Method and system for monitoring integrity of satellite-borne GNSS (Global Navigation Satellite System) observed quantity of low-orbit satellite

Abstract

The invention relates to the technical field of satellite data processing, in particular to a low-orbit satellite satellite-borne GNSS observed quantity integrity monitoring method and system, and the method comprises the steps: collecting low-orbit satellite satellite-borne GNSS observation data with a specified arc length through a sliding window; fitting the observation data by using a trigonometric function model to obtain a fitting residual sequence; fitting the fitting residual error sequence by using an autoregressive moving average model (ARIMA) and carrying out white noise detection on a fitting result residual error so as to obtain a low-orbit satellite-borne GNSS observed quantity predicted value under each epoch; comparing the low-orbit satellite-borne GNSS observed quantity predicted value with the observed quantity measured value to obtain an observed quantity prediction error under the corresponding epoch; and constructing a GNSS integrity verification statistical magnitude, and judging the integrity condition of the satellite-borne GNSS observed quantity of the low-orbit satellite according to a verification threshold. According to the method, the long-term and short-term change rule of satellite-borne GNSS observation data of the low-orbit satellite is considered, abnormal fault monitoring is advanced to the initial stage of data processing, and support is provided for safer and more reliable GNSS real-time data processing.

Description

Technical Field

[0001] The present invention relates to the technical field of satellite data processing, and particularly to a method and system for monitoring the integrity of on-board GNSS observables of low-earth orbit satellites. Background Art

[0002] Due to the influence of the harsh space environment, the safety and reliability of on-board GNSS data processing for low-earth orbit satellites have always been highly concerned. Autonomous integrity monitoring is an important technical approach among them. Autonomous integrity monitoring usually judges whether the current GNSS data is credible by constructing an appropriate test statistic and observing the value of the statistic. Therefore, how to construct an appropriate test statistic is the key to the success or failure of autonomous integrity monitoring.

[0003] The existing integrity monitoring methods mainly start from the estimation results, construct corresponding integrity detection statistics, and perform integrity monitoring by whether their values exceed the corresponding thresholds. However, the above monitoring process usually intervenes in the middle and later stages of data processing. Although it can support integrity monitoring well, due to the late intervention time, it is often difficult to react to "abnormal" data in a timely manner at the initial stage of data processing. Summary of the Invention

[0004] Therefore, the present invention provides a method and system for monitoring the integrity of on-board GNSS observables of low-earth orbit satellites, which solves the problem that it is difficult to process abnormal data in a timely manner at the initial stage of data processing when the existing GNSS integrity monitoring process is accessed in the middle and later stages of data processing. Starting from the observation data, taking into account the long-term and short-term variation laws of on-board GNSS observation data of low-earth orbit satellites, and using the integrity test statistic to advance the abnormal fault monitoring to the initial 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 invention, on the one hand, a method for monitoring the integrity of on-board GNSS observables of low-earth orbit satellites is provided, including:

[0006] Collecting on-board GNSS observation data of low-earth orbit satellites with a specified arc length by using a sliding window and preprocessing the observation data;

[0007] Fitting the preprocessed observation data by using a trigonometric function model to obtain a fitting residual sequence within the corresponding arc segment, and the fitting residual sequence is used to represent the difference sequence between the observable quantity calculated by using the trigonometric function model and the actual observable quantity at the corresponding epoch;

[0008] Fitting the fitting residual sequence by using an autoregressive moving average model ARIMA and performing white noise detection on the fitting result residuals;

[0009] If the residuals of the fitting result pass the white noise test, the predicted values of the trigonometric function model of the on-board GNSS observations of the low-Earth orbit satellite and the residuals predicted by the autoregressive moving average model ARIMA are used to obtain the predicted values of the on-board GNSS observations of the low-Earth orbit satellite at each epoch;

[0010] Compare the predicted values of the on-board GNSS observations of the low-Earth orbit satellite with the measured values of the observations to obtain the prediction error of the on-board GNSS observations of the low-Earth orbit satellite at the corresponding epoch;

[0011] Construct a GNSS integrity check statistic based on the prediction error of the observations, set a check threshold according to the false alarm rate requirement, and determine the integrity of the on-board GNSS observations of the low-Earth orbit satellite according to the integrity check statistic and the check threshold.

