CN118226481A - Big dipper high accuracy safety monitoring algorithm based on atmospheric delay constraint - Google Patents
Big dipper high accuracy safety monitoring algorithm based on atmospheric delay constraint Download PDFInfo
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- 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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- 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
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Abstract
The Beidou high-precision safety monitoring algorithm based on the atmospheric delay constraint comprises the steps of firstly acquiring satellite observation data of monitoring points and coordinates of a reference station, then processing the observation data, and then constructing an atmospheric constraint baseline resolving function model and a random model; then, carrying out baseline calculation on the observed value, carrying out quality control on a baseline calculation result, and then, calculating based on parameters with fixed ambiguity to obtain a baseline vector; and (3) carrying out quality evaluation on the baseline vector, carrying out quality control again after disqualification until qualification, carrying out three-dimensional unconstrained adjustment calculation on qualification, carrying out inspection, processing the disqualification until all the qualification, carrying out two-dimensional constrained adjustment calculation, and carrying out inspection again until qualification, thereby obtaining the coordinate information of the monitoring point. The invention is based on single difference processing and additional atmospheric delay constraint, and can be used for relaxing the requirements of baseline solution on the common-view satellite and the influence of insufficient atmospheric delay modeling information on the baseline solution precision, thereby meeting the requirements of high-precision safety monitoring in various scenes.
Description
Technical Field
The invention relates to improvement of a safety monitoring algorithm, belongs to the field of satellite positioning, and particularly relates to a Beidou high-precision safety monitoring algorithm based on atmospheric delay constraint.
Background
The double-difference observation model for traditional deformation monitoring (namely safety monitoring) has relatively simple structure, so that the error influence (clock difference and hardware delay) related to a satellite end and a receiver end can be effectively eliminated; weakening the influence of atmospheric delay errors (ionospheric delay, tropospheric delay) and satellite orbits, etc.; the integral characteristic of double-difference ambiguity is reserved, and the method is widely applied to real-time precise positioning, however, for safety monitoring, the safety monitoring is generally in a complex observation environment, for example, under the observation condition of typical dam deformation monitoring, each monitoring point can only observe satellites in partial altitude angles and azimuth angles due to shielding by surrounding mountain bodies, so that the high-precision deformation monitoring is adversely affected, the space-atmosphere correlation of a region where a site is located is strong, the weather condition of a vertical gradient corresponding to a large-altitude difference has non-stationary characteristic, the tropospheric delay difference is obvious, the number of common-view satellites between a reference point and the monitoring point is limited, the measurement of the dam deformation quantity is greatly affected, and the atmospheric delay influence caused by the large-altitude difference cannot be sufficiently eliminated in a double-altitude model, so that the high-precision complex scene safety monitoring cannot be realized.
The application number is CN202110394787.4, and the Chinese patent application of the application date is 2021, 4 months and 13 days discloses a Beidou/GNSS network RTK algorithm suitable for high-precision deformation monitoring, and relates to the technical field of deformation monitoring, and the implementation process is as follows: the monitoring end and GNSS continuous operation reference station data are transmitted back to the server end, and the server end divides the regional subnetwork according to the position of the reference station; constructing a GNSS observation equation of a reference station, fixing double-difference ambiguities of all baselines in a reference station network, and extracting delay information of an inclined path ionosphere and a zenith troposphere of the baselines of the GNSS reference station; the server side selects a proper reference station subnet and a main reference station according to the position of the monitoring station, and interpolates to obtain the information of the inclined path ionosphere and the zenith troposphere between the main reference station and the monitoring station; the atmospheric correction value and the precision information thereof are used as virtual observation values and are input into an observation equation between a built main reference station and a monitoring station, compared with a conventional GNSS (Global navigation satellite System) monitoring algorithm, the application range of the algorithm of a comparison file is wide, the applicability is strong, the monitoring cost can be effectively saved, the monitoring performance can be improved, but the problem that high-precision safety monitoring cannot be carried out in a complex scene is not solved in the technology.
The disclosure of this background section is only intended to increase the understanding of the general background of the present patent application and should not be taken as an admission or any form of suggestion that this information forms the prior art already known to a person of ordinary skill in the art.
