KR101633392B1 - System and method of real-time ionospheric threat adaptation using space weather forecasting for gnss augmentation systems - Google Patents

System and method of real-time ionospheric threat adaptation using space weather forecasting for gnss augmentation systems Download PDF

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KR101633392B1
KR101633392B1 KR1020150069568A KR20150069568A KR101633392B1 KR 101633392 B1 KR101633392 B1 KR 101633392B1 KR 1020150069568 A KR1020150069568 A KR 1020150069568A KR 20150069568 A KR20150069568 A KR 20150069568A KR 101633392 B1 KR101633392 B1 KR 101633392B1
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value
dst
threat model
space weather
prediction error
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이지윤
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한국과학기술원
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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S19/00—Satellite radio beacon positioning systems; Determining position, velocity or attitude using signals transmitted by such systems
    • G01S19/01—Satellite radio beacon positioning systems transmitting time-stamped messages, e.g. GPS [Global Positioning System], GLONASS [Global Orbiting Navigation Satellite System] or GALILEO
    • G01S19/13—Receivers
    • G01S19/20—Integrity monitoring, fault detection or fault isolation of space segment
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
    • G01C21/00—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S19/00—Satellite radio beacon positioning systems; Determining position, velocity or attitude using signals transmitted by such systems
    • G01S19/01—Satellite radio beacon positioning systems transmitting time-stamped messages, e.g. GPS [Global Positioning System], GLONASS [Global Orbiting Navigation Satellite System] or GALILEO
    • G01S19/03—Cooperating elements; Interaction or communication between different cooperating elements or between cooperating elements and receivers
    • G01S19/07—Cooperating elements; Interaction or communication between different cooperating elements or between cooperating elements and receivers providing data for correcting measured positioning data, e.g. DGPS [differential GPS] or ionosphere corrections

Abstract

The present invention relates to a method of applying a real-time ionospheric threat model of a global navigation satellite system (GNSS) augmentation system using a space weather forecasting system. More specifically, the present invention relates to a method of applying a real-time ionospheric threat model according to space weather rather than a worst threat model according to a prior art by determining a relation between space weather information and an ionospheric threat, which is a biggest error factor for the GNSS augmentation system, and utilizing the relation. According to the present invention, there is provided a technology which improves performance of a navigation system by mitigating the ionospheric threat and using an efficient modeling by utilizing the space weather information which is an external source. Since real-time space weather forecasting values are utilized, threat model values which are applied by a user are not set as a constant value but selected for respective steps. Therefore, a security level which corresponds to integrity information calculated by the user is lowered, thereby improving system availability. The system of applying a real-time ionospheric threat model of a GNSS augmentation system using a space weather forecasting system comprises: a space weather forecasting error statistical analysis module, a forecasting error bounding module, an ionospheric threat model applying module, a disturbance storm database, and a control unit.

Description

FIELD OF THE INVENTION [0001] The present invention relates to a GNSS reinforcement system using a space weather forecasting system, and a method and system for applying a real-time ionosphere threat model to a GNSS reinforcement system using a space weather forecasting system.

The present invention relates to a method for applying a real-time ionospheric threat model of a global navigation satellite system (GNSS) reinforcing system utilizing a space weather forecasting system, and more particularly, A method for applying real-time ionospheric threat model based on space weather using space weather forecast value instead of applying the worst threat model which is applied in present system by identifying and using relation with ionospheric threat will be.

The ground augmentation system (GBAS) is a system developed for precise airport access and automatic landing of aircraft by providing users with GNSS error correction information and integrity information. If severe Ionospheric storms occur during operation of the system, the ionospheric delay value changes rapidly, and when the user of the reinforcement system uses the correction information when such ionospheric anomaly occurs, the accuracy of the user's position estimate is significantly degraded. Users can be at great risk if the ionospheric anomalies falling within the category of the ionosphere threat model are not detected and notified in time by ground monitoring. Therefore, the ionosphere threat model is constructed and used in the ground based reinforcement system.

The development of the threat model of the GNSS reinforcement system is based on the data collection and analysis of the past periods, and the model which includes all the ionospheric conditions that have occurred so far. Therefore, users of existing GNSS augmentation systems always calculate the level of protection considering the worst ionosphere situation in calculating integrity level of user protection level.