[0012] As the method for monitoring the integrity of the on-board GNSS observations of the low-Earth orbit satellite in the present invention, further, the trigonometric function model is used to fit the preprocessed observation data, including:

[0013] Set the coefficients to be fitted, the order of the trigonometric function, and the angular frequency of the trigonometric function, and construct a trigonometric function fitting model based on the observation data;

[0014] Obtain the residuals of the observations with a specified arc length within the sliding window according to the fitting model, and represent the observation residuals in matrix form, so as to obtain the coefficients of the trigonometric function model based on the matrix form of the observation residuals and using the least squares principle;

[0015] Establish a trigonometric function model according to the coefficients of the trigonometric function model, so as to calculate the difference between the observed value and the actual observed value at the corresponding epoch using the trigonometric function model.

[0016] As the method for monitoring the integrity of the on-board GNSS observations of the low-Earth orbit satellite in the present invention, further, the fitting model is expressed as: where L j represents the on-board GNSS observation of the low-Earth orbit satellite at epoch t j , n represents the order of the trigonometric function, ω represents the angular frequency of the trigonometric function, a 0 , b i , c i are the coefficients of the model to be fitted.

[0017] As the method for monitoring the integrity of the on-board GNSS observations of the low-Earth orbit satellite in the present invention, further, the residuals of the observations with a specified arc length within the sliding window are expressed as:

[0018] where L 1 , L 2 , …, L m represent m groups of observations in the specified arc length.

[0019] As the method for monitoring the integrity of GNSS observations on-board low-earth orbit satellites of the present invention, further, an autoregressive integrated moving average model ARIMA is used to fit the fitting residual sequence, including:

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

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

[0022] As the method for monitoring the integrity of GNSS observations on-board low-earth orbit satellites of the present invention, further, the residual prediction model is expressed as: where p and q represent the model orders; d represents the number of differences; 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 t t the fitting residual at the epoch.

[0023] As the method for monitoring the integrity of GNSS observations on-board low-earth orbit satellites of the present invention, further, the GNSS integrity check statistic is expressed as: β 2 = ε m T P m ε m where β 2 represents the integrity check statistic, P m represents the weighting factor of the LEO on-board GNSS observations, and ε m represents the prediction error of the LEO on-board GNSS observations at the t m epoch.

[0024] On the other hand, the present invention also provides a system for monitoring the integrity of GNSS observations on-board low-earth orbit satellites, including: 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,

[0025] The data acquisition module is used to collect the on-board GNSS observation data of the low-earth orbit satellite with a specified arc length by using a sliding window and preprocess the observation data;

[0026] The data fitting module is used to fit the preprocessed observation data by using a trigonometric function model to obtain a fitting residual sequence within the corresponding arc segment, and the fitting residual sequence is used to represent the difference sequence between the observation quantity calculated by using the trigonometric function model and the actual observation quantity at the corresponding epoch;

[0027] A noise detection module, which is used to fit the fitting residual sequence by using the autoregressive integrated moving average model (ARIMA) and perform white noise detection on the fitting result residual;

[0028] A prediction estimation module, which is used to obtain the predicted values of the on-board GNSS observables of the low-earth orbit satellite at each epoch according to the predicted values of the trigonometric function model of the on-board GNSS observables of the low-earth orbit satellite and the predicted values of the ARIMA residuals when the fitting result residual passes the white noise detection;

[0029] An error calculation module, which is used to compare the predicted values of the on-board GNSS observables of the low-earth orbit satellite with the measured values of the observables to obtain the prediction error of the on-board GNSS observables of the low-earth orbit satellite at the corresponding epoch;

[0030] An integrity monitoring module, which is used to construct a GNSS integrity verification statistic based on the prediction error of the observables, set a verification threshold according to the false alarm rate requirement, and determine the integrity of the on-board GNSS observables of the low-earth orbit satellite according to the integrity verification statistic and the verification threshold.

