GNSS (Global Navigation Satellite System) high-precision data processing method taking into account shade shielding of forests and trees

Abstract

The invention discloses a GNSS (Global Navigation Satellite System) high-precision data processing method capable of considering the shade shielding of forests and trees, and relates to the technical field of GNSS data processing, and the method specifically comprises the steps: 1, dividing time periods, and determining satellite signal data observed by a GNSS receiver in each time period; 2, judging whether the GNSS observation area in different time periods belongs to a tree shade shielding area or not; step 3, preliminarily processing GNSS data, and screening and rejecting sampling points in a tree shade shielding area; 4, complementing the data at the vacant sampling points based on the conventional data, and then performing high-precision data processing; based on the method, the requirement of high-precision measurement can be met even in complex environments such as flourishing forests and tree shades, so that the GNSS high-precision positioning technology can be applied in more complex environments, the positioning precision and the data reliability are improved, and the application range of the GNSS technology is expanded.

Description

Technical Field

[0001] The present invention belongs to the technical field of GNSS data processing, and in particular relates to a GNSS high-precision data processing method that takes into account the shading of dense forest shade. Background Art

[0002] The Global Navigation Satellite System (GNSS) covers a variety of global and regional satellite navigation systems, such as the United States' Global Positioning System (GPS), China's Beidou Navigation Satellite System (BDS), Russia's Global Navigation Satellite System (GLONASS), Europe's Galileo Navigation Satellite System (Galileo), and Japan's Quasi-Zenith Satellite System (QZSS). Together, they have woven a positioning, navigation and timing service network covering the world or a specific region, and are widely used in all walks of life.

[0003] GNSS often faces the test of complex environments in practical applications, especially those covered by dense forests and shade. In these areas, dense trees not only block some satellite signals, but also introduce multipath effects through reflection and refraction, resulting in signal interference, interruption and a reduction in the number of visible satellites, which in turn affects the geometric distribution of satellite constellations, introduces a large number of gross errors into GNSS positioning, and seriously interferes with the accuracy and reliability of parameter estimation.

[0004] As users' expectations for the real-time, accuracy and reliability of GNSS positioning services grow, especially in complex scenarios such as multi-frequency and multi-system fusion applications and shade from dense forests, how to efficiently and accurately handle the impact of the environment on the accuracy of observation data has become a key technical problem that needs to be solved urgently. Summary of the invention

[0005] The purpose of the present invention is to provide a GNSS high-precision data processing method that takes into account the shade of dense forests, so as to solve the technical problem that it is difficult to realize GNSS high-precision data processing under the condition of taking into account the shade of dense forests in the prior art.

[0006] In order to achieve the above object, the present invention adopts the following technical solutions:

[0007] Step 1: Divide the time period and determine the satellite signal data observed by the GNSS receiver in each time period; divide a day into different time periods of equal length, determine the length of each time period as t, classify the time periods into morning, noon, and dusk according to the change of tree shade over time, and set the GNSS to obtain observation data in a dense forest environment, record the satellite signal data in each time period, and record it as dense forest data. The satellite signal data includes: the number of satellites, satellite signal strength and observed satellite numbers. The satellite number set of open data in n time periods includes the satellite number set of dense forest data.

[0008] Step 2: Determine whether the GNSS observation area at different time periods belongs to the tree shade area; obtain the GNSS forest data of n time periods in the real forest environment, simulate the real forest environment to establish a simulation environment, use the simulation experiment to set the tree shade area to have no effect on the accuracy of GNSS observation data, simulate each time period, obtain open data, determine the true values ​​of the forest data and open data in a certain time period, and obtain the observation factors of the forest data and open data in the time period respectively. The calculation formula of the observation factor G is as follows:

[0009]

[0010] Among them, K m Indicates the size of the time period type factor, m indicates the type of time period, which includes morning, noon and dusk, i indicates that the number of satellites observed is i, N indicates that a total of N satellites are observed in this time period, S i Indicates the signal strength of the satellite numbered i within a period of time t.

