CN109542084B - A satellite-based augmentation system integrity fault simulation method - Google Patents

Abstract

The invention provides an integrity fault simulation method of a satellite-based augmentation system, which belongs to the technical field of satellite-based augmentation of satellite navigation. Including: step 1, configure the station satellite and fault parameters for simulation through the human-computer interaction module; step 2, the data import module, according to the configured station and satellite parameters, the monitoring station selected by the user and the user station in a specified time. Import observation files and navigation files; step 3, the fault generation module generates corresponding types of faults according to the fault parameters configured in step 1, calculates the impact of the fault on the observation data, and adds it to the observation data; step 4, data output module Output the observation data with added faults in RINEX format. The invention provides a solution idea for the test and verification of the integrity monitoring capability of Beidou SBAS in my country by inputting the measured data, adding various integrity faults as simulation events, and outputting the processed data to the subsequent SBAS processing module.

Description

A satellite-based augmentation system integrity fault simulation method

technical field

The invention belongs to the technical field of satellite-based augmentation of satellite navigation, in particular to a method for simulating integrity faults of a satellite-based augmentation system.

Background technique

At present, the main core constellations of the global navigation satellite system (GNSS) include the global positioning system (GPS) of the United States, the global navigation satellite system (GLONASS) of Russia, and the Galileo positioning system of the European Union. and China's Beidou navigation system. Since the core constellation alone cannot meet the accuracy and integrity requirements of aviation users, augmented systems have emerged. Augmentation systems include satellite-based augmentation systems, space-based augmentation systems, and ground-based augmentation systems. Among them, the satellite-based augmentation system has a wide coverage and has more advantages in aviation and navigation applications.

The "White Paper on China's Beidou Satellite Navigation System" points out that in the construction of the third generation of Beidou, it is necessary to carry out the integrated construction of the augmentation system and the basic system at the same time, not only to realize the global basic navigation service, but also to provide satellite-based augmentation services for the Asia-Pacific region and improve the system service accuracy. and reliability.

Satellite-based augmentation system (SBAS) processes satellite signals from ground stations, calculates various corrections and integrity information, and broadcasts the information to users through geostationary satellites. One of its most important functions is to provide integrity information for the safety of aviation users. At present, most GNSS simulation software only has functions such as positioning and calculation, and lacks an integrity fault simulation module, which makes it difficult to support the testing and research of integrity monitoring methods. Since the probability of integrity faults in the measured data is very small, it is necessary to artificially add specific faults to generate simulation data to support the development and testing of the integrity monitoring module.

Integrity failures are divided into three categories: signal-in-space failures, monitoring station failures, and propagation segment failures.

Signal-in-space faults are faults generated at the transmitter of a navigation satellite, mainly including star clock faults and ephemeris faults. The failure of the satellite clock is caused by the defect or aging of the satellite atomic clock components, and the error can be divided into two forms: drift and jump. In the reference document [1], in the GPS standard positioning service performance specification, the rate of change of the star clock error should not be greater than 0.006m/s under normal circumstances. Reference [2] analyzed the GPS standard positioning service fault reports from 2000 to 2017, and found that the satellite clock skew fault is the most likely space signal fault in practice. When the star clock malfunctions and the rate of change increases abnormally, the ranging error caused within a hundred seconds can reach hundreds of meters or even thousands of meters. Due to the periodicity of satellite orbital motion, the planetary disturbances it receives are regular, so planetary disturbances do not belong to the scope of integrity failure research. Therefore, the main reason for the ephemeris failure is due to the maneuvering of the satellite, which is a planned interruption. The satellite will be forecasted in advance, and its health flag will be set as an unhealthy state.

The monitoring station failure is the failure of the signal receiving end. First, the electromagnetic interference around the monitoring station will cause a serious deterioration of the signal carrier-to-noise ratio and a sharp increase in the ranging error. The second situation is that the communication network of the monitoring station is interrupted and the quality of the observation data is degraded to the point where the service cannot be provided normally. During the time of failure, the failed station will not receive any valid observation files and navigation files, and the files being received are also forced to be interrupted.