[0031] Advantages of the present invention:

[0032] The present invention takes into account the characteristics of the operation law of LEO satellites themselves, and uses the combination of trigonometric functions and ARIMA to fully explore the long-term and short-term variation laws of LEO on-board GNSS observables and construct a corresponding observable prediction model. Compared with the traditional prediction mode based on the dynamic model, on the basis of ensuring the prediction accuracy, the model complexity can be effectively reduced, and the calculation efficiency of subsequent integrity monitoring implementation can be guaranteed; on the basis of constructing the prediction model of LEO on-board GNSS observables, a corresponding integrity test statistic is further constructed. According to the value of this statistic, the integrity of LEO on-board GNSS observables can be conveniently determined, thus providing certain support for the safe and reliable processing of LEO on-board GNSS data. Description of the drawings

[0033] Figure 1 It is a schematic diagram of the integrity monitoring process of the on-board GNSS observables of the low-earth orbit satellite in the embodiment;

[0034] Figure 2 It is a schematic diagram of the variation law of LEO on-board GNSS observables in the embodiment;

[0035] Figure 3 It is a schematic diagram of the integrity algorithm process of LEO on-board GNSS observation data in the embodiment. Detailed implementation manners

[0036] To make the objectives, technical solutions, and advantages of the present invention more clear and understandable, the present invention will be further described in detail below in conjunction with the accompanying drawings and technical solutions.

[0037] In an embodiment of the present invention, refer to Figure 1 As shown, a method for monitoring the integrity of LEO satellite-borne GNSS observables is provided, including:

[0038] S101. Collect LEO satellite-borne GNSS observation data of a specified arc length using a sliding window and preprocess the observation data.

[0039] The high-precision prediction of the LEO satellite-borne GNSS observable sequence is the key to establishing the integrity test statistic of the observable sequence in this project. Different from ground users, the operation of LEO satellites in space satisfies dynamic constraints. Therefore, the LEO satellite-borne GNSS observables themselves have obvious change characteristics. For example, Figure 2 As shown, the change of the carrier phase observable during a single transit of a GNSS satellite by a certain LEO satellite is given. Other LEO satellites are similar. It can be seen that its change characteristics are relatively consistent with trigonometric functions. However, it should also be noted that this is only the general change law, that is, the "long-term" change law. Due to various errors, even if this kind of long-term change law is removed, in order to improve the prediction accuracy level of the observables, there is still a "short-term" law that needs to be considered for the remaining part. In the embodiment of this case, on the basis of performing necessary preprocessing such as data format on the LEO satellite-borne GNSS observables, in order to ensure the real-time nature of the processing, a sliding window mode is adopted, that is, observation data of an arc length is collected, trigonometric function fitting and prediction are performed. After new real-time observation data is received, the new data is directly used while the expired old data is released, and trigonometric function fitting and prediction are performed again.

[0040] S102. Use a trigonometric function model to fit the preprocessed observation data to obtain a fitting residual sequence within the corresponding arc segment. The fitting residual sequence is used to represent the difference sequence between the observable calculated using the trigonometric function model and the actual observable at the corresponding epoch.

[0041] Among them, using a trigonometric function model to fit the preprocessed observation data can be designed to include:

[0042] Set the coefficients to be fitted, the order of the trigonometric function, and the angular frequency of the trigonometric function, and construct a trigonometric function fitting model based on the observation data;

[0043] Obtain the residuals of the observables of a specified arc length within the sliding window according to the fitting model, and represent the observable residuals in matrix form, so as to obtain the trigonometric function model coefficients based on the matrix form of the observable residuals and using the least squares principle;

[0044] A trigonometric function model is established based on the coefficients of the trigonometric function model to calculate the difference between the observed value and the actual observed value at the corresponding epoch by using the trigonometric function model.

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

[0046]

[0047] Among them, L j represents the LEO satellite-borne GNSS observable at epoch t; n represents the order of the trigonometric function, and ω represents the angular frequency of the trigonometric function. Both can be selected in combination with the operating characteristics of the low-earth orbit satellite; a j , b 0 , c i , c i are the model coefficients to be fitted.

[0048] Assume that there are m groups of LEO satellite-borne GNSS observables L 1 , L 2 , …, L m in the sliding window, then there is

[0049]

[0050] Record it in matrix form, there is

[0051] V = A·X - L(3)

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

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

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

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

[0056] On this basis, use the established trigonometric function model to calculate the observable at the corresponding epoch, and compare it with the actual observable to obtain the difference sequence within the fitting arc segment:

[0057]

[0058] Among them, represents the calculated value of the trigonometric function model at epoch t; L j represents the L at epoch t j represents the L at epoch tj Measured values of epoch LEO spaceborne GNSS observables; δL j Indicates the difference between the model calculated value and the measured value.