Disclosure of Invention
The invention aims to solve the problem that high-precision safety monitoring cannot be performed in a complex scene in the prior art, and provides a Beidou high-precision safety monitoring algorithm based on atmospheric delay constraint, which can perform high-precision safety monitoring in the complex scene.
In order to achieve the above object, the technical solution of the present invention is: the big Dipper high-precision safety monitoring algorithm based on the atmospheric delay constraint comprises the following steps:
Firstly, acquiring satellite observation data of monitoring points and reference station coordinates, preprocessing the observation data, detecting integrity, removing abnormal observation data, and constructing a Beidou base line calculation model of single difference between stations from a non-difference model by using the residual normal observation values;
Step two, adding an ionospheric pseudo-observation value to the Beidou baseline resolving model of the single difference between the stations and establishing an atmosphere constraint baseline resolving model by single difference troposphere delay information;
Step three, carrying out baseline calculation through a baseline calculation model of the atmospheric constraint information to obtain a baseline calculation value and a variance-covariance matrix thereof, and constructing a variance calculation random model according to the variance-covariance matrix;
Step four, quality control is carried out on the baseline solution data, then an unbiased atmosphere constraint baseline solution model is constructed, and then partial ambiguity fixed parameter estimation is carried out on the unbiased atmosphere constraint baseline solution model, so as to obtain a corresponding baseline vector;
Step five, carrying out quality evaluation on the baseline vector calculated by the baseline, if the baseline vector is qualified, carrying out step six, and if the baseline vector is not qualified, returning to step four to restart until the baseline vector is qualified;
Step six, firstly substituting the qualified baseline vector into a network adjustment solution function model to construct a three-dimensional unconstrained adjustment solution model, and then substituting the reference station coordinate into the three-dimensional unconstrained adjustment solution model to perform three-dimensional unconstrained adjustment solution to obtain a three-dimensional unconstrained adjustment solution result;
Step seven, checking a three-dimensional unconstrained adjustment solution result, and if the three-dimensional unconstrained adjustment solution result is checked to be qualified, converting qualified three-dimensional coordinates into a two-dimensional plane coordinate system by combining a variance-covariance matrix; detecting and eliminating rough differences in turn, carrying out three-dimensional unconstrained adjustment on the eliminated result to obtain three-dimensional coordinates after adjustment, then converting the three-dimensional coordinates after adjustment into a two-dimensional plane coordinate system by combining variance-covariance matrix, and carrying out two-dimensional constraint adjustment solution on the two-dimensional plane coordinate system to obtain a two-dimensional constraint adjustment solution result;
Step eight, checking a two-dimensional constraint adjustment solution result, if the two-dimensional constraint adjustment solution result is qualified, namely the coordinate information of the monitoring point, and ending the whole process; if the detection is unqualified, deleting the two-dimensional baseline observation value with larger error, then carrying out two-dimensional constraint adjustment calculation on the rest baseline observation value again, then carrying out detection again on the new two-dimensional constraint adjustment calculation result, and sequentially cycling until the detection is qualified, so as to obtain the coordinate information of the monitoring point, and ending the whole process.
The Beidou baseline resolving model for constructing the inter-station single difference by combining the non-difference model comprises the following specific steps: hypothetical subscriptAndRespectively representing the mobile station and the reference station, and obtaining the single difference Beidou observation equation between stations: ; wherein/> The number of the epoch is represented,Differential values between stations representing differential observations between GNSS pseudo-ranges and carrier phase stations minus geometrical distances between satellites to receivers, respectively,Representing mathematical expectations; /(I)An inter-station differential value representing a geometric distance between the satellite and the receiver; /(I)Representing station star pair linearization vectors,Respectively representing the clock differences of the receiver of the inter-station difference component; /(I)Representing inter-station differential component troposphere zenith wet delay; /(I)A projection function representing tropospheric wet delay; /(I)A first order ionospheric bias delay at a first frequency representing an inter-station differential; /(I)Representing ionospheric delay conversion coefficients between different frequencies; /(I)Respectively representing inter-station difference amounts of pseudo-range bias of a receiver end; /(I)A receiver end phase deviation representing the inter-station difference; /(I)Represents theCarrier wavelength of frequency; /(I)Indicating the integer ambiguity of the inter-station difference.
