KR102043712B1 - Method and system for gnss ionospheric observation validation - Google Patents

Abstract

The present invention relates to a system for verifying GNSS ionospheric spatial tilt measurements in areas where the GNSS ground station distribution is less dense. More specifically, the spatial and spatial analysis of ionospheric anomalies using ionospheric anomalies through geographic adjacent GNSS ground station search, satellite-ground station combination classification through pattern recognition, final gradient candidate extraction, and threat visualization are applied to the actual phenomena. It is related to a GNSS ionospheric spatial slope measurement verification system that can be used to verify whether or not due to a receiver or post-process error.
The present invention provides a system and method for verifying whether extreme GNSS ionospheric slope measurements are due to actual phenomena or satellite / post-processing errors in situations where the GNSS ground station distribution is dense.

Description

GNSS ionospheric measurement verification method and system {METHOD AND SYSTEM FOR GNSS IONOSPHERIC OBSERVATION VALIDATION}

The present invention relates to a system for verifying GNSS ionospheric spatial tilt measurements in areas where the GNSS ground station distribution is less dense. More specifically, the spatial and temporal analysis of ionospheric anomalies using ionospheric anomalies through geographic adjacent GNSS ground station search, satellite-ground station combination classification through pattern recognition, final gradient candidate extraction, and threat visualization are applied to real-world phenomena. It is related to a GNSS ionospheric spatial slope measurement verification system that can be used to verify whether or not due to a receiver or post-process error.

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 an extreme ionospheric space slope occurs during operation of the system, the ionospheric delay value changes rapidly with space. If the reinforcement system is not aware of this situation and broadcasts the correction information as it is, the accuracy of the position estimate of the aircraft user may be greatly degraded, which may cause serious threats.

The mitigation of such ionosphere threats is achieved through the development of an ionosphere threat model and the application of reinforcement systems. The ionosphere threat model expresses ionosphere anomalies occurring in each region by parameters such as space slope, width, maximum delay, and speed, and the category of each parameter is determined using actual GNSS ionosphere measurements. The ionospheric threat mitigation process consists of two parts: elimination of dangerous satellite geometry through error prediction and integrity information expansion based on the ionosphere threat model. The magnitude of the prediction error is largely determined by the extent of the spatial gradient of the threat model, and if a large prediction error occurs, many satellite geometries are unavailable, reducing the availability of the reinforcement system.

GNSS measurements may be recorded at actual values, for example due to satellite / receiver failure and post-processing errors during ionospheric delay calculations. GNSS measurement failure and error components can produce large ionospheric slope measurements that do not actually exist. If this happens, as mentioned above, the availability of the reinforcement system and the safety of the aircraft users are not guaranteed. Therefore, in order to completely eliminate the error of the GNSS ionospheric slope measurement and to correctly perform the ionospheric threat model development and threat mitigation, a verification technique (or system) for the GNSS ionospheric slope measurement should be developed.

KR 10-0980762 B1

The present invention has been devised to solve the above-mentioned problems, and a system for verifying whether the extreme GNSS ionospheric slope measurement is caused by a real phenomenon or a satellite / post-processing error in a situation where the GNSS ground station distribution is not dense. The purpose is to provide a method.

In order to achieve the above object, the GNSS ionospheric layer measurement verification system according to the present invention performs a verification on whether the GNSS ionotropic layer measurement actually occurs in the ionosphere abnormality, (a) detecting an occurrence of the ionosphere abnormality from a specific ground station. step; (b) searching a list of ground stations within a predetermined distance around the ground station; (c) classifying satellite-ground station combinations exhibiting an aspect within a predetermined reference range compared to the abnormality of the ground station-satellite combination in which the abnormality has occurred; (d) determining an ionospheric slope of the satellite-ground station combination candidates classified in step (c), and selecting a final candidate group for comparison with the ground station-satellite combination in which the abnormality has occurred by comparing with a preset threshold value; And (e) verifying whether the observed ionospheric slope is a real phenomenon by comparing the satellite-ground station combination with the observed abnormality and the final candidate group selected in step (d), or constructed on the basis of GNSS measurements. By using the ionosphere map to visualize the satellite-ground station combination in which an anomaly occurs and the ionosphere anomaly of the severe ionosphere candidate group, the method includes providing visualization data that allows the observer to directly determine the anomaly.

The ionospheric layer abnormality of the step (a) may mean that the slope of the ionospheric layer is larger than the preset normal value.

Determination of similarity between the ionospheric abnormality pattern of step (c) may be determined by applying a pattern recognition algorithm to determine whether the change pattern of total electron content (TEC) is within a preset reference range.

The ionospheric slope in step (d) is obtained by a time-step method using a single ground station that tracks a particular satellite in each ground station, and the slope by the time-step method is a continuous time. t ₁ , t ₂ It can be calculated by dividing the ionospheric measurements of TEC _{i , k} (t ₁ ) and TEC _{i, k} (t ₂ ) by dividing the line of sight vector travel at the TEC maximum density altitude during that time.