[0059] S103. Use the autoregressive moving average model ARIMA to fit the fitting residual sequence and perform white noise detection on the fitting result residuals.

[0060] For the time series δL 1 , δL 2 , …, δL m , again following the basic process of ARIMA model building, through steps such as stationarity analysis, model order determination, model estimation and testing, establish the following residual prediction model:

[0061]

[0062] In the formula, p and q represent the model orders; d represents the number of differences; 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 calculated value of the trigonometric function model from the LEO spaceborne GNSS observable.

[0063] Use the ARIMA model to fit the time series δL 1 , δL 2 , …, δL m Perform fitting, and perform white noise detection on the fitting result residuals. If the detection can pass, enter the next step of processing; otherwise, use the ARIMA model to fit the residual result until the fitting residuals can pass the white noise test.

[0064] S104. If the fitting result residuals pass the white noise detection, obtain the predicted values of the LEO spaceborne GNSS observables at each epoch based on the predicted values of the trigonometric function model of the LEO satellite spaceborne GNSS observables and the predicted values of the autoregressive moving average model ARIMA residuals.

[0065] By comprehensively using the trigonometric function and the ARIMA model, the predicted value of the LEO spaceborne GNSS observable under any epoch t m condition can be obtained:

[0066]

[0067] In the formula, represents t m the final predicted value of the LEO spaceborne GNSS observable at epoch; represents t mPredicted value of trigonometric function model for LEO satellite-borne GNSS observables at epoch; Denote the predicted value of the epoch residual obtained by the ARIMA residual prediction model at time t m Predicted value of the epoch residual.

[0068] S105. Compare the predicted value of the LEO satellite-borne GNSS observable with the measured value of the observable to obtain the prediction error of the LEO satellite-borne GNSS observable at the corresponding epoch.

[0069] The predicted value of the LEO satellite-borne GNSS observable at time t is obtained from Equation (7) m Predicted value of the LEO satellite-borne GNSS observable at epoch Subtract it from the measured value L of the LEO satellite-borne GNSS observable m to obtain

[0070]

[0071] where ε m denotes the prediction error of the LEO satellite-borne GNSS observable.

[0072] S106. Construct a GNSS integrity check statistic based on the prediction error of the observable, and set a check threshold according to the false alarm rate requirement, so as to determine the integrity of the LEO satellite-borne GNSS observable based on the integrity check statistic and the check threshold.

[0073] The integrity test statistic can be constructed as follows:

[0074] β 2 = ε m T P m ε m (9)

[0075] where β 2 denotes the integrity test statistic; P m denotes the weighting factor of the LEO satellite-borne GNSS observable, which can be set according to the elevation angle; other symbols are the same as in Equation (8).

[0076] The above test statistic follows a χ 2 distribution. According to the false alarm rate requirement, the test threshold can be calculated more conveniently, and based on this, it can be determined whether a fault has occurred:

[0077]

[0078] where H 0 denotes the fault-free condition; denotes the probability distribution function of the χ 2 distribution; P FA denotes the false alarm rate; T denotes the test threshold.

[0079] So far, according to the above conditions, the integrity of the LEO satellite-borne GNSS observables can be determined.

[0080] Therefore, in the embodiment of this case, the algorithm flow for the integrity monitoring of LEO satellite-borne GNSS observables can be as Figure 3 shown. First, perform necessary preprocessing on the LEO satellite-borne GNSS observation data, including data format arrangement, etc.; then, use the trigonometric function model to fit the LEO satellite-borne GNSS observation data and obtain the fitting residual sequence; then, perform stationarity analysis on the fitting residual sequence, and accordingly perform model order determination to determine the orders p and q of the ARIMA model used, as well as the number of differencing times d; use the ARIMA model to fit the fitting residual sequence again, and perform white noise detection on the fitting residuals. If it passes, proceed to the next step, otherwise, return to the stationarity analysis step and execute again; combine the trigonometric function model and the ARIMA model to obtain the predicted value of the LEO satellite-borne GNSS observation data, subtract it from the measured value of the LEO satellite-borne GNSS observation data at the current epoch, and construct an integrity detection statistic; according to the given false alarm rate requirement, determine whether the integrity detection statistic exceeds the detection threshold. If it does not exceed the detection threshold, 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 relevant personnel to process it in time.

