CN109542084B - Integrity fault simulation method for satellite-based augmentation system - Google Patents

Integrity fault simulation method for satellite-based augmentation system Download PDF

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CN109542084B
CN109542084B CN201811376580.9A CN201811376580A CN109542084B CN 109542084 B CN109542084 B CN 109542084B CN 201811376580 A CN201811376580 A CN 201811376580A CN 109542084 B CN109542084 B CN 109542084B
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李锐
刘禹彤
王君君
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Beihang University
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    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B23/00—Testing or monitoring of control systems or parts thereof
    • G05B23/02—Electric testing or monitoring
    • G05B23/0205—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults
    • G05B23/0218—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults characterised by the fault detection method dealing with either existing or incipient faults
    • G05B23/0243—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults characterised by the fault detection method dealing with either existing or incipient faults model based detection method, e.g. first-principles knowledge model
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
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Abstract

The invention provides a method for simulating integrity fault of a satellite-based augmentation system, and belongs to the technical field of satellite-based augmentation of satellite navigation. The method comprises the following steps: step 1, configuring station satellite and fault parameters for simulation through a human-computer interaction module; step 2, importing the real observation files and navigation files of the monitoring station and the user station selected by the user within a specified time by a data importing module according to the configured station satellite parameters; 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 adds the influences to the observation data; and 4, outputting the observation data added with the fault according to a RINEX format by a data output module. The invention provides a solution for the test and verification of the integrity monitoring capability of the Beidou SBAS in China by inputting measured data, adding various integrity faults as simulation events and outputting the processed data to a subsequent SBAS processing module.

Description

Integrity fault simulation method for satellite-based augmentation system
Technical Field
The invention belongs to the technical field of satellite-based augmentation of satellite navigation, and particularly relates to a method for simulating integrity fault of a satellite-based augmentation system.
Background
Currently, the main core constellation of Global Navigation Satellite System (GNSS) includes the Global Positioning System (GPS) in the united states, the russian global navigation satellite system (GLONASS), the galileo positioning system in the european union and the beidou navigation satellite system in china. The enhancement system comes in force because the core constellation alone cannot meet the requirements of aviation users on accuracy and integrity. The enhancement system comprises a star-based enhancement system, a space-based enhancement system and a foundation enhancement system. The satellite-based augmentation system is wide in coverage range and has advantages in aviation and navigation applications.
The white paper of the Beidou satellite navigation system in China indicates that the integrated construction of an enhancement system and a basic system needs to be carried out simultaneously in the Beidou third generation construction process, not only is the global basic navigation service realized, but also a satellite-based enhancement service is provided for Asia-Pacific region, and the service precision and reliability of the system are improved.
Satellite-based augmentation systems (SBAS) process satellite signals from ground stations, calculate various corrections and integrity information, and broadcast the information to users via geostationary orbit satellites. One of its most important functions is to provide integrity information to ensure the safety of the airline users. At present, most GNSS simulation software only has the functions of positioning, resolving and the like, lacks an integrity fault simulation module and is difficult to support the test and research of an integrity monitoring method. Because the probability of the integrity fault in the measured data is very small, specific faults need to be added manually to generate simulation data so as to support the development and the test of the integrity monitoring module.
Integrity failures are classified into three categories: spatial signal faults, monitoring station faults, and propagation segment faults.
The space signal fault is a fault generated at a transmitting end of a navigation satellite and mainly comprises a clock fault and an ephemeris fault. The satellite clock fault is caused by the defect or aging of the satellite atomic clock component, and the generated error can be divided into two forms of drift and jump. In the performance specification of the GPS standard positioning service in reference [1], the rate of change of the star clock error should normally be not more than 0.006 m/s. Reference [2] by analyzing the GPS standard positioning service fault report from 2000 to 2017, the star clock ramping fault is the spatial signal fault most likely to be generated actually. When the star clock generates faults and the change rate is abnormally increased, the maximum range error caused in hundreds of seconds can reach hundreds of meters or even thousands of meters. The satellite orbit motion has periodicity, so that the satellite orbit motion is subjected to planetary disturbance regularly, and the planetary disturbance does not belong to the integrity fault research range. Therefore, the ephemeris fault is mainly caused by the fact that the satellite maneuvers to adjust the orbit, the satellite is interrupted in a plan, the satellite forecasts in advance, and the health mark of the satellite is set to be in an unhealthy state.