The verification in step (e) of whether the observed ionospheric slope is a real phenomenon is performed by comparing the satellite-ground station combination where the extreme slope was observed with the extreme ionospheric slope candidate group to see if the observed ionospheric slope is a real phenomenon. It may be performed in a manner of verifying and determining whether to provide a result.

The visualization data may include a content of displaying a gaze vector of a finally selected satellite-ground station on a map to determine whether a slope larger than a predetermined reference value is displayed by the same phenomenon.

According to another aspect of the present invention, a device for verifying whether or not the measured ionospheric layer is an actual occurrence of the ionospheric anomaly in the GNSS ionosphere measurement, the ionosphere abnormalities that monitor whether the abnormality occurred in the ionosphere by monitoring the ionosphere of each ground station in the database. Occurrence monitoring unit; A neighboring ground station search unit for searching for a list of ground stations within a predetermined distance around the ground station where the ionospheric fault has occurred, when an abnormality occurs in the ionosphere; A similar ionospheric fault pattern ground station classification unit for classifying a ground station-satellite combination exhibiting an aspect within a preset reference range compared to the abnormality of the ground station-satellite combination in which the abnormality has occurred; An ionospheric slope estimator for determining an ionospheric slope of classified satellite-ground station combination candidates and selecting a final candidate group for comparison with the ground station-satellite combination in which the abnormality occurs by comparing with a preset threshold; By comparing the observed satellite-ground station combination with the final candidate group selected by the ionospheric gradient estimator, it is possible to verify whether the observed ionospheric slope is a real phenomenon or to construct an ionospheric map constructed based on GNSS measurements. An ionospheric measurement verification unit which visualizes an anomalous satellite-ground station combination and an ionospheric anomaly of an extreme ionosphere candidate group, thereby providing a visualization data that allows an observer to directly determine an anomaly; And a controller for controlling each module of the ionospheric measurement verification system to perform a series of processes for verifying whether a change in the ionospheric measurement is actually a phenomenon in the ionosphere.

The ionospheric layer abnormality may mean that the inclination of the ionospheric layer is larger than a predetermined range of normal values.

The similarity determination of the abnormality of the ionospheric layer may be determined by applying a pattern recognition algorithm based on whether a change pattern of total electron content (TEC) is within a predetermined reference range.

The ionospheric slope is obtained by a time-step method using a single ground station that tracks a specific satellite in a combination at each ground station, and the slope by the time-step method is a continuous time t ₁ , t ₂ . The ionospheric measurements can be calculated by dividing TEC _{i, k} (t ₁ ) and TEC _{i, k} (t ₂ ) by dividing the line of sight vector travel at the TEC maximum density altitude during that time.
Verification of whether the observed ionospheric slope is a real phenomenon is performed by comparing the satellite-ground station combination where the extreme slope is observed with the extreme ionospheric slope candidate group to determine whether the observed ionospheric slope is real. It may be performed in a manner to provide.
The visualization data may include a content of displaying a gaze vector of a finally selected satellite-ground station on a map to determine whether a slope larger than a predetermined reference value is displayed by the same phenomenon.

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According to the present invention, there is an effect of providing a system and method for verifying whether the extreme GNSS ionospheric slope measurement is caused by an actual phenomenon or by satellite / post-processing errors in a situation where the distribution of GNSS ground stations is not dense.

1 shows a station-pair method.
2 is a view showing a time-step method according to the present invention.
Figure 3 is a flow chart for performing GNSS ionospheric slope verification in accordance with the present invention.
4 is a slope graph of SAVO / SSA1-PRN 21.
FIG. 5 is a graph showing a change pattern of TEC spatial slope applying the amount of TEC change and a time-step method to five ground station-satellite combinations finally estimated; FIG.
6 is a graph showing the situation of time when the maximum slope of each satellite-ground station combination occurred.
7 is a diagram illustrating a configuration of an ionospheric measurement verification system according to the present invention.
8 is a simulation flowchart of ionospheric pseudorange error calculation of GBAS.
FIG. 9 is a diagram illustrating a situation in which an ionospheric pseudorange error calculation simulation parameter of a Local-Area Differential GNSS (LAD-GNSS) of the present invention is applied. FIG.
10 is a table showing an ionospheric pseudorange error calculation simulation parameter irradiation range.
11 is an embodiment of the maximum pseudo-range error results for each scenario according to the drone operating environment.
12 is a flowchart of a satellite geometry classification apparatus equipped with an unmanned aerial vehicle of LAD-GNSS to perform the satellite geometry classification method of the present invention.
Fig. 13 is a diagram showing the configuration of an unmanned aerial vehicle equipped satellite classification device;
FIG. 14 is a view showing the maximum pseudo range error calculation simulation result (a) in GBAS and the maximum pseudo distance error calculation simulation result (b) of the LAD-GNSS of the present invention. FIG.