[0011] Based on the calculation formula of the observation factor, the observation factor of the dense forest data in time period a is obtained, recorded as the dense forest factor, and the observation factor of the open data in time period a is recorded as the open factor. The ratio g of the dense forest factor and the open factor is calculated, and the shade shielding threshold L is set. When g is less than or equal to L, the GNSS observation area in time period a is judged to belong to the shade shielding area. Otherwise, the GNSS observation area in time period a is judged to belong to the open area. L is set according to actual needs and specific conditions, and all GNSS data belonging to the shade shielding area in different time periods are collected.

[0012] Step 3: Preliminary processing of GNSS data, screening and eliminating sampling points in the area blocked by tree shade; filtering the received GNSS data, using digital filtering technology to remove noise interference, such as Kalman filtering, wavelet transform or median filtering, etc., through digital filtering technology to suppress random noise, retain useful information in the signal, and improve the signal-to-noise ratio of the data. After filtering, the smoothness and stability of GNSS data are significantly improved. Real-time verification of GNSS data is performed to verify the integrity of the data and eliminate erroneous data, including: checking the integrity of the data packet, the continuity of the timestamp and the standardization of the data format, using redundant observations and internal consistency check methods to identify and eliminate potential erroneous data or abnormal data, and correcting possible data deviation problems, such as clock error, atmospheric environment and ionospheric delay, using the satellite clock error information provided by the precise ephemeris, through The influence of clock error is eliminated by differential technology. The atmospheric environment deviation is eliminated by building an atmospheric delay model and estimating it using meteorological parameters and observation data. The deviation of ionospheric delay is further weakened by linear combination technology using multi-frequency observation data. The changing trend of GNSS data in each time period is obtained through simulation experiments. The sampling frequency is determined based on the changing trend of GNSS data in each time period. The signal quality of GNSS data in different time periods in the shaded area is evaluated according to the sampling points. The signal-to-noise ratio and cycle slip indicators of the signal are evaluated, and the signal-to-noise ratio threshold and cycle slip threshold are set. When the signal-to-noise ratio or cycle slip of the signal is greater than or equal to the corresponding threshold, the data of the sampling point is eliminated to obtain a vacant sampling point, and the data that is not eliminated is recorded as regular data, where the signal-to-noise ratio threshold is obtained by integrating the historical GNSS Maolin data, and the cycle slip threshold is obtained by screening the outliers or mutation points in the data sequence in the GNSS data.

[0013] Step 4: Complete the data at the missing sampling points based on conventional data, and then perform high-precision data processing; collect the data of the first N sampling points of each missing sampling point to form a prediction data set, complete each missing sampling point in turn according to the time series, extract the data feature values ​​of the sampling points, such as pseudorange observation values ​​and carrier phase observation values, and use the trend extrapolation method to predict the data feature values ​​of the missing sampling points. By establishing a polynomial model, the data of the N+1th sampling point is predicted from the data of the first N sampling points. The polynomial model is specifically shown as follows:

[0014]

[0015] Where M(N+1) represents the characteristic data value of the N+1th sampling point, {a, a′, …, a (x)} represents the model coefficient, x represents the highest order of the polynomial model, and the range of the model coefficient is determined by comprehensively considering the data change trends of the N sampling points before the vacant sampling point. By predicting the data of the non-vacant sampling points, the true value is compared with the predicted value to determine the prediction accuracy of the polynomial model at different orders, thereby determining the highest order of the polynomial model.

[0016] The data in the missing sampling points are completed by a polynomial model, and the complete GNSS data is obtained after all the missing sampling points are completed. For the complete GNSS data, time correlation analysis is used to assist in the gross error detection of the data, and the gross error is processed by an interpolation method. The interpolation method includes: a classical spatiotemporal interpolation algorithm, an interpolation method based on a neural network, a baseline solution is performed on the GNSS data, and the obtained baseline solution result is adjusted. The accuracy of the baseline solution is adjusted by error analysis, the data processing process is monitored in real time, and the quality of the GNSS data after each data processing step is evaluated, for example: checking the baseline vector correction number, the constrained adjustment baseline vector correction number and the same-name unconstrained baseline vector correction number indicators, and the correctness of the data processing steps is monitored in real time.

