CN118031787B - GNSS deformation monitoring method - Google Patents
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01B—MEASURING LENGTH, THICKNESS OR SIMILAR LINEAR DIMENSIONS; MEASURING ANGLES; MEASURING AREAS; MEASURING IRREGULARITIES OF SURFACES OR CONTOURS
- G01B7/00—Measuring arrangements characterised by the use of electric or magnetic techniques
- G01B7/16—Measuring arrangements characterised by the use of electric or magnetic techniques for measuring the deformation in a solid, e.g. by resistance strain gauge
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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
- G01S19/01—Satellite radio beacon positioning systems transmitting time-stamped messages, e.g. GPS [Global Positioning System], GLONASS [Global Orbiting Navigation Satellite System] or GALILEO
- G01S19/13—Receivers
- G01S19/14—Receivers specially adapted for specific applications
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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
- G01S19/01—Satellite radio beacon positioning systems transmitting time-stamped messages, e.g. GPS [Global Positioning System], GLONASS [Global Orbiting Navigation Satellite System] or GALILEO
- G01S19/13—Receivers
- G01S19/35—Constructional details or hardware or software details of the signal processing chain
- G01S19/37—Hardware or software details of the signal processing chain
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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
- G01S19/396—Determining accuracy or reliability of position or pseudorange measurements
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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
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Abstract
The invention relates to a GNSS deformation monitoring method, which comprises the following steps: constructing a data set and a pseudo-range prediction model based on GNSS historical data; receiving online data of a measuring station and a reference station, and performing cycle slip detection; constructing double-difference pseudo-range and double-difference carrier phase based on the satellite data after cycle slip detection, and transmitting integer ambiguity floating solution; selecting a subset of ambiguities based on the integer ambiguity floating solution; and carrying out RTK positioning solution according to the selected subset. The dataset comprises: satellite signal elevation, carrier-to-noise ratio, azimuth, and pseudorange errors. The construction of the pseudo-range prediction model comprises the following steps: constructing a relation between input quantity and output quantity; combining the distribution target value and the predicted value; optimizing and obtaining a prediction model in the invention, under a specific monitoring environment, a data set is constructed by collecting GNSS historical data signals, then pseudo-range errors are matched and predicted based on signal-to-noise ratio, elevation angle and azimuth angle, the fixed rate of ambiguity is improved, and finally the precision of relative positioning of RTK is improved.
Description
Technical Field
The invention relates to the field of surveying and mapping science and technology, in particular to a GNSS deformation monitoring method.
Background
GNSS technology has the advantages of high automation degree, capability of simultaneously measuring three-dimensional displacement, all weather and the like in the deformation monitoring field, and has been widely applied to the fields such as buildings, bridges, dams, landslides and the like. The project of applying GNSS deformation monitoring abroad has been developed in many areas, and an exemplary research work of geological disaster monitoring early warning and emergency by utilizing the latest positioning technology and information technology is performed, so that a plurality of landslide early warning systems are established, and a good disaster prevention effect is achieved. The conventional GNSS deformation monitoring method has the following problems:
In the conventional GNSS deformation monitoring method, in the process of monitoring through GNSS deformation, because of shielding of surrounding environment of a receiver, the error of GNSS measurement information is large, so that the positioning accuracy can not meet the requirement of deformation monitoring positioning accuracy, and the reliability of relative positioning of RTK is greatly reduced.
And (II) in the conventional GNSS deformation monitoring method, due to the existence of noise, particularly under the condition that the environment is shielded, the ambiguity fixing rate is very low, so that the RTK positioning accuracy is reduced.
Disclosure of Invention
In view of the foregoing drawbacks of the prior art, an object of the present invention is to provide a GNSS deformation monitoring method, which solves one or more of the problems of the prior art.
In order to achieve the above purpose, the technical scheme of the invention is as follows:
A GNSS deformation monitoring method comprising the steps of:
Constructing a data set and a pseudo-range prediction model based on GNSS historical data;
receiving online data of a measuring station and a reference station, and performing cycle slip detection;
constructing double-difference pseudo-range and double-difference carrier phase based on the satellite data after cycle slip detection, and transmitting integer ambiguity floating solution;
selecting a subset of ambiguities based on the integer ambiguity floating solution;
And carrying out RTK positioning solution according to the selected subset.
Further, the dataset includes: satellite signal elevation, carrier-to-noise ratio, azimuth, and pseudorange errors.