Hereinafter, exemplary embodiments of the present invention will be described in detail with reference to the accompanying drawings. Prior to this, terms or words used in the specification and claims should not be construed as having a conventional or dictionary meaning, and the inventors should properly explain the concept of terms in order to best explain their own invention. Based on the principle that can be defined, it should be interpreted as meaning and concept corresponding to the technical idea of the present invention. Therefore, the embodiments described in the specification and the drawings shown in the drawings are only the most preferred embodiment of the present invention and do not represent all of the technical idea of the present invention, various modifications that can be replaced at the time of the present application It should be understood that there may be equivalents and variations.

Prior to verifying the GNSS ionospheric space slope, the ionospheric measurements are required. In the GNSS field, TEC (Total Electron Content) is used a lot. TEC is an important parameter that represents the density of electrons in the ionosphere, which means the total amount of electrons per square meter of gaze vector between GNSS satellites and ground stations. The TEC code (TEC _{ρ _ meas} ), carrier (TEC _{Φ _ meas} ) and CMC (Code-Minus-Carrier; TEC _{CMC _ meas} ) measurements are shown below and GNSS codes (ρ _L1 , ρ _L2 ) and carriers (Φ _L1 , Φ _L2). ) Can be generated using measurements.

(One)

(2)

(3)

(4)

c is the speed of light and K is the conversion constant 40.3 m ³ s ^{- 2} . IFB is the bias between receiver frequencies, τ _gd is the hardware bias of the satellite. Carrier measurements include the ambiguity constants λ _L1 N _L1 and λ _L2 N _L2 corresponding to the L1 and L2 frequencies f _L1 and f _L2 , but with less noise (ε _Φ << ε _ρ ). Code measurements have more noise than carrier measurements, but they have strong strengths in scintillation phenomena that cause signal disconnection and refraction due to ionosphere heterogeneity. Each TEC measurement is estimated using the Long-Term Ionospheric Anomaly Monitor (LTIAM) developed by the Korea Advanced Institute of Science and Technology.

1 is a diagram illustrating a station-pair method.

The ionospheric slope for estimating the ionosphere threat model is estimated using the Station-pair method developed by Stanford University. The station-pair method is shown in Figure 1 below and two GNSS ground stations i (10) tracking satellite k (200). And the ionospheric measurements TEC _{i , k} and TEC _{j , k} at the same time of j (20) are divided by the distance between the ground stations to estimate the ionospheric delay slope (or TEC slope) according to the space.

GNSS ionospheric slope verification can only be achieved when other ground stations or satellites observe similar patterns and extremes of similar magnitude. Conventional methods for verifying ionospheric tilt measurements include station-wide validation and satellite-wide validation. Station-wide validation is a test that utilizes a GNSS ground station near the GNSS ground station combination (consisting of two) where severe ionosphere gradients are observed. Satellite-wide validation is based on the use of other satellites passing through the vicinity at the time when the extreme ionosphere gradient is observed. Both methods can be verified by applying the station-pair method using nearby ground stations or satellites, and showing that the estimated ionospheric spatial slope is the same with the same slope pattern. If the neighboring ground stations are far apart, the distance between the ground stations is larger than that of the ionosphere change between the ground stations. Therefore, it is difficult to estimate a large slope value in the station-pair method. Therefore, the slope cannot be verified. For this reason, there are limitations in applying Station-wide validation and satellite-wide validation in areas with large distributions of ground stations (eg Brazil) or in areas with small spatial ionospheric anomalies.

2 is a diagram illustrating a time-step method.

As shown in Figure 2, the time-step method proceeds by utilizing the ground station i (100) tracking the satellite k (200) in combination. The slope is continuous time t ₁ , t ₂ The ionospheric measurements of TEC _{i , k} (t ₁ ) and TEC _{i , k} (t ₂ ) are then subdivided and the distance of eye vector travel at 350 km, the TEC maximum density altitude, is calculated for that time. This technique uses t ₁ , t ₂ By arbitrarily adjusting the time-window between them, there is an advantage of estimating the ionosphere spatial gradient of a desired distance (or space scale).

3 is a flowchart of performing GNSS ionospheric slope verification, and FIG. 4 is a slope graph of SAVO / SSA1-PRN 21.

The GNSS ionospheric slope measurement verification system using the technique is performed in the order as shown in FIG. 3. First, a list of neighboring ground stations is automatically searched by placing a limit radius (or distance limitation) from the ground station where the extreme ionosphere spatial slope (ie, more than the ionosphere) is observed (S310). The limit radius is arbitrarily set by the user according to the regional characteristics of each GNSS network.

A pattern recognition algorithm, for example, the 'K-means' method, is applied to classify satellite-ground station combinations that exhibit a similar pattern to the TEC change of the satellite-ground station combinations with extreme slopes (S320). Then, for satellite-ground station combinations classified as having similar aspects, the ionograph slopes of the classified candidates are transformed by applying the time-step method described above, and the final candidate group for threat visualization is classified by comparing with a preset threshold. (S330). The threshold set here may be set to 70% of the maximum observed extreme slope as an example.

Subsequently, it is verified whether the observed ionospheric slope is a real phenomenon by comparing the satellite-ground station combination where the extreme slope is observed and the extreme ionospheric slope candidate group (S340).

