertical delay value and location information of the ionospheric puncture point; the observation data includes: raw GNSS observation data, navigation message data, and precise ephemeris data;

[0044] Step S2: Store the vertical delay value and location information of the ionospheric puncture point according to the preset storage path and generate an ionospheric dataset;

[0045] Step S3: Obtain the delay values ​​of K ionospheric puncture points within the fitting domain around the point to be fitted in the ionospheric dataset, and calculate at least one monitoring statistic based on the delay values ​​of K ionospheric puncture points within the fitting domain around the point to be fitted.

[0046] Step S4: Determine the threshold of the monitoring statistics based on the MaxEnt threshold algorithm and the monitoring statistics.

[0047] It is understandable that raw GNSS observation data is a set of basic physical observations directly output by the receiver and recorded in standard formats such as RINEX. It mainly includes pseudorange, carrier phase, Doppler shift, and signal-to-noise ratio (SNR) categorized by satellite and frequency. Navigation message data is binary coded data transmitted by the satellite, mainly including satellite ephemeris, satellite clock bias, ionospheric delay model parameters, etc., which can be obtained through the navigation messages transmitted by the satellite and received in real time by the GNSS receiver. Raw GNSS observation data and navigation message data are important parameters for obtaining ionospheric delay. In this embodiment of the invention, taking the L1 and L2 frequency points of the GPS system as examples, the preprocessing process of the observation data includes: cycle slip detection, carrier smoothing pseudorange, estimation and correction of satellite and receiver DCB values, and calculation of ionospheric delay parameters based on the preprocessed data and navigation messages to obtain the vertical delay value of the ionospheric puncture point in the target area. And its location information. In the calculation of ionospheric delay parameters, precise ephemeris data can be used as auxiliary data to obtain more accurate ionospheric delay values. Precise ephemeris data is a high-precision product generated by professional institutions through post-processing. The above processing procedures are existing technologies and will not be elaborated upon here.

[0048] Furthermore, the vertical ionospheric delay values ​​of K ionospheric puncture points in the fitting region around the fitting point are extracted from the dataset. The value of K is determined according to the actual change of the number of ionospheric puncture points in the fitting region around the fitting point, and the value is at least 10.

[0049] Furthermore, the monitoring statistics type can be selected from parameters capable of identifying ionospheric anomalies, characterizing ionospheric states, or verifying the accuracy of ionospheric delay value estimation models. In this embodiment of the invention, the Time Gradient Exponential (TGI) monitoring statistics are used as an example to obtain the monitoring statistics threshold. Those skilled in the art will understand that if multiple monitoring statistics are calculated simultaneously, each monitoring statistics is substituted into the MaxEnt threshold algorithm to obtain the corresponding monitoring threshold.

[0050] Furthermore, the time gradient exponential monitoring statistics The calculation method is as follows:

[0051] ;

[0052] Where: m is the number of time gradients within the time interval. For the value of the i-th time gradient, The calculation method is as follows .

[0053] In implementation, the time interval is determined based on the update interval of the ionospheric delay correction information of the BeiDou satellite-based augmentation system. In this embodiment, the time interval is 5 minutes. Within a single time interval, m consecutive temporal gradient (TG) data points are selected, and the temporal gradient exponential monitoring statistic is calculated. It should be noted that the selection of the temporal gradient (TG) is to assess the short-term rapid changes in the local ionosphere. In this embodiment, m=10.

[0054] Furthermore, this invention uses the time gradient exponential monitoring statistic to capture the rapid gradient changes in the total electron content of the ionosphere caused by ionospheric anomalies.

[0055] Furthermore, the method for calculating the time gradient TG is as follows:

[0056] ;

[0057] in: , Represented as the vertical delay value of the ionospheric puncture point at different epochs. This represents the epoch interval between different epochs.

[0058] It is understandable that the epoch intervals between different epochs... The value of is determined based on the sensitivity requirements of the ionospheric delay monitoring system. In this embodiment of the invention, the epoch interval between different epochs... The value is 30 seconds.

[0059] Furthermore, the threshold for monitoring statistics is determined based on the MaxEnt threshold algorithm and the monitoring statistics, including:

[0060] A dataset is constructed based on monitoring statistics, and the discrete probability distribution vector P of the dataset is obtained.

[0061] Based on the undetermined threshold T * The dataset is divided into two parts: normal monitoring statistics N and abnormal monitoring statistics A.

