KR102270339B1 - Method and System for Reduction of Time to First Fix of High Integrity RTK-GNSS - Google Patents

Abstract

The present invention relates to a system for reducing the Time To First Fix (TTFF) of a Real Time Rinematic (RTK)-GNSS (Global Navigation Satellite System) that supports a high-precision/high-integrity navigation solution for an unmanned aerial vehicle.
According to the present invention, there is provided a system and method for shortening and determining the initial preparation time (TTFF) of RTK-GNSS for navigation support of an unmanned aerial vehicle.

Description

Method and System for Reduction of Time to First Fix of High Integrity RTK-GNSS

The present invention relates to a system for shortening the Time To First Fix (TTFF) of a Real Time Kinematic (RTK)-GNSS (Global Navigation Satellite System) that supports a high-precision/high-integrity navigation solution for an unmanned aerial vehicle. More specifically, it relates to a system that satisfies the integrity probability and TTFF required for UAV operation by applying multipath error artificial neural network model construction, multipath error prediction using neural networks, and TTFF analysis technique based on integrity requirement performance.

1 is a diagram illustrating a carrier-based satellite navigation system.

The carrier measurement-based RTK-GNSS system used for accurate navigation of an unmanned aerial vehicle consists of a satellite, a reference station, and a user. Unlike general code-based GNSS, the carrier-based system can estimate the user's location with cm-level accuracy by differentiating the carrier measurements of the user and the reference station. Due to the characteristics of the carrier, it is not known how many carrier cycles are included in the measured value.

The initial time it takes to solve the ambiguity integer and calculate the user's location is called TTFF (Time To First Fix), and the ambiguity integer once solved does not change unless there is a problem in the signal. In the case of a commercial RTK system, it takes about 5 minutes of TTFF, but an ambiguous integer solved in 5 minutes contains a lot of uncertainty, which can cause a large position error and cause an accident. To ensure this, the concept of integrity should be introduced, which detects large positional errors and alerts users. The current general RTK system does not guarantee the integrity, and in the case of an unmanned aerial vehicle system using low-cost equipment, a long TTFF of more than one hour is required to ensure the integrity. In this way, TTFF is determined by integrity requirement performance and ambiguity integer accuracy. Since the accuracy of the ambiguous integer is determined by the multipath error of the GNSS measurement, the TTFF can be reduced if the multipath error is reduced. Therefore, a technology (or system) that accurately calculates the ambiguity constant by predicting/reducing multipath error and determining the TTFF by reflecting the required integrity performance performance should be developed.

Carrier-based RTK-GNSS can estimate the user's location by solving the ambiguity constant. In order to solve the ambiguity, a double-difference method is used in which the measurements of the user and the reference station for two satellites are differentiated from each other. The user can measure the code measurement value and the carrier measurement value from the GNSS satellite, and each measurement value is expressed as Equation 1 below.

here,

ρ : chord measurement [m]

φ : Carrier measurement [cycle]

λ : carrier wave wavelength [m/cycle]

r is the actual distance between the receiver and the satellite [m]

b : Receiver clock error [m]

B : satellite clock error [m]

I : Delay error due to ionosphere [m]

T : delay error due to convection [m]

ν : multipath error ~ N ( 0 ,σ ² ) [m]

N : ambiguous integer [cycle]

Subscript: means user k

Superscript: means satellite i

Equation 2 below represents a single-difference measurement value in which the measurements of the user and the reference station are different from each other, and Equation 3 represents a double-difference measurement value in which the single-difference measurements for two satellites are different from each other.

