Method and system for integrity monitoring of low earth orbit satellite on-board gnss observations

Abstract

The present application relates to the technical field of satellite data processing, and particularly relates to a low-orbit satellite on-board GNSS observation integrity monitoring method and system, which adopts a sliding window to collect low-orbit satellite on-board GNSS observation data of a specified arc length; uses a trigonometric function model to fit the observation data to obtain a fitting residual sequence; uses an autoregressive moving average model ARIMA to fit the fitting residual sequence and to perform white noise detection on the fitting result residual to obtain low-orbit satellite on-board GNSS observation prediction values under each epoch; compares the low-orbit satellite on-board GNSS observation prediction values with measured values to obtain observation prediction errors under corresponding epochs; constructs a GNSS integrity check statistic, and determines the low-orbit satellite on-board GNSS observation integrity according to a check threshold. The present application takes into account the long-term and short-term variation rules of low-orbit satellite on-board GNSS observation data, and advances the abnormal fault monitoring to the initial stage of data processing, thereby providing support for more secure and reliable GNSS real-time data processing.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of satellite data processing, in particular to a low-orbit satellite on-board GNSS observation integrity monitoring method and system. BACKGROUND

[0002] Due to the influence of the harsh space environment, the safety and reliability of low-orbit satellite on-board GNSS data processing has always been a concern, and autonomous integrity monitoring is an important technical approach. Autonomous integrity monitoring usually relies on constructing appropriate test statistics to determine whether the current GNSS data is reliable through the value of the statistics, therefore, how to construct appropriate test statistics is the key to the success of autonomous integrity monitoring.

[0003] The existing integrity monitoring method mainly starts from the estimated value, constructs the corresponding integrity detection statistics, and monitors the integrity by whether the value exceeds the corresponding threshold. However, the above monitoring process usually intervenes in the later stage of data processing, although it can better support integrity monitoring, but due to the late intervention time, it is often difficult to respond to "abnormal" data in the early stage of data processing. SUMMARY

[0004] Therefore, the present application provides a low-orbit satellite on-board GNSS observation integrity monitoring method and system, which solves the problem that the existing GNSS integrity monitoring process is difficult to process abnormal data in the early stage of data processing when it is intervened in the later stage of data processing, and starts from the observation data, takes into account the long-term and short-term variation law of low-orbit satellite on-board GNSS observation data, and uses integrity test statistics to monitor abnormal faults in the early stage of data processing, providing support for more secure and reliable GNSS real-time data processing.

[0005] According to the design scheme provided by the present application, on the one hand, a low-orbit satellite on-board GNSS observation integrity monitoring method is provided, comprising:

[0006] Sliding window is used to collect low-orbit satellite on-board GNSS observation data of a specified arc length and pre-process the observation data;

[0007] The pre-processed observation data is fitted by using a trigonometric function model to obtain a fitting residual sequence in the corresponding arc segment, the fitting residual sequence is used to represent the difference sequence between the observation calculated by the trigonometric function model and the actual observation at the corresponding epoch;

[0008] An autoregressive moving average model ARIMA is used to fit the fitting residual sequence and to perform white noise detection on the fitting result residual;

[0009] If the residual of the fitting result passes the white noise detection, the prediction value of the low-orbit satellite on-board GNSS observation at each epoch is obtained according to the trigonometric function model prediction value of the low-orbit satellite on-board GNSS observation and the autoregressive moving average model ARIMA residual prediction value;

[0010] The prediction value of the low-orbit satellite on-board GNSS observation is compared with the measured value of the observation, and the prediction error of the low-orbit satellite on-board GNSS observation at the corresponding epoch is obtained.

[0011] The GNSS integrity check statistic is constructed based on the observation prediction error, and the check threshold is set according to the false alarm rate requirement, and the integrity of the low-orbit satellite on-board GNSS observation is determined according to the integrity check statistic and the check threshold.

[0012] As the low-orbit satellite on-board GNSS observation integrity monitoring method of the application, further, the preprocessed observation data is fitted by using the trigonometric function model, which comprises:

[0013] The coefficients to be fitted, the order of the trigonometric function and the angle frequency of the trigonometric function are set, and the trigonometric function fitting model is constructed according to the observation data;

[0014] The residual of the observation at the specified arc length in the sliding window is obtained according to the fitting model, and the observation residual is expressed in matrix form, and the coefficients of the trigonometric function model are obtained based on the matrix form of the observation residual and by using the least square principle;

[0015] The trigonometric function model is established according to the coefficients of the trigonometric function model, and the difference between the observation at the corresponding epoch and the actual observation is calculated by using the trigonometric function model.