[0017] Furthermore, precise ephemeris is used to reduce the impact of GNSS satellite orbit errors on the accuracy of baseline solutions. Multi-band observation data is used for integer solutions to improve the redundancy of observation data, separate and eliminate errors such as ionospheric delay, and fix the ambiguity parameters to integers through integer solutions to improve the accuracy of baseline solutions. Multi-band observation data is used to constrain the resolution of ambiguities and improve the stability of integer solutions. In the GNSS network adjustment process, an iterative algorithm is used to gradually approach the optimal solution to eliminate the probability that the optimal solution cannot be obtained when solving the adjustment equation. The iterative algorithm continuously adjusts the parameter estimates and recalculates the residual and covariance matrices until certain convergence conditions are met.

[0018] In summary, due to the adoption of the above technical solution, the beneficial effects of the present invention are:

[0019] 1. The present invention can accurately identify which areas are greatly affected by tree shade and which areas are less affected by tree shade by detailed time period division. For the time periods less affected by tree shade, these high-quality observation data can be fully utilized to improve the accuracy and reliability of overall data processing. By identifying and eliminating the time period data greatly affected by tree shade, the workload of data processing is reduced and the processing efficiency is improved.

[0020] 2. The present invention adjusts the sampling frequency to ensure that sufficient data can be obtained in the blocked area. Based on the prediction and analysis of high-quality non-blocked data, the data segments that are eliminated due to tree shade are filled, thereby maintaining the continuity and integrity of the data and reducing the impact of data mutations or outliers on the accuracy of GNSS data. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0022] Figure 1 A flow chart of a method for processing GNSS high-precision data taking into account shade from dense forests is shown;

[0023] Figure 2 A step diagram of a data processing method for improving the accuracy of complete data is shown. DETAILED DESCRIPTION

[0024] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0025] like Figure 1 , Figure 2 As shown, this embodiment proposes a GNSS high-precision data processing method that takes into account the shade of dense forests, and the method specifically includes:

[0026] Step 1: Divide the time period and determine the satellite signal data observed by the GNSS receiver in each time period; divide a day into different time periods of equal length, determine the length of each time period as t, classify the time periods into morning, noon, and dusk according to the change of tree shade over time, and set the GNSS to obtain observation data in a dense forest environment, record the satellite signal data in each time period, and record it as dense forest data. The satellite signal data includes: the number of satellites, satellite signal strength and observed satellite numbers. The satellite number set of open data in n time periods includes the satellite number set of dense forest data.

[0027] Step 2: Determine whether the GNSS observation area at different time periods belongs to the tree shade area; obtain the GNSS forest data of n time periods in the real forest environment, simulate the real forest environment to establish a simulation environment, use the simulation experiment to set the tree shade area to have no effect on the accuracy of GNSS observation data, simulate each time period, obtain open data, determine the true values ​​of the forest data and open data in a certain time period, and obtain the observation factors of the forest data and open data in the time period respectively. The calculation formula of the observation factor G is as follows:

[0028]

[0029] Among them, K m represents the size of the time period type factor, m represents the type of the time period, and the time period types include: morning, noon and dusk. In this embodiment, the time period type factors are: m =1.1, time period belongs to noon K m =0.8, the time period belongs to dusk K m =1, i means the number of the observed satellite is i, N means a total of N satellites were observed in this time period, S i Indicates the signal strength of the satellite numbered i within a period of time t.

[0030] Based on the calculation formula of the observation factor, the observation factor of the dense forest data in time period a is obtained, recorded as the dense forest factor, and the observation factor of the open data in time period a is recorded as the open factor. The ratio g of the dense forest factor and the open factor is calculated, and the shade shielding threshold L is set. When g is less than or equal to L, the GNSS observation area in time period a is judged to belong to the shade shielding area. Otherwise, the GNSS observation area in time period a is judged to belong to the open area. L is set according to actual needs and specific conditions, and all GNSS data belonging to the shade shielding area in different time periods are collected.