According to the inter-station differential observation equation, after inter-station differential, only satellite related deviation, such as satellite clock error and satellite phase deviation, can be eliminated, and at the moment, a corresponding GNSS inter-station single-difference model cannot be directly and independently estimated due to model rank deficiency caused by linear correlation among parameters, therefore, a full-value observation equation is required to be constructed through parameter recombination, and the equation is as follows:
Receiver clock error Receiver code biasPhase deviation from receiverRank deficiency between, selecting receiver code bias/>, in the form of ionosphere combinationFor this purpose, the differential observation equation at this time can be rewritten as:
；
In the belt The superscript parameters represent the reorganized parameters,Represented is a ionosphere combination;
for receiver phase deviation And ambiguityRank deficiency caused by linear correlation between the two, selecting one satellite/>, of each systemAmbiguitySelecting as a reference, the differential observation equation between the stations can be further rewritten as:
; the corresponding parameters at this time can be expressed as: /(I) ；
Receiver code biasReceiver phase offsetAnd ionospheric delayRank deficiency caused by linear correlation between the two, and receiver code deviation/>, of geometric independent combination, is selectedFor reference, eliminate reference frequencyThe pseudo-range hardware delay of (2) may be rewritten as:
；
specific, estimatable forms of the corresponding reorganization parameters are:
。
the method for constructing the atmosphere constraint baseline resolving model by adding the ionosphere pseudo-observed value and the single-difference troposphere delay information to the Beidou baseline resolving model of the single difference between stations specifically comprises the following steps:
The ionosphere pseudo-observation value constraint is added on the basis of a Beidou baseline solution model of single difference between stations to construct the following function model:
; in the/> The ionospheric pseudo-observation is in the following specific form:
；
In the method, in the process of the invention, Representing the situation when the frequency number is greater than 2, while the corresponding ionospheric stochastic model construction is inversely weighted/> according to distanceCorresponding variance-biasCan be expressed as:
， for the empirical distance, based on the actual monitoring area reference net distance selection, Height angle information corresponding to the base line;
Constructing a single difference troposphere information constraint model:
；
In the formula, a function model Corresponding fitting parameter informationRepresenting zenith troposphere polynomial coefficients, the coefficients being fit according to stable fiducial points; /(I)Respectively represent two-dimensional coordinate difference values from the measuring point to the reference measuring point,The geodetic height information of the measuring points is represented, and each measuring point is different due to geography and climate; in view of this, it is necessary to adaptively determine,/>, after the geographic environment test according to the baseline pair of each measuring pointNamely, an adjusting coefficient;
After the parameter recombination and the atmospheric delay constraint, the following atmospheric constraint baseline calculation model considering the atmospheric delay can be constructed:
。
In the fourth step, a bias-free atmosphere constraint baseline solution model is constructed through baseline solution data, and the model specifically comprises the following steps: after the Kalman filtering parameter estimation is completed, performing DIA parameter quality control, and processing parameter deviation to construct an unbiased atmosphere constraint baseline solution model, wherein the DIA quality control comprises detection, identification and adjustment and comprises the following specific processes:
The parameters and variances of the Kalman filter estimation are expressed in the form of: Wherein, observe residual/> ；
Expressed as filter gain,ForVariance of the time state vector;
1. Detecting: checking whether the dynamic model has rough difference, and constructing statistics according to the observation value residual error and the weight matrix Setting significance level valueIts critical value isIfThe test amount is obvious, and errors exist in the observed value, so that an identification stage is required to be entered;
2. identification: based on the fact that the model errors exist in the model in the previous step, the step can be used for searching the model errors and finding problematic observation data, and at the moment, the model errors which possibly exist are checked one by one, namely error items are gradually added The following observation equation is constructed:
And constructs test statistics/> : IfThen considerNotably, the error parameters of the most remarkable error term are taken as function model correction terms or the term data is eliminated.