A monitoring station fault is a fault generated at the signal receiving end. First, the electromagnetic interference around the monitoring station causes the signal-to-carrier-to-noise ratio to be seriously deteriorated, and the ranging error is sharply increased. The second situation is when the monitoring station communication network is interrupted, resulting in a degradation of the observed data quality to a point where it is not properly serviced. During the time of the failure, the failed station will not receive any valid observation files and navigation files, and the files being received are forced to be interrupted.
A propagation segment fault is a fault due to an anomaly in the propagation medium between the satellite and the receiver. Errors generated in the navigation signal propagation from the satellite to the receiver are mainly ionospheric errors, tropospheric errors and multipath interference. Among them, ionospheric delay is the most dominant source of error. Ionospheric anomalies are highly random and include mainly ionospheric storms and ionospheric flicker. The flicker is caused by small-scale inhomogeneous structures in an ionosphere, so that a tracking signal is unlocked, and a receiver cannot track one or more visible satellites in a short time. Ionospheric flicker has limited impact on the positioning results due to satellite redundancy.
The ionosphere storm is a phenomenon that the physical parameters of the ionosphere deviate seriously from the normal state due to disturbance of the geospatial environment caused by strong magnetic storm generated by the sun. With the occurrence of strong magnetic storms, the ionosphere is subject to drastic changes worldwide. The ionosphere storm causes larger electron density and electron content gradient change, which causes the reduction of the ionosphere time-space correlation and is an important factor influencing a navigation positioning system.
Reference [3] has two forms of ionospheric perturbation effects that SBAS may have: the 'bubble-shaped' influence means that in the environment surrounding of a quiet ionized layer, the ionized layer delay in a small range area is abnormally changed, and the abnormal disturbance is only generated in the range along with the change of time and does not move, and the ionized layer abnormal area is in a block shape. The wall-shaped influence is the disturbance influence which is superimposed on a quiet ionosphere environment and changes in a gradient manner along with the distance and moves along with the time, and the ionosphere abnormal area is in an extension shape.
Reference [4] the Stanford university GPS laboratory proposed a Local Area Augmentation System (LAAS) wedge model based on ionospheric anomaly data in the mid-latitudinal region of North America, in which the ionosphere under anomaly is considered to be a linear semi-open wedge front and is moving at a fixed speed relative to the ground. The wedge model is determined by three parameters of the ionosphere abnormal front end motion speed, width and gradient.
The 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 R M,SPARKS L.The WAAS ionospheric spatialthreat model[C]//The Institute of Navigation.Proceedings of the 14thInternational Technical Meeting of the Satellite Division of The Institute ofNavigation(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 threatfor LAAS:mitigation and tolerable threat space[C]//The Institute ofNavigation.Proceedings of the 2004National Technical Meeting.Manassas,VA:TheInstitute of Navigation,Inc.,2004:490-501.
disclosure of Invention
The invention provides a satellite-based augmentation system integrity fault simulation method aiming at the problems that GNSS simulation software of the existing satellite-based augmentation system only has the functions of positioning, resolving and the like, lacks an integrity fault simulation module and is difficult to support the test and research of an integrity monitoring method. The method has the advantages that the measured data are input, various integrity faults are added to serve as simulation events, the processed data are output to the subsequent SBAS processing module, and a solution idea is provided for testing and verifying the integrity monitoring capability of the Beidou SBAS in China.
The invention provides a satellite-based augmentation system integrity fault simulation method, which comprises the following four 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 fault electromagnetic faults and monitoring station receiver faults; ionospheric storm faults belong to propagation segment faults.
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 the exchange format RINEX.
And 3, generating a corresponding type of fault by the fault generation module according to the fault parameters configured in the step 1, calculating the influence of the fault on the observation data, and adding the calculated influence into the observation data by the data processing module.
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 moving at a constant 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.
Compared with the prior art, the invention has the following obvious advantages:
(1) the invention has flexible parameter configuration. A user can select parameters such as a monitoring station, a user station, a satellite constellation, a fault type and the like by himself through a man-machine interaction interface.
(2) The selected integrity failure types are comprehensive. The main failure types which can be generated by the three parts of satellite signal transmission, propagation and reception are involved.
(3) The data output format is standardized in the invention. The output observation files and navigation files adopt RINEX standard file formats, and can be directly used for various processing modules such as geometric method simulation, dynamic method simulation, ionosphere processing, enhanced message generation, user algorithm simulation, performance evaluation and the like so as to support various subsequent functional design and performance evaluation work of an SBAS simulation platform.