Further, the construction of the pseudo-range prediction model includes the following steps:
constructing a relation between input quantity and output quantity;
Combining the distribution target value and the predicted value;
And optimizing to obtain a prediction model.
Further, the cycle slip detection includes the steps of:
Recombining the dual-frequency observations into a MW combination and a GF combination;
judging whether the satellite signal generates cycle slip or not;
and if the cycle slip is generated, eliminating the satellite data, and if the cycle slip is not generated, acquiring the satellite data of the measuring station and the reference station.
Further, the determining whether the satellite signal generates the cycle slip includes:
GF combination: filtering by using polynomial fitting;
MW combinations: and filtering by adopting a recurrence formula.
Further, the acquisition of the satellite data of the measuring station comprises the following steps:
Single-point positioning and resolving of satellite data of a measuring station;
rule matching is carried out on the solved data and the data set;
And if the rule matching is successful, acquiring the corresponding pseudo range and carrier data, and correcting the pseudo range, otherwise, carrying out pseudo range prediction through a prediction model, and correcting the pseudo range according to the predicted pseudo range.
Further, the construction of the double-difference pseudo-range and the double-difference carrier phase includes:
Obtaining a single difference between the pseudo range and the carrier wave between the two receivers;
a secondary difference is made between the pseudorange and the carrier between the two satellites.
Further, the selecting of the ambiguity subset includes: and selecting a multi-strategy ambiguity subset.
Further, the multi-strategy includes:
Sequentially removing satellites with the smallest elevation angles until the whole cycle ambiguity is successfully fixed or the number of subsets is smaller than a threshold value;
sequentially removing satellites with the minimum carrier-to-noise ratio until the whole-cycle ambiguity is successfully fixed or the number of subsets is smaller than a threshold value;
sequentially removing satellites with minimum double-difference residual errors until the whole-cycle ambiguity is successfully fixed or the number of subsets is smaller than a threshold value;
Sequentially removing satellites with maximum whole-cycle ambiguity variance until the whole-cycle ambiguity is successfully fixed or the number of subsets is smaller than a threshold value;
the above-mentioned rejection processes are parallel.
Further, the selecting the ambiguity subset includes: and selecting the largest subset of the satellite numbers with the fixed integer ambiguity as the final subset.
Compared with the prior art, the invention has the following beneficial technical effects:
Under a specific monitoring environment, the invention constructs a data set by collecting GNSS historical data signals, matches and predicts pseudo-range errors based on signal-to-noise ratio, elevation angle and azimuth angle, improves the fixed rate of ambiguity, and finally improves the precision of relative positioning of RTK.
And (II) the invention respectively uses the double-difference residual error, the elevation angle and the carrier-to-noise ratio as the standards to select four partial ambiguity fixed subsets, selects the subset with the largest ambiguity fixed number as the final subset, and ensures the reliability of RTK positioning accuracy while ensuring that the whole-cycle ambiguity can be fixed.
And thirdly, in the method, the influence caused by multipath is effectively relieved by matching the data set and predicting the pseudo-range error in a severe environment when the pseudo-range has a large error, so that the positioning accuracy of the RTK is improved.
Drawings
Fig. 1 shows a flow chart of a GNSS deformation monitoring method according to an embodiment of the invention.
Fig. 2 is a schematic flow chart of a GNSS deformation monitoring method according to an embodiment of the invention.
Fig. 3 is a schematic diagram showing a positioning effect before a GNSS deformation monitoring method according to an embodiment of the present invention is used.
Fig. 4 shows a schematic diagram of a positioning effect after a GNSS deformation monitoring method according to an embodiment of the present invention is used.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, a GNSS deformation monitoring method according to the present invention will be described in further detail with reference to the accompanying drawings and detailed description. The advantages and features of the present invention will become more apparent from the following description. It should be noted that the drawings are in a very simplified form and are all to a non-precise scale, merely for the purpose of facilitating and clearly aiding in the description of embodiments of the invention. For a better understanding of the invention with objects, features and advantages, refer to the drawings. It should be understood that the structures, proportions, sizes, etc. shown in the drawings are for illustration purposes only and should not be construed as limiting the invention to the extent that any modifications, changes in the proportions, or adjustments of the sizes of structures, proportions, or otherwise, used in the practice of the invention, are included in the spirit and scope of the invention which is otherwise, without departing from the spirit or essential characteristics thereof.