In this regard, the ionospheric threat model constructed and used for the LAAS (Local Area Augmentation System), which is a conventional GBAS reinforcing system, will be described below.

Considering the rapid increase of ionospheric delay in spontaneously and horizontally during the ionospheric storm phenomenon in the mid - latitude region of the United States, the ionospheric threat model uses a linear tilt model with constant velocity.

In order to construct the ionosphere threat space, the dual frequency GPS data must be processed through a series of processes through the ground station network. The existing US ionospheric threat model uses high precision ionospheric delay error generated by collecting and processing data from WAAS (Wide Area Augmentation System) ground station and US CORS (Continuously Operating Reference Station) ground station. To construct the LAAS threat area in the United States, we selected data from 2000 to 2005 in the solar peak area where a large number of ground stations are concentrated, allowing comparison of ionospheric delays for several tens of km of baselines do. We process the data of the selected ground stations and perform the high precision ionospheric delay error estimation process for the ionosphere threat model. Precise ionospheric delay error values generated by the Jet Propulsion Laboratory (JPL) were used for LAAS ionospheric threat models. Data collected from CORS ground stations and WAAS ground stations were processed through algorithms developed by Komjathy et al. For cycle slip detection, singularity removal, ambiguity resolution, and frequency-to-frequency bias estimation.

1 is a diagram showing an embodiment of a ground station combining method for estimating the ionospheric slope.

In order to estimate the ionospheric slope, the ground station combination method as shown in FIG. 1 is used. In this method, it is assumed that the two ground stations are each a combination of LGF and user receiver (aircraft). time

Figure 112015047777675-pat00001
Satellite that can be observed simultaneously in both ground stations 1 and 2
Figure 112015047777675-pat00002
The calculated ionospheric delay error
Figure 112015047777675-pat00003
Wow
Figure 112015047777675-pat00004
Is divided by the distance between two ground stations, the ionospheric slope
Figure 112015047777675-pat00005
Can be obtained. This method is applied to all receiver combinations looking at each satellite to estimate the ionospheric slope. here
Figure 112015047777675-pat00006
and
Figure 112015047777675-pat00007
Represents the position vector of the ground stations 1 and 2.

Figure 112015047777675-pat00008

In the process of constructing the LAAS ionosphere threat model, we obtain each parameter limit value in the ionospheric slope threat space shown in Table 1 by the methods mentioned above. In the threat model, the threshold of ionospheric slope is expressed as a function of the altitude of the satellite, has an ionospheric slope value of 375mm / km for low altitude satellites, and has an altitude of 1 ° increase for satellite altitudes in the range of 15 ° to 65 ° And the maximum ionospheric slope has a value of 425 mm / km at a satellite altitude of 65 ° or more. As a result, the maximum ionospheric delay time error reaches 50 m.

Figure 112015047777675-pat00009

Thus, in LAAS, the worst ionospheric threat model defined in Table 1 is applied regardless of space weather. This can cause a lot of conservativeness in the calculation of the protection level. "Conservativeness" means a high degree of user protection. In other words, when the 'user' is assumed to be an aircraft, the greater the conservativeness, the greater the protection area for the aircraft, the higher the safety of the aircraft. However, the availability of the system such as the aircraft landing precision through GNSS reinforcement system .

KR 10-2009-0042193 A

The present invention has been developed in order to overcome such a problem. By identifying the relationship between the space weather information and the GNSS reinforcement system, which is the biggest error factor, the ionosphere threat, the worst threat model And to apply a real-time ionospheric threat model based on space weather. To this end, we aim to provide a technique to apply the real-time ionosphere threat model with probability matching the system's integrity requirement by modeling the provided weather forecast error and providing bounded error bound value with a certain probability.