[0016] As the low-orbit satellite on-board GNSS observation integrity monitoring method of the application, further, the fitting model is expressed as: Wherein, L j represents the low-orbit satellite on-board GNSS observation at t j epoch, n represents the order of the trigonometric function, ω represents the angle frequency of the trigonometric function, a0, b i , c i are the model coefficients to be fitted.

[0017] As the low-orbit satellite on-board GNSS observation integrity monitoring method of the application, further, the residual of the observation at the specified arc length in the sliding window is expressed as:

[0018] Wherein, L1, L2, …, L m represent m groups of observations at the specified arc length.

[0019] As the low-orbit satellite on-board GNSS observation integrity monitoring method of the application, further, the autoregressive moving average model ARIMA is used to fit the fitting residual sequence, including:

[0020] The residual prediction model is established according to the autoregressive moving average model ARIMA modeling process, and the autoregressive moving average model ARIMA modeling process includes stationarity analysis, model order determination, model estimation and test;

[0021] The fitting residual sequence is fitted by using the residual prediction model, and the fitting result residual is white noise detected.

[0022] As the low-orbit satellite on-board GNSS observation integrity monitoring method of the application, further, the residual prediction model is expressed as: Wherein, p and q represent the model order; d represents the difference number; B represents the lag operator; φ i And θ i Respectively represent the autoregressive coefficient and the moving average coefficient; ε t Represents white noise; δL t Represents the fitting residual at t t Epoch.

[0023] As the low-orbit satellite on-board GNSS observation integrity monitoring method of the application, further, the GNSS integrity check statistic is expressed as: β 2 = ε m T P m ε m , wherein, β 2 Represents the integrity test statistic, P m Represents the LEO on-board GNSS observation weighted factor, ε m Represents the prediction error of low-orbit satellite on-board GNSS observation at t m Epoch.

[0024] In another aspect, the application also provides a low-orbit satellite on-board GNSS observation integrity monitoring system, comprising: data acquisition module, data fitting module, noise detection module, prediction estimation module, error calculation module and integrity monitoring module, wherein,

[0025] The data acquisition module is used for collecting low-orbit satellite on-board GNSS observation data of specified arc length by using sliding window and pre-processing the observation data;

[0026] The data fitting module is used for fitting the pre-processed observation data by using the trigonometric function model to obtain the fitting residual sequence in the corresponding arc segment, and the fitting residual sequence is used to represent the difference sequence between the observation calculated by using the trigonometric function model and the actual observation at the corresponding epoch.

[0027] a noise detection module configured to fit the residual series by using an autoregressive moving average model (ARIMA) and perform white noise detection on the fitting result residual;

[0028] a prediction estimation module configured to, in the case that the fitting result residual passes the white noise detection, obtain low-earth orbit (LEO) satellite-borne global navigation satellite system (GNSS) observation prediction values at each epoch according to a trigonometric function model prediction value of the LEO satellite-borne GNSS observation and an autoregressive moving average model (ARIMA) residual prediction value;

[0029] an error calculation module configured to compare the LEO satellite-borne GNSS observation prediction values with measured values to obtain LEO satellite-borne GNSS observation prediction errors at corresponding epochs;

[0030] a integrity monitoring module configured to construct a GNSS integrity check statistic based on the observation prediction errors, set a check threshold according to a false alarm rate requirement, and determine the integrity of the LEO satellite-borne GNSS observation according to the integrity check statistic and the check threshold.

[0031] Advantages of the present application:

[0032] The present application takes into account the operating regularity characteristics of LEO satellites, uses a combination of trigonometric functions and ARIMA to fully exploit the long-term and short-term variation rules of LEO satellite-borne GNSS observations and construct a corresponding observation prediction model, which, compared with traditional prediction models based on dynamic models, can effectively reduce the model complexity on the basis of ensuring the prediction accuracy, and ensure the calculation efficiency of subsequent integrity monitoring implementation; on the basis of constructing the LEO satellite-borne GNSS observation prediction model, a corresponding integrity test statistic is further constructed, and according to the value of the statistic, the integrity of the LEO satellite-borne GNSS observation can be determined relatively conveniently, thereby providing certain support for safe and reliable LEO satellite-borne GNSS data processing. BRIEF DESCRIPTION OF DRAWINGS

[0033] Figure 1 A low-earth orbit (LEO) satellite-borne GNSS observation integrity monitoring process in an embodiment;

[0034] Figure 2 A LEO satellite-borne GNSS observation variation rule in an embodiment;

[0035] Figure 3 A LEO satellite-borne GNSS observation data integrity algorithm process in an embodiment. DETAILED DESCRIPTION

[0036] In order to make the purpose, technical scheme and advantages of the present application more clear, specific, the present application is described in further detail below in combination with the drawings and technical scheme.