A GNSS deformation monitoring method comprising the steps of:
step S1: a data set is constructed based on the GNSS historical data, and a pseudo-range prediction model is constructed based on the constructed data set.
The dataset comprises: satellite signal elevation angle (elr), carrier-to-noise ratio (C/N 0), azimuth angle (ax), and pseudorange error (Δρ). Specifically, the data set is defined as d= (X, Δρ), where x= [ elr ax C/N 0 ], Δρ is the pseudorange error obtained by inverting the pseudorange with reference to the position. Under the condition that the reference station coordinates are known, the position coordinates are pulled to the measuring station coordinate positions in the severe environment by using the total station to obtain the measuring station coordinate positions, then the GNSS signals are received by using the GNSS receivers of the measuring stations, and the elevation angle, the carrier-to-noise ratio, the azimuth angle and the pseudo range errors of the satellite signals are obtained through calculation. Specifically, in the process of acquiring a data set, GNSS satellite signals with satellite elevation angles not more than 15 degrees and carrier-to-noise ratios not more than 25db in received GNSS signal data need to be removed, so as to improve satellite signal quality.
Further, an inverted pseudo-range ρ t is obtained given the receiver position, in particular:
wherein (x s,ys,zs) denotes the satellite position, For the clock-difference that the receiver has solved,For correction of satellite clock difference, I is ionospheric delay obtained by Klobuchar model, T represents tropospheric delay obtained by Saastamoinen model.
The calculation formula of the pseudo-range error is as follows:
where ρ represents the actual received pseudoranges, ρ t represents the inverted simulated pseudoranges, AndThe residuals of receiver clock and satellite clock, respectively, Δi and Δt represent the ionospheric delay not obtained by the Klobuchar model and the tropospheric delay not obtained by the Saastamoinen model, respectively, and epsilon represents noise.
In the invention, a pseudo-range prediction model is constructed based on Bayesian optimized Gaussian process regression, and the method comprises the following steps:
Step S11: the relation between the input quantity (satellite signal elevation angle, carrier-to-noise ratio, azimuth angle) and the output quantity (pseudo-range error) is constructed and expressed as: y n＝f(Xn)+εn, wherein f (·) represents a function mapping relationship, ε n is Gaussian white noise and obeys normal distribution Wherein, the kernel function of f (·) is:
In the method, in the process of the invention, The signal variance, c, representing the kernel function is well-established.
Step S12: the process of training a model in supervision studies typically involves using a set of input data (features) to predict a corresponding target value, by combining a distributed target value with a predicted value, the target value being the actual value that the model is expected to predict or estimate. The predicted value is an estimated value generated by the model according to the input data, and when the model receives new input data, a learned rule or relation is used to generate a corresponding predicted value. In the regression problem, the predicted value is an estimate of the target value by the model, and in the classification problem, the predicted value is an estimate of the class to which the input data belongs by the model. Specifically, in the present invention, the joint distribution between the target value and the predicted value is expressed as:
Where K (X *,X)＝K(X,x*),x* represents a new input point, i.e., the point at which the prediction was made, in Gaussian process regression, the entire function space is modeled using training data X and corresponding target values Y, and for the new input point X *, its corresponding target value is predicted:
P(f(x*)|X,Y,x*)～N(μ*,∑*)，
Wherein mu * is the mean value, sigma * is the variance, and the super parameter is continuously optimized by the constructed data level to obtain a final super parameter value, which can be expressed as: the joint distribution of three hyper-parameters among the kernel function, the target value and the predicted value.
Step S13: optimizing to obtain a prediction model, specifically, obtaining an optimal solution of f (·) through Bayesian optimization, wherein the optimal solution is expressed as: x * = argminf (x).
The predictive model is expressed as: y=gpr (X), where x= [ elr az C/N 0 ], y=gpr (·) represents the trained predictive model.
Further, in the embodiment of the present invention, because of some characteristic differences among the systems, the prediction models are respectively constructed for the GPS, BDS2, BDS3, and GAL systems.
Step S2: and receiving online data of the measuring station and the reference station, and performing cycle slip detection to acquire satellite data of the measuring station and the reference station.
Further, the cycle slip detection includes the steps of:
Step S210: the double-frequency observations (two observations of one satellite) are recombined into a standard phase-pseudo code combination (MW combination) and a geometric combination-free combination (GF combination), and the MW combination and the GF combination are further matched for use.