In order to achieve the above object, there is provided a method for applying a real-time ionosphere threat model of a global navigation satellite system (GNSS) reinforcement system using a weather forecasting system according to the present invention, comprising the steps of: (a) calculating a prediction error (differential Dst, DDst), which is a value obtained by subtracting a Dst exponent true value for each specific time point from a disturbance storm index predicted value Dst; (b) calculating a probability density function for the prediction error; (c) modifying the probability density function to bind (include) all values of the prediction error in the probability density function; (d) calculating an overband value of the DDst standard deviation; (e) calculating a predicted Dst bound from an exponential predicted value and an over-bound value of the DDst standard deviation; And (f) calculating an ionospheric slope value with respect to the prediction error bound value.

Receiving a Dst exponent predictive value for a plurality of specific time points from the (a1) Dst exponent predictive value providing system before the step (a); And (a02) receiving the Dst exponent true value for each particular time point from the Dst exponent true value providing system.

(A11) calculating an average and a standard deviation of each prediction Dst interval with respect to the prediction error between the step (a) and the step (b), wherein the probability density of the step (b) The function is a normalized probability density function and can be computed from the mean and standard deviation.

The correction of the probability density function in the step (c) may be performed by calculating an expanded Gaussian distribution by applying an expansion coefficient to the probability density function so as to bind (include) all the values of the prediction error, ), The overband value of the DDst standard deviation can be obtained by applying the expansion coefficient.

The prediction error bound value may be defined as a value obtained by subtracting a value obtained by multiplying an over-bound value of the DD st standard deviation by a constant for setting a reliability of a prediction error to a specific probability, from the exponential prediction value.

(g) calculating an integrity parameter using the ionospheric slope value calculated in the step (f); And (h) transmitting the integrity parameter to an operating aircraft

And the integrity parameter may be data that allows the aircraft to navigate within the protection range defined by the integrity parameter.

According to another aspect of the present invention, there is provided a system for applying a GNSS-reinforced real-time ionospheric threat model using a space weather forecasting system, wherein a disturbance storm index predicted value (Dst) (Dst, DDst), which is a value obtained by subtracting the true value of the Dst exponent, a probability density function with respect to the prediction error, and calculates a probability density function A space weather prediction error statistical analysis module for calculating an overband value of the DDst standard deviation; A prediction error bounding module for calculating a predicted Dst bound from the exponential predicted value and an over-bound value of the DDst standard deviation; An ionospheric threat model application module for calculating an ionospheric slope value with respect to the prediction error bound value; A Dst database storing the Dst exponent predicted value and the Dst exponent true value; And a controller for controlling each of the modules and performing a series of processes related to application of a GNSS-reinforced real-time ionosphere threat model using a space weather forecasting system.

The GNSS reinforcement system real-time ionospheric threat model application system utilizing the space weather forecasting system receives Dst exponent predicted values at a plurality of specific time points from the Dst exponent predictive value providing system, And a Dst data receiving module that receives the Dst exponent true value.

The space-time prediction error statistical analysis module may further include a function of calculating an average and a standard deviation of each prediction Dst section with respect to the prediction error, and the space-time prediction error statistical analysis module may calculate a probability density function Can be a normalized probability density function and can be calculated from the mean and standard deviation.

The correction of the probability density function of the space-time prediction error statistical analysis module may be performed by calculating an expanded Gaussian distribution by applying an expansion coefficient to the probability density function so as to bind (include) all the values of the prediction error, The overband value of the DDst standard deviation can be obtained by applying the expansion coefficient.

The prediction error bound value may be defined as a value obtained by subtracting a value obtained by multiplying an over-bound value of the DD st standard deviation by a constant for setting a reliability of a prediction error to a specific probability, from the exponential prediction value.

The ionosphere threat model application module may further include a function of calculating an integrity parameter using the calculated ionospheric slope value, and the GNSS reinforcing system real time ionospheric threat model application system utilizing the space weather forecast system may include: And an integrity parameter transfer module for transferring the calculated integrity parameters to the aircraft by the ionosphere threat model application module.

According to the present invention, a technique for mitigating the ionosphere threat by utilizing an external source of space weather information and improving the performance of the navigation system through efficient modeling is invented, and a threat model value Can be selected and applied in a stepwise manner rather than a fixed value. This reduces the protection level, which is the integrity information that the user calculates, and improves system availability accordingly.