[0037] The embodiment of the present application, see Figure 1 As shown in the figure, a low-orbit satellite on-board GNSS observation integrity monitoring method is provided, comprising:

[0038] S101, the specified arc length of low-orbit satellite on-board GNSS observation data is collected by using sliding window and the observation data is pretreated.

[0039] The high-precision prediction of LEO on-board GNSS observation sequence is the key to establish the observation sequence integrity test statistic of the project. Unlike ground users, LEO satellites run in space to meet the dynamics constraints, therefore, LEO on-board GNSS observation itself has obvious change characteristics, such as Figure 2 As shown in the figure, the carrier phase observation change of a LEO satellite passing through a GNSS satellite is given, and other LEO satellites are similar. It can be seen that its change characteristics are consistent with the trigonometric function, but it should also be noted that this is only the general change rule, that is, the "long-term" change rule. Due to the influence of various errors, even if the long-term change rule is removed, in order to improve the observation prediction accuracy, the remaining part also has a "short-term" rule to be considered. In the embodiment of the case, on the basis of necessary pretreatment of low-orbit satellite on-board GNSS observation such as data format, in order to ensure the real-time of processing, the mode of sliding window is adopted, that is, a certain arc length of observation data is collected, trigonometric function fitting and prediction are carried out, after receiving new real-time observation data, the new data is directly used, the old data is released, and trigonometric function fitting and prediction are carried out again.

[0040] S102, the pretreated observation data is fitted by using the trigonometric function model, and the fitting residual sequence in the corresponding arc segment is obtained, the fitting residual sequence is used to represent the difference sequence between the observation calculated by using the trigonometric function model and the actual observation under the corresponding ephemeris.

[0041] Among them, the pretreated observation data is fitted by using the trigonometric function model, which can be designed to contain:

[0042] The coefficients to be fitted, the order of trigonometric function and the angle frequency of trigonometric function are set, and the trigonometric function fitting model is constructed according to the observation data;

[0043] The residual of the observation in the sliding window with specified arc length is obtained according to the fitting model, and the observation residual is expressed in matrix form, and the coefficients of the trigonometric function model are obtained based on the matrix form of the observation residual and by using the least square principle;

[0044] The trigonometric function model is established according to the trigonometric function model coefficients, so as to calculate the difference between the observation at the corresponding epoch and the actual observation by using the trigonometric function model.

[0045] The trigonometric function fitting LEO satellite-borne GNSS observation model is as follows:

[0046]

[0047] Wherein, L j represents the LEO satellite-borne GNSS observation at t j epoch; n represents the order of the trigonometric function, and ω represents the angle frequency of the trigonometric function, both of which can be selected in combination with the running characteristics of the low earth orbit satellite; a0, b i and c i are model coefficients to be fitted.

[0048] It is assumed that there are m groups of LEO satellite-borne GNSS observations L1, L2, …, L m in the sliding window, so that

[0049]

[0050] It is recorded in the matrix form as follows:

[0051] V = A·X - L (3)

[0052] Wherein, V represents the residual vector; A represents the design matrix; X represents the parameter vector to be estimated; and L represents the LEO satellite-borne GNSS observation vector in the sliding window.

[0053] Then, the trigonometric function model coefficients can be estimated according to the least square principle:

[0054] X = (A T PA) -1 (A T PL)(4)

[0055] Wherein, P represents the weight of the LEO satellite-borne GNSS observation, which is obtained according to the observation elevation angle weighting, and other symbols are the same as those in formula (3).

[0056] On this basis, the observation at the corresponding epoch is calculated by using the established trigonometric function model, and compared with the actual observation, so as to obtain the difference sequence in the fitting arc segment:

[0057]

[0058] Wherein, represents the trigonometric function model calculation value at t j epoch; L j represents the LEO satellite-borne GNSS observation measured value at t j epoch; and δLj The difference between the model calculation value and the measured value is represented.

[0059] S103, fitting the fitting residual sequence by using the autoregressive moving average model ARIMA and performing white noise detection on the fitting result residual.