Drawings
FIG. 1 is a schematic diagram of a satellite based augmentation system integrity failure simulation system of the present invention;
FIG. 2 is a schematic diagram of a human-computer interaction primary interface of the present invention;
FIG. 3 is a monitoring station fault addition flow diagram of the present invention;
FIG. 4 is a flow chart of the spatial signal fault addition of the present invention;
FIG. 5 is a schematic cross-sectional view of a circular truncated cone storm model of the present invention;
fig. 6 is a ionospheric storm fault addition flow diagram in accordance with the present invention.
Detailed Description
To facilitate understanding and practice of the invention by those of ordinary skill in the art, the invention is described in further detail below with reference to the accompanying drawings.
The integrity fault simulation system of the satellite-based augmentation system comprises five modules of human-computer interaction, data import, fault generation, data processing and data output, as shown in figure 1. Parameter configuration is carried out on a monitoring station, a satellite and integrity faults through a graphical interface of a man-machine interaction module, and space signal faults, monitoring station faults and propagation section faults are added in observation data to serve as simulation events, so that the processed data is output to provide a data source for a subsequent SBAS processing module, and development and testing of the SBAS integrity monitoring method are supported.
In the invention, the integrity faults are divided into three categories, including: spatial signal faults, monitoring station faults, and propagation segment faults. The space signal fault is a fault generated at a transmitting end of a navigation satellite, and comprises a step fault and a slope fault which are all clock faults. The monitoring station faults are faults generated by a signal receiving end and comprise monitoring station electromagnetic faults of electromagnetic interference around the monitoring station and monitoring station receiver faults which enable observation data not to be normally received due to communication network interruption. The propagation segment fault is an ionospheric storm fault due to propagation medium anomalies between the satellite to the receiver, the most significant source of which is the ionospheric storm. The simulation method of the invention realizes the simulation of the fault.
The integrity fault simulation method of the satellite-based augmentation system comprises four steps, and each step is described below.
Step 1, a user carries out flexible parameter configuration through a man-machine interaction module. The parameters that need to be configured include: the station satellite parameters and the fault parameters are configured through a man-machine interaction main interface.
FIG. 2 is a schematic diagram of a human-computer interaction main interface according to the present invention. Firstly, parameter configuration is carried out through a man-machine interaction main interface, and the configured parameters comprise: the user selects the time of data processing, the zone of the monitoring station, the list of monitoring stations and subscriber stations, the satellite constellation, and the type of added fault. Meanwhile, the man-machine interaction main interface can also display the positions of the monitoring station and the user station so as to facilitate the station selection of the user.
And then, according to the selected fault type, configuring corresponding fault parameters through the popped up sub-window. The types of faults include: spatial signal faults, monitoring station electromagnetic faults, ionospheric storm faults, and monitoring station receiver faults. Spatial signal fault types include: step faults and ramp faults. The spatial signal fault parameters include: fault star number, fault type selection (step or ramp), fault start-stop time, step value, or ramp value. The electromagnetic fault parameters of the monitoring station comprise: fault site name, fault start-stop time, and electromagnetic fault variance factor-no fault type. The monitoring station receiver fault parameters include: fault station name, fault start-stop time. The propagation segment faults are mainly ionosphere storm faults. Ionospheric burst fault parameters include: the parameters of the circular truncated cone and the motion parameters. The parameters of the circular truncated cone comprise: upper base circle radius, lower base circle radius, and maximum retardation value. The motion parameters include: storm center initial longitude, storm center initial latitude, storm start-stop time, storm movement direction, and storm movement speed.
And 2, importing the real observation file and the navigation file of the monitoring station and the user station selected by the user in a specified time into the integrity fault simulation system by the data import module according to the station satellite parameters configured by the man-machine interaction module, and storing the real observation file and the navigation file into the data processing module.
The file format of the observation file and the navigation file adopts a receiver-independent exchange format rinex (receiver independent exchange format).
And 3, generating integrity faults according to fault parameters input by a user and adding the integrity faults into the observation data file.
And the fault generation module generates different types of fault events according to the integrity fault parameters configured by the man-machine interaction module and calculates the influence of the selected fault on the observation data. The code pseudo range and carrier phase described in the observation file simulate observation data. And the data processing module superposes the influence caused by the corresponding integrity fault in the imported observation data according to the fault event selected by the user to obtain the code pseudo-range and carrier phase simulation observation data after the fault is added.
The simulation process for adding different types of faults is described below.
And step 301, adding a monitoring station fault. The present invention considers monitoring station failures in two situations.