와

는 무시할 수 있을 만큼 작아지게 되고, 측정치 간에 독립적인 오차요소인 다중경로 오차가 모호정수 및 상대위치만 남게된다. 따라서 이중차분 코드측정치와 이중차분 반송파 측정치를 이용하여 모호정수 및 상대위치를 결정한다. 반송파의 다중경로 오차는 코드 다중경로 오차에 비해 무시할 수 있을 만큼 작기 때문에 모호정수의 정확도는 코드 다중경로 오차(ν _ρ )에 의해 결정된다고 할 수 있다. 다중경로 오차는 주변 장애물에 의해 반사된 신호가 원래의 신호와 간섭을 일으켜 발생하는 지연 오차를 의미한다. GNSS 위성은 일정 주기를 갖고 지구를 공전하고 있으므로 고정된 수신기에 대해 다중경로 오차는 주기적으로 반복되며 주변 환경에 영향을 많이 받는다. 이러한 특성을 가진 다중경로 오차를 완화시키기 위해 1)수신기에서 하드웨어/소프트웨어 적으로 완화하는 방법과 2)발생한 다중경로 오차를 추정하여 보상해주는 방법이 있다. 수신기의 하드웨어 관점에서 초크링(Choke Ring) 또는 배열 안테나를 사용 하는 방법이 있고 소프트웨어 관점에서 협대역 상관기(Narrow Correlator) 안테나를 사용하는 방법이 있다. 하지만 위의 방법들은 비싸고 무겁기 때문에 무인기에 적용하기에는 무리가 있다. 다중경로 오차를 추정하여 보상하는 방법에는 전날 데이터에 적응필터를 이용하여 추정하는 방법이 있다. 하지만 적응필터 방법의 경우 고정된 수신기에 대해서만 사용 가능하다. 따라서 비행환경에 따라 다중경로 오차가 변화하는 무인기에 사용하기에는 한계가 있다.In the process of double differentiation, the error is due to the correlation between the measured values.

Wow

becomes small enough to be negligible, and the multipath error, which is an independent error factor between measurements, leaves only ambiguity constants and relative positions. Therefore, the ambiguity constant and the relative position are determined using the double-difference code measurement value and the double-differential carrier measurement value. Since the multipath error of the carrier is negligibly small compared to the code multipath error, the accuracy of the ambiguity constant can be said to be determined by the code multipath error (ν _{ρ ).} The multipath error means a delay error caused by the interference of the signal reflected by the surrounding obstacle with the original signal. Since the GNSS satellite orbits the earth with a certain period, the multipath error for a fixed receiver is repeated periodically and is greatly affected by the surrounding environment. In order to mitigate multipath errors with these characteristics, there are 1) a hardware/software mitigation method in the receiver, and 2) a method for estimating and compensating the generated multipath error. There is a method of using a choke ring or an array antenna from a hardware point of view of the receiver, and a method of using a narrowband correlator antenna from a software point of view. However, the above methods are expensive and heavy, so it is difficult to apply them to an unmanned aerial vehicle. As a method of estimating and compensating for multipath error, there is a method of estimating using an adaptive filter on the previous day's data. However, in the case of the adaptive filter method, it can be used only for a fixed receiver. Therefore, there is a limit to using it for an unmanned aerial vehicle whose multipath error varies depending on the flight environment.

US 10-0980762 B1

The present invention was devised to solve the above-mentioned problems, and an object of the present invention is to provide a system and method for shortening and determining the initial preparation time (TTFF) of RTK-GNSS for navigation support of an unmanned aerial vehicle.

In order to achieve the above object, the method for reducing the initial preparation time of high-safety RTK-GNSS according to the present invention is (a) input parameters of an artificial neural network model (hereinafter referred to as 'artificial neural network model') for code multipath error estimation. obtaining a; (b) inputting the input parameters into the artificial neural network model; (c) outputting the code multipath error of the current measurement value in the artificial neural network model in real time; and (d) correcting the code measurement value using the code multipath error calculated in step (c), wherein in step (d), the corrected code measurement value is determined from the previously measured code measurement value. It is calculated by subtracting the code multipath error of step (d).

The input parameter may include a parameter indicating a direction in which the receiver looks at the satellite; signal to noise ratio (SNR); And it may include one or more of the inclination of the antenna installed in the unmanned aerial vehicle.

Between the steps (c) and (d), (c1) collecting the code multipath error output from the artificial neural network model for a preset time; (c2) obtaining an average of the collected code multipath errors, and estimating the average as a code multipath error;

The code multipath error in step (d) may be the code multipath error estimated in step (c2).