3. And (3) adjusting: after finding the model error based on the identification, correcting the influence of the parameter estimation, and if a plurality of errors exist in the model, repeating the step identification until all error items are not obvious; assume the firstAndIf there is a significant error in the observed value, the corresponding adjustment process can be expressed as:
。
In the step seven, in the process of checking the three-dimensional unconstrained adjustment calculation result, the passing result is based on the absolute value of the three-dimensional unconstrained adjustment baseline component correction The following requirements/>, should be satisfiedIn the above, the ratio ofBaseline accuracy specified for the corresponding level, ifAnd if the result does not meet the requirement, adopting a corresponding method to process, and repeating the steps five to six until the detection adjustment solution is not exceeded, and outputting a final result.
In the seventh step, the two-dimensional plane coordinate system is changed, and the conversion process is carried out according to the following requirements:
，。
in the step eight, in the step of checking the two-dimensional constraint adjustment solution result, the absolute value of the same baseline of the passing result unconstrained adjustment with poor corresponding correction should meet the following requirements: in the above, the ratio of/> If the result does not meet the requirement, the known coordinates and the like as constraints are considered to have some larger-error values, and the larger-error constraint values are deleted and steps seven to eight are repeated until the requirement is met.
Compared with the prior art, the invention has the beneficial effects that:
1. According to the Beidou high-precision safety monitoring algorithm based on the atmospheric delay constraint, an observation equation is built by combining visual satellite observation data corresponding to a reference station and a monitoring station, the clock difference and the code deviation of a receiver, the phase deviation of the receiver, the rank deficiency between ambiguity and an ionosphere are eliminated based on parameter recombination and a rank deficiency elimination theory, a full-rank baseline solution model is built, the requirement of baseline solution on the number of co-vision satellites is relaxed, and the limitation of serious signal shielding on high-precision safety monitoring in a complex environment is solved. Therefore, the invention can cope with various complex scenes, and the safety monitoring precision is higher.
2. According to the Beidou high-precision safety monitoring algorithm based on the atmospheric delay constraint, on a Beidou base line resolving model of single difference between stations, the limitation of atmospheric delay of complex scenes such as high slopes and landslide bodies where the safety monitoring points of complex scenes are located on to the high-precision safety monitoring precision is considered, a multi-parameter plane fitting model related to elevation and position factors is constructed, the influence of residual troposphere delay caused by large fall can be effectively weakened, the integer ambiguity fixing rate is improved, and the rapid high-precision positioning and troposphere delay parameter estimation of each measuring point are realized. Therefore, the invention can reduce the influence of various troposphere delays and rapidly realize high-precision positioning.
3. According to the Beidou high-precision safety monitoring algorithm based on the atmospheric delay constraint, due to the fact that the distribution range of the safety monitoring regional measuring points is limited, the geometric configuration of the regional measuring points where the stations are located is basically consistent, the corresponding space atmosphere correlation is strong, the ionosphere of the two measuring points corresponding to the base line is basically similar, by adding the single difference ionosphere pseudo-observation value and taking the base line length as an independent variable, a random model function is constructed to reasonably constrain the single difference atmosphere between the stations, the model strength is optimized, and the high-precision safety monitoring is promoted. Therefore, the method has higher model accuracy and higher monitoring accuracy on the target.
Drawings
Fig. 1 is a flow chart of the present invention.
Detailed Description
The invention is described in further detail below with reference to the accompanying drawings and detailed description.