The first condition is electromagnetic fault of a monitoring station, the signal carrier-to-noise ratio is seriously deteriorated due to electromagnetic interference around the monitoring station, and the distance measurement error is increased sharply. Because the electromagnetic influence has randomness, Gaussian noise with the average value of 0 is used as the influence of the abnormal change of the electromagnetic field on the pseudo range and the carrier phase observed value. The variance of the Gaussian noise is configured by a user on the man-machine interaction module. In the occurrence time of the electromagnetic fault, the influence of electromagnetic interference is simulated by superposing errors obeying corresponding Gaussian distribution, namely Gaussian noise, on code pseudo-ranges and carrier phase observed values of all satellites received by a fault monitoring station.
The second situation is a monitoring station receiver failure, which causes the monitoring station communication network to be interrupted and the quality of the observed data to degrade to a point where it cannot be serviced properly. During the time of the fault occurrence, the fault monitoring station will not receive any valid observation data and navigation files, and the files being received are forced to be interrupted. Such faults are therefore simulated by clearing all observations of the fault station.
The process of adding the monitoring station fault is shown in fig. 3, according to monitoring station electromagnetic fault parameters input by a human-computer interaction console, including fault station names, fault types, electromagnetic fault variance factors and fault start-stop time, a data processing module reads observation files in RINEX format, traverses the monitoring stations one by one, judges whether the station names are fault stations, and directly outputs observation data without adding the monitoring station fault if the station names are not fault stations; if the monitoring station is a fault station, selecting the observation data within the fault occurrence time for processing, and if the monitoring station is a receiver fault, clearing the observation data; if the fault is an electromagnetic fault, the fault generation module calculates a Gaussian noise interference value with the average value of 0, and the data processing module respectively superposes the Gaussian noise interference value on the double-frequency code pseudo-range observed value and the carrier phase observed value. And finally outputting the observation file after the monitoring station faults are superposed.
Step 302, add spatial signal faults.
The star clock fault is the most frequently occurring spatial signal fault, and the generated error can be divided into two forms of drift and jump. Where clock drift is simulated by superimposing a ramp value that varies linearly with time, and clock transitions are simulated by superimposing a constant value. An overlay constant value, i.e., a step value.
The invention divides the space signal faults into two types, namely slope faults and step faults. The slope value configured by the user in the man-machine interaction module is the change rate of the pseudo-range observed quantity increment of the frequency point code of the GPS L1, and the unit is m/s; the step value is an increment of the code pseudorange observations in m. The observed value increment of other frequency points or the observed value increment of the carrier phase can be obtained through a simple conversion relation.
The process of adding spatial signal faults of the present invention is shown in fig. 4. According to space signal fault parameters input by a human-computer interaction console, including a fault satellite number, fault start-stop time, a fault type, a step value or a slope value, a data processing module reads a navigation file in a RINEX format, traverses the satellite and judges whether the satellite is a fault satellite, and if not, space signal faults are not added to observation data of the satellite; and if the satellite is a fault satellite, selecting the observation data of the fault satellite at the fault occurrence time for processing. If step faults are added, constant errors are added to all observed values of the fault satellite; if a ramp fault is added, the fault generation module calculates an error delta that varies linearly with time. And the data processing module adds step errors or calculated slope error increments to the double-frequency code pseudo range and the carrier phase observed value of the fault satellite and finally outputs the observed data added with the space signal fault.
And step 303, adding a propagation section fault.
The ionospheric storm is mainly modeled in the propagation section fault simulation. In combination with the 'bubble' shaped, 'wall' shaped and wedge-shaped models, the ionosphere storm simulation model is established by adopting the round table which moves at a constant speed. The storm model is added to the ionospheric vertical delay data for the quiet period to simulate the observed data for the storm period. The model can adjust the size of the parameters to simulate the scale, direction, speed, front end gradient, etc. of different ionospheric storms. An ionospheric storm is an ionospheric storm.
Fig. 5 shows a cross section of the circular cone storm model of the present invention. The model is actually a two-dimensional circular ionospheric vertical delay increment added on the basis of an ionospheric thin-shell model. Within the maximum radius, the ionosphere is affected by storms and the delay value increases. Within the minimum radius, the delay increment is kept at a maximum value, and the delay increment changes linearly between the minimum radius and the maximum radius. The maximum delay value is the GPS L1 frequency bin ionospheric vertical delay increment value.
The process of adding ionospheric storm faults in the present invention, as shown in fig. 6, includes the following parameters input according to the console for human-computer interaction: the data processing module reads the observation file in the RINEX format to obtain the position of the monitoring station and observation data, reads the navigation file in the RINEX format and calculates the position of the satellite.