Specifically, the geometric combination-free (GF combination) model is:
Where lambda 1 denotes the carrier wavelength of L1, lambda 2 denotes the carrier wavelength of L2, Represents the phase observations of L1,The phase observations of L2, f 2, the frequency of L2, f 1, the integer ambiguity of L1, N 1, the integer ambiguity of L2, and N 2, where L1 and L2 represent two sets of signal data generated by a satellite for the same time of a receiver.
GF combining eliminates the effects of orbital errors, receiver clock errors and satellite clock errors, as well as tropospheric errors, including only the effects of ionospheric residuals and real-time ambiguity combining of the two carriers.
Specifically, the standard phase-pseudo code combination (MW combination) model is:
MW combining eliminates the effects of ionosphere, satellite clock bias, receiver clock bias, and satellite-to-receiver geometric distance errors, where the combined ambiguity is theoretically constant when the phase observations do not cycle slip, and fluctuates around a certain value due to the effects of observation noise. Both GF and MW combinations are well suited for cycle slip detection of dynamic data, since they eliminate the influence of the geometrical distance of the satellite to the receiver, i.e. independent of the motion state of the receiver.
Step S211: whether the satellite signal generates cycle slip is determined.
The judging method for GF combination comprises the following steps: and filtering by using polynomial fitting.
Specifically, firstly, the current epoch i, the observed values of the previous epochs and the prior information are utilized to calculate the predicted value of the next epoch i+1, meanwhile, the fitting standard deviation is solved, when the observed value of the epoch i+1 is received, the predicted value is compared with the GF combined observed value, if the difference value of the predicted value and the GF combined observed value is larger than a set threshold (+/-3σ), the epoch i+1 is considered to have cycle slip, and if the predicted value is within the threshold range, the epoch i+1 is considered to have no cycle slip.
The judging method for MW combination comprises the following steps: and filtering by adopting a recurrence formula.
In particular, the method comprises the steps of,
Where < N σ>i is the average of i epoch ambiguity mutually differences N σ,For the ambiguity of the ith epoch to be mutually bad,For the variance of the ambiguity difference N σ of the ith epoch, more specifically, if the 7i+1th epoch needs to be detected, the average value < N σ>i of the ambiguity difference N σ of the previous ith epoch is calculated first, and the observed values MW of the ith epoch and the 1 th epoch are combinedCompared with < N σ>i, whenIf so, the i+1 epoch is considered to have cycle slip, otherwise, the i+1 epoch is considered to have no cycle slip.
Step S212: and if the cycle slip is generated, eliminating the satellite data, and if the cycle slip is not generated, acquiring the satellite data of the measuring station and the reference station.
Further, the step of acquiring the satellite data of the measuring station includes the following steps:
Step S220: for single-point positioning calculation of satellite data of a measuring station, specifically, data characteristics of each satellite can be known through ephemeris and observation data of the measuring station, including: azimuth, elevation, carrier-to-noise ratio, satellite system.
Step S221: and carrying out rule matching on the calculated data and the data set.
Further, the matching rule is: and respectively matching the azimuth angle, the elevation angle and the carrier-to-noise ratio of the satellite with the data set, and if the matching of the azimuth angle, the elevation angle and the carrier-to-noise ratio is successful, judging that the satellite is successfully matched, otherwise, the matching is failed.
Step S222: if the rule matching is successful, the corresponding pseudo range and carrier data are acquired, the step S223 is proceeded, otherwise, the pseudo range prediction is carried out through the prediction model, and the step S223 is proceeded again.
Step S223: and (5) pseudo-range correction.
Further, the pseudo-range correction is expressed as:
where Δρ represents the pseudorange correction value, ρ represents the measured station pseudorange received by the receiver, Representing the corrected pseudoranges.
The acquiring the reference station satellite data includes: the pseudo range of the reference station and the carrier data are acquired, and the observation environment of the reference station is better than that of the measuring station, so that satellite data can be directly acquired without correction. In the invention, the influence caused by multipath is relieved by means of data set matching and pseudo-range error prediction
Step S3: and constructing double-difference pseudo-range and double-difference carrier phase based on the satellite data after cycle slip detection, and transmitting integer ambiguity floating solution.