The artificial neural network model may be formed by learning by layers arranged in the same manner as the artificial neural network model.

The step (a) may include calculating, by the receiver, a parameter indicating a direction in which the receiver looks at the satellite from GPS data received from the satellite, and obtaining it as an input parameter of the artificial neural network model.

The step (a) may include calculating, by the receiver, a signal to noise ratio (SNR) from GPS data received from the satellite, and obtaining it as an input parameter of the artificial neural network model.

The step (a) may include calculating the tilt of the antenna installed in the unmanned aerial vehicle from the data obtained from the sensor unit, and obtaining it as an input parameter of the artificial neural network model.

According to another aspect of the present invention, a system for reducing the initial preparation time of high safety RTK-GNSS includes: at least one processor; and at least one memory storing computer-executable instructions, wherein the computer-executable instructions stored in the at least one memory are executed by the at least one processor for: (a) code multipath error estimation; obtaining input parameters of an artificial neural network model (hereinafter referred to as an 'artificial neural network model'); (b) inputting the input parameters into the artificial neural network model; (c) outputting the code multipath error of the current measurement value in the artificial neural network model in real time; and (d) correcting the code measurement value using the code multipath error calculated in step (c), wherein in step (d), the corrected code measurement value is determined from the previously measured code measurement value, It is calculated by subtracting the code multipath error of step (d).

The system for reducing the initial preparation time of the high-safety RTK-GNSS may further include a receiver for receiving GPS data for calculating the input parameters of the artificial neural network model from the satellite.

The input parameter may include a parameter indicating a direction in which the receiver looks at the satellite; and signal to noise ratio (SNR).

The system for reducing the initial preparation time of the high-safety RTK-GNSS may further include a sensor unit for calculating the inclination of the antenna installed in the unmanned aerial vehicle.

According to another aspect of the present invention, a computer program for reducing the initial preparation time of the high-safety RTK-GNSS is stored in a non-transitory storage medium, and by the processor, (a) an artificial neural network model for code multipath error estimation obtaining an input parameter of (hereinafter referred to as an 'artificial neural network model'); (b) inputting the input parameters into the artificial neural network model; (c) outputting the code multipath error of the current measurement value in the artificial neural network model in real time; and (d) executing the step of correcting the code measurement value using the code multipath error calculated in the step (c), wherein in the step (d), the corrected code measurement value is It is calculated by subtracting the code multipath error of step (d) from the code measurement value.

According to the present invention, it is effective to provide a system and method for shortening and determining the initial preparation time (TTFF) of RTK-GNSS for navigation support of an unmanned aerial vehicle.

1 is a diagram illustrating a carrier-based satellite navigation system;
Fig. 2 shows a flow diagram of a multipath error mitigation algorithm;
3 is a diagram illustrating an antenna gain change according to an antenna inclination;
4 is a diagram showing the configuration of a system for reducing the initial preparation time of a high safety RTK-GNSS.
5 is a diagram for estimating a multipath error according to an environment;
6 is a view showing a first result of the multipath error standard deviation according to the elevation angle.
7 is a diagram for estimating a multipath error according to an antenna inclination;
8 is a diagram showing a second result of the multipath error standard deviation according to the elevation angle.
9 is a diagram illustrating multipath error standard deviation and integrity probability-based TTFF.

Hereinafter, preferred embodiments of the present invention will be described in detail with reference to the accompanying drawings. Prior to this, the terms or words used in the present specification and claims should not be construed as being limited to conventional or dictionary meanings, and the inventor should properly understand the concept of the term in order to best describe his invention. Based on the principle that it can be defined, it should be interpreted as meaning and concept consistent with the technical idea of the present invention. Therefore, the configuration shown in the embodiments and drawings described in the present specification is only the most preferred embodiment of the present invention and does not represent all of the technical spirit of the present invention, so at the time of the present application, various It should be understood that there may be equivalents and variations.

