CN117724125A - Quality control method and device for observed data based on consistency - Google Patents
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Abstract
The embodiment of the invention provides a quality control method and device of observed data based on consistency, and relates to the technical field of satellite navigation. The method comprises the following steps: screening an observation data set of a GNSS global navigation satellite system satellite to obtain an observation data subset, and calculating a first parameter value based on the observation data subset; generating residual data by substituting the first parameter value back into the observation data set, and taking a satellite corresponding to the residual data meeting the preset requirement in the residual data as a satellite sample; sample data is obtained based on the satellite samples, and target parameters are calculated based on the satellite samples and the sample data. Because the data in the observation data set is screened, the observation data containing the gross errors is removed in the screening process, the accuracy of the data of the obtained observation data subset is higher, and the satellite corresponding to the data meeting the preset requirement is selected as the satellite sample, so that the gross error data can be further screened and removed, and the reliability of the data quality is improved.
Description
Technical Field
The embodiment of the invention relates to the technical field of satellite navigation technology, in particular to a quality control method and device of observed data based on consistency.
Background
With the rise and development of GNSS (Global Navigation Satellite System), the GNSS technology is widely used in many positioning-related fields. Along with the widening of the application field, the Real-Time positioning requirement of high precision and high reliability is continuously improved, and the higher requirement is put forward on the data quality control capability of RTK (Real-Time Kinematic) positioning.
However, in complex environments such as urban canyons and under trees, due to interference and influence of the environments, signals are reflected, diffracted and shielded, so that cycle slip and gross errors occur in GNSS observation data, and the accuracy of RTK positioning is affected. Especially, as the number of satellites increases, the probability of coarse differences, especially multiple coarse differences and continuous coarse differences, in the observed data in the complex environment increases, so that the reliability of the observed data calculation is poor.
As can be seen, the problem of poor data processing results in the problem of reduced reliability of the result of the solution, and the prior art does not have a better strategy for coping with how poor data is.
Disclosure of Invention
The embodiment of the invention provides a quality control method and device for observed data based on consistency, which at least solve the problem that the prior art has no better strategy for coping with how coarse and poor data.
According to an embodiment of the present invention, there is provided a quality control method of observed data based on consistency, including:
screening an observation data set of a GNSS global navigation satellite system satellite to obtain an observation data subset, and calculating a first parameter value based on the observation data subset;
step two, the first parameter value is replaced to the observation data set to generate residual data, and a satellite corresponding to the residual data meeting the preset requirement in the residual data is taken as a satellite sample;
and thirdly, acquiring sample data based on the satellite samples, and calculating target parameters based on the satellite samples and the sample data.
In an exemplary embodiment, after the satellite corresponding to the residual data meeting the preset requirement in the residual data is taken as a satellite sample, the method further includes:
sequentially executing the first step and the second step for N times to obtain a satellite sample set, wherein the satellite sample set comprises at least 2 satellite samples, N is more than or equal to 1, and N is an integer;
the acquiring sample data based on the satellite samples and calculating target parameters based on the satellite samples and the sample data includes:
acquiring sample data based on the satellite sample set, and calculating a target parameter based on the satellite sample set and the sample data;
and under the condition that the step one and the step two are sequentially executed for N times, the number of satellite samples in the satellite sample set meets the preset condition.
In an exemplary embodiment, the screening the set of observations of GNSS satellites to obtain the subset of observations includes at least one of:
according to the quality of satellite data, sequencing data in an observation data set of GNSS satellites, and determining an observation data subset in the observation data in a sequencing range;
and eliminating data in the observation data set of the GNSS satellite according to the characteristics of the satellite data, and determining an observation data subset in the eliminated observation data set.
In an exemplary embodiment, before the ordering of the data in the set of observations of GNSS satellites according to the quality of the satellite data, the method further comprises at least one of:
determining the quality of satellite data in an observation data set of the GNSS satellites based on the altitude and the signal-to-noise ratio of the satellites;
based on the historical prediction information of the satellites, a quality of satellite data in an observation set of the GNSS satellites is determined.
In an exemplary embodiment, the removing data in the GNSS satellite observation data set according to the features of the satellite data includes at least one of:
based on the space structure of the satellite, eliminating data in an observation data set of the GNSS satellite;
and removing data in the GNSS satellite observation data set based on the system characteristics of the satellite observation data.
In an exemplary embodiment, the taking, as a satellite sample, a satellite corresponding to residual data satisfying a preset requirement in the residual data includes:
and taking a satellite corresponding to residual data smaller than a preset threshold value in the residual data as a satellite sample, wherein the preset threshold value is related to the quality of the satellite data.