The construction of the double difference carrier wave specifically comprises the following steps: in the present embodiment, assuming that the satellite P and the satellite Q transmit satellite signals to the receiver a and the receiver B simultaneously, a single difference is found between the receiver a and the receiver B, expressed as:
Where lambda represents the wavelength of the carrier wave, Representing the carrier phase observations of satellite P to receiver a,Representing the geometrical distance of the receiver a from the satellite P,Representing the clock difference for receiver a, V ion represents the ionospheric delay, V trop calibrates the tropospheric delay,Other errors (multipath errors, hardware noise, etc.) are represented.
Further set up: The above equation can be further simplified to:
Assuming that receiver a is very close to receiver B, the ionospheric and tropospheric delays can be eliminated, i.e The above equation can be further simplified to:
after the difference between the receivers, satellite ephemeris error, ionospheric delay, and tropospheric delay can be eliminated. A single difference observation is obtained, on the basis of which a secondary difference is found between satellite P and satellite Q, expressed as:
Further set up: The above equation can be further simplified to:
After the difference between satellites, the influence of the receiver clock error can be eliminated, and the high positioning accuracy can be obtained by introducing the baseline vector, the integer ambiguity, the receiver clock error and the satellite clock error into an observation equation, wherein the position parameters are about 10. Preferably, in this embodiment, the method for constructing the double-difference pseudo range may refer to the construction of the double-difference carrier, which is expressed as: and will not be described in detail herein.
Furthermore, the ambiguity floating solution is obtained based on the constructed double-difference pseudo-range and carrier wave, and in this embodiment, the ambiguity floating solution can be extracted by a least square method, which can be specifically expressed as:
In the method, in the process of the invention, Representing the double-difference pseudorange observations,Representing a double difference carrier phase observation, deltap representing a correction amount of the position of the measuring station, G n×3 representing a coefficient matrix, I n×n representing an n-order identity matrix,Representing the double difference observables of the measurement station approximate coordinate inversion,Representing a measurement station ambiguity resolution.
Step S4: a subset of ambiguities is selected based on the integer ambiguity floating solution.
Further, the selecting of the ambiguity subset includes: and selecting a multi-strategy ambiguity subset.
The method comprises the following specific steps:
step S41: and arranging the elevation angles of all satellites according to the height, sequentially removing satellites with the smallest elevation angles, fixing the integer ambiguity by using an LAMBDA algorithm until the integer ambiguity is successfully fixed or the number of subsets is smaller than a threshold value A, and ending the strategy.
Step S42: and arranging the carrier-to-noise ratios of all satellites according to the height, sequentially removing satellites with the lowest carrier-to-noise ratios, fixing the integer ambiguity by using an LAMBDA algorithm until the integer ambiguity is successfully fixed or the number of subsets is smaller than a threshold value A, and ending the strategy.
Step S43: arranging double-difference residuals of all satellites according to the size, sequentially removing the satellite with the largest double-difference residual, fixing the whole-cycle ambiguity by using an LAMBDA algorithm until the whole-cycle ambiguity is successfully fixed or the number of subsets is smaller than a threshold value A, ending the strategy, and specifically, expressing the double-difference residuals as:
Step S44: and arranging the whole-cycle ambiguity variances of all satellites according to the sizes, sequentially fixing the whole-cycle ambiguity of the satellite with the largest whole-cycle ambiguity variance by using an LAMBDA algorithm until the whole-cycle ambiguity is successfully fixed or the number of subsets is smaller than a threshold value A.
The integer ambiguity variance specifically obtains an original variance matrix through the double-difference pseudo-range and the double-difference carrier equation in the step S3 after the least square solution, but the original variance matrix has strong correlation, and further reduces the correlation among the integer ambiguities through a de-correlation process to obtain a de-correlation variance matrix, wherein diagonal elements of the de-correlation variance matrix are integer ambiguity variances, and in the embodiment of the invention, the calculation of the integer ambiguity variances is common knowledge in the field and is not repeated herein.
The LAMBDA algorithm, namely a least square ambiguity reduction correlation adjustment method, is an ambiguity fixing method widely adopted at present, and mainly comprises two parts, wherein the first part is a multidimensional integer transformation for reducing the correlation between ambiguity parameters; and the second part is to search the ambiguity in the converted space, and then convert the result back into the ambiguity space, so as to obtain the integer ambiguity solution. The specific algorithm content can be found in RTKLIB open source software, and the partial function can be directly called when in use.