[0031] Step 3: Preliminary processing of GNSS data, screening and eliminating sampling points in the area blocked by tree shade; filtering the received GNSS data, using digital filtering technology to remove noise interference, such as Kalman filtering, wavelet transform or median filtering, etc., through digital filtering technology to suppress random noise, retain useful information in the signal, and improve the signal-to-noise ratio of the data. After filtering, the smoothness and stability of GNSS data are significantly improved. Real-time verification of GNSS data is performed to verify the integrity of the data and eliminate erroneous data, including: checking the integrity of the data packet, the continuity of the timestamp and the standardization of the data format, using redundant observations and internal consistency checks, etc., to identify and eliminate potential erroneous data or outliers, and to correct possible data deviations, such as clock error, atmospheric environment and ionospheric delay. Clock error correction uses the satellite clock error information provided by the precise ephemeris. The influence of clock error is eliminated by differential technology, and the atmospheric environment error is eliminated by constructing an atmospheric delay model and estimating the error using meteorological parameters and observation data. The influence of ionospheric delay is further weakened by using multi-frequency observation data and linear combination technology. The changing trend of GNSS data in each time period is obtained through simulation experiments. The sampling frequency is determined based on the changing trend of GNSS data in each time period. The signal quality of GNSS data in the tree shade area in different time periods is evaluated according to the sampling points. The signal-to-noise ratio and cycle slip indicators of the signal are evaluated, and the signal-to-noise ratio threshold and cycle slip threshold are set. When the signal-to-noise ratio or cycle slip of the signal is greater than or equal to the corresponding threshold, the data of the sampling point is eliminated to obtain a vacant sampling point, and the data that is not eliminated is recorded as regular data, where the signal-to-noise ratio threshold is obtained by integrating historical GNSS Maolin data, and the cycle slip threshold is obtained by screening outliers or mutation points in the data sequence in the GNSS data.

[0032] Step 4: Complete the data at the missing sampling points based on conventional data, and then perform high-precision data processing; collect the data of the first N sampling points of each missing sampling point to form a prediction data set, complete each missing sampling point in turn according to the time series, extract the data features of the sampling points, such as pseudorange observation values, carrier phase observation values, and use the trend extrapolation method to predict the data feature values ​​of the missing sampling points. By establishing a polynomial model, the data of the N+1th sampling point is predicted from the data of the first N sampling points. The polynomial model is specifically shown as follows:

[0033]

[0034] Where M(N+1) represents the characteristic data value of the N+1th sampling point, {a, a′, …, a (x)} represents the model coefficient, x represents the highest order of the polynomial model, and the range of the model coefficient is determined by comprehensively considering the data change trends of the N sampling points before the vacant sampling point. By predicting the data of the non-vacant sampling points, the true value is compared with the predicted value to determine the prediction accuracy of the polynomial model at different orders, thereby determining the highest order of the polynomial model.

[0035] The data in the missing sampling points are completed by a polynomial model, and the complete GNSS data is obtained after all the missing sampling points are completed. For the complete GNSS data, time correlation analysis is used to assist in the gross error detection of the data, and the gross error is processed by an interpolation method. The interpolation method includes: a classical spatiotemporal interpolation algorithm, an interpolation method based on a neural network, a baseline solution is performed on the GNSS data, and the obtained baseline solution result is adjusted. The accuracy of the baseline solution is adjusted by error analysis, the data processing process is monitored in real time, and the quality of the GNSS data after each data processing step is evaluated, for example: checking the baseline vector correction number, the constrained adjustment baseline vector correction number and the same-name unconstrained baseline vector correction number indicators, and the correctness of the data processing steps is monitored in real time.

[0036] Furthermore, precise ephemeris is used to reduce the impact of GNSS satellite orbit errors on the accuracy of baseline solutions. Multi-band observation data is used for integer solutions to improve the redundancy of observation data, separate and eliminate errors such as ionospheric delay, and fix the ambiguity parameters to integers through integer solutions to improve the accuracy of baseline solutions. Multi-band observation data is used to constrain the resolution of ambiguities and improve the stability of integer solutions. In the GNSS network adjustment process, an iterative algorithm is used to gradually approach the optimal solution to eliminate the probability that the optimal solution cannot be obtained when solving the adjustment equation. The iterative algorithm continuously adjusts the parameter estimates and recalculates the residual and covariance matrices until certain convergence conditions are met.