BRIEF DESCRIPTION OF THE DRAWINGS Figure 1 shows an embodiment of a ground station combining method for estimating the ionospheric slope.
FIG. 2 is a graph showing the relationship between the Dst index and the ionospheric slope statistic

Figure 112015047777675-pat00010
).
3 is a graph showing the correlation between the ionospheric slope statistics and the Dst index.
Fig. 4 is a diagram showing each point of the LAAS CAT-I ionosphere threat model and the Dst index when the point occurs, on the y-axis and the x-axis, respectively.
FIG. 5 is a diagram comparing a true value with a space-time predicted value used for reliability analysis; FIG.
6 is a graph showing the DDst value with time.
7 shows the DDst standard deviation (
Figure 112015047777675-pat00011
A graph that shows the overbound value of an object.
8 is a diagram showing a distribution of prediction errors drawn through a normalization process;
FIG. 9 shows a graph of < RTI ID = 0.0 >
Figure 112015047777675-pat00012
≪ RTI ID = 0.0 > and < / RTI > multiplying an exponent corresponding to each probability set in an overbound value.
10 is a diagram showing a finally obtained prediction error bound value;
11 shows an ionospheric threat model in which the prediction error reliability analysis result is lowered by applying the worst ionospheric slope relationship according to the Dst index in FIG.
12 is a data frequency graph according to the predicted Dst value;
FIG. 13 is a flow chart summarizing the steps of applying the GNSS reinforcement system real time ionosphere threat model using the weather forecasting system.
14 is a diagram showing a configuration of a GNSS-reinforced real-time ionosphere threat model application system utilizing a space weather forecast system;

Hereinafter, preferred embodiments of the present invention will be described in detail with reference to the accompanying drawings. Prior to this, terms and words used in the present specification and claims should not be construed as limited to ordinary or dictionary terms, and the inventor should appropriately interpret the concepts of the terms appropriately It should be interpreted in accordance with the meaning and concept consistent with the technical idea of the present invention based on the principle that it can be defined. Therefore, the embodiments described in this specification and the configurations shown in the drawings are merely the most preferred embodiments of the present invention and do not represent all the technical ideas of the present invention. Therefore, It is to be understood that equivalents and modifications are possible.

 The description with reference to FIG. 2 to FIG. 13 will now be made in order to lower the water conservancy as described above, by applying the ionospheric threat model in real time using the weather forecast value, which is externally obtainable information, To apply a reliable real-time ionosphere threat model using space weather forecast values while increasing system availability.

GNSS Reinforcement System Using the Space Weather Forecasting System A method for applying the real time ionospheric threat model is a GNSS Reinforcement System Real Time Ionospheric Threat Model Application System using the weather forecasting system according to the present invention for controlling the operation of an aircraft in a ground station 100).

end. Analysis of Correlation between Space Weather Information and Ionospheric Space Slope

FIG. 2 is a graph showing the relationship between the Dst index and the ionospheric slope statistic

Figure 112015047777675-pat00013
) In the second embodiment.

In order to mitigate the ionosphere threat model by applying space weather information, first, correlation analysis between space weather information and ionospheric space slope should be preceded. In the present invention, the Dst (Disturbance Storm) index is selected considering the correlation with the ionospheric space slope as the space weather information to be used, the provision of the forecast value, and the update period.

Ionospheric slope statistical analysis was performed based on data obtained from the CONS (Continuous Operating Reference Stations) reference station of CONUS (Conterminous United States). Correlation analysis was performed through data analysis of 9 consecutive days in 2004, when solar activity was active. As can be seen from FIG. 2, it can be confirmed that the ionospheric slope statistic value and the change in Dst are in good agreement with each other.

3 is a graph showing the correlation between the ionospheric slope statistics and the Dst index. Referring to the figure, the correlation index is 0.88, indicating that there is a large correlation.

I. Identifying the worst ionospheric slope relationship based on space weather information

In order to lower the slope of the ionosphere by applying the weather forecast, it is necessary to identify the worst ionospheric slope relationship according to each meteorological information. In the present invention, the relationship between the DST index and the worst ionospheric slope relation corresponding to the DST index and its index was determined through the relationship between the US LAAS CAT-I ionosphere threat model and the Dst index.