[0062] Calculate the entropy values ​​corresponding to the normal monitoring statistic N and the abnormal monitoring statistic A, respectively.

[0063] Calculate the total entropy value of the normal monitoring statistic N and the abnormal monitoring statistic A according to the Shannon entropy definition. ;

[0064] Based on the total entropy value Take the largest T * Let T be the optimal threshold and denoted as the final monitoring threshold. In response to the fact that the number of monitoring statistics is not the same, the final monitoring threshold corresponding to each monitoring statistic is calculated according to the definition of Shannon entropy.

[0065] in: .

[0066] Furthermore, this invention uses the MaxEnt threshold algorithm to verify and select the rationality of the monitor threshold setting, which can effectively reduce the problem of small user ionospheric correction residuals but high false alarm rate of the monitor, and improve the availability of system services.

[0067] Specifically, the expression for the discrete probability distribution vector P is:

[0068] ;

[0069] in: The probability of the i-th order of magnitude monitoring statistic occurring is equal to the number of times the i-th order of magnitude monitoring statistic occurs within the interval divided by the total data volume, L. limit To monitor the lower boundary of the statistic, U limit To monitor the upper boundary of the statistic, This defines the interval between the lower and upper boundaries.

[0070] Furthermore, by obtaining the discrete probability distribution vector P of the data set, this invention can clearly display the distribution pattern of the monitoring statistics, quantify the degree of separation between the normal monitoring statistics N and the abnormal monitoring statistics A, avoid errors caused by subjective experience, locate the optimal segmentation threshold, reduce the blindness of threshold selection, and improve the segmentation effect and algorithm stability.

[0071] Specifically, based on the undetermined threshold T* ( The dataset is divided into two parts: normal monitoring statistics N and abnormal monitoring statistics A, including:

[0072] The expression for the normal monitoring statistic N is:

[0073] ;

[0074] in: To define the cumulative probability of the normal monitoring statistic N calculated based on the undetermined threshold T*, we set... The calculation formula is: In the formula, 0 indicates that the cumulative probability of the monitoring statistic being greater than the undetermined threshold T* is 0.

[0075] The expression for the anomaly monitoring statistic A is:

[0076] ;

[0077] in: Based on the undetermined threshold T * The cumulative probability of the anomaly monitoring statistic A is set as follows: The calculation formula is: In the formula, 0 indicates that the cumulative probability of the monitoring statistic being less than the undetermined threshold T* is 0.

[0078] Furthermore, this invention sets an undetermined threshold T* as a candidate boundary for dividing the normal monitoring statistic N and the abnormal monitoring statistic A, dividing them into two non-overlapping regions, providing a basic basis for subsequent entropy calculation. By traversing and optimizing, the optimal value is determined, that is, the value that maximizes the total information entropy (i.e., the final threshold T), thus achieving accurate separation of the two types of monitoring statistics.

[0079] Specifically, the entropy values ​​corresponding to the normal monitoring statistic N and the abnormal monitoring statistic A are calculated separately, including:

[0080] Entropy value of normal monitoring statistic N The calculation formula is:

[0081] ;

[0082] Entropy value of anomaly monitoring statistic A The calculation formula is:

[0083] .

[0084] Furthermore, based on the total entropy value Calculating the optimal threshold T involves iterating through all possible undetermined thresholds T* and calculating the corresponding total entropy value. Select the total entropy value The undetermined threshold T* at its maximum is the optimal threshold T, and the total entropy value is... The calculation formula is: ;

[0085] in: The entropy value of the normal monitoring statistic N, Let A be the entropy value of the anomaly monitoring statistic A.

[0086] The formula for calculating the optimal threshold T is: .

[0087] Those skilled in the art will understand that all or part of the steps of the above method embodiments can be implemented by hardware related to program instructions. The aforementioned program can be stored in a computer-readable storage medium. When the program is executed, it performs the steps of the above method embodiments. The aforementioned storage medium includes various media capable of storing program code, such as mobile storage devices, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0088] Alternatively, if the integrated units of this invention are implemented as software functional modules and sold or used as independent products, they can also be stored in a computer-readable storage medium. Based on this understanding, the technical solutions of the embodiments of this invention, or the parts that contribute to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the methods of the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as mobile storage devices, ROM, RAM, magnetic disks, or optical disks.

