CN111175796A - Method for rapidly resolving long baseline ambiguity in network RTK - Google Patents
Method for rapidly resolving long baseline ambiguity in network RTK Download PDFInfo
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S19/00—Satellite radio beacon positioning systems; Determining position, velocity or attitude using signals transmitted by such systems
- G01S19/38—Determining a navigation solution using signals transmitted by a satellite radio beacon positioning system
- G01S19/39—Determining a navigation solution using signals transmitted by a satellite radio beacon positioning system the satellite radio beacon positioning system transmitting time-stamped messages, e.g. GPS [Global Positioning System], GLONASS [Global Orbiting Navigation Satellite System] or GALILEO
- G01S19/42—Determining position
- G01S19/43—Determining position using carrier phase measurements, e.g. kinematic positioning; using long or short baseline interferometry
- G01S19/44—Carrier phase ambiguity resolution; Floating ambiguity; LAMBDA [Least-squares AMBiguity Decorrelation Adjustment] method
Abstract
The invention discloses a method for quickly resolving long baseline ambiguity in a network RTK, which comprises the steps of acquiring CORS station observation data, establishing a GNSS network RTK reference station double-difference data resolving model, carrying out weighting constraint on ionospheric parameters, assisting in quickly resolving the baseline ambiguity, obtaining a floating solution of ambiguity and atmospheric delay errors and a corresponding covariance matrix according to parameter joint resolution, selecting an ambiguity fixed subset according to a set ambiguity resolving strategy, calculating a fixed value of a joint parameter according to a set constraint condition, analyzing and comparing the relation of the standard deviation of the double-difference ionospheric before and after ambiguity is fixed along with an epoch, feeding back in real time epoch by epoch, introducing the calculated atmospheric factor into the GNSS RTK reference station double-difference data resolving model for resolving, and overcoming the problem of long convergence time due to initialization of the long baseline ambiguity in a satellite lifting network RTK, the success rate of baseline ambiguity fixing is improved.
Description
Technical Field
The invention relates to the technical field of satellite navigation positioning, in particular to a method for quickly resolving long baseline ambiguity in network RTK.
Background
With the continuous expansion of the application field of satellite navigation positioning technology and the development and construction of GNSS systems, the operation range of the conventional RTK technology can not meet the requirements of users, the network RTK technology is developed by taking advantage of the fact that a plurality of base stations form a base station network to provide high-precision differential correction data for the mobile station within the network coverage range, so that the base station length is expanded to the maximum extent, and the high-precision positioning of the regional mobile station is realized. The real-time performance and the reliability of the positioning of the rover user are directly influenced by the quality of the network RTK baseline ambiguity resolution, but because the distance between network RTK base stations is often more than 20km, ionospheric delay and tropospheric delay are the largest factors restricting ambiguity resolution, and the ambiguity can not be completely eliminated through the double-difference operation between the satellites, so that the baseline ambiguity can be fixed in a long time. Meanwhile, as the dimension of the satellite increases, systematic deviations such as corresponding atmospheric residuals and the like still exist along with the satellite lifting in the resolving process, which is one of the main factors for restricting the rapid resolving of the network RTK baseline ambiguity.
Disclosure of Invention
The invention aims to provide a method for rapidly resolving the long baseline ambiguity in the network RTK, which solves the problem of long initial convergence time of the long baseline ambiguity in the satellite lifting network RTK and improves the success rate of baseline ambiguity fixing.
In order to achieve the above object, the present invention provides a method for rapidly resolving a long baseline ambiguity in a network RTK, comprising:
acquiring CORS station observation data, and establishing a GNSS network RTK reference station double-difference data resolving model;
carrying out weighting constraint on ionospheric parameters to assist in fast resolving of baseline ambiguity;
jointly resolving according to the parameters to obtain floating point values of the ambiguity and the atmospheric delay error and corresponding covariance matrixes;
selecting an ambiguity fixed subset according to a set ambiguity resolution strategy;
calculating a fixed value of the joint parameter according to a set constraint condition;
and analyzing and comparing the relation of the standard deviation of the double-difference ionized layer with the change of the epoch before and after the ambiguity is fixed, and feeding back the relation in real time from epoch to complete resolving.