FIG. 4 is a diagram showing each point of the LAAS CAT-I ionosphere threat model and the Dst index when the point occurs, on the y-axis and the x-axis, respectively. That is, each point represents an actual Dst index (x coordinate) at a specific point in time and an ionospheric slope (y coordinate) measured at that point.

Referring to the figure, the red line shows a graph of the relationship of the worst ionospheric slope value according to each Dst index value obtained as a result.

All. Statistical analysis of weather forecast errors and prediction error bounding step

FIG. 5 is a graph comparing the predicted value of the spacecraft and the true value used for the reliability analysis.

The weather forecast values used to lower the ionosphere threat model applied to real-time users can not be free from forecast errors. Therefore, in the present invention, space weather prediction value reliability analysis was performed by comparing the space weather prediction value with the true value.

In the graph of FIG. 5, the Dst prediction value (Predicted Dst) provided by the University of Colorado was used for the reliability analysis performed in the present invention (x axis). DST prediction data used were data provided every 10 minutes from 1995 to 2009, and a total of 788976 data were used.

Also, the true value of the Dst index (Final Dst) was the data provided by the WDC (y-axis).

In other words, at each point in FIG. 5, the x-axis value of the point means the Dst value predicted beforehand for a specific time, and the y-axis value means the actual Dst value at that point. For example, points in the area below the line 510 corresponding to y = x (indicated by 32.39%) indicate that the Dst value whose absolute value is larger than the predicted Dst value is actually measured points. That is, this 32.39% area is where the actual Dst absolute value (Final Dst) is greater than the predicted Dst absolute value (Predicted Dst), which is a more predictive , And it can be confirmed that there is uncertainty in the prediction as described above.

Therefore, in the present invention, DDst (Differential Dst) obtained by subtracting a true value from a predicted value is defined and used as a prediction error.

FIG. 6 is a graph showing the obtained DDst value with time, and it can be confirmed that a large DDst value appears on some days (11/06/01 and 11/20/03).

7 shows the DDst standard deviation (

Figure 112015047777675-pat00014
FIG. 8 is a graph showing a distribution of a prediction error DDst drawn through a normalization process, that is, a probability density function (PDF).

For the statistical analysis of the weather forecast error using the DDst calculated as described with reference to FIG. 5, the obtained DDst is normalized by using the mean and standard deviation of each prediction Dst. The blue line and the pink line in FIG. 7 are lines showing mean and standard deviation of the predicted Dst interval, respectively. The distribution of the prediction error drawn through the normalization process is shown in FIG. As shown in FIG. 8, it can be seen that the prediction error does not follow the Gaussian normal distribution and the fat-tail distribution having a large probability in the tail portion is shown. Therefore, the inflation factor was applied to model the prediction error with the Gaussian distribution, and the expanded Gaussian was calculated. A graph that bounds (includes) all errors by applying the expansion coefficient is shown by red solid line 810. Using the thus obtained expansion coefficient, the final standard deviation

Figure 112015047777675-pat00015
The overbound value is indicated by red solid line 710 in FIG.

In this way,

Figure 112015047777675-pat00016
Through the overbound value, it is necessary to finally bind the error with the probability required by the system. Currently, the LAAS system in the United States guarantees system reliability with a high probability that faults are allowed once every 10 7 . Thus, the expanded standard deviation obtained to set the reliability of the prediction error to a probability of 1-10 - 7
Figure 112015047777675-pat00017
The overband value is multiplied by 5.33.

FIG. 9 is a graph of the bounded prediction error value

Figure 112015047777675-pat00018
And an exponent corresponding to each probability set in an overbound value. The red line 910 of FIG. 9 represents the error bounding probability with a probability of 1-10 - 7 to be finally used in the present invention.

Then, a predicted Dst bound to be applied to the relation of FIG. 4 is finally calculated. The prediction error bound value can be obtained by subtracting the bounded prediction error from the Dst prediction value received by the user as shown in Equation 2 below.

Figure 112015047777675-pat00019

FIG. 10 is a graph showing prediction error bound values according to different reliability levels as a graph in which a finally obtained prediction error bound value (Predicted Dst bound ) appears on the y-axis.

la. Real-time application of ionosphere threat model applying prediction error reliability analysis result

FIG. 11 is a diagram illustrating an ionospheric threat model lowered by applying a prediction error reliability analysis result to the worst ionospheric slope relationship according to the Dst index in FIG. 4, and FIG. 12 is a graph of data frequency according to a predicted Dst value.