According to an embodiment of the present invention, there is provided a quality control apparatus for consistency-based observation data, including:
the screening module is used for executing the first step, wherein the first step comprises the steps of screening an observation data set of a GNSS global navigation satellite system satellite to obtain an observation data subset, and calculating a first parameter value based on the observation data subset;
the generation module is used for executing a second step, wherein the second step comprises the steps of substituting the first parameter value back into the observation data set to generate residual data, and taking satellites corresponding to residual data meeting preset requirements in the residual data as satellite samples;
and the calculating module is used for executing a third step, wherein the third step comprises the steps of acquiring sample data based on the satellite samples and calculating target parameters based on the satellite samples and the sample data.
In an exemplary embodiment, the apparatus further comprises:
the execution module is used for executing the step one and the step two for N times in sequence to obtain a satellite sample set, wherein the satellite sample set comprises at least 2 satellite samples, N is more than or equal to 1, and N is an integer;
the computing module is specifically configured to:
acquiring sample data based on the satellite sample set, and calculating a target parameter based on the satellite sample set and the sample data;
and under the condition that the step one and the step two are sequentially executed for N times, the number of satellite samples in the satellite sample set meets the preset condition.
According to yet another embodiment of the present invention, there is also provided a computer readable storage medium having a computer program stored therein, wherein the computer program is configured to perform the steps of any one of the embodiments of the above-described quality control method based on consistency observation data when run.
According to yet another embodiment of the present invention, there is also provided an electronic device comprising a memory having stored therein a computer program and a processor arranged to run the computer program to perform the steps of any of the embodiments of a method of quality control of consistency-based observational data described above.
According to the invention, the data in the observation data set is screened, the observation data containing the gross errors is removed in the screening process, and the obtained data of the observation data subset has higher precision, so that the reliability of the quality of the observation data is improved. And the satellite corresponding to the data meeting the preset requirements is selected as a satellite sample, and the data is acquired based on the satellite sample, so that the coarse data can be further screened and removed, and the data quality is improved. In addition, as the observation data containing the rough differences are screened and removed, a large number of subsets can be prevented from being traversed, and the calculation efficiency is improved.
Drawings
Fig. 1 is a hardware configuration diagram of a mobile terminal of a quality control method based on consistency observation data according to an embodiment of the present invention;
FIG. 2 is a flow chart of a method of quality control of observed data based on consistency, in accordance with an embodiment of the present invention;
FIG. 3 is a flow chart of a method of quality control of observed data based on consistency, in accordance with an embodiment of the present invention;
FIG. 4 is a block diagram of a quality control apparatus based on consistency observations in accordance with an embodiment of the present invention;
FIG. 5 is a block diagram of a quality control device based on consistency observations in accordance with an embodiment of the present invention;
FIG. 6 is a satellite motion trajectory diagram of an embodiment of the present invention;
FIG. 7 is a sequence diagram of coordinate errors for a fixed solution for different solution strategies;
FIG. 8 is a chart of station observation data time periods for an embodiment of the present invention;
FIG. 9 is a graph showing a comparison of the coordinate sequences of the fixed solutions of different methods;
fig. 10 is a horizontal error distribution diagram under different calculation strategies, wherein fig. (a) shows a calculation strategy corresponding to a conventional method, fig. (b) shows a calculation strategy corresponding to an robust kalman filter method, and fig. (c) shows a calculation strategy corresponding to the method of the present invention.
Detailed Description
Embodiments of the present invention will be described in detail below with reference to the accompanying drawings in conjunction with the embodiments.
It should be noted that the terms "first," "second," and the like in the description and the claims of the present invention and the above figures are used for distinguishing between similar objects and not necessarily for describing a particular sequential or chronological order.
The method embodiments provided in the embodiments of the present application may be performed in a mobile terminal, a computer terminal or similar computing device. Taking the mobile terminal as an example, fig. 1 is a block diagram of a hardware structure of a mobile terminal according to a quality control method of observed data based on consistency according to an embodiment of the present invention. As shown in fig. 1, a mobile terminal may include one or more (only one is shown in fig. 1) processors 102 (the processor 102 may include, but is not limited to, a microprocessor MCU or a processing device such as a programmable logic device FPGA) and a memory 104 for storing data, wherein the mobile terminal may also include a transmission device 106 for communication functions and an input-output device 108. It will be appreciated by those skilled in the art that the structure shown in fig. 1 is merely illustrative and not limiting of the structure of the mobile terminal described above. For example, the mobile terminal may also include more or fewer components than shown in fig. 1, or have a different configuration than shown in fig. 1.