By comparing the satellite-ground station combination with the extreme slope and the extreme ionospheric slope candidate group, it is possible to verify whether the observed ionospheric slope is a real phenomenon and provide the result, or to map the ionospheric map constructed based on the GNSS measurement. By visualizing the satellite-ground station combination and the ionospheric anomaly of the extreme ionosphere candidate group, the viewer can provide visualization data to directly determine the anomaly. At this time, in the visualization data provided, the gaze vector of the finally selected satellite-ground station can be displayed on a map so that it can be checked whether a large slope is shown due to the same phenomenon (see FIG. 6).

Number of Ground Stations-Satellite Combinations () Total number of ground stations-satellite combinations in Brazil
(As of December 10, 2013) 2871 Step 1: Search for adjacent ground stations (<1500 km) 1706 Step 2: perform pattern recognition 152 Step 3: Exclude slope candidates (> 2.15 TECU / km) 5

The system proposed in the present invention was utilized to verify the actual extreme GNSS ionospheric spatial slope measurement and to verify the effectiveness of the proposed system. Referring to Table 1, the GNSS ground station in Brazil has a wider distribution of GNSS ground stations than the US, Japan, and Korea, making it difficult to verify GNSS ionospheric slope measurements. A large slope value of 3.09 TECU / km was observed in the ground station SAVO / SSA1-PRN 21 combination on December 10, 2013 at 23:53:30 UT (Universal Time). The normal ionospheric layer shows a spatial gradient of 0.024 TECU / km. The total number of ground station-satellite combinations as of that date is 2871. In the first phase, 1706 ground station-satellite combinations were located within a radius of 1500 km from SAVO and SSA1 ground stations. In the second phase, the K-means algorithm was applied to classify 152 ground station-satellite combinations with similar TEC patterns. Finally, a total of five ground station-satellite combinations were finally retrieved, including the SAVO-PRN 21 and SSA1-PRN 21 combinations, which experienced extreme ionospheric slopes, with a slope candidate exclusion step with a threshold set to 2.15 TECU / km. .

FIG. 5 (a) shows the TEC change amount of the five ground station-satellite combinations finally estimated, and FIG. 5 (b) shows the TEC spatial slope change pattern using the time-step method.

It can be seen from FIG. 5 (a) that the above five ground station-satellite combinations have similar TEC change patterns, and it can be inferred that the change in the amount of electrons has been affected by the same ionosphere abnormalities. It can be seen that an extreme slope corresponding to 3.09 TECU / km observed through FIG. 5 (b) is observed.

6 is a graph showing the situation of time when the maximum slope of each satellite-ground station combination occurred.

In each of the graphs (a) to (e), the map was constructed using the GNSS ionospheric measurements of the day and visualized the ionosphere anomalies. The darker the background, the higher the TEC value. Yellow triangles indicate the location of each ground station. The circle represents the position of the IPS (Ionospheric Pierce Point) between the ground stations connected to each satellite and the line, and the color of the IPP represents the magnitude of the time difference slope at that time. Here, IPP is the intersection of 350km, the altitude of which TEC represents the maximum value, and the hypocrisy vector.

As a result of comprehensive analysis of FIGS. 5 and 6, the phenomenon is a plasma bubble formed diagonally from the northwest to the southeast. Plasma bubbles occur mainly at night in low latitudes, such as Brazil, with extreme TECU depletion, and move from west to east. It can be inferred that the phenomenon moved to the east and caused a sudden change in electron quantity by sequentially affecting SSA1, SAVO, CRAT, and SEAJ, starting from the BATF ground station. In addition, it was estimated that the plasma bubble moved eastward at a speed of about 170 m / s. In conclusion, it can be verified that the magnitude of the ionospheric spatial gradient of the extreme magnitude generated between the SAVO-SSA1 (PRN 21) ground stations of FIG. 4 is caused by the actual ionospheric anomaly and not by the receiver / post-processing error through the verification system proposed in the present invention. Can be.

7 is a diagram showing the configuration of the ionospheric measurement verification system 300 according to the present invention.

The controller 301 controls each module below of the ionospheric measurement verification system 300 to perform a series of processes for verifying whether a change in the ionospheric measurement is actually a phenomenon in the ionosphere.

The ionospheric fault occurrence monitoring unit 302 monitors the ionosphere of each ground station on the database 307 to monitor whether an anomaly has occurred in the ionosphere, that is, an extreme ionospheric space tilt occurs.

The neighboring ground station search unit 303 automatically searches for a list of neighboring ground stations by setting a limit radius (or distance limitation) from the ground station when an error occurs in the ionosphere. The limit radius is arbitrarily set by the user according to the regional characteristics of each GNSS network.

Similar ionospheric fault pattern The ground station classification unit 304 classifies a ground station-satellite combination exhibiting a similar ionosphere fault pattern to the ground station-satellite combination in which the abnormality has occurred. The ionospheric anomaly can be judged by the similarity of the observed TEC change of the satellite-ground station combination, and the similarity can be determined by applying a pattern recognition algorithm such as the 'K-means' technique. Classify the satellite-ground station combinations that are similar to the observed TEC change in the observed satellite-ground station combination.