In the case of FIG. 4, each point represents the actual value of the Dst at that point in time, that is, the true Dst value as the x axis, and the ionospheric slope measured at that point as the y- 4 as described above.

At this stage, the predicted Dst bound calculated as Equation 2 is applied to the x-axis value in FIG. That is, the x-axis values in FIG. 4 final Dst values, instead, replaced with the prediction error bound value (Predicted Dst bound, y-axis values in FIG. 10) calculated by the equation (2), and the ionospheric slope measurement at that point y 11, and the graph when the constant K for setting the reliability of the prediction error to a specific probability is 5.33 is shown by red solid line 1110 ) And the worst ionospheric slope model graph 1120 is shown.

The thus calculated ionospheric slope data of FIG. 11 is used in the calculation of the integrity parameter transmitted to the aircraft in operation, and the aircraft is operated within the protection range determined by such data.

As a result, it can be seen that the ionospheric slope lower than the existing fixed ionospheric slope value of 425 mm / km (1120) can be applied in the interval of the forecast value of -50 or more. This result can be interpreted as the result that the threat model can be lowered during the data analysis period of 94% of the last 15 years, as shown in Fig. The lowered ionosphere threat model can lower the conservativeness of user 's protection level calculation process to ensure reliability and higher availability.

13 is a flow chart summarizing the steps of applying the GNSS reinforcement system real time ionosphere threat model using the above-described space weather forecasting system as described above with reference to FIGS. 2 to 12.

First, a Dst exponent predicted value for a plurality of specific time points is received from a disturbance storm (DST) providing system (S1301). Then, the Dst exponent true value for each specific time point is received from the Dst index true value providing system (S1302). 5, since the Dst prediction value (Predicted Dst) uses the data provided by the University of Colorado, the Dst index prediction value providing system becomes the system of Colorado University, and the true value of the Dst index (Final Dst ) Uses the data provided by the WDC, the Dst index true value providing system becomes the WDC system.

(Dst, DDst) which is a value obtained by subtracting the Dst exponent true value from the received Dst exponent predicted value is calculated (S1303), and an average and a standard deviation of each prediction Dst interval with respect to the prediction error are calculated (S1304) . Then, a normalized probability density function for the prediction error is calculated from the average and standard deviation (S1305). This process has been described in detail with reference to FIGS. 5 to 7. FIG.

Then, a probability density function to which the expansion coefficient is applied is calculated (S1306) so as to bind (include) all the values of the prediction error in the probability density function. That is, as described above with reference to FIG. 8, since the prediction error does not follow the Gaussian normal distribution and fat-tail distribution having a large probability at the tail portion is shown, the inflation factor to obtain the expanded Gaussian probability density function (810).

By applying the expansion coefficient thus obtained, the overstatement of the DDst standard deviation

Figure 112015047777675-pat00020
overbound value 710 is calculated (S1307).

Then, a predicted Dst bound is calculated from an exponential predicted value Dst and an over-bound value of the DDst standard deviation (S1308), and the predicted error bound value is calculated as the DDst standard deviation And a constant (K) for setting the reliability of the prediction error to a specific probability is subtracted from the over-bound value of the prediction error, and the expression is expressed by Equation (2).

11, the calculated slope value of the ionosphere is calculated (S 1309), and the calculated slope value of the ionosphere is used for calculating an integrity parameter to be transmitted to the aircraft in operation (S 1310) It can be operated within the protection range determined by the data.

FIG. 14 is a diagram showing a configuration of a GNSS-reinforced real-time ionosphere-threat model application system 100 using a space weather forecasting system.

The GNSS reinforcement system using the space weather forecasting system The real-time ionosphere threat model applying system 100 is a GNSS reinforcement system utilizing the space weather forecasting system according to the present invention as described with reference to FIGS. 2 to 13, It may be installed in a ground station system that controls the operation of the aircraft.

