A method for processing observation data

Abstract

The present invention discloses an observation data processing method, which belongs to the field of digital information transmission technology, and particularly relates to a method for calculating the availability of GNSS observation data. Specifically, the method considers factors affecting the quality of actual observation data, such as missing frequency point type, signal-to-noise ratio mutation, multipath anomaly, cycle slip, etc., instead of just counting the available ratio of observation data from the overall number of satellite frequency points, thereby overcoming the technical defects of single GNSS observation data quality evaluation function, inaccurate and incomplete analysis.

Description

Technical Field

[0001] The invention discloses an observation data processing method, belongs to the technical field of digital information transmission, and particularly relates to an availability calculation method for GNSS observation data. Background Art

[0002] The present invention belongs to the field of GNSS (Global Navigation Satellite System) positioning, and specifically relates to a method for calculating the availability of GNSS observation data. The quality of GNSS observation data directly affects the accuracy and reliability of positioning, so it is very important to accurately evaluate GNSS observation data. In the prior art, the availability of GNSS observation data is mainly evaluated by calculating the ratio of the actual number of observation epochs of all satellites during the observation period to the theoretical number of epochs. Since this method only counts the available ratio of observation data from the satellite epoch dimension, it does not consider the frequency availability problem, resulting in an overly optimistic estimate of data quality. For example, although some epochs have data, they lack core frequency types, or some frequencies have problems such as signal-to-noise ratio mutations, multipath anomalies, and cycle jumps, which may greatly weaken the quality of observation data and lead to a significant decrease in positioning performance. Therefore, the current method of evaluating the quality of observation data from the epoch dimension alone has the technical defects of a single evaluation function and inaccurate and incomplete analysis.

[0003] The Chinese patent application, application number CN107544077 B, published on November 29, 2019, discloses a GNSS data quality detection and analysis system and its analysis method, wherein the system includes a differential data parsing and format conversion module connected to a network RTD / RTK differential service platform, an original satellite observation data format conversion module connected to a user terminal, and a graphical display and warning function module, etc. Although it can directly display the relationship between GNSS metadata and positioning results, discover positioning problems, influencing factors and causes, and issue warnings, as for the quality of GNSS observation data, it mainly evaluates the available ratio of GNSS observation data by calculating the ratio of the actual number of observation epochs of all satellites during the observation period to the theoretical number of epochs, and does not fully consider the potential factors that seriously affect the quality of actual observation data, such as the missing core first frequency type or the signal-to-noise ratio mutation, multipath anomaly, cycle slip, etc., of certain frequency points, and has technical defects such as single quality evaluation function, inaccurate and incomplete analysis, which leads to the problem of overestimation of data quality and positioning performance. Summary of the invention

[0004] 1. Technical issues to be solved

[0005] In view of the existing GNSS observation data quality evaluation method in the prior art, which mainly calculates the available ratio of GNSS observation data by calculating the ratio of the actual number of observation values ​​of all satellite frequency points in the observation period to the theoretical number, and does not fully consider the factors affecting the quality of actual observation data, such as missing frequency point type, signal-to-noise ratio mutation, multipath anomaly, cycle slip, etc., resulting in the problems of single quality evaluation function, inaccurate and incomplete analysis, the present invention provides an observation data processing method, which can evaluate the quality of GNSS observation data more comprehensively, objectively and accurately.

[0006] 2. Technical solution

[0007] The purpose of the present invention is achieved through the following technical solutions. The specific observation data processing method comprises the following steps:

[0008] An observation data processing method comprises the following steps: detecting and counting the a priori effective frequency points of all epochs in the observed physical data according to the Rinex format observation value file received by the GNSS base station within a set time period; obtaining the abnormal frequency point number after calculating the physical data in the observation value file; performing a difference operation on the a priori effective frequency point number and the calculated abnormal frequency point number to obtain the a posteriori effective frequency point number; calculating the quotient of the a posteriori effective frequency point number and the theoretical frequency point number, and obtaining the comprehensive availability rate of the base station observation data by the obtained ratio.

[0009] Furthermore, the statistics of the a priori effective frequency point number refers to recording the effective frequency point epoch, counting the effective frequency point number of the epoch, and obtaining the a priori effective frequency point number of all epochs in the observation data.

[0010] Furthermore, the method for determining a satellite with a priori valid frequency points is to detect whether each satellite in each epoch contains a first frequency point: if the first frequency point is not included, the a priori valid frequency point number of this satellite is 0; if the first frequency point is included, the satellite has a valid frequency point at this moment.

[0011] Furthermore, the a priori valid frequency epoch is an observation epoch of all satellites including the first frequency; and the a priori valid frequency number is the total number of frequencies of all satellites in all valid frequency epochs.

[0012] Furthermore, the number of abnormal frequency points refers to the number obtained by performing a union operation on all epochs and then performing an intersection operation with the prior valid frequency point epochs.