The ionospheric slope estimator 305 converts the ionospheric slopes of the classified candidates by applying a time-step method to the satellite-ground station combinations classified as being similar, and compares them with a preset threshold for final visualization of the threat. Classify candidate groups.

The ionospheric measurement verification unit 306 verifies whether the observed ionospheric slope is a real phenomenon or compares the satellite-ground station combination where the extreme slope is observed with the extreme ionospheric slope candidate group, or builds on the basis of the GNSS measurement. By using the previous ionospheric map, the satellite-ground station combination with anomalies and the ionospheric anomalies of the extreme ionosphere candidates can be visualized to provide the data for the observer to directly determine the anomalies. At this time, in the visualization data provided, the gaze vector of the finally selected satellite-ground station may be displayed on a map so that it may be checked whether a large inclination is exhibited by the same phenomenon.

The method and apparatus for verifying the GNSS ionospheric measurement value and the verification system, which have been described with reference to FIGS. 1 to 7 so far, are described below with reference to FIGS. 8 to 14. In order to build a model reliably, it is an invention that must be preemptively selected. In view of the above, the invention of GNSS ionospheric measurement verification method and verification system thereof is an invention that is closely related to the invention of the unmanned aerial vehicle-based satellite geometry classification method and apparatus for mitigating ionospheric threats. From the description, a system combining the two inventions can be constructed.

That is, the following describes the invention of an unmanned aerial vehicle-mounted satellite geometry classification method and device model for mitigating the ionospheric threat as a separate system with reference to FIGS. 8 to 14, but such an unmanned aerial vehicle for mitigating the ionospheric threat of FIGS. Incorporating the GNSS ionospheric measurement verification method and the verification system described above with reference to FIGS. 1 to 7 to perform verification of the ionospheric measurement first in order to reliably construct the onboard satellite geometric classification method and device model, FIG. It can be fully understood that the description of FIGS. 1 to 14 can be sufficiently derived and implemented.

8 is a simulation flowchart of ionospheric pseudorange error calculation of GBAS.

The description of the conventional geometric classification technique of GBAS is as follows. Prospective ionospheric threat simulations yield maximum pseudorange errors and use them to calculate vertical ionospheric position errors. The following describes the explanation in order.

First, the pseudorange error caused by the ionospheric slope is calculated as shown in the above figure. GBAS uses smoothed Carrier Smoothed Code (CSC) measurements to mitigate signal noise. The difference between the measured values using the same time constant of 100 seconds in the aircraft and the ground station becomes an error and checks whether or not it is detected by the CCD monitor. In the simulation of this scenario, the magnitude, width, movement speed, and initial position of the ionosphere slope are adjusted, and all cases that can be generated by the combination of variables are simulated, and then the maximum value for each Δv is found.

Next, to calculate the vertical ionospheric position error, we assume a situation where two satellites are simultaneously affected by the ionospheric slope as the worst ionospheric anomaly scenario. At this time, the vertical position error is expressed by the following equation.

IEV _{i, j} is the vertical ionospheric position error of the i, j th satellite, and R is the maximum pseudorange error that can occur due to the ionospheric slope. It uses the tabled values from the simulation in advance and is expressed as a function of Δv, which is the difference between the velocity of the ionospheric slope and the moving speed of the satellite. S _{v, i} is the vertical satellite geometric coefficient of the i-th satellite and is a constant that converts from the pseudorange error region to the position error region.

The vertical satellite geometric coefficients are explained as follows.

By indicating the position of the satellite in the NED (North, East, Down) coordinate system with reference to the position of the receiver, the geometrical information of the satellites, El, Elevation, and Azimuth, can be obtained. Using the elevation and azimuth angles for the N satellites, the matrix G (size: N by 4) is calculated as shown in the following equation: The matrix G is a matrix containing the distribution information of the satellites, which is essential for GPS positioning calculation. .

The matrix W (size: N by N) is a weighting matrix and consists of the variance of the pseudorange error probability distribution for each satellite.

The vertical satellite geometric coefficients refer to elements corresponding to the vertical components of the matrix S below by combining the matrix G and the matrix W in the least squares method.

The largest of the derived IEVs among all possible satellite pairs obtained from a particular satellite geometry is defined as MIEV. When N satellites are observed from the GBAS terrestrial system at a given point in time, the subset geometry of the satellite is generated from N to N-2 combinations as shown below. One MIEV is calculated taking into account all satellite pairs per subset geometry.

In this worst case ionospheric anomaly scenario, the location error MIEV derived should not exceed the TEL. The TEL was derived from the Obstacle Clearance Surface (OCS) for precise aircraft access, for example, at 28.79m at a 200ft altitude. If the MIEV value exceeds the TEL value, GBAS determines that a potential integrity threat exists.