The memory 104 may be used to store a computer program, for example, a software program of application software and a module, such as a computer program corresponding to a quality control method for consistency-based observation data in an embodiment of the present invention, and the processor 102 executes the computer program stored in the memory 104 to perform various functional applications and data processing, that is, implement the above-mentioned method. Memory 104 may include high-speed random access memory, and may also include non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some examples, the memory 104 may further include memory remotely located relative to the processor 102, which may be connected to the mobile terminal via a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.
The transmission device 106 is used to receive or transmit data via a network. Specific examples of the network described above may include a wireless network provided by a communication provider of the mobile terminal. In one example, the transmission device 106 includes a network adapter (Network Interface Controller, simply referred to as NIC) that can connect to other network devices through a base station to communicate with the internet. In one example, the transmission device 106 may be a Radio Frequency (RF) module, which is configured to communicate with the internet wirelessly.
The embodiment provides a quality control method of observed data based on consistency, wherein fig. 2 is a flowchart of the quality control method of observed data based on consistency according to the embodiment of the invention, and as shown in fig. 2, the flowchart includes the following steps:
step S201, screening an observation data set of a GNSS satellite to obtain an observation data subset, and calculating a first parameter value based on the observation data subset.
In this embodiment, a set of GNSS satellite observations may be acquired as an observation set and a subset of data determined from the observation set.
For example, when the subset of satellites that need to be observed is of the size ofWhen it is, +.>Subset combination, wherein->For the numerical value determined according to satellite observation environment and number, the value range is +.>，/>For the number of parameters to be estimated, +.>To observe the number of satellites.
Because the number of the subset combinations is large, the total calculation and the inspection can seriously affect the calculation efficiency, so that the observation data can be further screened to obtain the data with higher data quality and better reliability. The accuracy and reliability of the first parameter values calculated based on the subset of observations is high due to the high reliability of the subset of observations.
Optionally, the screening the set of observations of the GNSS satellites to obtain the subset of observations includes at least one of:
according to the quality of satellite data, sequencing data in an observation data set of GNSS satellites, and determining an observation data subset in the observation data in a sequencing range;
and eliminating data in the observation data set of the GNSS satellite according to the characteristics of the satellite data, and determining an observation data subset in the eliminated observation data set.
In this embodiment, the data in the set of observations of the GNSS satellites may be ordered according to the quality of the satellite data. For example, in order of good to bad data quality, or in order of bad to good. Therefore, the observation data with better quality can be obtained according to the sorting.
In addition, part of data in the satellite data can be removed according to the characteristics of the satellite data, for example, satellite combination with poor satellite space structure and incomplete satellite system is skipped directly, the follow-up calculation is not participated, the data with obvious unreasonable data or larger deviation can be removed, and the time can be saved.
Optionally, before ordering the data in the set of observation data of the GNSS satellites according to the quality of the satellite data, the method further comprises at least one of:
determining the quality of satellite data in an observation data set of the GNSS satellites based on the altitude and the signal-to-noise ratio of the satellites;
based on the historical prediction information of the satellites, a quality of satellite data in an observation set of the GNSS satellites is determined.
In this embodiment, the quality of the satellite data in the observation data set may be acquired first. In general, in a good environment, the higher the altitude angle, the greater the signal-to-noise ratio value. When the satellite altitude is higher and the signal-to-noise ratio is obviously lower, the satellite is more likely to be affected by interference, reflection, diffraction and the like, so that the signal-to-noise ratio is lower, and the possibility that the observed data contains coarse differences is higher.
Therefore, the index is used in screening satellitesSelecting satellites with good observation quality as far as possible, wherein SNR is a value of signal-to-noise ratio, +.>A high-angle-SNR model constructed based on data in good environments.
In addition, the quality of the satellite data can be predicted by the historical data of the satellite data. Predicting satellites with excessive deviation of current epoch observation data from measured data based on historical data, and potentially containing coarse differences, and utilizing indexes when constructing satellite subsetsData with too large difference are not selected as far as possible, wherein +.>For actual observation data +.>Is a predicted value based on historical information. Based on the above mode, the quality of the satellite data can be determined more accurately, and the reliability of the data is better.
Optionally, the removing the data in the observation data set of the GNSS satellites according to the features of the satellite data includes at least one of the following:
based on the space structure of the satellite, eliminating data in an observation data set of the GNSS satellite;
and removing data in the GNSS satellite observation data set based on the system characteristics of the satellite observation data.
In this embodiment, the data with poor reliability in the satellite observation data is removed, and the calculation efficiency is improved.
In particular, subsets may be screened and culled from the satellite space structure. The GNSS calculation result is influenced by the space structure of the satellite, and when the space structure of the satellite is poor, the obtained positioning result is not stable enough. Although the satellite space structure can be accurately analyzed by calculating the GDOP value, there is a problem in that the calculation amount is large.