The method for acquiring CORS station observation data and establishing a GNSS network RTK reference station double-difference data resolving model comprises the following steps:
obtaining a geometric distance from a satellite to an observation station, a double-difference troposphere delay value, a double-difference ionosphere delay value, a double-difference whole-cycle ambiguity value, a wavelength and a random noise error to obtain a corresponding double-difference carrier phase observation value and a corresponding pseudo-range observation value, obtaining a linear relation between the ambiguity and the ionosphere delay after double-frequency subtraction, and establishing a double-difference data resolving model of the GNSS network RTK reference station.
Wherein, the weighting constraint of the ionospheric parameters and the rapid solution of the baseline ambiguity are assisted, comprising:
and carrying out weighting constraint on ionosphere parameters according to the GNSS network RTK reference station double-difference data resolving model, carrying out simultaneous calculation on the double-difference carrier phase observation value and the pseudo-range observation value, and combining a Gaussian Markov model to rapidly resolve the baseline ambiguity.
Wherein, the jointly resolving floating point values of the ambiguity and the atmospheric delay error and the corresponding covariance matrix according to the parameters comprises:
and acquiring a combined resolving parameter of the ambiguity and the atmospheric delay error, and obtaining a corresponding floating solution and a covariance matrix by using an extended Kalman filtering technology.
Wherein the selecting the ambiguity-fixed subset according to the partial ambiguity resolution strategy comprises:
and sequencing all the non-reference satellites by an elevation sequence, selecting an initial ambiguity fixed subset and a corresponding floating solution and variance covariance matrix according to a set cut-off height threshold, searching and fixing by using an LAMBDA (label-based dynamic range data acquisition) method, and comparing with a set condition to obtain an ambiguity fixed subset.
Wherein, the selecting the ambiguity fixed subset according to the partial ambiguity resolution strategy further comprises:
the ambiguity integration success rate calculated according to the initial ambiguity fixed subset and the variance covariance matrix is greater than a set threshold, meanwhile, the integral value obtained by the anonymous function algorithm is used for RATIO detection and is greater than a set RATIO threshold, and the ambiguity number in the initial ambiguity fixed subset is greater than a set satellite number, so that a corresponding ambiguity fixed subset is obtained; and if any one of the ambiguity rounding success rate, the integer value and the ambiguity number does not meet the set threshold, the set ratio threshold and the set satellite number respectively, converting the set cut-off height threshold into any satellite elevation.
Wherein after converting the set cut-off altitude threshold into any satellite elevation angle, the method further comprises:
and after the set cut-off height threshold value is converted into any satellite elevation angle, sequencing iterative calculation is carried out according to the elevation angle threshold value, satellites which do not participate in calculation are excluded, then anonymous functions are continuously utilized to search to obtain an initial ambiguity fixed subset, and the ambiguity fixed subset is selected according to a corresponding floating solution and a variance covariance matrix.
Wherein, after selecting the ambiguity fixed subset according to the corresponding floating solution and variance covariance matrix, the method further comprises:
and carrying out RATIO detection on the integer value obtained by the anonymous function algorithm, comparing the integral value with a set value, outputting the ambiguity fixed subset if the integral value is larger than the set value, and carrying out resolving again if the integral value is smaller than the set value.
Wherein, according to the set constraint condition, calculating the fixed value of the joint parameter, including:
and calculating an atmospheric delay error value by using a least square method according to a set constraint condition of the ambiguity value of the whole period.
Wherein, the analysis compares the relation of the standard deviation of the double-difference ionized layer with the change of the epoch before and after the ambiguity is fixed, and feeds back the relation in real time from epoch to complete resolving, and comprises the following steps:
when the ambiguity is located the floating solution, the precision of the double-difference ionosphere delay is set to be decimeter-centimeter level, when the ambiguity value is fixed, the precision of the double-difference ionosphere delay is centimeter-millimeter level, and the double-difference ionosphere delay feeds back in real time epoch by epoch, and the atmospheric factor to be calculated is introduced into the GNSS network RTK reference station double-difference data resolving model for resolving.