Propagation segment failures are failures caused by anomalies in the propagation medium between the satellite and the receiver. The errors generated during the propagation of the navigation signal from the satellite to the receiver mainly include ionospheric error, tropospheric error and multipath interference. Among them, the ionospheric delay is the main error source. Ionospheric anomalies are highly random, mainly including ionospheric storms and ionospheric scintillations. Scintillation is caused by the structure of small-scale inhomogeneities in the ionosphere, which can cause the tracking signal to lose lock and the receiver to be unable to track one or more visible satellites in a short period of time. Due to the redundancy of satellites, ionospheric scintillation has limited impact on positioning results.

An ionospheric storm is a phenomenon in which the physical parameters of the ionosphere are seriously deviated from the normal state due to the disturbance of the earth's space environment due to the strong magnetic storm of the solar eruption. With the occurrence of strong magnetic storms, the ionosphere changes drastically on a global scale. Ionospheric bursts cause large changes in electron density and electron content gradients, which will reduce the spatial-temporal correlation of the ionosphere, which is an important factor affecting the navigation and positioning system.

Reference [3] has two forms of ionospheric disturbance effects that may exist in SBAS: "bubble-like" effects refer to the abnormal changes in ionospheric delays in a small area surrounded by a calm ionospheric environment, which change with time. Anomalous disturbances only occur within this range, and no movement occurs, and the anomalous area of the ionosphere is block-shaped. The "wall"-like effect refers to the superimposed disturbance effect that changes with distance gradient and moves with time on the calm ionospheric environment, and the ionospheric anomaly area is extended.

In reference [4], the GPS Laboratory of Stanford University proposed a local area augmentation system (LAAS) wedge model based on the ionospheric anomaly data in the mid-latitude region where North America is located. In this model, the ionosphere under abnormal conditions Considered a linear semi-open wedge-shaped front end, and moving at a fixed speed relative to the ground. The wedge-shaped model is determined by three parameters, the velocity, width and gradient of the anomalous front of the ionosphere.

References are as follows:

[1]U.S.Department of Defense.Global positioning system standardpositioning service performance standard[EB/OL].(2008-09)[2018-05-08].

http://www.gps.gov/technical/ps/2008-SPS-performance-standard.pdf.

[2]WILLIAM J.Global positioning system(GPS)standard positioningservice(SPS)performance analysis report,Appendix C:Performance analysis(PAN)problem report[EB/OL].[2018-05-08].http://www .nstb.tc.faa.gov/DisplayArchive.htm.

[3] ALTSHULER E S, FRIES RM, SPARKS L. The WAAS ionospheric spatialthreat model[C]//The Institute of Navigation. Proceedings of the 14th International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GPS 2001).Salt Lake City , UT: The Institute of Navigation, Inc., 2001: 2463-2467.

[4]LUO M,PULLEN S,WALTER T,et al.Ionosphere spatial gradient threat for LAAS:mitigation and tolerable threat space[C]//The Institute of Navigation.Proceedings of the 2004National Technical Meeting.Manassas,VA:TheInstitute of Navigation, Inc., 2004:490-501.

SUMMARY OF THE INVENTION

Aiming at the problems that the current GNSS simulation software of the satellite-based augmentation system only has functions such as positioning and calculation, lacks an integrity fault simulation module, and is difficult to support the testing and research of the integrity monitoring method, a satellite-based augmentation system integrity is proposed. Fault simulation method. Input the measured data and add various integrity faults as simulation events, and output the processed data to the subsequent SBAS processing module, which provides a solution for the test and verification of the integrity monitoring capability of Beidou SBAS in my country.

A method for simulating the integrity fault of a satellite-based augmentation system proposed by the present invention includes the following four steps:

Step 1. Configure the station satellite parameters and fault parameters for simulation through the human-computer interaction module.

Station and satellite parameters include user-selected data processing time, monitoring station area, monitoring station and user station list, satellite constellation, and added fault types; fault types include space signal faults, monitoring station faults, and ionospheric storm faults; monitoring station faults Including monitoring station failure electromagnetic failure and monitoring station receiver failure; ionospheric storm failure belongs to the propagation section failure.

Step 2: The data import module imports the real observation files and navigation files of the monitoring station selected by the user and the user station within the specified time according to the station and satellite parameters configured by the human-computer interaction module; the file format adopts the exchange format RINEX.