[0081] Furthermore, based on the above method, the embodiment of the present invention also provides a low-earth orbit satellite-borne GNSS observable integrity monitoring system, including: 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, where,

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

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

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

[0085] A prediction and estimation module, configured to, when the residual of the fitting result passes the white noise detection, obtain the predicted value of the on-board GNSS observables of the LEO satellite at each epoch according to the predicted value of the trigonometric function model of the on-board GNSS observables of the LEO satellite and the predicted value of the autoregressive integrated moving average model (ARIMA) residual;

[0086] An error calculation module, configured to compare the predicted value of the on-board GNSS observables of the LEO satellite with the measured value of the observables, and obtain the prediction error of the on-board GNSS observables of the LEO satellite at the corresponding epoch;

[0087] An integrity monitoring module, configured to construct a GNSS integrity verification statistic based on the prediction error of the observables, set a verification threshold according to the false alarm rate requirement, and determine the integrity of the on-board GNSS observables of the LEO satellite according to the integrity verification statistic and the verification threshold.

[0088] Unless otherwise specifically stated, the relative steps, numerical expressions, and values of the components and steps set forth in these embodiments do not limit the scope of the present invention.

[0089] Each embodiment in this specification is described in a progressive manner. The key point of each embodiment is to illustrate the differences from other embodiments. The same or similar parts among the embodiments can be referred to each other. For the systems disclosed in the embodiments, since they correspond to the methods disclosed in the embodiments, the description is relatively simple, and the relevant parts can be referred to the description of the method part.

[0090] The units and method steps of each example described in combination with the embodiments disclosed herein can be implemented by electronic hardware, computer software, or a combination of the two. To clearly illustrate the interchangeability of hardware and software, the composition and steps of each example have been generally described according to functions in the above description. Whether these functions are executed in a hardware or software manner depends on the specific application and design constraints of the technical solution. Those of ordinary skill in the art can use different methods to implement the described functions for each specific application, but such implementation is not considered to exceed the scope of the present invention.

[0091] Those of ordinary skill in the art can understand that all or part of the steps in the above methods can be completed by instructing relevant hardware through a program. The program can be stored in a computer-readable storage medium, such as a read-only memory, a magnetic disk, or an optical disc, etc. Optionally, all or part of the steps of the above embodiments can also be implemented using one or more integrated circuits. Correspondingly, each module / unit in the above embodiments can be implemented in the form of hardware or in the form of a software functional module. The present invention is not limited to any specific form of the combination of hardware and software.

[0092] Finally, it should be noted that the above-described embodiments are only specific embodiments of the present invention, used to illustrate the technical solutions of the present invention, rather than limiting it. The protection scope of the present invention is not limited thereto. Although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that any technician familiar with the technical field of the present invention can still modify the technical solutions recorded in the foregoing embodiments or can easily think of changes, or perform equivalent replacements for some of the technical features; and these modifications, changes or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be covered by the protection scope of the present invention. Therefore, the protection scope of the present invention should be subject to the protection scope of the claims.

Claims

1. A method for monitoring the integrity of GNSS observations on low-orbit satellites, characterized in that: Include: A sliding window is used to collect the GNSS observation data of low-orbit satellites with a specified arc length and pre-process the observation data; The preprocessed observation data are fitted using a trigonometric function model to obtain a fitting residual sequence in the corresponding arc segment, wherein the fitting residual sequence is used to represent a difference sequence between the observation amount calculated using the trigonometric function model and the actual observation amount at the corresponding epoch; The autoregressive moving average model ARIMA is used to fit the residual sequence and the residual of the fitting result is tested for white noise; If the residual of the fitting result passes the white noise detection, the predicted value of the low-orbit satellite onboard GNSS observation at each epoch is obtained based on the predicted value of the trigonometric function model of the low-orbit satellite onboard GNSS observation and the predicted value of the autoregressive moving average model ARIMA residual; Compare the predicted value of the low-orbit satellite onboard GNSS observation quantity with the measured value of the observation quantity to obtain the prediction error of the low-orbit satellite onboard GNSS observation quantity at the corresponding epoch; The GNSS integrity verification statistics are constructed based on the observation prediction error, and the verification threshold is set according to the false alarm rate requirement. The integrity of the GNSS observations onboard the low-orbit satellite is judged based on the integrity verification statistics and the verification threshold.