[0013] Furthermore, all the above epochs are: signal-to-noise ratio abnormal epochs, multipath abnormal epochs, multipath abnormal epochs between two adjacent epochs, and cycle slip abnormal epochs.

[0014] It should be noted that the signal-to-noise ratio abnormal epoch refers to the corresponding epoch where the difference between the signal-to-noise ratios of two adjacent observation epochs and the previous epoch exceeds the set value;

[0015] The multipath anomaly epochs refer to the epochs at which the multipath anomaly epochs of each frequency point of the output satellite are greater than the corresponding epochs of the multipath error greater than the set value;

[0016] The said two adjacent epoch multipath abnormal epoch refers to the corresponding epoch whose difference with the previous epoch exceeds the set value when the difference of the two adjacent epoch multipaths is calculated in sequence;

[0017] The cycle slip anomaly epochs mentioned refer to the corresponding epochs in the cycle slip epochs of each frequency point of the satellite where a whole cycle jump occurs.

[0018] Furthermore, the theoretical frequency number is obtained by accumulating the theoretical frequency number of each satellite during the observation period.

[0019] Furthermore, the calculation method of the theoretical frequency number of each satellite during the observation period refers to multiplying the theoretical epoch number of each satellite by the frequency number.

[0020] Furthermore, the theoretical epoch number refers to the epoch number of all satellites above the cutoff altitude angle during the observation period.

[0021] Furthermore, the altitude angle refers to the altitude angle of each satellite at each epoch relative to the data receiving reference station calculated by using the reference station coordinates and the broadcast ephemeris.

[0022] 3. Beneficial effects

[0023] Compared with the prior art, the advantages of the present invention are:

[0024] The present invention uses the first frequency point of the satellite as a benchmark to count the number of effective frequency points, fully considering the dependence on the first frequency point of the satellite in actual navigation and positioning applications, and can improve the reliability of GNSS observation data quality evaluation; incorporates satellite signal-to-noise ratio mutations into the comprehensive quality evaluation of observation data, realizes quantitative evaluation of satellite signal quality, and provides a basis for signal interference and occlusion analysis; incorporates multipath mutations into the comprehensive quality evaluation of observation data, and further improves the reliability of multipath effect quantitative evaluation; and through collaborative analysis of satellite effective frequency points, signal-to-noise ratio mutations, multipath anomalies and cycle slips to count the comprehensive efficiency of reference station observation data, the reference station observation quality can be evaluated more comprehensively and accurately, and the reference station observation environment can be inferred without going to the station, saving inspection costs. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] Figure 1 The present invention provides a flowchart of the observation data processing method. DETAILED DESCRIPTION

[0026] The present invention is described in detail below in conjunction with the accompanying drawings and specific embodiments.

[0027] An observation data processing method comprises the following steps: detecting and counting the a priori effective frequency points of all epochs in the observed physical data according to the Rinex format observation value file received by the GNSS base station within a set time period; obtaining the abnormal frequency point number after calculating the physical data in the observation value file; performing a difference operation on the a priori effective frequency point number and the calculated abnormal frequency point number to obtain the a posteriori effective frequency point number; calculating the quotient of the a posteriori effective frequency point number and the theoretical frequency point number, and the obtained ratio is the comprehensive availability of the observation data of the base station, and the index is used to evaluate the overall observation data quality level of the base station.

[0028] Output the epochs of a priori valid frequency points and calculate the number of a priori valid frequency points. The a priori valid frequency point epoch is the observation epoch of all satellites containing the first frequency point; the number of a priori valid frequency points is the total number of frequencies of all satellites in all valid frequency point epochs. The data source is the Rinex observation value file of the base station for a period of time. First, check whether each epoch of each satellite in the file contains the first frequency point: if not, the number of a priori valid frequency points of the satellite is 0; if so, it means that the satellite has a priori valid frequency point, and the valid frequency point epoch t is recorded. 0 , count the effective frequency points of the satellite, then count the effective frequency points of the epoch, and finally, count the a priori effective frequency points of all epochs in the observation data, that is, Signum_pre=count(t0);

[0029] Output the epoch of the sudden change of the signal-to-noise ratio of each satellite frequency point. Calculate the difference of the signal-to-noise ratio of two adjacent observation epochs in turn, and record the corresponding epoch t1 whose difference with the previous epoch exceeds ±2dBHz; output the epoch of multipath anomaly at each satellite frequency point, and record the corresponding epoch t2 whose multipath error is greater than 0.5m. 2 At the same time, the multipath differences of two adjacent epochs are calculated in turn, and the corresponding epochs whose differences with the previous epoch exceed ±0.3m are recorded. 3 ; Output the satellite frequency cycle jump epoch, record the corresponding epoch t that produces the whole cycle jump 4 The theoretical frequency point calculation method is:

[0030] Calculate the elevation angle of each satellite at each epoch relative to the reference station using the reference station coordinates and broadcast ephemeris; calculate the epoch number of all satellites above the cutoff elevation angle during the observation period, i.e. the theoretical epoch number D j ; The frequency point number Frenum broadcast by each satellite is a known constant. Then the theoretical frequency point number of each satellite is obtained by multiplying the epoch number of each satellite by the frequency point number. The theoretical frequency point number of all satellites in the observation period is obtained by accumulating them. The formula is:

[0031]

[0032] Among them, D j : Theoretical epoch number; Frenum: The frequency number broadcast by the satellite.