[0060] For the time series δL1, δL2, …, δL m , again according to the basic process of ARIMA modeling, through the steps of stationarity analysis, model order determination, model estimation and test, the residual prediction model is established as follows:

[0061]

[0062] In the formula, p and q represent the model order; d represents the difference number; B represents the lag operator; φ i and θ i represent the autoregressive coefficient and the moving average coefficient respectively; ε t represents white noise; δL t represents the residual value obtained by subtracting the calculation value of the trigonometric function model from the LEO satellite-borne GNSS observation.

[0063] The time series δL1, δL2, …, δL m is fitted by using the ARIMA model, and the fitting result residual is subjected to white noise detection. If it can pass the detection, the next step is entered, otherwise, the residual result is fitted again by using the ARIMA model until the fitting residual can pass the white noise test.

[0064] S104, if the fitting result residual passes the white noise detection, the prediction value of the low-orbit satellite-borne GNSS observation at each epoch is obtained according to the trigonometric function model prediction value of the low-orbit satellite-borne GNSS observation and the autoregressive moving average model ARIMA residual prediction value.

[0065] The trigonometric function and ARIMA model are comprehensively utilized, so that the prediction value of the LEO satellite-borne GNSS observation under the condition of any epoch t m can be obtained:

[0066]

[0067] In the formula, represents the final prediction value of the LEO satellite-borne GNSS observation at epoch t m ; and represents the trigonometric function model prediction value of the LEO satellite-borne GNSS observation at epoch t m ; and represents the residual prediction value at epoch t m obtained by the ARIMA residual prediction model.

[0068] S105, compare the low-orbit satellite on-board GNSS observation prediction value with the measured value of the observation, and obtain the low-orbit satellite on-board GNSS observation prediction error corresponding to the epoch.

[0069] Obtain t from formula (7) m Epoch LEO on-board GNSS observation prediction value Subtract it from the measured value of the LEO on-board GNSS observation L m , and obtain

[0070]

[0071] In the formula, ε m represents the LEO on-board GNSS observation prediction error.

[0072] S106, construct a GNSS integrity check statistic based on the observation prediction error, and set a check threshold according to the false alarm rate requirement, to determine the integrity of the low-orbit satellite on-board GNSS observation according to the integrity check statistic and the check threshold.

[0073] The integrity check statistic can be expressed as follows:

[0074] β 2 = ε m T P m ε m (9)

[0075] In the formula, β 2 represents the integrity check statistic; P m represents the LEO on-board GNSS observation weighting factor, which can be set according to the elevation angle; other symbols are the same as formula (8).

[0076] The above test statistic obeys χ 2 distribution, according to the false alarm rate requirement, the test threshold can be calculated more conveniently, according to which it can be determined whether a fault occurs:

[0077]

[0078] Among them, H0 represents the no-fault condition; represents the probability distribution function of χ 2 distribution; P FA represents the false alarm rate; T represents the test threshold.

[0079] At this point, according to the above conditions, the integrity of the LEO on-board GNSS observation can be determined.

[0080] To this end, the algorithm flow for integrity monitoring of LEO satellite-borne GNSS observations in the case of the present embodiment can be as shown in Figure 3 First, the LEO satellite-borne GNSS observation data is preprocessed as necessary, including data format arrangement, etc.; then, the LEO satellite-borne GNSS observation data is fitted using a trigonometric function model, and a fitting residual sequence is obtained; then, stationarity analysis is performed on the fitting residual sequence, and model order is determined therefrom, to determine the order p and q of the ARIMA model to be used, as well as the number of differences d; the fitting residual sequence is fitted again using the ARIMA model, and white noise detection is performed on the fitting residual, if passed, the next step is entered, otherwise, the stationarity analysis step is re-executed; the LEO satellite-borne GNSS observation data prediction value is obtained by combining the trigonometric function model and the ARIMA model, the difference between the prediction value and the actual measurement value of the LEO satellite-borne GNSS observation data at the current epoch is calculated, and an integrity detection statistic is constructed; according to the given false alarm rate requirement, whether the integrity detection statistic exceeds the detection threshold, if not, it is determined that the current LEO satellite-borne GNSS observation data is normal, otherwise, it is determined that the current LEO satellite-borne GNSS observation data is abnormal, to remind the relevant personnel to handle in time.