The data processing module calculates the longitude and latitude and the inclination factor of an Ionosphere Penetration Point (IPP) according to the position of the monitoring station and the position of the satellite. Let λuAnd
Figure GDA0002446603470000061
respectively longitude and latitude, lambda of the ground userippAnd
Figure GDA0002446603470000062
respectively, the latitude and longitude of the ionosphere penetration point. Based on the geometric relationship of the positions, the latitude and longitude of the ionosphere penetration point can be calculated as follows [ reference 5 ]]：
Figure GDA0002446603470000063
Figure GDA0002446603470000064
Figure GDA0002446603470000071
Therein, ΨippIs the geocentric angle; a is azimuth [ reference 6]And E is an elevation angle [ reference 6]]，ReIs the approximate radius of the earth (6378.1363 km), hIThe height of the ionosphere with the maximum electron density content is also the height of a reference plane of the thin shell model (the value is 350 km).
Using a tilt factor function FippThe ionospheric delay on the signal line-of-sight path can be converted into the vertical ionospheric delay in the zenith direction in the thin-shell model, and the tilt factor is defined as the ratio of the line-of-sight ionospheric delay to the vertical ionospheric delay and can be calculated as follows [ reference 7 ]]：
Figure GDA0002446603470000072
The 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 entry. global positioning system-signal, measurement and performance (second edition) [ M ]. beijing: electronics 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.
And the fault generation module calculates to obtain each epoch in the circular table storm model, and the corresponding ionosphere vertical delay value increment in the circular latitude and longitude range. And after calculating the longitude and latitude and the inclination factor of the ionosphere penetration point, the data processing module selects all observed values of the IPP longitude and latitude in the storm range for processing. And judging whether the IPP longitude and latitude are in the circular storm area, if not, not adding the ionosphere storm fault, if so, calculating and changing a code pseudo range and a carrier phase observation value according to the obtained tilt factor and the ionosphere vertical delay increment, and finally outputting an observation file added with the ionosphere storm fault.
And (4) calculating according to the formulas (1), (2), (3) and (4) to obtain the double-frequency code pseudorange and the carrier phase value after the simulated ionosphere storm model is added.
Figure GDA0002446603470000073
Figure GDA0002446603470000074
Figure GDA0002446603470000075
Figure GDA0002446603470000076
Wherein: rho1And ρ2Are respectively pseudo-range observed values of the original dual-frequency codes, phi1And phi2Respectively is an original dual-frequency carrier phase observed value;
Figure GDA0002446603470000077
and
Figure GDA0002446603470000078
respectively adding the modified dual-frequency code pseudo range observed values after the ionospheric storm,
Figure GDA0002446603470000079
and
Figure GDA00024466034700000710
respectively, modified dual-frequency carrier phase observations after the addition of an ionospheric storm. c is the speed of light in vacuum, f1And f2Representing a dual frequency bin. FippIs the tilt factor and h is the increment of the ionospheric vertical delay value of the L1 frequency bin.
And 4, outputting the processed file data according to a RINEX standard file format by the data output module for subsequent use.
The invention provides a satellite-based augmentation system integrity fault simulation method. Parameter configuration can be carried out on monitoring stations, satellites and integrity faults through a human-computer interaction graphical interface, space signal faults, monitoring station faults and propagation section faults are added in observation data to serve as simulation events, and data are provided for a follow-up SBAS processing module. The method realizes model simulation of integrity faults, and adopts a step model and a slope model for spatial signal faults; a 0-mean Gaussian noise model is adopted for electromagnetic faults of a monitoring station; and a motion circular truncated cone model is adopted for ionosphere storm faults of the propagation section. Errors generated by simulation faults are directly superposed on the true code pseudo-range and the carrier phase observed value, so that the data source is close to the actual condition, and the subsequent analysis and verification of the data before and after the faults are added are facilitated. 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 lambdaippAnd
Figure FDA0002427289430000021
the following were used:
Figure FDA0002427289430000022
Figure FDA0002427289430000023
Figure FDA0002427289430000024
wherein λ isuAnd
Figure FDA0002427289430000025
respectively longitude and latitude, Ψ of the ground userippIs the geocentric angle, A is the azimuth angle, E is the elevation angle, ReIs the approximate radius of the earth, hIThe height with the largest ionized layer electron density content;
tilt factor
Figure FDA0002427289430000026
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
Figure FDA0002427289430000027
And
Figure FDA0002427289430000028
the dual-frequency carrier phase observed values are respectively
Figure FDA0002427289430000029
And
Figure FDA00024272894300000210
the following were used:
Figure FDA00024272894300000211
Figure FDA00024272894300000212
wherein h is L1 frequency point ionospheric vertical delay increment, FippIs the tilt factor, f1And f2Representing the dual frequency bin, c is the speed of light in vacuum.
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