Step 3. The fault generation module generates a corresponding type of fault according to the fault parameters configured in step 1, and calculates the impact of the fault on the observed data, and the data processing module adds the calculated impact to the observed data.

For the electromagnetic fault of the monitoring station, the Gaussian noise with a mean value of 0 is used as the influence of the abnormal changes of the electromagnetic field on the pseudorange and carrier phase observations; for the receiver fault of the monitoring station, all the observation data of the faulty station are cleared; the space signal fault is divided into order Step fault and slope fault; for step fault, add a constant error to all observations of the faulty satellite; for slope fault, calculate the error increment that varies linearly with time; The simulation model calculates the ionospheric vertical delay value increment within the circular latitude and longitude range corresponding to each epoch in the circular typhoon storm model, and calculates and changes all the observed values of the ionospheric penetration point latitude and longitude within the storm range.

Step 4. The data output module outputs the observation data with added faults in RINEX format.

Compared with the prior art, the present invention has the following obvious advantages:

(1) Each parameter configuration in the present invention is flexible. Users can choose parameters such as monitoring station, user station, satellite constellation, fault type, etc. by themselves through the man-machine interface.

(2) The selected integrity fault types in the present invention are comprehensive. It involves the main types of failures that may occur in the three parts of satellite signal transmission, propagation and reception.

(3) The data output format is standardized in the present invention. The output observation files and navigation files are in the RINEX standard file format, which can be directly used in various processing modules such as geometric method simulation, dynamic method simulation, ionospheric processing, enhanced message generation, user algorithm simulation, and performance evaluation to support the follow-up of the SBAS simulation platform. Various functional design and performance evaluation work.

Description of drawings

1 is an architecture diagram of a satellite-based augmentation system integrity fault simulation system of the present invention;

Fig. 2 is the main interface schematic diagram of human-computer interaction of the present invention;

Fig. 3 is the flow chart of monitoring station fault adding of the present invention;

Fig. 4 is a flow chart of adding a signal-in-space fault of the present invention;

Fig. 5 is the cross-sectional schematic diagram of the round table storm model of the present invention;

FIG. 6 is a flow chart of adding an ionospheric storm fault according to the present invention.

Detailed ways

In order to facilitate those skilled in the art to understand and implement the present invention, the present invention will be further described in detail below with reference to the accompanying drawings.

The integrity fault simulation system of the satellite-based augmentation system of the present invention, as shown in Figure 1, includes five modules: human-computer interaction, data import, fault generation, data processing and data output. The parameters of monitoring stations, satellites and integrity faults are configured through the graphical interface of the human-computer interaction module, and space signal faults, monitoring station faults and propagation segment faults are added to the observation data as simulation events, so that the processed data is output for subsequent SBAS. The processing module provides data sources to support the development and testing of SBAS integrity monitoring methods.

In the present invention, integrity faults are divided into three categories, including: space signal faults, monitoring station faults and propagation section faults. Signal-in-space faults are faults generated at the transmitter of the navigation satellite, including step faults and ramp faults, all of which are star clock faults. The monitoring station fault is the fault generated by the signal receiving end, including the electromagnetic fault of the monitoring station due to the electromagnetic interference around the monitoring station and the fault of the monitoring station receiver that cannot receive the observation data normally due to the interruption of the communication network. Propagation segment failure is an ionospheric storm failure caused by the abnormal propagation medium between the satellite and the receiver, and its main source is the ionospheric storm. The simulation method of the present invention realizes the simulation of the above-mentioned fault.

The satellite-based augmentation system integrity fault simulation method of the present invention includes four steps, and each step is described below.

Step 1. The user performs flexible parameter configuration through the human-computer interaction module. The parameters that need to be configured include: station satellite parameters and fault parameters. The parameter configuration is carried out through the main interface of human-computer interaction.

As shown in FIG. 2 , it is a schematic diagram of the main interface of human-computer interaction of the present invention. First, configure the parameters through the main interface of human-computer interaction. The parameters to be configured include: the time when the user selects the data processing, the area of the monitoring station, the list of monitoring stations and user stations, the satellite constellation and the type of added fault. At the same time, the main interface of human-computer interaction will also display the location of the monitoring station and the user station, which is convenient for the user to select the station.