The controller 110 controls each module to perform a series of processes related to the application of the GNSS-reinforced real-time ionosphere threat model using the weather forecasting system.

The Dst data reception module 120 receives the Dst exponent predictive value for a plurality of specific time points from the disturbance storm (DST) providing system first, and then, from the Dst index true value providing system, And receives the true value of the Dst index for.

The Dst database 130 stores the received Dst exponent predicted value and the Dst exponent true value.

The space weather prediction error statistical analysis module 140 calculates a prediction error (differential Dst, DDst), which is a value obtained by subtracting the Dst exponent true value from the received Dst exponential predicted value, and calculates an average of each prediction Dst interval And standard deviation. Then, a normalized probability density function for the prediction error is calculated from the average and standard deviation. Then, the probability density function is calculated by applying the expansion coefficient to bind (include) all the values of the prediction error in the probability density function. The fat-tail distribution in which the prediction error does not follow the Gaussian normal distribution and has a large probability in the tail portion The inflation factor is applied to model the prediction error with the Gaussian distribution, and the expanded Gaussian probability density function is calculated (810). By applying the expansion coefficient thus obtained, the overstatement of the DDst standard deviation

Figure 112015047777675-pat00021
overbound (710).

The prediction error bounding module 150 calculates a predicted Dst bound from an exponential predicted value Dst and an overbound value of the DDst standard deviation, The overdub value of the DDst standard deviation is set to a value obtained by subtracting a value obtained by multiplying a reliability (K) for setting the reliability of the prediction error to a specific probability, and the expression is expressed by Equation (2).

The ionospheric threat model application module 160 calculates the ionospheric slope value as shown in FIG. 11 with respect to the prediction error bound value, calculates an integrity parameter to be delivered to the aircraft in operation using the calculated ionospheric slope value, Allow aircraft to operate within the scope of protection provided by such data.

The integrity parameter transmission module 170 delivers the integrity parameters calculated by the ionosphere threat model application module 160 to the aircraft.

100: GNSS reinforcement system using space weather forecast system Real time ionosphere threat model application system
410: Conventional worst ionospheric slope relationship graph
420: The worst real-time slope relation graph in real time according to Dst index
510: A line (y = x) in which the predicted Dst (x axis) and the true value (final axis) Dst (y axis)
710: The final standard deviation obtained by applying the expansion coefficient

Figure 112015047777675-pat00022
Overbound Value Graph
810: Gaussian distributed DDst probability density function graph with all error bounds by applying expansion coefficient
910:
Figure 112015047777675-pat00023
Graph showing the overbound value multiplied by an exponent corresponding to a probability of 1-10 - 7
1110: Graph when the index (K) corresponding to the probability set in the ionosphere threat model applying the prediction error reliability analysis result is 5.33
1120: Worst Ionospheric Slope Model Graph

Claims (12)