[0037] The above description is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can make equivalent replacements or changes according to the technical scheme and inventive concept of the present invention within the technical scope disclosed by the present invention, which should be covered by the protection scope of the present invention.

[0038] The preferred embodiments of the present invention disclosed above are only used to help explain the present invention. The preferred embodiments do not describe all the details in detail, nor do they limit the invention to only specific implementation methods. Obviously, many modifications and changes can be made according to the content of this specification. This specification selects and specifically describes these embodiments in order to better explain the principles and practical applications of the present invention, so that those skilled in the art can understand and use the present invention well. The present invention is limited only by the claims and their full scope and equivalents.

Claims

1. A GNSS high-precision data processing method taking into account the shade of dense forests, characterized in that: include: Step 1: Divide the time periods and determine the satellite signal data observed by the GNSS receiver in each time period; Step 2: Determine whether the GNSS observation area at different time periods belongs to the tree shade area; Collect GNSS data from n time periods in a real dense forest environment. Through simulation experiments, obtain open data where tree shade has no effect on GNSS observations in the same environment. Comprehensively analyze the time periods when GNSS is affected by tree shade in a real dense forest environment. Step 3: Preliminary processing of GNSS data to screen out sampling points within the tree shade area; Through preliminary processing, the influence of noise, abnormal data and data deviation is eliminated. After preliminary processing, the signal quality of each GNSS sampling point is evaluated to screen out the data at the sampling points that are greatly affected by tree shade. Step 4: fill in the data at the missing sampling points based on the conventional data, and then perform high-precision data processing; The data of the missing sampling points are predicted through the trend extrapolation method, and the data parts that are eliminated at the missing sampling points are completed to obtain complete data, and high-precision data processing is performed on the complete data.

2. According to claim 1, a GNSS high-precision data processing method taking into account the shade of dense forests is characterized in that: The specific method of step one is: A day is divided into different time periods of the same length, and the length is determined as t. According to the changes in tree shade over time, the time periods are classified into morning, noon, and dusk. At the same time, GNSS is set to obtain observation data in a dense forest environment, and the satellite signal data in each time period is recorded, which is recorded as dense forest data. The satellite signal data includes: the number of satellites, satellite signal strength, and the observed satellite number.

3. According to claim 1, a GNSS high-precision data processing method taking into account the shade of dense forests is characterized in that: Comprehensive analysis is performed to obtain the time period when GNSS is affected by tree shade in a real dense forest environment. The specific method is as follows: Obtain GNSS Maolin data for n time periods in a real Maolin environment, restore the real Maolin environment to establish a simulation environment, determine the true values ​​of Maolin data and open data in a certain time period, and use the formula The observation factors of Maolin data and open data are obtained, where K m Indicates the size of the type factor for time period m. Time period types include: morning, noon and dusk. Each time period type corresponds to a type factor size. i indicates that the number of the observed satellite is i, and N indicates that a total of N satellites are observed in this time period. S i It represents the signal strength of the satellite with satellite number i in time t. The time period belonging to the tree shade area is determined based on the calculation formula of the observation factor, and the time period when the GNSS is affected by the tree shade in the real dense forest environment is obtained.

4. According to claim 3, a GNSS high-precision data processing method taking into account the shade of dense forests is characterized in that: The calculation formula based on the observation factor is used to determine the time period belonging to the tree shade area. The specific method is as follows: For time period a, based on the calculation formula of observation factor, the observation factor of dense forest data in time period a is obtained, recorded as dense forest factor, and the observation factor of open data in time period a is recorded as open factor. The ratio g of dense forest factor and open factor is calculated, and the shade shielding threshold L is set. When g is less than or equal to L, it is judged that the GNSS observation area in time period a belongs to the shade shielding area. Otherwise, it is judged that the GNSS observation area in time period a belongs to the open area. L is set according to actual needs and specific circumstances, and all GNSS data belonging to the shade shielding area in different time periods are collected.