2. The method for monitoring the integrity of GNSS observations onboard a low-orbit satellite according to claim 1, characterized in that: The preprocessed observation data is fitted using a trigonometric function model, including: Set the coefficients to be fitted, the order of the trigonometric function and the angular frequency of the trigonometric function, and build a trigonometric function fitting model based on the observed data; Obtain the residual of the specified arc length observation in the sliding window according to the fitting model, and express the observation residual in matrix form, so as to obtain the trigonometric function model coefficient based on the matrix form of the observation residual and the least square principle; A trigonometric model is established based on the trigonometric model coefficients to calculate the difference between the observed value and the actual observed value at the corresponding epoch using the trigonometric model.

3. The method for monitoring the integrity of GNSS observations onboard a low-orbit satellite according to claim 2, characterized in that: The fitting model is expressed as: Among them, L j Indicates t j The GNSS observations on low-orbit satellites in the epoch, n represents the order of the trigonometric function, ω represents the angular frequency of the trigonometric function, a0, b i 、c i are the coefficients of the model to be fitted.

4. The method for monitoring the integrity of GNSS observations onboard a low-orbit satellite according to claim 3, characterized in that: The residual error of a specified arc length observation within the sliding window is expressed as: Among them, L1, L2, …, L m Represents m groups of observations in a specified arc length.

5. The method for monitoring the integrity of GNSS observations onboard a low-orbit satellite according to claim 1, characterized in that: The autoregressive moving average model ARIMA is used to fit the residual sequence, including: A residual prediction model is established according to an autoregressive moving average (ARIMA) modeling process, wherein the autoregressive moving average (ARIMA) modeling process includes stationarity analysis, model order determination, model estimation and testing; The residual prediction model is used to fit the fitting residual sequence, and the residual of the fitting result is tested for white noise.

6. The method for monitoring the integrity of GNSS observations onboard a low-orbit satellite according to claim 5, characterized in that: The residual prediction model is expressed as: Where p and q represent the model order; d represents the number of differences; 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 Indicates t t The residuals of the fit at epoch.

7. The method for monitoring the integrity of GNSS observations onboard a low-orbit satellite according to claim 1, characterized in that: The GNSS integrity verification statistic is expressed as: β 2 =ε m T P m ε m , where β 2 represents the integrity test statistic, P m represents the weighting factor of LEO satellite-borne GNSS observations, ε m Indicates t m Prediction error of GNSS observations on low-orbit satellites in epochs.

8. A low-orbit satellite onboard GNSS observation integrity monitoring system, characterized in that: It includes: data acquisition module, data fitting module, noise detection module, prediction estimation module, error calculation module and integrity monitoring module, among which, A data acquisition module, used to collect GNSS observation data of low-orbit satellites of a specified arc length using a sliding window and pre-process the observation data; A data fitting module is used to fit the preprocessed observation data using a trigonometric function model to obtain a fitting residual sequence in a corresponding arc segment, wherein the fitting residual sequence is used to represent a difference sequence between the observation amount calculated using the trigonometric function model and the actual observation amount at the corresponding epoch; Noise detection module, used to fit the fitting residual sequence using the autoregressive moving average model ARIMA and perform white noise detection on the residual of the fitting result; The prediction and estimation module is used to obtain the prediction value of the low-orbit satellite onboard GNSS observation quantity at each epoch based on the prediction value of the trigonometric function model of the low-orbit satellite onboard GNSS observation quantity and the autoregressive moving average model ARIMA residual prediction value when the residual of the fitting result passes the white noise detection; The error calculation module is used to compare the predicted value of the low-orbit satellite onboard GNSS observation quantity with the measured value of the observation quantity to obtain the predicted error of the low-orbit satellite onboard GNSS observation quantity at the corresponding epoch; The integrity monitoring module is used to construct GNSS integrity verification statistics based on the observation prediction error, set the verification threshold according to the false alarm rate requirement, and judge the integrity of the low-orbit satellite onboard GNSS observations based on the integrity verification statistics and the verification threshold.

9. An electronic device, characterized in that: include: 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 according to any one of claims 1 to 7.

10. 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 according to any one of claims 1 to 7 can be implemented.

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