The invention relates to a method for rapidly resolving long baseline ambiguity in network RTK, which comprises the steps of obtaining CORS station observation data, establishing a GNSS network RTK reference station double-difference data resolving model, carrying out weighting constraint on ionospheric parameters, assisting in rapidly resolving the baseline ambiguity, obtaining a floating solution of ambiguity and atmospheric delay error and a corresponding covariance matrix according to parameter joint resolution, selecting an ambiguity fixed subset according to a set ambiguity resolving strategy, calculating a fixed value of a joint parameter according to a set constraint condition, analyzing and comparing the change relation of the standard deviation of the ionospheric double-difference before and after ambiguity is fixed along with an epoch, feeding back in real time epoch by epoch, introducing the calculated atmospheric factor into the GNSS network RTK reference station double-difference data resolving model for resolving, and overcoming the problem of long convergence time due to initialization of the long baseline ambiguity in a satellite lifting network RTK, the problem of long initial convergence time of the long baseline ambiguity in the RTK of the satellite lifting network is solved, and the success rate of baseline ambiguity fixing is improved.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
Fig. 1 is a schematic step diagram of a method for rapidly resolving a long baseline ambiguity in network RTK provided by the present invention.
Fig. 2 is a schematic diagram of a GNSS network RTK positioning principle provided by the present invention.
FIG. 3 is a three-step data flow diagram for long baseline ambiguity resolution in GNSS network RTK provided by the present invention.
FIG. 4 is a graph of the standard deviation of the double difference ionospheric delay before and after a long ambiguity fix in a GNSS network RTK provided by the present invention.
Fig. 5 is a graph of the variation of satellite number, RATIO and RATIO test results obtained by processing ionosphere weighting model data during two satellite lifting provided by the invention.
Fig. 6 is a sequence diagram of the ratio value variation during long baseline ambiguity resolution in the GNSS network RTK provided by the present invention, and the resolution result of the selected baseline three-dimensional component.
Detailed Description
Reference will now be made in detail to embodiments of the present invention, examples of which are illustrated in the accompanying drawings, wherein like or similar reference numerals refer to the same or similar elements or elements having the same or similar function throughout. The embodiments described below with reference to the drawings are illustrative and intended to be illustrative of the invention and are not to be construed as limiting the invention.
Referring to fig. 1, the present invention provides a method for fast resolving a long baseline ambiguity in a network RTK, including:
s101, obtaining CORS station observation data, and establishing a GNSS network RTK reference station double-difference data resolving model.
Specifically, a geometric distance rho from a satellite to a CORS station (the CORS station is a reference station established by using a network RTK technology), a double-difference tropospheric delay value T and a double-difference ionospheric delay value I are obtained1Double difference whole cycle ambiguity value a1And a2Wavelength lambda of1And λ2Random noise error epsilon of carrier phase and pseudo range on GNSS double frequency1、ε2、e1And e2Obtaining a corresponding double-difference carrier phase observed value (phi)1And phi2) And pseudorange observations (p)1And p2) Wherein, in the step (A),
obtaining a linear relation between ambiguity and ionospheric delay after performing double-frequency differencing, and establishing a double-difference data resolving model of the GNSS network RTK reference station, wherein the model comprises a plurality of satellites and a plurality of reference stations as shown in fig. 2, and finally transmitting data to a user, hiding double subscripts of a receiver and the satellites, and using the subscripts to represent carrier frequency, and the double-difference data resolving model of the GNSS network RTK reference station is as follows:
φ1＝ρ+T-u1I1+λ1a1+ε1
φ2＝ρ+T-u2I1+λ2a2+ε2
p1＝ρ+T+u1I1+e1
p2(i)＝ρ+T+u2I1+e2
the linear relationship between the ambiguity and the ionospheric delay is:
φ1-φ2＝-(u1-u2)I1+(λ1a1-λ2a2)+ε1-ε2
p1-p2＝(u1-u2)I1+e1-e2
s102, conducting weighting constraint on ionosphere parameters and assisting in fast resolving of baseline ambiguity.