5. The method for processing GNSS high-precision data taking into account shade from dense forests according to claim 1, characterized in that: Eliminate the impact of noise, abnormal data and data deviation by: Eliminate the influence of noise; filter the received GNSS data, using digital filtering techniques including Kalman filtering, wavelet transform or median filtering to suppress random noise and remove noise interference; Eliminate the impact of abnormal data; Real-time verification of GNSS data includes: checking the integrity of data packets, the continuity of timestamps, and the standardization of data formats, using redundant observations and internal consistency check methods to identify and eliminate potential erroneous or abnormal data; Eliminate the impact of data bias; correct possible data bias problems, including: using the satellite clock error information provided by the precise ephemeris to eliminate the clock error through differential technology; building an atmospheric delay model and eliminating the atmospheric environment bias through meteorological parameters and observation data estimation; using multi-frequency observation data to further weaken the ionospheric delay bias through linear combination technology.

6. The method for processing GNSS high-precision data taking into account shade from dense forests according to claim 1, characterized in that: Screen out the data at sampling points that are greatly affected by tree shade. The specific method is as follows: Through simulation experiments, the changing trend of GNSS data in each time period is obtained. Based on the changing trend of GNSS data in each time period, the sampling frequency is determined, and the signal quality of GNSS data belonging to the tree shade area in different time periods is evaluated according to the sampling points. The signal-to-noise ratio and cycle slip indicators of the signal are evaluated, and the signal-to-noise ratio threshold and cycle slip threshold are set. When the signal-to-noise ratio or cycle slip of the signal is greater than or equal to the corresponding threshold, the data of the sampling point is eliminated to obtain the vacant sampling point, and the data that is not eliminated is recorded as regular data, where the signal-to-noise ratio threshold is obtained by integrating the historical GNSS Maolin data, and the cycle slip threshold is obtained by screening the outliers or mutation points in the data sequence in the GNSS data.

7. The method for processing GNSS high-precision data taking into account shade from dense forests according to claim 1, characterized in that: The data of the missing sampling points are predicted by trend extrapolation method to complete the data that are removed at the missing sampling points. The specific method is as follows: Collect the data of the first N sampling points of each vacant sampling point to form a prediction data set. Complete each vacant sampling point in turn according to the time series, extract the data characteristic values ​​of the sampling points, such as pseudorange observation values ​​and carrier phase observation values, and use the trend extrapolation method to predict the data characteristic values ​​of the vacant sampling points. By establishing a polynomial model, the data of the N+1th sampling point is predicted from the data of the first N sampling points, and the formula is used. represents a polynomial model, where M(N+1) represents the characteristic data value of the N+1th sampling point, {a, a′, …, a (x) } represents the model coefficient, x represents the highest order of the polynomial model, and the range of the model coefficient is determined by comprehensively considering the data change trends of the N sampling points before the missing sampling point. By predicting the data of the non-vacant sampling points, the true value is compared with the predicted value to determine the prediction accuracy of the polynomial model at different orders, thereby determining the highest order of the polynomial model, and using the polynomial model to complete the data in the missing sampling points.

8. The method for processing GNSS high-precision data taking into account shade from dense forests according to claim 1, characterized in that: Perform high-precision data processing on the complete data. The specific method is as follows: For complete GNSS data, time correlation analysis is first used to assist in the detection of gross errors in the data, and the interpolation method is used to handle the gross errors. Then, the GNSS data is baseline solved, and the baseline solution results are adjusted. The accuracy of the baseline solution is adjusted through error analysis. The data processing process is monitored in real time, and the quality of GNSS data after each data processing step is evaluated. Precise ephemeris is used to eliminate the impact of the orbit error of the GNSS satellite on the accuracy of the baseline solution. Multi-band observation data is used for integer solution. Through integer solution, the ambiguity parameters are fixed to integers. Multi-band observation data is used to constrain the resolution of ambiguities. In the process of GNSS network adjustment, an iterative algorithm is used to gradually approach the optimal solution. The iterative algorithm continuously adjusts the parameter estimates and recalculates the residual and covariance matrices until the optimal solution is obtained.

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