Then, according to the selected fault type, configure the corresponding fault parameters through the pop-up sub-window. The types of failures include: signal-in-space failures, monitoring station electromagnetic failures, ionospheric storm failures, and monitoring station receiver failures. Signal-in-space fault types include: step fault and ramp fault. The space signal fault parameters include: fault star number, fault type selection (step or ramp), fault start and end time, step value or ramp value. The electromagnetic fault parameters of monitoring station include: fault station name, fault start and end time and electromagnetic fault variance factor - no fault type. The fault parameters of the monitoring station receiver include: fault station name, fault start and end time. Propagation segment faults are mainly ionospheric storm faults. The ionospheric storm fault parameters include: circular cone parameters and motion parameters. The parameters of the circular cone include: the radius of the upper bottom surface, the radius of the lower bottom surface, and the maximum delay value. Movement parameters include: the initial longitude of the storm center, the initial latitude of the storm center, the start and end time of the storm, the direction of the storm, and the speed of the storm.

Step 2: The data import module imports the monitoring station selected by the user and the real observation files and navigation files of the user station within the specified time into the integrity fault simulation system according to the station-star parameters configured by the human-computer interaction module, and stores them in the data processing module .

The file format of observation files and navigation files adopts the receiver-independent exchange format RINEX (receiverindependent exchange format).

Step 3. According to the fault parameters input by the user, the integrity fault is generated and added to the observation data file.

The fault generation module generates different types of fault events according to the integrity fault parameters configured by the human-computer interaction module, and calculates the impact of the selected faults on the observed data. Code pseudorange and carrier phase simulation observation data recorded in the observation file. The data processing module superimposes the influence caused by the corresponding integrity fault in the imported observation data according to the fault event selected by the user, and obtains the code pseudorange and carrier phase simulation observation data after adding the fault.

The simulation process for adding different types of faults is described below.

Step 301 , adding monitoring station faults. The present invention considers monitoring station failures in two cases.

The first case is the electromagnetic failure of the monitoring station. The electromagnetic interference around the monitoring station will cause serious deterioration of the signal carrier-to-noise ratio, and the ranging error will increase sharply. Due to the randomness of electromagnetic effects, Gaussian noise with a mean value of 0 is used as the effect of abnormal changes in the electromagnetic field on the pseudorange and carrier phase observations. Among them, the variance of Gaussian noise is configured by the user on the human-computer interaction module. During the electromagnetic fault occurrence time, the influence of electromagnetic interference is simulated by superimposing the error obeying the corresponding Gaussian distribution on the code pseudorange and carrier phase observations of all satellites received by the fault monitoring station. The error of the Gaussian distribution is Gaussian noise.

The second situation is that the monitoring station receiver fails, and the communication network of the monitoring station is interrupted, resulting in the degradation of the quality of the observation data and the inability to provide services normally. During the failure time, the failure monitoring station will not receive any valid observation data and navigation files, and the files being received are also forced to be interrupted. Therefore, such failures are simulated by clearing all observations of the faulty station to zero.

The process of adding a monitoring station fault in the present invention is shown in Figure 3. According to the electromagnetic fault parameters of the monitoring station input in the man-machine interactive console, including the fault station name, fault type, electromagnetic fault variance factor and fault start and end time, the data processing module Read the observation file in RINEX format, traverse the monitoring stations one by one, and determine whether the station name is a faulty station. If it is not a faulty station, it will directly output the observation data without adding the monitoring station fault; if it is a faulty station, select the time when the fault occurred. The observation data is processed. If the monitoring station receiver is faulty, the observation data will be cleared; if it is an electromagnetic fault, the fault generation module will calculate the Gaussian noise interference value with a mean value of 0, and the data processing module will observe the dual-frequency code pseudorange. The Gaussian noise interference value is superimposed on the value and the carrier phase observation respectively. Finally, output the observation file after the superposition monitoring station fails.

Step 302, adding a signal-in-space fault.

The star clock failure is the most common space signal failure, and the error can be divided into two forms: drift and jump. Where clock drift is simulated by superimposing ramp values that vary linearly with time, and clock jumps can be simulated by superimposing constant values. Superimpose constant values, ie step values.