Specifically, the ionosphere parameters are weighted and constrained according to the GNSS network RTK reference station double-difference data resolving model, the fast resolving of baseline ambiguity is assisted, the double-difference carrier phase observed value and the pseudo-range observed value are calculated simultaneously, the reliability of a pseudo-observation equation is enhanced, the GNSS network RTK reference station double-difference data resolving model is resolved fast by combining a Gaussian Markov model, and the expression is as follows:
wherein E (-) is an expected operator, E is an identity matrix, W is a mapping function coefficient written out in a matrix form, and i is a random process model corresponding to the added ionosphere prior information:
wherein D (-) represents a dispersion operator,
and
respectively, non-differential carrier phase observations on the GNSS dual-band and variance factors of the pseudoranges,
variance factor, D, representing ionospheric pseudo-observationsnIs a difference operator between receivers, DmIs a difference operator between satellites, WmA weight matrix that models the altitude dependence of the satellites,
representing the Kronecker product.
For network RTK inter-base station data processing, the double-difference ionospheric observation sample value i of the model is initially set to zero, and the ionospheric variance factor
The prior accuracy model related to the baseline length is selected as follows:
ci＝l×9.9×10-4
wherein l is the base length in meters.
S103, jointly resolving according to the parameters to obtain floating point values of the ambiguity and the atmospheric delay error and corresponding covariance matrixes.
Specifically, obtaining the joint solution parameters
Ignoring integer constraints of ambiguity, and obtaining floating solutions of two joint parameters and corresponding covariance matrixes by using an extended Kalman filtering technology:
wherein the content of the first and second substances,
and
are each a real number of the respective value,
and
respectively, corresponding variance-covariance matrices.
And S104, selecting the ambiguity fixed subset according to a set ambiguity resolution strategy.
Specifically, all non-reference satellites are ordered in the elevation sequence to obtain:
ele＝{e1,e2,...,en|e1＜e2＜…＜en}
setting a cut-off height angle threshold to ecut＝e1At 35 deg., excluding satellites not participating in the solution, selecting a fixed subset of initial ambiguities
And its corresponding floating point solution
Sum variance covariance matrix
Using LAMBDA to carry out search and fix, and obtaining the result when the following 3 conditions are met
calculating ambiguity bootstrapping integration success rate P according to the initial ambiguity fixed subset and the variance covariance matrix, wherein P is more than or equal to P0(P0Set threshold 99.9%);
secondly, performing RATIO detection by using an integer value obtained by an anonymous function algorithm, wherein the integral value is greater than a set RATIO threshold value 2;
the fuzzy number in the initial fuzzy fixed subset is larger than the set minimum satellite number (10 pieces);
if the three conditions are not met simultaneously, the set cut-off height threshold value is converted into any satellite elevation angle, namely ecut＝eiAnd according to the elevation angle limit eiN (the value of i is determined according to the number of times of iteration of elevation angle), excluding satellites not participating in calculation, and continuing ambiguity search acquisition
When the subset of ambiguities is
After fixing, the floating point solution is then based on the fixed subset
Selecting a fixed subset of ambiguities from a sum-variance covariance matrix
The method comprises the following steps:
meanwhile, the integral value obtained by the anonymous function algorithm is subjected to RATIO detection and is compared with a set value, and if the integral value is larger than the set value, the ambiguity fixed subset is output
If the RATIO is smaller than the set value, the calculation is carried out again, wherein the RATIO detection is as follows:
typically, ambiguity integer values are considered to be fixed passes when the ratio threshold is greater than 1/2.
And S105, calculating a fixed value of the joint parameter according to the set constraint condition.