Step S45: the above strategies proceed in parallel. Preferably, through four partial ambiguity fixed subset selection strategies, the reliability of RTK positioning accuracy is ensured while the whole-cycle ambiguity can be fixed.
Step S45: and selecting the largest subset of the satellite numbers with the fixed integer ambiguity as the final subset.
Step S5: referring to fig. 3 and 4, an RTK (real-time dynamic differential positioning) positioning solution is performed according to the selected subset to obtain the position change condition of the measuring station for GNSS deformation monitoring.
The technical features of the above-described embodiments may be arbitrarily combined, and all possible combinations of the technical features in the above-described embodiments are not described for brevity of description, however, as long as there is no contradiction between the combinations of the technical features, they should be considered as the scope of the description.
The above examples illustrate only a few embodiments of the invention, which are described in detail and are not to be construed as limiting the scope of the invention. It should be noted that it will be apparent to those skilled in the art that several variations and modifications can be made without departing from the spirit of the invention, which are all within the scope of the invention. Accordingly, the scope of protection of the present invention is to be determined by the appended claims.
Claims (7)
1. A method for GNSS deformation monitoring, comprising the steps of:
Constructing a data set and a pseudo-range prediction model based on GNSS historical data;
Receiving online data of a measuring station and a reference station, and performing cycle slip detection; if cycle slip is generated, eliminating satellite data of the measuring station and the reference station, and if cycle slip is not generated, acquiring satellite data of the measuring station and the reference station;
constructing double-difference pseudo-range and double-difference carrier phase based on the satellite data after cycle slip detection, and transmitting integer ambiguity floating solution;
selecting a subset of ambiguities based on the integer ambiguity floating solution;
performing RTK positioning solution according to the selected subset;
Wherein, the cycle slip detection comprises the following steps:
Recombining the dual-frequency observations into a MW combination and a GF combination;
judging whether the satellite signal generates cycle slip or not;
The acquisition of the satellite data of the measuring station comprises the following steps:
Single-point positioning and resolving of satellite data of a measuring station;
rule matching is carried out on the solved data and the data set;
If the rule matching is successful, acquiring a corresponding pseudo range and carrier data, and correcting the pseudo range, otherwise, carrying out pseudo range prediction through a prediction model, and correcting the pseudo range according to the predicted pseudo range;
Wherein the selecting of the ambiguity subset includes: and selecting a multi-strategy ambiguity subset.
2. The GNSS deformation monitoring method of claim 1, wherein: the dataset comprises: satellite signal elevation, carrier-to-noise ratio, azimuth, and pseudorange errors.
3. The GNSS deformation monitoring method of claim 2, wherein: the construction of the pseudo-range prediction model comprises the following steps:
constructing a relation between input quantity and output quantity;
Combining the distribution target value and the predicted value;
And optimizing to obtain a prediction model.
4. The GNSS deformation monitoring method of claim 1, wherein: the determining whether the satellite signal generates cycle slip includes:
GF combination: filtering by using polynomial fitting;
MW combinations: and filtering by adopting a recurrence formula.
5. The GNSS deformation monitoring method of claim 1, wherein: the construction of the double-difference pseudo-range and the double-difference carrier phase comprises the following steps:
Obtaining a single difference between the pseudo range and the carrier wave between the two receivers;
a secondary difference is made between the pseudorange and the carrier between the two satellites.
6. The GNSS deformation monitoring method of claim 1, wherein: the multi-strategy includes:
Sequentially removing satellites with the smallest elevation angles until the whole cycle ambiguity is successfully fixed or the number of subsets is smaller than a threshold value;
sequentially removing satellites with the minimum carrier-to-noise ratio until the whole-cycle ambiguity is successfully fixed or the number of subsets is smaller than a threshold value;
sequentially removing satellites with minimum double-difference residual errors until the whole-cycle ambiguity is successfully fixed or the number of subsets is smaller than a threshold value;
Sequentially removing satellites with maximum whole-cycle ambiguity variance until the whole-cycle ambiguity is successfully fixed or the number of subsets is smaller than a threshold value;
the above-mentioned rejection processes are parallel.
7. The method for monitoring deformation of a GNSS as defined in claim 6, wherein: the ambiguity subset selection includes: and selecting the largest subset of the satellite numbers with the fixed integer ambiguity as the final subset.
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