The present invention divides signal-in-space faults into two categories, ramp faults and step faults. The ramp value configured by the user in the human-computer interaction module is the rate of change of the GPS L1 frequency point code pseudorange observation increment, in m/s; the step value is the incremental value of the code pseudorange observation, in m. The observation value increment of other frequency points or the carrier phase observation value increment can be obtained through a simple conversion relationship.

The process of adding a signal-in-space fault in the present invention is shown in FIG. 4 . The data processing module reads the navigation file in RINEX format, traverses the satellites, and determines whether If it is a faulty satellite, if it is not, no space signal fault will be added to the observation data of the satellite; if it is a faulty satellite, the observation data of the faulty satellite at the time of the fault occurrence will be selected for processing. If a step fault is added, a constant error is added to all observations of the faulty satellite; if a ramp fault is added, the fault generation module calculates an error increment that varies linearly with time. The data processing module adds the step error or the calculated slope error increment to the dual-frequency code pseudorange and carrier phase observations of the faulty satellite, and finally outputs the observation data after adding the signal-in-space fault.

Step 303, adding a propagation segment fault.

Ionospheric storms are mainly modeled in the propagation segment fault simulation. Combined with "bubble", "wall" and wedge-shaped models, the present invention uses a circular trough moving at a uniform speed to establish an ionospheric storm simulation model. The storm model was added to the ionospheric vertical delay data during the calm period to simulate the observational data during the storm period. The model can adjust the size of parameters to simulate the scale, direction, speed, front-end gradient, etc. of different ionospheric storms. Ionospheric storms are ionospheric storms.

As shown in Figure 5, it is the cross section of the round typhoon storm model of the present invention. This model is actually a two-dimensional circular ionospheric vertical delay increment based on the ionospheric thin shell model. At the maximum radius, the ionosphere is affected by the storm and the delay value increases. Within the minimum radius, the delay increment remains at the maximum value, and between the minimum and maximum radius, the delay increment changes linearly. The maximum delay value is the ionospheric vertical delay increment value at the GPS L1 frequency.

The process of adding ionospheric storm faults in the present invention, as shown in FIG. 6 , the parameters input by the console according to human-computer interaction include: circular platform parameters, initial longitude and latitude of the storm center position, storm moving direction, storm moving speed, and storm start and end Time, the data processing module reads the observation file in RINEX format, obtains the position of the monitoring station and the observation data, reads the navigation file in RINEX format, and calculates the satellite position.

分别为地面用户的经纬度，λ_ipp和

分别为电离层穿透点的经纬度。根据位置的几何关系，电离层穿透点的经纬度可按下式计算[参考文献5]：The data processing module calculates the longitude, latitude and inclination factor of the ionosphere pierce point (IPP) according to the position of the monitoring station and the satellite. Let λ _u and

are the latitude and longitude of the ground user, λ _ipp and

are the latitude and longitude of the ionospheric penetration point, respectively. According to the geometric relationship of the location, the latitude and longitude of the ionospheric penetration point can be calculated as follows [Ref. 5]:

Among them, Ψ _ipp is the geocentric angle; A is the azimuth angle [Reference 6], E is the elevation angle [Reference 6], _Re is the approximate radius of the earth (valued at 6378.1363 km), and h _I is the ionospheric electron density The height with the largest content is also the reference plane height of the thin shell model (the value is 350km).

The ionospheric delay on the line-of-sight path of the signal can be converted to the vertical ionospheric delay in the zenith direction in the thin-shell model using the tilt factor function F _ipp . The tilt factor is defined as the ratio of the line-of-sight ionospheric delay to the vertical ionospheric delay. Press Formula calculation [Reference 7]:

References are as follows:

[5]RTCA/DO-229C.MINIMUM OPERATIONAL PERFORMANCE STANDAR-DS FOR GLOBALPOSITIONING SYSTEM/WIDE AREA AUGMENTATION SYSTEM AIRBOR-NE EQUIPMENT[S].2001.

[6] Pratap Misra, Per Enge. Global Positioning System - Signal, Measurement and Performance (Second Edition) [M]. Beijing: Electronic Industry Press. 2008.

[7] Lawrence, Sparks, et, al. Estimating ionospheric delay using kriging: 1. Methodology [J]. RADIO SCIENCE, 2011, 46(6), RS0D21, doi: 10.1029/2011RS004667.