[0081] Further, based on the above method, the embodiment of the present application also provides a low-orbit satellite satellite-borne GNSS observation integrity monitoring system, comprising: a data acquisition module, a data fitting module, a noise detection module, a prediction estimation module, an error calculation module and an integrity monitoring module, wherein,

[0082] The data acquisition module is used to collect low-orbit satellite satellite-borne GNSS observation data of a specified arc length using a sliding window and to preprocess the observation data;

[0083] The data fitting module is used to fit the preprocessed observation data using a trigonometric function model to obtain a fitting residual sequence within the corresponding arc segment, wherein the fitting residual sequence is used to represent the difference sequence between the observation calculated using the trigonometric function model and the actual observation at the corresponding epoch;

[0084] The noise detection module is used to fit the fitting residual sequence using an autoregressive moving average model ARIMA and to perform white noise detection on the fitting result residual;

[0085] The prediction estimation module is used to obtain the prediction value of the low-orbit satellite satellite-borne GNSS observation at each epoch in the case that the fitting result residual passes the white noise detection, according to the trigonometric function model prediction value of the low-orbit satellite satellite-borne GNSS observation and the autoregressive moving average model ARIMA residual prediction value;

[0086] an error calculation module, configured to compare the low-orbit satellite on-board GNSS observation prediction value with the observation measured value, to obtain low-orbit satellite on-board GNSS observation prediction error corresponding to an epoch;

[0087] an integrity monitoring module, configured to construct a GNSS integrity check statistic based on the observation prediction error, and to set a check threshold according to a false alarm rate requirement, and to determine the integrity of the low-orbit satellite on-board GNSS observation according to the integrity check statistic and the check threshold.

[0088] Unless specifically stated otherwise, the relative arrangement of components and steps in the embodiments described herein do not limit the scope of the present application.

[0089] The various embodiments are described in a progressive manner in the present specification, and each embodiment focuses on the differences from other embodiments. The same or similar parts between the various embodiments can be understood by referring to each other. For the system disclosed in the embodiments, the description is relatively simple because it corresponds to the method disclosed in the embodiments. The relevant parts can be understood by referring to the description of the method.

[0090] The units and method steps of each example described in combination with the embodiments disclosed herein can be realized in electronic hardware, computer software or a combination of both. In order to clearly illustrate the interchangeability of hardware and software, the composition and steps of each example have been described in the above description in general terms. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation does not exceed the scope of the present application.

[0091] Those skilled in the art can understand that all or part of the steps in the above method can be instructed by a program to complete the relevant hardware, and the program can be stored in a computer readable storage medium, such as a read-only memory, a magnetic disk or an optical disk, etc. Alternatively, all or part of the steps of the above embodiments can also be implemented using one or more integrated circuits, and accordingly, each module / unit in the above embodiments can be implemented in the form of hardware or in the form of a software function module. The present application is not limited to any specific form of combination of hardware and software.

[0092] Finally, it should be noted that the above-described embodiments are merely specific embodiments of the present application, which are used to illustrate the technical solutions of the present application, but not to limit the same. The protection scope of the present application is not limited thereto. Although the present application has been described in detail with reference to the foregoing embodiments, it should be understood by those skilled in the art that any person skilled in the art can still modify or easily think of changes to the technical solutions recorded in the foregoing embodiments, or make equivalent replacements to some of the technical features, within the technical scope disclosed by the present application. The modifications, changes or replacements do not cause the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present application, and should be covered within the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the protection scope of the claims.

Claims

1. A method for low earth orbit satellite on-board GNSS observation integrity monitoring, characterized in that, The application relates to a low-orbit satellite on-board GNSS (Global Navigation Satellite System) observation data error prediction method and device. The low-orbit satellite on-board GNSS observation data of a specified arc length is collected by using a sliding window and the observation data is pretreated; The preprocessed observation data is fitted by using a trigonometric function model to obtain a fitting residual sequence in a corresponding arc segment, and the fitting residual sequence is used to represent a difference sequence between an observation calculated by using the trigonometric function model and an actual observation at a corresponding epoch, wherein the trigonometric function model is represented as: L j t j represents a low-orbit satellite on-board GNSS observation at an epoch, n represents a trigonometric function order, ω represents a trigonometric function angular frequency, a0, b i , and c i are model coefficients to be fitted. An ARIMA model is used to fit the residual sequence and to perform white noise detection on the fitting result residual; If the fitting result residual passes the white noise detection, the low-orbit satellite on-board GNSS observation prediction value of each epoch is obtained according to the low-orbit satellite on-board GNSS observation trigonometric function model prediction value and the ARIMA residual prediction value; The low-orbit satellite on-board GNSS observation prediction value is compared with the measured value to obtain the low-orbit satellite on-board GNSS observation prediction error of the corresponding epoch; A GNSS integrity check statistic is constructed based on the observation prediction error, a check threshold is set according to the false alarm rate, and the low-orbit satellite on-board GNSS observation integrity is judged according to the integrity check statistic and the check threshold.