GBAS expands the Vertical Position Level (VPL) to mitigate these potential integrity threats. GBAS terrestrial devices determine the level of protection for all subset satellite geometries at a given point in time and location by:

K _ffmd is the coefficient of undetected probability under the assumption that there is no measurement failure, and σ _i is the standard deviation of the error distribution for satellite i. The VPL _Ho derived from the above values is calculated for every subset of satellite geometries and compared with the preliminarily established vertical alarm limits. Subset geometries above the vertical alarm limit are not approved for use by the GBAS ground system.

The statistics used to calculate the vertical protection level are in the order of four error elements as above, in order of the ground system error term, the error term for the residual error associated with the convective layer, the error term for the measurement of the satellite in-air receiver, and the spatial under the ionospheric static state. It consists of error terms for residual errors due to decorrelation.

The GBAS terrestrial system expands the overall level of vertical protection by multiplying the expansion coefficient by the existing σ _vig for all subset geometries determined to be potential integrity threats in the worst ionosphere anomalies, i.e. all subset geometries whose MIEV is greater than the TEL value. Make the value larger than the vertical alarm limit. Broadcast the expanded integrity parameter by determining I _vig , which makes the vertical protection level greater than the vertical alarm limit value. The aircraft will not permit the use of the subset geometry corresponding to the above conditions (MIEV> TEL) by causing the vertical protection level calculated using the expanded to exceed the vertical alarm limit. In this way, it mitigates the potential integrity threats that occur in the worst ionospheric anomaly scenarios.

FIG. 9 is a diagram illustrating a situation in which an ionospheric pseudo-range error calculation simulation parameter of a Local-Area Differential GNSS (LAD-GNSS) of the present invention is applied.

Hereinafter, LAD-GNSS will be used as a term encompassing an unmanned aerial vehicle navigation system using GNSS, that is, a ground station system and an unmanned aerial vehicle mounted system.

The following three major changes are applied to the conventional GBAS complex satellite classification technique to better suit the unmanned aerial navigation system.

1. System Architecture Change from Ground Equipment to Onboard Navigation System

-The onboard navigation system (user) knows the combination of satellite geometries used for its location, and does not require computation for many satellite combinations.

2. Worst case of ionospheric anomaly scenario

Unlike the scenario in GBAS, where two satellites are simultaneously affected by ionospheric slope, LAD-GNSS defines that only one satellite is affected.

3. Direct comparison of MIEV and alarm limit values, not vertical protection level expansion

-Unlike in GBAS, which determines the availability of a subset of a particular satellite geometry by expanding the level of vertical protection, LAD-GNSS, which is performed only for one satellite geometry, determines the availability by directly comparing the MIEV value with the alarm limit value. This enables faster navigation control for unmanned aerial vehicles.

The following is a description of the simplified satellite geometry classification algorithm of LAD-GNSS. First, as in GBAS, the maximum pseudorange error that is not detected by the monitor is calculated based on the simulation in advance.

9 is a diagram illustrating parameters of pseudo range error simulation. Unlike simulation in GBAS, which does not consider directionality, LAD-GNSS with DSIGMA affected by directionality includes the direction angle of ground station, the direction of inclined plane and the direction of drone.

FIG. 10 is a table illustrating an investigation range of simulation parameter for ionospheric pseudorange error calculation, and FIG. 11 is an example of a maximum pseudorange error result for each scenario according to an unmanned aerial vehicle operating environment.

The table of FIG. 10 shows the minimum and maximum values, the intervals according to the irradiation range set for each parameter. 9, which is not shown in FIG. 9, represents a relative distance between the drone and the ionospheric slope at the end of the simulation, and β denotes a difference value between the moving direction of the drone and the direction of the ionospheric slope. Example results of the maximum pseudorange errors for the two scenarios for the irradiation range of the table of FIG. 10 are shown in the table of FIG. 11.

FIG. 12 is a flowchart in which a satellite geometry classification apparatus 500 mounted on an unmanned aerial vehicle of LAD-GNSS performs the satellite geometry classification method of the present invention, and FIG. 13 illustrates a configuration of the satellite geometry classification apparatus 500 mounted on an unmanned aerial vehicle. Drawing.

Hereinafter, a process for calculating the vertical ionospheric layer position error will be described with reference to FIGS. 12 and 13.

The pseudorange error refers to an error still remaining due to an ionospheric error or the like even when the correction information received from the ground station is applied to the position information obtained from the unmanned aerial vehicle equipped navigation system.

First, in the unmanned aerial vehicle equipped satellite classification device 500, the controller 501 controls each module described below to perform a series of processes related to satellite geometry classification. The satellite geometry classification means determining whether the current position information of the unmanned aerial vehicle calculated by the information from the navigation system such as the unmanned aerial vehicle-equipped navigation system or the ground station is correct.

The parameter is collected by the maximum pseudo range error calculation parameter collecting unit 502 (S510). As described above with reference to FIG. 9, the parameters may include the direction angles of the ground stations, the moving direction of the inclined plane, the moving direction of the drone, and the like, which are parameters affected by the directionality. In addition, the parameters may be the same as those described with reference to FIGS. 9 and 10.