The fault generation module calculates and obtains each epoch in the circular typhoon storm model, which corresponds to the increment of the vertical ionospheric delay value within the circular latitude and longitude range. After the data processing module calculates the longitude and latitude of the ionospheric penetration point and the inclination factor, it selects all the observation values of the IPP longitude and latitude within the storm range for processing. Determine whether the IPP latitude and longitude is within the circular storm area, if not, do not add the ionospheric storm fault, if so, calculate and change the code pseudorange and carrier phase observations based on the obtained tilt factor and ionospheric vertical delay increment , and finally output the observation file after adding the ionospheric storm fault.

According to equations (1), (2), (3), and (4), the pseudorange and carrier phase values of the dual-frequency code after adding the simulated ionospheric storm model are obtained.

和

分别加入电离层风暴后的更改的双频码伪距观测值，

和

分别是加入电离层风暴后的更改的双频载波相位观测值。c是真空中的光速，f₁和f₂代表双频频点。F_ipp是倾斜因子，h是L1频点电离层垂直延迟值增量。Among them: ρ ₁ and ρ ₂ are the original dual-frequency code pseudorange observations, respectively, and φ ₁ and φ ₂ are the original dual-frequency carrier phase observations, respectively;

and

The modified dual-frequency code pseudorange observations after adding the ionospheric storm, respectively,

and

are the altered dual-frequency carrier phase observations after the addition of ionospheric storms. c is the speed of light in vacuum, and f ₁ and f ₂ represent dual frequency points. F _ipp is the tilt factor, and h is the increment of the ionospheric vertical delay value at the L1 frequency.

Step 4: The data output module outputs the processed file data according to the RINEX standard file format for subsequent use.

The invention proposes a method for simulating the integrity fault of a satellite-based augmentation system. The parameters of monitoring stations, satellites and integrity faults can be configured through the human-computer interaction graphical interface, and space signal faults, monitoring station faults and propagation segment faults can be added to the observation data as simulation events to provide data for subsequent SBAS processing modules. The invention realizes the model simulation of the integrity fault, adopts the step model and the slope model for the space signal fault; adopts the 0-mean Gaussian noise model for the electromagnetic fault of the monitoring station; adopts the moving circular cone model for the ionospheric storm fault in the propagation section . The error generated by the simulated fault is directly superimposed on the real code pseudorange and carrier phase observations, which not only ensures that the data source is close to the actual situation, but also facilitates the subsequent analysis and verification of the data before and after adding the fault. The invention has the advantages of flexible parameter configuration, comprehensive fault types, standardized output format and the like.

Claims (6)

1. A satellite-based augmentation system integrity fault simulation method is characterized by comprising the following steps:

step 1, station satellite parameters and fault parameters for simulation are configured through a human-computer interaction module;

the satellite parameters comprise the time for processing data selected by a user, the area of a monitoring station, a list of the monitoring station and a subscriber station, a satellite constellation and an added fault type; the fault types comprise a space signal fault, a monitoring station fault and an ionosphere storm fault; the monitoring station faults comprise monitoring station electromagnetic faults and monitoring station receiver faults; the ionospheric storm fault belongs to a propagation section fault;

step 2, the data import module imports real observation files and navigation files of a monitoring station and a user station selected by a user within a specified time according to the station satellite parameters configured by the man-machine interaction module; the file format adopts an exchange format RINEX;

step 3, the fault generation module generates faults of corresponding types according to the fault parameters configured in the step 1, calculates the influence of the faults on the observation data, and the data processing module adds the calculated influence to the observation data;

for electromagnetic faults of the monitoring station, Gaussian noise with the average value of 0 is used as the influence of abnormal changes of an electromagnetic field on pseudo-range and carrier phase observed values; for the fault of a monitoring station receiver, clearing all observation data of the fault station; the space signal faults are divided into step faults and slope faults; for step faults, adding constant errors to all observed values of a fault satellite; for a slope fault, calculating an error increment which changes linearly along with time; for ionospheric storm faults, a simulation model is established by adopting a round table which moves at a uniform speed, the ionospheric vertical delay value increment in a round longitude and latitude range corresponding to each epoch in the round table storm model is calculated, and all observed values of the ionospheric penetration point longitude and latitude in the storm range are calculated and changed;

and 4, outputting the observation data added with the fault according to a RINEX format by a data output module.