Therefore, the correlation of the satellite pairs in the sample can be analyzed for screening, for example, if the satellite pairs with the excessively high spatial correlation are found in the sample, the current combination is directly removed, the calculated amount is reduced, and the calculation efficiency is improved.
In another embodiment, the culling subset may also be screened systematically or comprehensively based on the characteristics of the data. For example, the subsets are filtered and rejected according to parameters to be estimated. In single point positioning, if the subset does not contain all the system observation data required for calculation, the receiver clock correction of the corresponding satellite system cannot be calculated correctly, and the combination can be removed directly.
By the method, the satellite subset set which possibly does not contain the gross error can be constructed, so that the obtained data has higher accuracy and better reliability.
Step S202, the first parameter value is replaced to the observation data set to generate residual data, and a satellite corresponding to the residual data meeting the preset requirement in the residual data is taken as a satellite sample.
Wherein the first parameter value may be an approximate estimate of the parameter to be estimated based on the subset solution. And generating residual vectors by substituting the obtained first parameter values into all satellite data sets.
The preset requirement may be that the absolute value of the residual data is less than a certain threshold, or that the residual data is less than a threshold associated with the satellite.
Optionally, the taking, as the satellite sample, a satellite corresponding to residual data satisfying a preset requirement in the residual data includes:
and taking a satellite corresponding to residual data smaller than a preset threshold value in the residual data as a satellite sample, wherein the preset threshold value is related to the quality of the satellite data.
In this embodiment, for example, satellites having residual absolute values smaller than a certain threshold are used as satellites having no gross errors based on the current parameter estimation value, and the number of satellites having no gross errors is counted as an evaluation index of the parameter.
The GNSS mathematical model can be expressed as:
（1）
wherein,constant vector for carrier and pseudorange observations, +.>For the parameter vector to be estimated, +.>Residual error vector for observed value,/>Respectively corresponding coefficient matrixes of parameters, +.>For observations +.>And a corresponding weight matrix. Solution of parameters to be estimated from the subset +.>Error in Unit weight->Variance of each satellite->Post-test residual->：
(2)
Wherein,、/>and->The coefficient matrix, the weight matrix and the constant term corresponding to the screening subset are respectively +.>Is the number of parameters to be estimated. And obtaining all satellite post-verification residuals: />And posterior of all satellitesThe difference is:. Utilizing the residual absolute value of each satellite to follow the variance of each satellite in the post-test variancesAnd constructing an evaluation index. In order to avoid too few available observation data in a complex environment due to an excessively absolute statistical mode, the stability of a result is affected, and an IGGIII-like evaluation method is adopted:
(3)
wherein,for the observation type weighting factor, carrier is 1, pseudo-range is 0.01, ++>、/>For adjusting the coefficient, the recommended value ranges are 1.5-2,3.0-5.0,/L respectively>Is->Every variable in>Is the standard deviation.
Based on the analysis, different thresholds are set for satellite data with different qualities, so that the problems of unstable resolving results caused by too few satellites and too bad satellite space structures due to too strict judging methods in complex environments are solved.
Optionally, after the satellite corresponding to the residual data meeting the preset requirement in the residual data is taken as a satellite sample, the method further includes:
sequentially executing the first step and the second step for N times to obtain a satellite sample set, wherein the satellite sample set comprises at least 2 satellite samples, N is more than or equal to 1, and N is an integer;
the acquiring sample data based on the satellite samples and calculating target parameters based on the satellite samples and the sample data includes:
acquiring sample data based on the satellite sample set, and calculating a target parameter based on the satellite sample set and the sample data;
and under the condition that the step one and the step two are sequentially executed for N times, the number of satellite samples in the satellite sample set meets the preset condition.
In this embodiment, after the first and second steps are performed, the steps are repeatedly performed N times, that is, the subset is repeatedly constructed and calculated and evaluated until the requirement of a certain number of times N is satisfied.
For example, the current optimal model has a sample number ratio satisfying the threshold, i.e. the interior point ratio isWhen the iteration times k times find the optimal sample, the probability is: />. Thus, a confidence probability is obtained that is set in advance>The calculation formula of the minimum iteration number N is as follows: />。
As shown in fig. 3, fig. 3 is a flowchart of a quality control method based on consistency observation data according to an embodiment of the present invention, including the steps of: preprocessing data to generate a sample, resolving the sample, and returning to generate the sample again when resolving fails; and when the solution is successful, further checking whether the sample can be optimized, if so, updating the minimum iteration number, and when the minimum iteration number is reached, fixing the ambiguity, thereby entering the next epoch.