[0033] The present invention adopts a method of outputting the epochs of a priori effective frequency points and calculating the number of a priori effective frequency points, and statistically obtains the number of a priori effective frequency points of all epochs in the observation data; outputs the epochs of signal-to-noise ratio mutations, multipath anomalies and cycle skips of each satellite frequency point. Calculate the number of abnormal frequency points, perform union calculation on all epochs, and then perform intersection calculation with the epochs of the a priori effective frequency points to obtain the number of abnormal frequency points, calculate the number of posterior effective frequency points, and calculate the quotient of the posterior effective frequency points and the calculated theoretical frequency point number to obtain the comprehensive availability of the reference station observation data. Because the actual navigation and positioning application fully considers the dependence of the satellite's first frequency point, the satellite signal-to-noise ratio mutation, the multipath mutation anomaly and other factors, the reliability and accuracy of the GNSS observation data quality evaluation are improved. At the same time, the comprehensive efficiency of the reference station observation data is statistically analyzed by collaboratively analyzing the satellite effective frequency points, signal-to-noise ratio mutations, multipath anomalies and cycle skips. The observation environment of the reference station can be inferred without going to the station, saving the inspection cost.

[0034] Example 1

[0035] like Figure 1 The present invention is a flowchart of the observation data processing method provided by the present invention.

[0036] like Figure 1 As shown in the figure, the physical data in the Rinex format observation value file received from the GNSS base station is processed as follows: output a priori valid frequency point epochs, satellite signal-to-noise ratio mutation epochs of each frequency point, output satellite multipath anomaly epochs of each frequency point, and output satellite cycle slip epochs of each frequency point. Since some user terminals only support receiving satellite single-frequency (first frequency point) data, in order to ensure the compatibility of terminal and base station data, the standard for judging the a priori valid frequency point epoch is whether each satellite in the epoch contains the first frequency point. This method is not commonly used in the field. Generally, the availability is judged only based on the number of frequencies. For example, as long as the second and third frequencies of the satellite are included, it is also judged to be available, which will result in the failure to take into account the data compatibility with single-frequency terminals. Based on the first frequency point judgment criterion, all a priori valid frequency point epochs in the output observation data are traversed, and the a priori valid frequency point number of all epochs is counted; the a priori valid frequency point epoch is the observation epoch of all satellites containing the first frequency point; the a priori valid frequency point number is the total number of frequencies of all satellites in all valid frequency point epochs. First check whether each satellite in each epoch contains the first frequency point: if not, the a priori valid frequency point number of the satellite is 0; if included, it means that the satellite has a priori valid frequency point, and the valid frequency point epoch t is recorded. 0 , count the effective frequency points of the satellite, then count the effective frequency points of the epoch, and finally, count the a priori effective frequency points of all epochs in the observation data, that is, Signum_pre=count(t 0 ).

[0037] Due to factors such as signal interference and obstruction, satellite observation data will produce problems such as signal-to-noise ratio mutation, multipath anomaly and whole-cycle jump, which will have an adverse impact on data quality. Although traditional methods have analyzed multipath and cycle jump, they did not eliminate abnormal periods when counting data availability. The present invention eliminates data during periods of signal-to-noise ratio mutation, multipath anomaly and cycle change, so as to comprehensively and quantitatively evaluate the level of data availability. The specific implementation method is to analyze and output the epochs of signal-to-noise ratio mutation, multipath anomaly and cycle jump at each frequency point of the satellite, and superimpose and analyze all abnormal frequency point epochs with the prior valid frequency point epochs to obtain all abnormal frequency point epochs, count the number of abnormal frequency points, and output the epochs of signal-to-noise ratio mutation at each frequency point of the satellite. The signal-to-noise ratio of two adjacent observation epochs is subtracted in turn, and the corresponding epochs t whose difference with the previous epoch exceeds ±2dBHz are recorded. 1 .

[0038] Output the multipath anomaly epochs of each satellite frequency point, and record the corresponding epoch t2 with a multipath error greater than 0.5m; at the same time, calculate the difference of the multipaths of two adjacent epochs in turn, and record the corresponding epoch t3 with a difference of more than ±0.3m from the previous epoch;

[0039] Output the satellite frequency cycle jump epochs and record the corresponding epoch t4 that produces the full cycle jump.