The maximum pseudo distance error calculation unit 503 calculates the maximum pseudo distance error (S520). The maximum pseudorange error calculation may be obtained through calculation of parameters in real time (S520), but the maximum pseudorange error corresponding to the collected parameters is provided in the database 506 with a parameter table as shown in FIG. 10. It may be found by finding in the database 506 (S520).

In LAD-GNSS, the worst ionospheric anomaly scenario assumes that one satellite is affected by the ionospheric slope. At this time, the vertical position error is expressed as Equation 1, whereby the vertical ionospheric position error calculator 504 calculates the vertical ionospheric position error (S530).

IEV _i is the vertical ionospheric position error of the i-th satellite, and R is the maximum pseudorange error that can occur due to the ionospheric slope. It uses values that have been tabled in advance by simulation. S _{v, i} is the vertical satellite geometric coefficient of the i-th satellite and is a constant that converts from the pseudorange error region to the position error region. This is an element corresponding to the vertical direction component of S of Equation 1 as described above.

Since the LAD-GNSS's unmanned aerial vehicle navigation system (user) knows its satellite geometry, there is no need to find the ionospheric vertical position error with respect to the subset of the satellite geometry, like GBAS. The vertical ionospheric position error calculation unit 504 selects the largest value (hereinafter referred to as 'MIEV') of the derived IEVs among all possible satellites obtained from the satellite geometries (S540).

When N satellites are being observed from the GBAS terrestrial system at a particular point in time, the MIEV is determined to be the largest of the N IEVs. Navigation system availability determination unit 505 is a navigation system that is currently operating in real time through a direct comparison between the MIEV value calculated in real time and the position error limit (hereinafter referred to as 'alarm limit') required in the operating area (for example, For example, it is determined whether or not the unmanned aerial vehicle navigation device (S550) is available (S550), and if the alarm limit is exceeded, the use of the corresponding navigation system is stopped to secure the safety of the user from the ionosphere threat.

FIG. 14 is a view showing the maximum pseudo range error calculation simulation result (a) in GBAS compared with the simulation result (b) of the maximum pseudo distance error of LAD-GNSS according to the present invention.

In FIG. 14 (a), the results of the simulation of calculating the maximum pseudo distance error performed in the existing GBAS are represented by relative speed as shown in the following figure. The blue line is the maximum pseudo-range error calculated by the actual simulation, and the red line is a model modeled linearly to facilitate the calculation. In actual operation, the red line value is used. The closer the relative speed is to zero, the less likely the ionospheric slope is to be detected by CCD monitors operating in GBAS ground stations, so the maximum pseudorange error near zero usually has a maximum value.

In FIG. 14 (b), on the other hand, in the LAD-GNSS of the present invention, which is a simplified system for UAV (unmanned aerial vehicle), it is assumed that the worst case is a situation where only one satellite is affected by the ionospheric slope. Since GBAS is a system that supports the navigation of manned manned aircraft, the worst case scenario is the situation where two satellites with a small probability of occurrence are affected at the same time.However, UAV is more dangerous than human aircraft. It only considers the situation where only one satellite is affected in order to increase the availability and usability of the system.

In this scenario, since there is no basis for determining the direction of movement of the ionosphere inclined plane as in GBAS, the vertical ionospheric position error is calculated for all directions. The relative speed will have a different value depending on the direction of movement of the inclined plane.

In Equation 2, when looking at the vertical ionospheric position error calculation formula for the i-th satellite, s _{v, i} is a parameter that is determined according to the distribution of all satellites currently received regardless of the direction of movement of the ionospheric slope. Therefore, the process of finding the maximum vertical ionospheric position error for all the moving directions is the same as the process of finding the maximum value of the maximum pseudorange error. In other words, the worst case scenario is a situation in which the ionospheric slope is moved to have a relative speed when the maximum pseudorange error has a maximum value. For this reason, LAD-GNSS uses the maximum pseudo distance error as a constant model.

The figure below shows a graph of the results of the simulation of the maximum pseudorange error calculated from LAD-GNSS, where each colored line is the maximum pseudorange error calculated according to the size of the ionospheric slope (g), and the black lines cover all potential ionospheric threat ranges. It represents the maximum pseudorange error model of the constant form calculated for. As with GBAS, the closer the relative speed is to zero, the larger the pseudorange error is, as the CCD monitors operating at ground stations and DSIGMAs operating on payloads are less likely to detect slopes.