2 is a diagram illustrating a flow chart of a multipath error mitigation algorithm, and FIG. 3 is a diagram illustrating an antenna gain change according to an antenna tilt.

The present invention uses artificial neural networks to estimate, compensate and mitigate code multipath errors in real time.

Referring to FIG. 2 , an artificial neural network model for estimating code multipath error is built by learning using previously collected data (S210), and the multipath error of the current measurement is estimated in real time using the learned model. The multipath error is mitigated by performing (S230) and correcting (S240). Since it is a model for estimating multipath error, the output of the artificial neural network should be a multipath error, and the input should be a parameter that can well represent the multipath error characteristics and can be used in real time. Therefore, as input to the model, parameters indicating the direction in which the receiver looks at the satellite, Signal to Noise Ratio (SNR), and the slope of the antenna are used as inputs.

The parameters indicating the direction in which the receiver looks at the satellite may be, for example, elevation and azimuth, and this data can be calculated and used in real time from GPS data received from the satellite in the receiver. Since the multipath error changes the reflected signal according to the relative position between the receiver and the satellite, not the absolute position of the satellite, information about the environment around the receiver can be given to the artificial neural network model.

The SNR is the ratio of the signal strength received by the antenna to the noise strength, and the receiver can calculate and use it in real time from GPS data received from the satellite. When the reflected signal interferes with the direct signal, there is a change in the SNR as well as the multipath error. In this case, the multipath error and the change in SNR have an in-phase relationship with respect to the difference in propagation distance between the direct signal and the reflected signal. Although it is difficult to derive an exact relationship between multipath error and SNR, it can be seen that SNR has a strong correlation with multipath error. Therefore, although it cannot be distinguished by elevation and azimuth angles that are not very sensitive to the movement of the drone, the environment around the drone that changes little by little can be distinguished through SNR.

The tilt of the antenna can be used in real time through the sensor unit 20 of the UAV (refer to FIG. 4 ). The sensor unit 20 (refer to FIG. 4 ) may include an inertial measurement unit (IMU) sensor. Since the gain of the antenna has different values depending on the direction with respect to the antenna, the received signal differs depending on the direction in which the signal is received even for the same signal. As shown in FIG. 3 , since the UAV moves by tilting it, the antenna attached to the UAV also tilts and the pattern of antenna gain changes. Therefore, a difference occurs in the received signal, and the multipath error also changes.

In the step (S210) of constructing an artificial neural network model for estimating the code multipath error, i) parameters indicating the direction the receiver looks at the satellite, such as elevation and azimuth, ii) SNR, iii) tilt of the antenna, etc. An artificial neural network model is trained by taking a plurality of data sets included as an input and using the code multipath error calculated for each data set as a predetermined output.

Thereafter, data such as parameters indicating the direction in which the receiver looks at the satellite, SNR, and the inclination of the antenna are acquired in real time and used as an input of the learned and constructed artificial neural network model ( S220 ).

In the artificial neural network model, a code multipath error is output with respect to such an input. The code multipath error output in this way is collected for a predetermined period of time using an artificial neural network model, averaged, and finally estimated as a code multipath error. The averaging may mean obtaining a root mean square (RMS) value of the collected code multipath error values as an embodiment.

가 된다. 여기서 i는 인공위성의 번호, j는 무인기에 장착된 수신기의 번호이다.As such, the final estimated code multipath error is

becomes where i is the number of the satellite, and j is the number of the receiver mounted on the UAV.

로부터, 이와 같이 최종적으로 추정된 코드 다중경로 오차

를 제거하는 보정을 하여(S240) 이와 같이 보정된 값인

을, 보정된 코드 측정치

로 결정하게 된다(S240).Also in Equation 4, the pre-measured code measurement value

From , this finally estimated code multipath error

By correcting to remove (S240), the corrected value

, the calibrated code measurements

is determined (S240).