2. The LEO satellite on-board GNSS observation integrity monitoring method according to claim 1, characterized in that, The pretreated observation data is fitted by using a trigonometric function model, which comprises the following steps: The coefficients to be fitted, the trigonometric function order and the trigonometric function angular frequency are set, and a trigonometric function fitting model is constructed according to the observation data; The residual of the observation of a specified arc length in the sliding window is obtained according to the fitting model, and the observation residual is expressed in a matrix form, so that the trigonometric function model coefficients are obtained based on the matrix form of the observation residual and by using the least square principle; The trigonometric function model is established according to the trigonometric function model coefficients, so that the difference between the corresponding epoch observation and the actual observation is calculated by using the trigonometric function model.

3. The LEO satellite on-board GNSS observation integrity monitoring method of claim 1, wherein, The residual of the observation of a specified arc length in the sliding window is expressed as: where L1, L2,..., L m denotes the m sets of observations in the specified arc length.

4. The LEO satellite on-board GNSS observation integrity monitoring method of claim 1, wherein, The fitting residual sequence is fitted by using an ARIMA model, which comprises the following steps: A residual prediction model is established according to the ARIMA modeling process, and the ARIMA modeling process comprises stationarity analysis, model order determination, model estimation and test; The fitting residual sequence is fitted by using the residual prediction model, and the fitting result residual is subjected to white noise detection.

5. The LEO satellite on-board GNSS observation integrity monitoring method according to claim 4, characterized in that, The residual prediction model is expressed as: where p and q represent the model order; d represents the difference order; B represents the lag operator; φ i and θ i represent the autoregressive and moving average coefficients, respectively; ε t represents white noise; δL t represents the fitting residual at t t epoch.

6. The LEO satellite on-board GNSS observation integrity monitoring method of claim 1, wherein, The GNSS integrity check statistics is represented as: β 2 = ε m T P m ε m where β 2 represents the integrity check statistics, P m represents the LEO spaceborne GNSS observation weighting factor, and ε m represents the t m epoch spaceborne GNSS observation prediction error of the low earth orbit satellite.

7. A low earth orbit satellite on-board GNSS observation integrity monitoring system, characterized by, The application relates to a low-orbit satellite on-board GNSS (Global Navigation Satellite System) observation data error prediction method and device. The low-orbit satellite on-board GNSS observation data of a specified arc length is collected by using a sliding window and the observation data is pretreated; A data fitting module is configured to fit the preprocessed observation data by using a trigonometric function model to obtain a fitting residual sequence in a corresponding arc segment, the fitting residual sequence being used to represent a difference sequence between an observation calculated by using the trigonometric function model and an actual observation at a corresponding epoch, wherein the trigonometric function model is represented as: L j t j GNSS observation of a low-orbit satellite at an epoch, n represents a trigonometric function order, ω represents a trigonometric function angular frequency, a0, b i , and c i are model coefficients to be fitted. An ARIMA model is used to fit the residual sequence and to perform white noise detection on the fitting result residual; If the fitting result residual passes the white noise detection, the low-orbit satellite on-board GNSS observation prediction value of each epoch is obtained according to the low-orbit satellite on-board GNSS observation trigonometric function model prediction value and the ARIMA residual prediction value; The low-orbit satellite on-board GNSS observation prediction value is compared with the measured value to obtain the low-orbit satellite on-board GNSS observation prediction error of the corresponding epoch; A GNSS integrity check statistic is constructed based on the observation prediction error, a check threshold is set according to the false alarm rate, and the low-orbit satellite on-board GNSS observation integrity is judged according to the integrity check statistic and the check threshold. The integrity monitoring module is configured to construct a GNSS integrity check statistic based on an observation prediction error, set a check threshold according to a false alarm rate requirement, and determine the integrity of the low-orbit satellite on-board GNSS observation according to the integrity check statistic and the check threshold.

8. An electronic device, comprising: The method comprises the steps of: At least one processor and a memory coupled to the at least one processor; The memory stores a computer program, and the computer program can be executed by the at least one processor to implement the method of any one of claims 1-6.

9. A computer-readable storage medium, characterized in that, The computer readable storage medium stores a computer program, and when the computer program is executed, the method of any one of claims 1-6 can be implemented.

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