10,20: ground station
100: ground station
200: satellite
300: ionospheric measurement verification system
500: satellite geometry classification device

Claims (14)

A method in which a GNSS ionospheric measurement verification system performs verification on whether GNSS ionospheric measurement is actually an abnormal ionosphere or more.
(a) detecting the occurrence of an ionospheric fault from a particular ground station;
(b) searching a list of ground stations within a predetermined distance around the ground station;
(c) classifying satellite-ground station combinations exhibiting an aspect within a predetermined reference range compared to the abnormality of the ground station-satellite combination in which the abnormality has occurred;
(d) determining an ionospheric slope of the satellite-ground station combination candidates classified in step (c), and selecting a final candidate group for comparison with the ground station-satellite combination in which the abnormality has occurred by comparing with a preset threshold value; And
(e) by comparing the observed satellite-ground station combination with the final candidate group selected in step (d) to verify whether the observed ionospheric slope is a real phenomenon, or an ionospheric layer constructed based on GNSS measurements. Using the map to visualize the satellite-ground station combination where the abnormality occurred and the ionospheric anomaly of the extreme ionosphere candidate group, providing the viewer with visualization data to directly determine the anomaly.
GNSS ionospheric measurement verification method comprising a. The method according to claim 1,
More than the ionizing layer of the step (a),
Means that the ionospheric slope is greater than the preset normal range
GNSS ionospheric measurement verification method characterized in that. The method according to claim 1,
Determination of similarity of the ionospheric abnormality phase of step (c),
The pattern recognition algorithm is applied to determine whether changes in TEC (Total Electron Content) are within a predetermined reference range.
GNSS ionospheric measurement verification method characterized in that.
delete The method according to claim 1,
The ionospheric slope in step (d) is
In each ground station, it is obtained through the time-step method by using the combination of a single ground station that tracks a specific satellite.
The slope by the time-step method,
Sequential time t ₁ , t ₂ To _calculate the ionospheric measurements of TEC _{i , k} (t ₁ ) and TEC _{i , k} (t ₂ ) and divide by the line of sight vector travel at the TEC maximum density altitude during that time.
GNSS ionospheric measurement verification method characterized in that. The method according to claim 1,
The verification in step (e) of whether the observed ionospheric slope is a real phenomenon,
By comparing the satellite-ground station combination where the extreme slope is observed with the extreme ionospheric slope candidate group, verifying whether the observed ionospheric slope is a real phenomenon, and determining and providing the result.
GNSS ionospheric measurement verification method characterized in that. The method according to claim 6,
In the visualization data,
The line of sight of the last selected satellite-ground station is displayed on the map and includes information that can confirm whether the slope is greater than the preset reference value by the same phenomenon.
GNSS ionospheric measurement verification method characterized in that. A device for verifying whether an anomalous anomalous occurrence actually occurs with respect to GNSS ionospheric measurement.
An ionospheric fault occurrence monitoring unit for monitoring an ionospheric layer of each ground station on a database to monitor whether an anomaly occurs in the ionosphere;
A neighboring ground station search unit for searching for a list of ground stations within a predetermined distance around the ground station where the ionospheric fault has occurred, when an abnormality occurs in the ionosphere;
A similar ionospheric fault pattern ground station classification unit for classifying a ground station-satellite combination exhibiting an aspect within a preset reference range compared to the abnormality of the ground station-satellite combination in which the abnormality has occurred;
An ionospheric slope estimator for determining an ionospheric slope of classified satellite-ground station combination candidates and selecting a final candidate group for comparison with the ground station-satellite combination in which the abnormality occurs by comparing with a preset threshold;
By comparing the observed satellite-ground station combination with the final candidate group selected by the ionospheric gradient estimator, it is possible to verify whether the observed ionospheric slope is a real phenomenon or to construct an ionospheric map constructed based on GNSS measurements. An ionospheric measurement verification unit which visualizes an anomalous satellite-ground station combination and an ionospheric anomaly of an extreme ionosphere candidate group, thereby providing a visualization data that allows an observer to directly determine an anomaly; And
Controlling each module of the ionospheric measurement verification system to perform a series of steps to verify whether a change in the ionospheric measurement is actually a phenomenon in the ionosphere.
GNSS ionospheric measurement verification system comprising a. The method according to claim 8,
The ionospheric layer or more,
Means that the ionospheric slope is greater than the preset normal range
GNSS ionospheric measurement verification system, characterized in that. The method according to claim 8,
Determination of similarity of the ionospheric abnormality phase,
The pattern recognition algorithm is applied to determine whether changes in TEC (Total Electron Content) are within a predetermined reference range.
GNSS ionospheric measurement verification system, characterized in that.
delete The method according to claim 8,
The ionospheric slope is
In each ground station, it is obtained through the time-step method by using the combination of a single ground station that tracks a specific satellite.
The slope by the time-step method,
Sequential time t ₁ , t ₂ To _calculate the ionospheric measurements of TEC _{i , k} (t ₁ ) and TEC _{i , k} (t ₂ ) and divide by the line of sight vector travel at the TEC maximum density altitude during that time.
GNSS ionospheric measurement verification system, characterized in that. The method according to claim 8,
Verification of whether the observed ionospheric slope is a real phenomenon is
By comparing the satellite-ground station combination where the extreme slope is observed with the extreme ionospheric slope candidate group, verifying whether the observed ionospheric slope is a real phenomenon, and determining and providing the result.
GNSS ionospheric measurement verification system, characterized in that. The method according to claim 13,
In the visualization data,
The line of sight of the last selected satellite-ground station is displayed on the map and includes information that can confirm whether the slope is greater than the preset reference value by the same phenomenon.
GNSS ionospheric measurement verification system, characterized in that.

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