Step S203, acquiring sample data based on the satellite samples, and calculating target parameters based on the satellite samples and the sample data.
In this step, the reliability of the sample data acquired based on the satellite sample is high, and the target parameter to be acquired may be further calculated based on the satellite sample and the sample data, wherein the target parameter may be any parameter to be calculated based on the satellite. Thus, the acquired target parameters have higher accuracy and good reliability.
Because the reliability of the current sample is good, the current optimal sample can be further stored. And the acquired optimal sample and the screened satellite meeting the threshold are utilized for calculation together, so that the data of the sample can be increased, and the calculation accuracy is improved.
According to the embodiment of the invention, the observation data set of the GNSS satellite is screened to obtain the observation data subset, and the first parameter value is calculated based on the observation data subset; generating residual data by substituting the first parameter value back into the observation data set, and taking a satellite corresponding to residual data meeting preset requirements in the residual data as a satellite sample; sample data is obtained based on the satellite samples, and target parameters are calculated based on the satellite samples and the sample data.
The data in the observation data set is screened, the observation data containing rough differences are removed in the screening process, and the obtained data of the observation data subset has higher precision, so that the reliability of the quality of the observation data is improved. And the satellite corresponding to the data meeting the preset requirements is selected as a satellite sample, and the data is acquired based on the satellite sample, so that the coarse data can be further screened and removed, and the data quality is improved. In addition, as the observation data containing the rough differences are screened and removed, a large number of subsets can be prevented from being traversed, and the calculation efficiency is improved.
According to the embodiment of the invention, the RANSAC algorithm is introduced into RTK data calculation, so that the influence of continuity and multiple coarse differences can be effectively resisted. And a threshold setting method suitable for RTK is constructed, a segmentation evaluation idea is introduced into a sample evaluation system, and an observation value evaluation method suitable for GNSS is formulated, so that the problems of too few satellites and too bad satellite space structures and unstable resolving results caused by too strict judging methods in complex environments are solved.
Specifically, the invention constructs an observation data subset by utilizing information such as GNSS altitude angle, signal-to-noise ratio, satellite system, satellite space structure, history prediction information and the like; then solving an approximate solution of the parameter to be estimated by utilizing the subset and evaluating the approximate solution; and finally, screening out the optimal sample obtained by iterating for a certain number of times, removing the observation data containing the gross error, and solving a stable solution of GNSS positioning. The method fully utilizes the existing information of the GNSS satellite, effectively avoids traversing a large number of subsets, and ensures the calculation efficiency; by means of a more reasonable satellite subset evaluation mode, the risk of insufficient observation data and poor satellite space structure caused by removing too many satellites in a complex environment is reduced.
From the description of the above embodiments, it will be clear to a person skilled in the art that the method according to the above embodiments may be implemented by means of software plus the necessary general hardware platform, but of course also by means of hardware, but in many cases the former is a preferred embodiment. Based on such understanding, the technical solution of the present invention may be embodied essentially or in a part contributing to the prior art in the form of a software product stored in a storage medium (e.g. ROM/RAM, magnetic disk, optical disk) comprising instructions for causing a terminal device (which may be a mobile phone, a computer, a server, or a network device, etc.) to perform the method according to the embodiments of the present invention.
In this embodiment, an apparatus for controlling quality of observed data is further provided, and the apparatus is used to implement the foregoing embodiments and preferred embodiments, and will not be described in detail. As used below, the term "module" may be a combination of software and/or hardware that implements a predetermined function. While the means described in the following embodiments are preferably implemented in software, implementation in hardware, or a combination of software and hardware, is also possible and contemplated.
FIG. 4 is a block diagram of a consistency-based observed data quality control apparatus, as shown in FIG. 4, according to an embodiment of the present invention, the apparatus comprising:
the screening module 401 is configured to perform step one, where step one includes screening an observation data set of a GNSS global navigation satellite system satellite to obtain an observation data subset, and calculating a first parameter value based on the observation data subset;
a generating module 402, configured to execute a second step, where the second step includes generating residual data by substituting the first parameter value back into the observation data set, and taking a satellite corresponding to residual data that meets a preset requirement in the residual data as a satellite sample;
a calculating module 403, configured to perform a third step, where the third step includes obtaining sample data based on the satellite samples, and calculating a target parameter based on the satellite samples and the sample data.
Optionally, the apparatus further includes:
the execution module is used for executing the step one and the step two for N times in sequence to obtain a satellite sample set, wherein the satellite sample set comprises at least 2 satellite samples, N is more than or equal to 1, and N is an integer;
the computing module is specifically configured to:
acquiring sample data based on the satellite sample set, and calculating a target parameter based on the satellite sample set and the sample data; and under the condition that the step one and the step two are sequentially executed for N times, the number of satellite samples in the satellite sample set meets the preset condition.