가 무시할 수 있을 정도로 작은 값이므로, 단계 S240에서 보정된 값

과 기 측정된 반송파 측정치

을 대입하면, 수학식 3의 최종식에서 모호정수

를 결정할 수 있다.In the final expression of Equation 3 representing the carrier measurement value, as described above, the multipath error

Since is a negligibly small value, the corrected value in step S240

Previously measured carrier measurements

By substituting , an ambiguous integer in the final expression of Equation 3

can be decided

에서 모호정수 항인

을 제거함으로써 바로 수신기 j와 위성 i 사이의 실제 거리인

를 구할 수 있게 되고, 이와 같은 방식으로

를 4개의 위성에 대하여 구하게 되면 무인기의 현재 위치를 계산해 낼 수 있다.In addition, since the ambiguity constant determined as described above does not change unless a problem occurs in the signal, the carrier measurement value of Equation 3 for determining the location of the UAV during future flight

Ambiguous integer term in

By removing , the actual distance between receiver j and satellite i is

can be obtained, and in this way

If , is obtained for four satellites, the current position of the UAV can be calculated.

As described above, the code multipath error output using the artificial neural network model is collected for a predetermined period of time, averaged, and finally estimated as the code multipath error. The time required for this is TTFF (Time To First) Fix). As described above, averaging may mean obtaining a root mean square (RMS) value of the collected code multipath error values as an embodiment.

In the conventional method of estimating the code multipath error, the deviation is very severe, and it takes a long time to collect the code multipath error data. However, as in the present invention, the method of estimating the code multipath error using an artificial neural network model Since the deviation of the code multipath error values output from the artificial neural network model is small, it has the advantage of finally estimating a reliable code multipath error even by collecting code multipath error data for a much shorter period of time.

4 is a diagram showing the configuration of the system 100 for reducing the initial preparation time of the high-safety RTK-GNSS.

The system 100 for reducing the initial preparation time of the high safety RTK-GNSS, the receiver 300, and the sensor unit 20 are mounted on the unmanned aerial vehicle.

The receiver 300 receives the GPS data from the satellite 10 and serves to transmit it to the system 100 for reducing the initial preparation time of the high safety RTK-GNSS.

The system 100 for reducing the initial preparation time of the high safety RTK-GNSS includes a processor 110 , a non-volatile storage unit 120 for storing programs and data, a volatile memory 130 for storing programs being executed, and a receiver 300 ) and a communication unit 140 for performing communication, and a bus that is an internal communication path between these devices. The running program may include a device driver, an operating system, and various applications. Although not shown, the electronic device includes a power supply unit such as a battery.

Applications include a code multipath error estimation application 220, a GPS data processing application 230, and the like.

The code multipath error estimation application 220 includes an artificial neural network model for estimating code multipath error as described above with reference to FIG. 2, and estimates the code multipath error therefrom.

The GPS data processing application 230 calculates input data to the artificial neural network model for code multipath error estimation as described above with reference to FIG. 2 . That is, from the GPS data received from the receiver 300, parameters indicating the direction in which the receiver looks at the satellite, such as elevation and azimuth, or signal to noise ratio (SNR) is calculated to estimate the code multipath error application ( 220) to the artificial neural network model for multipath error estimation.

In addition, the sensor control driver 210 calculates the tilt of the antenna based on the data received from the sensor unit 20 and transmits it to the artificial neural network model for estimating the code multipath error. As described above, the sensor unit may include an inertial measurement unit (IMU) sensor. Alternatively, the sensor unit 20 without the sensor control driver 210 directly calculates the slope of the antenna using the sensed signal and transmits it to the code multipath error estimation application 220, thereby estimating the code multipath error application 220. can be used as input for artificial neural network models of

5 is a diagram of estimating a multipath error according to an environment, FIG. 6 is a diagram illustrating a first result of a multipath error standard deviation according to an elevation angle, and FIG. 7 is a diagram of estimating a multipath error according to an antenna slope. , FIG. 8 is a diagram showing the second result of the multipath error standard deviation according to the elevation angle.

FIG. 5 is an example of estimating a code multipath error by an artificial neural network model for code multipath error estimation of the code multipath error estimation application 220 described with reference to FIG. 2 .