[0040] Calculate the number of abnormal frequency points. Perform a union operation on all the above epochs, and then perform an intersection operation with the above a priori valid frequency epochs to calculate the number of abnormal frequency points, that is, Signum_abn=count((t 1 ∪t 2 ∪t 3 ∪t 4 )∩t 0 ).

[0041] Calculate the a posteriori effective frequency number. Subtract the abnormal frequency number from the a priori effective frequency number calculated above, and the difference is the a posteriori effective frequency number, that is, Signum_pos=Signum_pre-Signum_abn. The quotient of the a posteriori effective frequency number and the obtained theoretical frequency number is the comprehensive availability of the base station observation data. Divide the calculated a posteriori effective frequency number by the theoretical frequency number calculated based on the satellite broadcast ephemeris, the precise coordinates of the base station and the observation cut-off altitude angle, and the obtained ratio is the comprehensive availability of the base station observation data, that is, Avai=Signum_pos / Signum_the. Among them, the theoretical frequency number calculation method adopts the conventional means, and the specific method is: first, calculate the altitude angle of each satellite and each epoch relative to the base station through the base station coordinates and broadcast ephemeris; then, calculate the epoch number of all satellites above the cut-off altitude angle during the observation period, that is, the theoretical epoch number D j; Since the frequency point number Frenum broadcast by each satellite is known, the theoretical frequency point number of each satellite is obtained by multiplying the epoch number of each satellite by the frequency point number. Finally, the theoretical frequency point number of all satellites in the observation period is obtained by accumulating them, that is:

[0042] .

[0043] The present invention provides a GNSS base station observation data processing method, which utilizes the satellite abnormal frequency point data elimination and effective frequency point data statistical method and principle, solves the technical problems of frequency point missing type analysis and signal-to-noise ratio mutation, multipath anomaly, cycle slip and other abnormal data processing, and achieves the effect of comprehensively, objectively and accurately quantitatively evaluating the quality of GNSS observation data.

[0044] The above schematically introduces in detail a GNSS reference station observation data quality evaluation method provided by the present invention. In this article, specific examples are used to illustrate the principles and implementation methods of the present invention. The explanation is not restrictive. The description of the above embodiments is only used to help understand the method and core ideas of the present invention. Without departing from the spirit or basic principles of the present invention, the present invention can be implemented in other specific forms. Figure 1 What is shown in the figure is only one of the embodiments of the invention, and the actual embodiments are not limited thereto. Therefore, if a person skilled in the art is inspired by the invention and designs a technical solution and an embodiment similar to the technical solution without creativity without departing from the purpose of the invention, they shall all fall within the protection scope of this patent. In summary, the content of this specification shall not be understood as limiting the invention.

Claims

1. A method for processing observation data, comprising the following steps: According to the Rinex format observation value files received by the GNSS base station within the set time period, the a priori effective frequency points of all epochs in the observed physical data are detected and counted; the physical data in the observation value file is calculated to obtain the abnormal frequency points; the a priori effective frequency points and the calculated abnormal frequency points are calculated to obtain the a posteriori effective frequency points; the a posteriori effective frequency points and the theoretical frequency points are calculated to obtain the comprehensive availability of the base station observation data; The statistics of a priori effective frequency points refer to recording effective frequency point epochs, counting the effective frequency points of the epochs, and obtaining the a priori effective frequency points of all epochs in the observation data; The method for determining a satellite with a priori valid frequency point is to detect whether each satellite in each epoch contains the first frequency point: if the first frequency point is not included, the a priori valid frequency point number of this satellite is 0; if the first frequency point is included, the satellite has a valid frequency point at this moment; The a priori valid frequency epoch is the observation epoch of all satellites including the first frequency; the a priori valid frequency number is the total number of frequencies of all satellites in all valid frequency epochs; The abnormal frequency points mentioned above refer to the number obtained by performing a union operation on all epochs and then performing an intersection operation with the a priori valid frequency point epochs; All the epochs mentioned are: signal-to-noise ratio abnormal epochs, multipath abnormal epochs, multipath abnormal epochs of two adjacent epochs, and cycle slip abnormal epochs; The theoretical frequency number is obtained by accumulating the theoretical frequency number of each satellite during the observation period; The method for calculating the theoretical number of frequency points of each satellite during the observation period is to multiply the theoretical number of epochs of each satellite by the number of frequency points.

2. The observation data processing method according to claim 1, characterized in that: The theoretical epoch number refers to the epoch number of all satellites above the cut-off altitude angle during the observation period.

3. The observation data processing method according to claim 2, characterized in that: The altitude angle refers to the altitude angle of each satellite at each epoch relative to the data receiving reference station calculated by using the reference station coordinates and the broadcast ephemeris.

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