Optionally, the screening module includes at least one of:
the sequencing sub-module is used for sequencing the data in the observation data set of the GNSS satellite according to the quality of the satellite data, and determining an observation data subset from the observation data in a preset range of sequencing;
and the eliminating sub-module is used for eliminating the data in the observation data set of the GNSS satellite according to the characteristics of the satellite data, and determining an observation data subset in the observation data set after eliminating.
Optionally, the apparatus further comprises at least one of:
a first determining module for determining the quality of satellite data in the set of observation data of the GNSS satellites based on the altitude and the signal-to-noise ratio of the satellites;
the second determining module is used for determining the quality of satellite data in the observation data set of the GNSS satellites based on the historical prediction information of the satellites.
Optionally, the rejection submodule includes at least one of:
the first rejecting unit is used for rejecting data in the observation data set of the GNSS satellite based on the space structure of the satellite;
the second eliminating unit is used for eliminating the data in the GNSS satellite observation data set based on the system characteristics of the satellite observation data.
Optionally, the generating module is specifically configured to:
and taking a satellite corresponding to residual data smaller than a preset threshold value in the residual data as a satellite sample, wherein the preset threshold value is related to the quality of the satellite data.
According to the quality control device for the observed data based on consistency, which is provided by the embodiment of the invention, the data in the observed data set is screened, the observed data containing rough differences is removed in the screening process, and the accuracy of the obtained data of the observed data subset is higher, so that the reliability of the observed data quality is improved. And the satellite corresponding to the data meeting the preset requirements is selected as a satellite sample, and the data is acquired based on the satellite sample, so that the coarse data can be further screened and removed, and the data quality is improved. In addition, as the observation data containing the rough differences are screened and removed, a large number of subsets can be prevented from being traversed, and the calculation efficiency is improved.
It should be noted that each of the above modules may be implemented by software or hardware, and for the latter, it may be implemented by, but not limited to: the modules are all located in the same processor; alternatively, the above modules may be located in different processors in any combination.
The present application is illustrated by the following specific examples. In order to better implement the method provided by the present invention, as shown in fig. 5, fig. 5 is a block diagram of a quality control device based on consistency observation data according to an embodiment of the present invention, which includes an input device 501, a processing device 502, an output device 503, a ROM504, and a RAM505. Wherein:
the input device 501 mainly includes a GNSS antenna and a board card for acquiring GNSS observation data.
The processing device 502 processes the received data by using the method provided by the invention to obtain a processing result.
The output device 503 outputs the result of the operation and processing, and mainly includes outputting the result directly to the built-in storage or outputting the result to the background server by means of 4G communication or the like.
In order to verify the effectiveness of the algorithm, the result is calculated and analyzed by comparing the simulation experiment with the actual project data.
Two domestic receivers are utilized to collect GPS/BDS double-frequency data in the open roof environment, the sampling interval is 1 second, the sampling time is 20 minutes, and the base line length is about 8 meters. The satellite motion trajectory of the GPS/BDS is shown in fig. 6. Selecting G05, C02 and C13 satellites for the validity of the inspection method in 200 th, 400 th and 600 th epochs by using a coordinate result of static processing as a reference coordinate, adding 0.5 week coarse difference into an L1 carrier observation value, adding 100 m coarse difference into a pseudo-range observation value, and simulating a discontinuous multi-coarse difference environment; g05, C02, C13 satellites are selected from 800 to 1000 epochs, plus/minus 0.5 week gross error is continuously added to the carrier L1 observation, plus/minus 100 meters gross error is added to the pseudo range, and the continuous gross error environment is simulated. Based on the observed values, dynamic RTK positioning calculation of different strategy machine types is adopted to test the effectiveness of the algorithm.
To facilitate review of the ambiguity fixing, FIG. 7 plots the coordinate error sequences of the fixed solutions for different resolution strategies. As shown in fig. 7, when the observation environment is good and there is no rough difference, good results can be obtained by different solutions. When the discontinuous multi-coarse difference situation occurs, the traditional solution is easy to be influenced by the coarse difference, the condition that the ambiguity is difficult to fix occurs, and the influence of the coarse difference can be effectively resisted by adopting the robust Kalman filtering method and the text method. When multiple coarse differences of continuous epochs occur, the correct resolving result cannot be obtained by adopting the traditional method and robust Kalman filtering resolving. The validity of the method can be intuitively seen by calculating the ambiguity fixing rate under different resolving strategies.