The first example collects data for two days for two environments (type A and type B) and trains a neural network model using data from one day. It can be confirmed that the model trained in various environments can estimate the multipath error by distinguishing the environment around the drone well.

The results of testing using data from different days for each environment are shown in FIG. 5 . Blue and black represent actual data, and red represent estimated values. As shown in FIG. 5, it can be seen that the multipath error is estimated by distinguishing the two environments from each other. Looking at the above results statistically, in the case of type A, the standard deviation of the unmoderated multipath error is about 0.19 m, and the standard deviation of the multipath error mitigated by compensating for the multipath error using an artificial neural network is about 0.13 m, which is 31%. The multipath error of Type B is also 0.20 m when not relaxed and 0.14 m when relaxed, confirming an error reduction of about 30%.

The first result of the standard deviation of the multipath error according to the elevation angle of the satellite is graphed as shown in FIG. 6 , and it can be seen that the standard deviation decreases for most satellite elevation angles.

The second example shows how well the multipath error can be estimated depending on the antenna slope. In the same environment, data for two cases of 0 degrees and 30 degrees were collected for two days, and then the neural network was trained using the data of one day. A test result using data from another day is shown in FIG. 7 . Blue and black represent actual data, and red represent estimated values.

As shown in FIG. 7 , it can be confirmed that the multipath error is well classified and estimated for each angle. In the case of 0 degree, it can be seen that the standard deviation of the multipath error is reduced by about 42% from about 0.16m to 0.09m, and in the case of 30 degree, it can be confirmed that it is reduced by about 30% from 0.19m to 0.13m.

9 is a diagram illustrating multipath error standard deviation and integrity probability-based TTFF.

)과 다중경로 오차의 표준편차에 대한 TTFF를 나타내고 있다. 도 9에서 보이듯이 다중경로 오차의 표준편차가 작을수록 무결성 요구조건이 완화될수록 TTFF가 감소하는 것을 볼 수 있다. 시스템에서는 도 9와 같이 2단계서 완화시킨 다중경로 오차의 표준편차와 운영하고자 하는 시스템의 무결성 요구조건을 기반으로 TTFF를 설정한다. 무결성 요구조건은 무인기 임무에 따라 기존에 미리 설정하는 설계 파라미터이다. 도 9는 RTK-GNSS 시스템이 반송파 이중차분 측정치와 TTFF 시간동안 필터링되는 Geometry-free 측정치를 기반으로 운영되는 것을 가정하였다.The TTFF is determined based on the estimated code multipath error information and the integrity information required by the system. 9 shows the integrity probability (

) and the TTFF for the standard deviation of the multipath error. As shown in FIG. 9 , it can be seen that the smaller the standard deviation of the multipath error, the lower the TTFF is as the integrity requirement is relaxed. As shown in FIG. 9, the system sets the TTFF based on the standard deviation of the multipath error relaxed in step 2 and the integrity requirements of the system to be operated. The integrity requirement is a design parameter that is previously set in advance according to the mission of the UAV. In FIG. 9, it is assumed that the RTK-GNSS system is operated based on the carrier double difference measurement value and the geometry-free measurement value filtered during the TTFF time.

10: satellite
20: IMU sensor unit
100: High safety RTK-GNSS initial preparation time reduction system
210: sensor control driver
220: code multipath error estimation application
230: GPS data processing application
300: receiver
500: drone

Claims (14)