Specifically, a fixed integer ambiguity value is used as a constraint condition, wherein the set constraint condition not only constrains a fixed value, but also sets different elevation angle reference satellites, a more accurate atmospheric delay error value is calculated through least square estimation, and the accuracy of ionospheric delay estimation through the step depends on a high quality value of the integer ambiguity estimated after the ionospheric parameters are subjected to weighting constraint. Wherein, the calculation formula of the least square method is as follows:
as shown in FIG. 3, when network RTK baseline ambiguity estimation is performed, the involved atmospheric delay model parameters and ambiguity parameters are solved in three steps jointly. Since each epoch is repeatedly solved for ambiguity by the anonymity function and RATIO detection. Usually before passing through the RATIO detection, the ambiguity float solution obtained in the first step must converge, which may be as long as tens of minutes, while the fixed solution is repeated for each epoch, the current solution being determined comparatively from the last epoch, in order to ensure that the current value is guaranteed to be correct over time. However, if the results contain early satellites, although RATIO detection may prevent errant integer solution fixes, it may affect the integrity of the previously accepted integer ambiguities. Considering that the number of GNSS systems is increased by times, all ambiguity parameters are unnecessary to fix, an optimized partial ambiguity resolution method (M-PAR) is provided for an ambiguity resolution scheme of a network RTK reference station under the condition that the number of participating satellites is enough, and the risk of ambiguity fixing failure can be reduced by selecting an ambiguity partial subset for fixing.
And S106, analyzing and comparing the change relation of the double-difference ionosphere standard deviation with the epoch before and after the ambiguity is fixed, and feeding back the relation in real time from epoch to complete resolving.
Specifically, as shown in fig. 4, the curves in the graph are, from top to bottom: the method comprises the steps of low elevation angle-ambiguity floating point, high elevation angle-ambiguity floating point, low elevation angle-ambiguity fixing and high elevation angle-ambiguity fixing, analyzing and comparing the change relation of the standard deviation of the double-difference ionosphere along with the epoch before and after ambiguity fixing, obtaining that when the ambiguity is in a floating solution, the ionosphere precision is kept at a decimeter-centimeter level, and along with the ambiguity fixing, the ionosphere precision is improved to a centimeter-millimeter level.
The influence of the ionized layer on the ambiguity estimation value is definitely reduced by improving the delay precision of the ionized layer, and accordingly, the calculated atmospheric factor is introduced into a double-difference data resolving model of the GNSS network RTK reference station for resolving through epoch-by-epoch real-time feedback, so that the resolving requirement of the next stage of network RTK high-precision positioning is met.
The proposed ambiguity resolution method is expressed by an English letter M-PAR algorithm, and in order to verify the effectiveness of the proposed method, a verification result is obtained through data experiment simulation. The data comes from the 10 th, 17 th of the NGS website CORS network 2019, the length of a base line is selected to be 100km, and the sampling rate is 10 s. The ionosphere of the same data was considered and modeled using a weighting of i-10 cm. Fig. 5 shows a graph of the RATIO and RATIO test results of the satellite number variation obtained by ionosphere weighting model data processing during two satellite lifting (1 is passed and 0 is failed). The ionospheric weighting is derived such that the initial convergence time, whether it be at the start of the solution or at the new satellite rise, is reduced from an unweighted time of about 6 minutes to only 1 minute, and from unweighted convergence times of 60 and 32 seconds to only 15 and 11 seconds for two satellite rises.