A method for applying a real-time ionosphere threat model of a global navigation satellite system (GNSS) reinforcement system using a space weather forecasting system,
(a) calculating a prediction error (differential Dst, DDst) which is a value obtained by subtracting a Dst exponent true value for each specific time point from a disturbance storm predicted value Dst for a plurality of specific time points;
(b) calculating a probability density function for the prediction error;
(c) modifying the probability density function to bind (include) all values of the prediction error in the probability density function;
(d) calculating an overband value of the DDst standard deviation;
(e) calculating a predicted Dst bound from an exponential predicted value and an over-bound value of the DDst standard deviation; And
(f) calculating an ionospheric slope value with respect to the prediction error bound value
A method of applying real - time ionosphere threat model to GNSS reinforcement system using space weather forecasting system. The method according to claim 1,
Prior to step (a)
(a01) receiving a Dst exponent predictive value for a plurality of specific time points from a Dst exponent value providing system; And
(a02) receiving a Dst exponential true value for each specific time point from a Dst exponent value providing system
A method for applying a real-time ionosphere threat model to a GNSS-enhanced system utilizing a space weather forecasting system. The method according to claim 1,
Between the step (a) and the step (b)
(a11) calculating an average and standard deviation of each prediction Dst section with respect to the prediction error,
Further comprising:
Wherein the probability density function of step (b) is a normalized probability density function, and is calculated from the mean and standard deviation
A method of applying a real - time ionosphere threat model to a GNSS reinforcement system using a space weather forecasting system. The method according to claim 1,
Modification of the probability density function in step (c)
And applying an expansion coefficient to the probability density function to bound (include) all the values of the prediction error to calculate an expanded Gaussian distribution,
The overband value of the DDst standard deviation in the step (d)
Those obtained by applying the expansion coefficient
A method of applying a real - time ionosphere threat model to a GNSS reinforcement system using a space weather forecasting system. The method according to claim 1,
The predictive error bound value may be calculated by:
A value obtained by subtracting a value obtained by multiplying the overbund value of the DDst standard deviation by a constant for setting the reliability of the prediction error to a specific probability,
A method of applying a real - time ionosphere threat model to a GNSS reinforcement system using a space weather forecasting system. The method according to claim 1,
(g) calculating an integrity parameter using the ionospheric slope value calculated in the step (f); And
(h) transmitting the integrity parameter to the aircraft in operation
Further comprising:
Wherein the integrity parameter comprises:
It is data that allows the aircraft to operate within the scope of protection set by its integrity parameters.
A method of applying a real - time ionosphere threat model to a GNSS reinforcement system using a space weather forecasting system. GNSS reinforcement system using space weather forecasting system As a real time ionosphere threat model application system,
(Dst, DDst), which is a value obtained by subtracting a Dst exponent true value for each specific time from a Dst (disturbance storm) index predicted value (Dst) for a plurality of specific time points, and calculates a probability density function A space-time prediction error statistical analysis module that modifies the probability density function to include all the values of the prediction error in the probability density function, and calculates an overbound value of the DDst standard deviation;
A prediction error bounding module for calculating a predicted Dst bound from the exponential predicted value and an over-bound value of the DDst standard deviation;
An ionospheric threat model application module for calculating an ionospheric slope value with respect to the prediction error bound value;
A Dst database storing the Dst exponent predicted value and the Dst exponent true value; And
A controller for controlling each of the modules to perform a series of processes related to application of a GNSS-reinforced real-time ionosphere threat model using a space weather forecasting system
A Real - time Ionosphere Threat Model Application System for GNSS Reinforcement System Using Space Weather Forecasting System. The method of claim 7,
A Dst data reception module for receiving a Dst exponent predictive value for a plurality of specific time points from the Dst exponent predictive value providing system and receiving a Dst exponential true value for each specific time from the Dst exponent true value providing system,
A GNSS reinforcement system using a space weather forecast system, and a real time ionosphere threat model application system. The method of claim 7,
The space-time prediction error statistical analysis module includes:
A function of calculating the average and standard deviation of each prediction Dst section with respect to the prediction error
Further comprising:
Wherein the probability density function calculated by the space weather prediction error statistical analysis module is a normalized probability density function and is calculated from the average and standard deviation
Real time ionosphere threat model application system using GNSS reinforcement system using space weather forecast system. The method of claim 7,
Modification of the probability density function of the space-time prediction error statistical analysis module may be performed,
And applying an expansion coefficient to the probability density function to bound (include) all the values of the prediction error to calculate an expanded Gaussian distribution,
The overband value of the DDst standard deviation is calculated by:
Those obtained by applying the expansion coefficient
Real time ionosphere threat model application system using GNSS reinforcement system using space weather forecast system. The method of claim 7,
The predictive error bound value may be calculated by:
A value obtained by subtracting a value obtained by multiplying the overbund value of the DDst standard deviation by a constant for setting the reliability of the prediction error to a specific probability,
Real time ionosphere threat model application system using GNSS reinforcement system using space weather forecast system. The method of claim 7,
The ionospheric threat model application module includes:
The function to calculate the integrity parameter using the calculated ionospheric slope value
Further comprising:
An integrity parameter transmission module for delivering the integrity parameter calculated by the ionosphere threat model application module to the aircraft
A GNSS reinforcement system using a space weather forecast system, and a real time ionosphere threat model application system.
KR1020150069568A 2015-01-29 2015-05-19 System and method of real-time ionospheric threat adaptation using space weather forecasting for gnss augmentation systems KR101633392B1 (en)

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