Claims

1. A method for determining the ionospheric anomaly monitoring threshold of a BeiDou satellite-based augmentation system, characterized in that, include: Acquire observation data within the target area, and preprocess and calculate the observation data to obtain the vertical delay value of the ionospheric puncture point. and its location information; The observation data includes: raw GNSS observation data, navigation message data, and precise ephemeris data; Vertical delay value of the ionospheric puncture point Its location information is stored according to a preset storage path and an ionospheric dataset is generated; Obtain the delay values ​​of K ionospheric puncture points within the fitting domain around the point to be fitted in the ionospheric dataset, and calculate at least one monitoring statistic based on the delay values ​​of K ionospheric puncture points within the fitting domain around the point to be fitted. The threshold for the monitoring statistics is determined based on the MaxEnt threshold algorithm and the monitoring statistics.

2. The method for determining the ionospheric anomaly monitoring threshold of the BeiDou satellite-based augmentation system according to claim 1, characterized in that, The step of determining the monitoring statistic threshold based on the MaxEnt threshold algorithm and the monitoring statistic includes: A dataset is constructed based on the monitoring statistics, and the discrete probability distribution vector P of the dataset is obtained. Based on the undetermined threshold T * The dataset is divided into two parts: normal monitoring statistics N and abnormal monitoring statistics A. Calculate the entropy values ​​corresponding to the normal monitoring statistic N and the abnormal monitoring statistic A, respectively. Calculate the total entropy value of the normal monitoring statistic N and the abnormal monitoring statistic A according to the Shannon entropy definition. ; According to the total entropy value Take the largest T * Let T be the optimal threshold and denoted as the final monitoring threshold. In response to the fact that the number of monitoring statistics is not one, the final monitoring threshold corresponding to each monitoring statistic is calculated according to the definition of Shannon entropy.

3. The method for determining the ionospheric anomaly monitoring threshold of the BeiDou satellite-based augmentation system according to claim 2, characterized in that, The expression for the discrete probability distribution vector P is: ； in: Let L be the probability of the occurrence of the monitoring statistic of the i-th order of magnitude. limit To monitor the lower boundary of the statistic, U limit To monitor the upper boundary of the statistic, This defines the interval between the lower and upper boundaries.

4. The method for determining the ionospheric anomaly monitoring threshold of the BeiDou satellite-based augmentation system according to claim 3, characterized in that, The condition based on the undetermined threshold T * The dataset is divided into two parts: normal monitoring statistics N and abnormal monitoring statistics A. The expression for normal monitoring statistics N is: ； in: Based on the undetermined threshold T * The cumulative probability of the normal monitoring statistic N is calculated; The calculation formula is: .

5. The method for determining the ionospheric anomaly monitoring threshold of the BeiDou satellite-based augmentation system according to claim 4, characterized in that, The expression for the anomaly monitoring statistic A is: ； in: Based on the undetermined threshold T * The cumulative probability of the calculated anomaly monitoring statistic A; The calculation formula is: .

6. The method for determining the ionospheric anomaly monitoring threshold of the BeiDou satellite-based augmentation system according to claim 4, characterized in that, The condition based on the undetermined threshold T * The data set is divided into segments, where the undetermined threshold T is... * The range of values ​​for is: .

7. The method for determining the ionospheric anomaly monitoring threshold of the BeiDou satellite-based augmentation system according to claim 6, characterized in that, The calculation of the entropy values ​​corresponding to the normal monitoring statistic N and the abnormal monitoring statistic A includes: The entropy value of the normal monitoring statistic N The calculation formula is: ； The entropy value of the anomaly monitoring statistic A The calculation formula is: 。 8. The method for determining the ionospheric anomaly monitoring threshold of the BeiDou satellite-based augmentation system according to claim 7, characterized in that, The total entropy value The calculation formula is: ; in: The entropy value of the normal monitoring statistic N, Let A be the entropy value of the anomaly monitoring statistic A.

9. The method for determining the ionospheric anomaly monitoring threshold of the BeiDou satellite-based augmentation system according to claim 2, characterized in that, The total entropy value Take the largest T * The optimal threshold, denoted as T, is calculated using the following formula: 。 10. The method for determining the ionospheric anomaly monitoring threshold of the BeiDou satellite-based augmentation system according to claim 1, characterized in that, The preprocessing and calculation of the observation data includes the following steps for the raw GNSS observation data: sequentially performing cycle slip detection, carrier smoothing pseudorange, satellite and receiver DCB value estimation and correction on the raw GNSS observation data; and calculating the ionospheric delay parameter based on the preprocessed data and navigation message to obtain the vertical delay value of the ionospheric penetration point in the target area. and its location information.

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