TABLE 1 comparison of fixed Rate and initialization time for different solution methods
| Ambiguity resolution method | Rate of fixation of degree of ambiguity | Initialization time(s) |
| LAMBDA (full) | 18.6％ | 3680 |
| FAR | 82.3％ | 270 |
| PAR | 92.6％ | 20 |
| M-PAR | 97.6％ | 20 |
By analyzing the ratio value change condition during ambiguity resolution along with the rising and falling or the non-acceptance of the satellite and a baseline three-dimensional component resolution result sequence diagram (light color represents a floating solution and dark color represents a fixed solution) of a group of data, as shown in fig. 6, and table 1 gives the statistical results of the fixing rate and the initialization time based on different ambiguity resolution methods, the ratio value can be steeply dropped and raised or not stable along with the rising and falling or the acceptance of the satellite, and even reaches the degree that 0 cannot be fixed; the ambiguity resolution scheme which is processed only by the LAMBDA algorithm without adopting part of fixed strategy and all data has the lowest fixed rate of only 18.6 percent, the maximum convergence time of the initialization process is 61.3 minutes, and the observation quantity is seriously influenced and the ambiguity is basically difficult to fix under the condition that the satellites which rise and fall initially and are not suitable for being involved in resolution are not removed. Meanwhile, by adopting the FAR algorithm and the PAR algorithm, the fixing rate respectively reaches 82.3 percent and 92.6 percent, the initial time is respectively reduced to 270 seconds and 20 seconds, which shows that both the FAR algorithm and the PAR algorithm can remove satellites which are not suitable for being involved in resolving, and the fixing rate of the ambiguity of the reference station can be obviously improved compared with the LAMBDA processing method for all data. Compared with the FAR and conventional PAR methods, the provided M-PAR method can shorten the initial convergence time of the floating solution, improve the fixation rate of the ambiguity to 97.6%, optimize the positioning precision effect, and keep the northeast direction at decimeter-centimeter level.
The invention relates to a method for rapidly resolving long baseline ambiguity in network RTK, which comprises the steps of obtaining CORS station observation data, establishing a GNSS network RTK reference station double-difference data resolving model, carrying out weighting constraint on ionospheric parameters, assisting in rapidly resolving the baseline ambiguity, obtaining a floating solution of ambiguity and atmospheric delay error and a corresponding covariance matrix according to parameter joint resolution, selecting an ambiguity fixed subset according to a set ambiguity resolving strategy, calculating a fixed value of a joint parameter according to a set constraint condition, analyzing and comparing the change relation of the standard deviation of the ionospheric double-difference before and after ambiguity is fixed along with an epoch, feeding back in real time epoch by epoch, introducing the calculated atmospheric factor into the GNSS network RTK reference station double-difference data resolving model for resolving, and overcoming the problem of long convergence time due to initialization of the long baseline ambiguity in a satellite lifting network RTK, the success rate of baseline ambiguity fixing is improved.
While the invention has been described with reference to a preferred embodiment, it will be understood by those skilled in the art that various changes in form and detail may be made therein without departing from the spirit and scope of the invention as defined by the appended claims.
Claims (10)
1. A method for rapidly resolving long baseline ambiguity in network RTK is characterized by comprising the following steps:
acquiring CORS station observation data, and establishing a GNSS network RTK reference station double-difference data resolving model;
carrying out weighting constraint on ionospheric parameters to assist in fast resolving of baseline ambiguity;
jointly resolving according to the parameters to obtain floating point values of the ambiguity and the atmospheric delay error and corresponding covariance matrixes;
selecting an ambiguity fixed subset according to a set ambiguity resolution strategy;
calculating a fixed value of the joint parameter according to a set constraint condition;
and analyzing and comparing the relation of the standard deviation of the double-difference ionized layer with the change of the epoch before and after the ambiguity is fixed, and feeding back the relation in real time from epoch to complete resolving.
2. The method for fast resolving the long baseline ambiguity in the network RTK as claimed in claim 1, wherein the acquiring the CORS station observation data and establishing the GNSS network RTK reference station double difference data resolving model comprises:
obtaining a geometric distance from a satellite to an observation station, a double-difference troposphere delay value, a double-difference ionosphere delay value, a double-difference whole-cycle ambiguity value, a wavelength and a random noise error to obtain a corresponding double-difference carrier phase observation value and a corresponding pseudo-range observation value, obtaining a linear relation between the ambiguity and the ionosphere delay after double-frequency subtraction, and establishing a double-difference data resolving model of the GNSS network RTK reference station.