2. The method according to claim 1, wherein in step 1, the fault parameter configuration is performed for the selected fault type, and the parameters to be configured for each fault type include:

spatial signal fault types include: step fault and slope fault, the space signal fault parameters include: fault star number, fault type selection, fault start-stop time, step value or ramp value; the electromagnetic fault parameters of the monitoring station comprise: fault station name, fault start-stop time and electromagnetic fault variance factor; the monitoring station receiver fault parameters comprise fault station names and fault start-stop time; the ionosphere storm fault comprises a circular table parameter and a motion parameter; the parameters of the circular truncated cone comprise: the radius of the upper bottom circle, the radius of the lower bottom circle and the maximum delay value; the motion parameters include: storm center initial longitude and latitude, storm start and stop times, storm movement direction, and storm movement speed.

3. The method according to claim 1 or 2, wherein in step 3, when the monitoring station configured in step 1 is added with a fault, the data processing module reads an observation file in a RINEX format, traverses the monitoring station, judges whether the station name is the fault station, and directly outputs the observation data without adding the fault of the monitoring station if the station name is not the fault station; if the fault station is the fault station, the observation data in the fault occurrence time is selected to be processed, if the fault station is the fault of the monitoring station receiver, the observation data is cleared, if the fault station receiver is the electromagnetic fault, the fault generation module calculates Gaussian noise with the mean value of 0, and the data processing module superposes the Gaussian noise on the code pseudo-range observation value and the carrier phase observation value.

4. The method according to claim 1 or 2, wherein in step 3, when the spatial signal fault configured in step 1 is added, the data processing module reads an observation file in a RINEX format, traverses the satellite, determines whether the satellite is a faulty satellite, and if not, does not add the spatial signal fault to the observation data of the satellite; if the fault satellite is the fault satellite, selecting observation data of the fault satellite at the fault occurrence time for processing, and if a step fault is added, adding a constant error on a code pseudo range and a carrier phase observation value of the fault satellite by a data processing module; if slope faults are added, the fault generation module calculates error increment which changes linearly along with time, and the data processing module adds the calculated slope error increment to code pseudo range and carrier phase observed value of a fault satellite.

5. The method according to claim 1, wherein in the step 3, when the ionosphere storm fault configured in the step 1 is added, the data processing module reads an observation file in a RINEX format to obtain a monitoring station position and observation data, reads a navigation file in the RINEX format to calculate a satellite position, and calculates and obtains the longitude and latitude and the inclination factor of an ionosphere penetration point according to the monitoring station position and the satellite position;

wherein, the longitude and latitude of the ionosphere penetration point are respectively lambda_ippAnd

the following were used:

wherein λ is_uAnd

respectively longitude and latitude, Ψ of the ground user_ippIs the geocentric angle, A is the azimuth angle, E is the elevation angle, R_eIs the approximate radius of the earth, h_IThe height with the largest ionized layer electron density content;

tilt factor

6. The method according to claim 1 or 5, wherein in the step 3, after the longitude and latitude of the ionosphere penetration point and the tilt factor are calculated, the data processing module selects all observed values of the ionosphere penetration point longitude and latitude within a storm range for processing; the fault generation module calculates and obtains each epoch in the cone storm model according to the cone parameters and the motion parameters, and the corresponding ionosphere vertical delay value increment in the circular longitude and latitude range; the data processing module calculates a change code pseudo range and a carrier phase observed value according to the inclination factor and the ionosphere vertical delay value increment, and outputs an observation file added with the ionosphere storm fault;

let the pseudo-range observed values of the original dual-frequency codes be rho₁And ρ₂The original dual-frequency carrier phase observed values are respectively phi₁And phi₂After ionosphere storm fault is added, the corresponding pseudo-range observed values of the double-frequency codes are respectively

And

the dual-frequency carrier phase observed values are respectively

And

the following were used:

wherein h is L1 frequency point ionospheric vertical delay increment, F_ippIs the tilt factor, f₁And f₂Representing the dual frequency bin, c is the speed of light in vacuum.

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