As a method of reducing the initial preparation time of high safety RTK-GNSS,
(a) obtaining input parameters of an artificial neural network model (hereinafter referred to as an 'artificial neural network model') for code multipath error estimation;
(b) inputting the input parameters into the artificial neural network model;
(c) outputting the code multipath error of the current measurement value in the artificial neural network model in real time; and
(d) correcting the code measurement value using the code multipath error calculated in step (c).
including,
In step (d), the corrected code measurement is
Calculated by correcting the value obtained by subtracting the code multipath error of step (d) from the previously measured code measurement value,
A method of reducing the initial preparation time of high-safety RTK-GNSS. The method according to claim 1,
The input parameters are
a parameter indicating a direction in which the receiver looks at the satellite;
signal to noise ratio (SNR); and
Tilt of the antenna installed on the drone
A method of reducing the initial preparation time of high safety RTK-GNSS comprising at least one of. The method according to claim 1,
Between step (c) and step (d),
(c1) collecting code multipath errors output from the artificial neural network model for a preset time;
(c2) obtaining an average of the collected code multipath errors and estimating the average as a code multipath error;
A method of reducing the initial preparation time of the high-safety RTK-GNSS further comprising a. 4. The method according to claim 3,
The code multipath error of step (d) is,
The code multipath error estimated in step (c2)
A method of shortening the initial preparation time of high-safety RTK-GNSS, characterized by The method according to claim 1,
The artificial neural network model is
Formed by learning by layers arranged in the same way as the artificial neural network model
A method of shortening the initial preparation time of high-safety RTK-GNSS, characterized by The method according to claim 1,
The step (a) is,
Calculating, by the receiver, a parameter indicating a direction in which the receiver looks at the satellite from the GPS data received from the satellite, and obtaining it as an input parameter of the artificial neural network model;
A method of reducing the initial preparation time of high safety RTK-GNSS comprising a. The method according to claim 1,
The step (a) is,
calculating, by a receiver, a signal to noise ratio (SNR) from GPS data received from a satellite, and obtaining it as an input parameter of the artificial neural network model;
A method of reducing the initial preparation time of high safety RTK-GNSS comprising a. The method according to claim 1,
The step (a) is,
Calculating the slope of the antenna installed in the UAV from the data acquired by the sensor unit, and acquiring it as an input parameter of the artificial neural network model
A method of reducing the initial preparation time of high safety RTK-GNSS comprising a. delete As a system to reduce the initial preparation time of high safety RTK-GNSS,
at least one processor; and
at least one memory for storing computer-executable instructions;
The computer-executable instructions stored in the at least one memory are executed by the at least one processor,
(a) obtaining input parameters of an artificial neural network model (hereinafter referred to as an 'artificial neural network model') for code multipath error estimation;
(b) inputting the input parameters into the artificial neural network model;
(c) outputting the code multipath error of the current measurement value in the artificial neural network model in real time; and
(d) correcting the code measurement value using the code multipath error calculated in step (c).
to run,
In step (d), the corrected code measurement is
Calculated by correcting the value obtained by subtracting the code multipath error of step (d) from the previously measured code measurement value,
A system for reducing the initial preparation time of high-safety RTK-GNSS. 11. The method of claim 10,
A receiver for receiving GPS data for calculating an input parameter of the artificial neural network model from a satellite
High safety RTK-GNSS initial preparation time reduction system, characterized in that it further comprises. 12. The method of claim 11,
The input parameters are
a parameter indicating a direction in which the receiver looks at the satellite; and
signal to noise ratio (SNR)
A system for reducing the initial preparation time of high safety RTK-GNSS, characterized in that it includes one or more of. 11. The method of claim 10,
A sensor unit for calculating the inclination of the antenna installed in the UAV
High safety RTK-GNSS initial preparation time reduction system, characterized in that it further comprises. As a computer program to shorten the initial preparation time of high-safety RTK-GNSS,
It is stored in a non-transitory storage medium, and by the processor,
(a) obtaining input parameters of an artificial neural network model (hereinafter referred to as an 'artificial neural network model') for code multipath error estimation;
(b) inputting the input parameters into the artificial neural network model;
(c) outputting the code multipath error of the current measurement value in the artificial neural network model in real time; and
(d) correcting the code measurement value using the code multipath error calculated in step (c).
contains a command that causes it to be executed,
In step (d), the corrected code measurement is
Calculated by correcting the value obtained by subtracting the code multipath error of step (d) from the previously measured code measurement value,
A computer program to shorten the initial preparation time of high-safety RTK-GNSS.

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