3. The method as claimed in claim 2, wherein the performing weighted constraint on ionospheric parameters to assist in the fast solution of baseline ambiguity comprises:
and carrying out weighting constraint on ionosphere parameters according to the GNSS network RTK reference station double-difference data resolving model, carrying out simultaneous calculation on the double-difference carrier phase observation value and the pseudo-range observation value, and combining a Gaussian Markov model to rapidly resolve the baseline ambiguity.
4. The method as claimed in claim 1, wherein the fast resolving of the long baseline ambiguity in network RTK, which jointly resolves the floating point values of the ambiguity and the atmospheric delay error and the corresponding covariance matrix according to the parameters, comprises:
and acquiring a combined resolving parameter of the ambiguity and the atmospheric delay error, and obtaining a corresponding floating solution and a covariance matrix by using an extended Kalman filtering technology.
5. The method for fast resolving long baseline ambiguity in network RTK as claimed in claim 1, wherein said selecting the ambiguity-fixed subset according to the partial ambiguity resolution strategy comprises:
and sequencing all the non-reference satellites by an elevation sequence, selecting an initial ambiguity fixed subset and a corresponding floating solution and variance covariance matrix according to a set cut-off height threshold, searching and fixing by using an LAMBDA (label-based dynamic range data acquisition) method, and comparing with a set condition to obtain an ambiguity fixed subset.
6. The method for fast resolving long baseline ambiguity in network RTK as claimed in claim 5, wherein said selecting the ambiguity-fixed subset according to the partial ambiguity resolution strategy further comprises:
the ambiguity integration success rate calculated according to the initial ambiguity fixed subset and the variance covariance matrix is greater than a set threshold, meanwhile, the integral value obtained by the anonymous function algorithm is used for RATIO detection and is greater than a set RATIO threshold, and the ambiguity number in the initial ambiguity fixed subset is greater than a set satellite number, so that a corresponding ambiguity fixed subset is obtained; and if any one of the ambiguity rounding success rate, the integer value and the ambiguity number does not meet the set threshold, the set ratio threshold and the set satellite number respectively, converting the set cut-off height threshold into any satellite elevation.
7. The method of claim 6, wherein after converting the set cut-off altitude threshold to any satellite elevation angle, the method further comprises:
and after the set cut-off height threshold value is converted into any satellite elevation angle, sequencing iterative calculation is carried out according to the elevation angle threshold value, satellites which do not participate in calculation are excluded, then anonymous functions are continuously utilized to search to obtain an initial ambiguity fixed subset, and the ambiguity fixed subset is selected according to a corresponding floating solution and a variance covariance matrix.
8. The method of claim 7, wherein after selecting the ambiguity-fixed subset based on the corresponding floating solution and covariance matrix, the method further comprises:
and carrying out RATIO detection on the integer value obtained by the anonymous function algorithm, comparing the integral value with a set value, outputting the ambiguity fixed subset if the integral value is larger than the set value, and carrying out resolving again if the integral value is smaller than the set value.
9. The method for rapidly resolving the long baseline ambiguity in the network RTK as claimed in claim 1, wherein the calculating the fixed value of the joint parameter according to the set constraint condition comprises:
and calculating an atmospheric delay error value by using a least square method according to a set constraint condition of the ambiguity value of the whole period.
10. The method as claimed in claim 1, wherein the analyzing and comparing the relationship between the standard deviation of the double-difference ionosphere and the change of the standard deviation with the epoch before and after the ambiguity is fixed, and feeding back the relationship in real time from epoch to complete the solution comprises:
when the ambiguity is located the floating solution, the precision of the double-difference ionosphere delay is set to be decimeter-centimeter level, when the ambiguity value is fixed, the precision of the double-difference ionosphere delay is centimeter-millimeter level, and the double-difference ionosphere delay feeds back in real time epoch by epoch, and the atmospheric factor to be calculated is introduced into the GNSS network RTK reference station double-difference data resolving model for resolving.
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