When the traditional method is adopted, the resolving is easily affected by the rough difference, the situations of ambiguity fixing failure and error are easy to occur, and the ambiguity fixing rate is the lowest and is only 80.7%. The anti-difference Kalman filtering can effectively resist discontinuous gross errors, but is difficult to effectively inhibit the influence of the continuous gross errors, and the ambiguity fixing rate is 88.6%. When the method is adopted for eliminating the discontinuous coarse difference, the discontinuous coarse difference and the continuous coarse difference can be effectively treated, the reliability of the calculation result is effectively ensured, and the ambiguity fixing rate is the highest (98.75%).
In order to verify the effectiveness of the algorithm, an actual experiment is carried out by using monitoring data in an actual project, the observation data adopts the observation data of a GPS system and a Beidou system, the sampling rate is 5s, the cut-off height angle is 15 degrees, the baseline length is about 4.6 kilometers, and signal lock loss frequently occurs in the data of a monitoring station due to the influence of factors such as signal shielding and receiver faults. As shown in fig. 8, fig. 8 is a chart of observation data of a station according to an embodiment of the present invention, wherein a dark color indicates that the satellite has lost its signal lock in a corresponding period. From the figure, it can be found that the observed data is from 17: the Beidou satellite starts to lose lock at 00, and 17: at 30, GPS and Beidou satellites all start to lose lock for a plurality of times, at 18:00, all satellites lose lock, 18: the 30 signals are gradually normal, and serious unlocking occurs in the signals around 23:23. In general, satellite observation quality is poor, and frequent and continuous signal unlocking exists.
Fig. 9 is a graph comparing coordinate sequences of the results of the conventional method, the robust kalman filtering method and the fixed solution method, wherein the time coordinate sequence is empty because the observed data is too seriously out of lock and there are not enough carrier observed values to participate in the solution. The coordinate sequence subjected to robust filtering is used as a reference coordinate sequence, and the darkest color, the darkest color and the lightest color are respectively the coordinate deviations of E, N, U directions of the reference coordinate sequence.
As can be seen from the graph, the problem that the resolution result of the traditional method has larger deviation (such as near 14400 seconds) and low ambiguity fixing rate due to the influence of signal out-of-lock and coarse error is solved, and the anti-error Kalman filtering algorithm can reduce error deviation to a certain extent and improve the ambiguity fixing rate. However, in the scene of serious unlocking and poor quality of the observed data, the effective fixing of the result is still difficult. Most of the solutions have smaller deviation except that the noise is larger near 5400 seconds.
To clearly show the error distribution of the solutions, RMSE and ambiguity statistics of the solutions of different solutions strategies are shown in table 1, and the horizontal error distribution map (fig. 10) under different solutions is plotted. As shown in fig. 10, fig. 10 (a) shows a solution strategy corresponding to the conventional method, fig. b shows a solution strategy corresponding to the robust kalman filter method, and fig. c shows a solution strategy corresponding to the method of the present invention. The figure can clearly find that the traditional method lacks necessary quality control means, when the observed data is poor, the calculated result has obvious deviation, and some deviation even exceeds 20cm. The robust Kalman filtering and the invention method have the advantages that the calculation result basically meets normal distribution, the invention method has smaller noise of the coordinate sequence and more centralized distribution. As can be seen from Table 1, the conventional method has significantly larger RMSE in the complex environment, and has significantly affected the accuracy and reliability of RTK in the elevation direction exceeding 15cm, and the method of the invention has a minimum 41% improvement in three directions and a 4% improvement in the ambiguity fixing rate compared with the robust Kalman filtering method.
TABLE 1 calculation results of different calculation strategies RMSE statistics
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Embodiments of the present invention also provide a computer readable storage medium having a computer program stored therein, wherein the computer program is arranged to perform the steps of any of the method embodiments described above when run.
In one exemplary embodiment, the computer readable storage medium may include, but is not limited to: a usb disk, a Read-Only Memory (ROM), a random access Memory (Random Access Memory, RAM), a removable hard disk, a magnetic disk, or an optical disk, or other various media capable of storing a computer program.
An embodiment of the invention also provides an electronic device comprising a memory having stored therein a computer program and a processor arranged to run the computer program to perform the steps of any of the method embodiments described above.
In an exemplary embodiment, the electronic apparatus may further include a transmission device connected to the processor, and an input/output device connected to the processor.
Specific examples in this embodiment may refer to the examples described in the foregoing embodiments and the exemplary implementation, and this embodiment is not described herein.
It will be appreciated by those skilled in the art that the modules or steps of the invention described above may be implemented in a general purpose computing device, they may be concentrated on a single computing device, or distributed across a network of computing devices, they may be implemented in program code executable by computing devices, so that they may be stored in a storage device for execution by computing devices, and in some cases, the steps shown or described may be performed in a different order than that shown or described herein, or they may be separately fabricated into individual integrated circuit modules, or multiple modules or steps of them may be fabricated into a single integrated circuit module. Thus, the present invention is not limited to any specific combination of hardware and software.
The above description is only of the preferred embodiments of the present invention and is not intended to limit the present invention, but various modifications and variations can be made to the present invention by those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the principle of the present invention should be included in the protection scope of the present invention.
Claims (10)
1. A method for quality control of observed data based on consistency, comprising:
screening an observation data set of a GNSS global navigation satellite system satellite to obtain an observation data subset, and calculating a first parameter value based on the observation data subset;
step two, the first parameter value is replaced to the observation data set to generate residual data, and a satellite corresponding to the residual data meeting the preset requirement in the residual data is taken as a satellite sample;
and thirdly, acquiring sample data based on the satellite samples, and calculating target parameters based on the satellite samples and the sample data.
2. The method according to claim 1, wherein after taking a satellite corresponding to residual data satisfying a preset requirement as a satellite sample, the method further comprises:
sequentially executing the first step and the second step for N times to obtain a satellite sample set, wherein the satellite sample set comprises at least 2 satellite samples, N is more than or equal to 1, and N is an integer;
the acquiring sample data based on the satellite samples and calculating target parameters based on the satellite samples and the sample data includes:
acquiring sample data based on the satellite sample set, and calculating a target parameter based on the satellite sample set and the sample data;
and under the condition that the step one and the step two are sequentially executed for N times, the number of satellite samples in the satellite sample set meets the preset condition.
3. The method according to claim 1 or 2, wherein the screening of the set of observations of satellites of the GNSS global navigation satellite system for a subset of observations comprises at least one of:
according to the quality of satellite data, sequencing data in an observation data set of GNSS satellites, and determining an observation data subset in the observation data in a sequencing range;
and eliminating data in the observation data set of the GNSS satellite according to the characteristics of the satellite data, and determining an observation data subset in the eliminated observation data set.
4. A method according to claim 3, wherein before ordering the data in the set of observations of GNSS satellites according to the quality of the satellite data, the method further comprises at least one of:
determining the quality of satellite data in an observation data set of the GNSS satellites based on the altitude and the signal-to-noise ratio of the satellites;
based on the historical prediction information of the satellites, a quality of satellite data in an observation set of the GNSS satellites is determined.
5. A method according to claim 3, wherein the culling of data from the set of observations of GNSS satellites based on the characteristics of the satellite data comprises at least one of:
based on the space structure of the satellite, eliminating data in an observation data set of the GNSS satellite;
and removing data in the GNSS satellite observation data set based on the system characteristics of the satellite observation data.
6. The method according to claim 1, wherein the taking, as satellite samples, satellites corresponding to residual data satisfying a preset requirement among the residual data includes:
and taking a satellite corresponding to residual data smaller than a preset threshold value in the residual data as a satellite sample, wherein the preset threshold value is related to the quality of the satellite data.
7. A quality control apparatus for consistency-based observation data, comprising:
the screening module is used for executing the first step, wherein the first step comprises the steps of screening an observation data set of a GNSS global navigation satellite system satellite to obtain an observation data subset, and calculating a first parameter value based on the observation data subset;
the generation module is used for executing a second step, wherein the second step comprises the steps of substituting the first parameter value back into the observation data set to generate residual data, and taking satellites corresponding to residual data meeting preset requirements in the residual data as satellite samples;
and the calculating module is used for executing a third step, wherein the third step comprises the steps of acquiring sample data based on the satellite samples and calculating target parameters based on the satellite samples and the sample data.
8. The apparatus of claim 7, wherein the apparatus further comprises:
the execution module is used for executing the step one and the step two for N times in sequence to obtain a satellite sample set, wherein the satellite sample set comprises at least 2 satellite samples, N is more than or equal to 1, and N is an integer;
the computing module is specifically configured to:
acquiring sample data based on the satellite sample set, and calculating a target parameter based on the satellite sample set and the sample data;
and under the condition that the step one and the step two are sequentially executed for N times, the number of satellite samples in the satellite sample set meets the preset condition.
9. A computer readable storage medium, characterized in that the computer readable storage medium has stored therein a computer program, wherein the computer program is arranged to perform the method of any of claims 1 to 6 when run.
10. An electronic device comprising a memory and a processor, characterized in that the memory has stored therein a computer program, the processor being arranged to run the computer program to perform the method of any of claims 1 to 6.
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