Referring to fig. 1, a Beidou high-precision safety monitoring algorithm based on atmospheric delay constraint comprises the following steps:
Firstly, acquiring satellite observation data of monitoring points and reference station coordinates, preprocessing the observation data, detecting integrity, removing abnormal observation data, and constructing a Beidou base line calculation model of single difference between stations from a non-difference model by using the residual normal observation values;
Step two, adding an ionospheric pseudo-observation value to the Beidou baseline resolving model of the single difference between the stations and establishing an atmosphere constraint baseline resolving model by single difference troposphere delay information;
Step three, carrying out baseline calculation through a baseline calculation model of the atmospheric constraint information to obtain a baseline calculation value and a variance-covariance matrix thereof, and constructing a variance calculation random model according to the variance-covariance matrix;
Step four, quality control is carried out on the baseline solution data, then an unbiased atmosphere constraint baseline solution model is constructed, and then partial ambiguity fixed parameter estimation is carried out on the unbiased atmosphere constraint baseline solution model to obtain a corresponding baseline vector;
Step five, carrying out quality evaluation on the baseline vector calculated by the baseline, if the baseline vector is qualified, carrying out step six, and if the baseline vector is not qualified, returning to step four to restart until the baseline vector is qualified;
Step six, firstly substituting the qualified baseline vector into a network adjustment solution function model to construct a three-dimensional unconstrained adjustment solution model, and then substituting the reference station coordinate into the three-dimensional unconstrained adjustment solution model to perform three-dimensional unconstrained adjustment solution to obtain a three-dimensional unconstrained adjustment solution result;
Step seven, checking a three-dimensional unconstrained adjustment solution result, and if the three-dimensional unconstrained adjustment solution result is checked to be qualified, converting qualified three-dimensional coordinates into a two-dimensional plane coordinate system by combining a variance-covariance matrix; detecting and eliminating rough differences in turn, carrying out three-dimensional unconstrained adjustment on the eliminated result to obtain three-dimensional coordinates after adjustment, then converting the three-dimensional coordinates after adjustment into a two-dimensional plane coordinate system by combining variance-covariance matrix, and carrying out two-dimensional constraint adjustment solution on the two-dimensional plane coordinate system to obtain a two-dimensional constraint adjustment solution result;
Step eight, checking a two-dimensional constraint adjustment solution result, if the two-dimensional constraint adjustment solution result is qualified, namely the coordinate information of the monitoring point, and ending the whole process; if the detection is unqualified, deleting the two-dimensional baseline observation value with larger error, then carrying out two-dimensional constraint adjustment calculation on the rest baseline observation value again, then carrying out detection again on the new two-dimensional constraint adjustment calculation result, and sequentially cycling until the detection is qualified, so as to obtain the coordinate information of the monitoring point, and ending the whole process.
The Beidou baseline resolving model for constructing the inter-station single difference by combining the non-difference model comprises the following specific steps: hypothetical subscriptAndRespectively representing the mobile station and the reference station, and obtaining the single difference Beidou observation equation between stations: ; wherein/> The number of the epoch is represented,Differential values between stations representing differential observations between GNSS pseudo-ranges and carrier phase stations minus geometrical distances between satellites to receivers, respectively,Representing mathematical expectations; /(I)An inter-station differential value representing a geometric distance between the satellite and the receiver; /(I)Representing station star pair linearization vectors,Respectively representing the clock differences of the receiver of the inter-station difference component; /(I)Representing inter-station differential component troposphere zenith wet delay; /(I)A projection function representing tropospheric wet delay; /(I)A first order ionospheric bias delay at a first frequency representing an inter-station differential; /(I)Representing ionospheric delay conversion coefficients between different frequencies; /(I)Respectively representing inter-station difference amounts of pseudo-range bias of a receiver end; /(I)A receiver end phase deviation representing the inter-station difference; /(I)Represents theCarrier wavelength of frequency; /(I)Indicating the integer ambiguity of the inter-station difference.
According to the inter-station differential observation equation, after inter-station differential, only satellite related deviation, such as satellite clock error and satellite phase deviation, can be eliminated, and at the moment, a corresponding GNSS inter-station single-difference model cannot be directly and independently estimated due to model rank deficiency caused by linear correlation among parameters, therefore, a full-value observation equation is required to be constructed through parameter recombination, and the equation is as follows:
Receiver clock error Receiver code biasPhase deviation from receiverRank deficiency between, selecting receiver code bias/>, in the form of ionosphere combinationFor this purpose, the differential observation equation at this time can be rewritten as:
；
In the belt The superscript parameters represent the reorganized parameters,Represented is a ionosphere combination;
for receiver phase deviation And ambiguityRank deficiency caused by linear correlation between the two, selecting one satellite/>, of each systemAmbiguitySelecting as a reference, the differential observation equation between the stations can be further rewritten as:
; the corresponding parameters at this time can be expressed as: /(I) ；
Receiver code biasReceiver phase offsetAnd ionospheric delayRank deficiency caused by linear correlation between the two, and receiver code deviation/>, of geometric independent combination, is selectedFor reference, eliminate reference frequencyThe pseudo-range hardware delay of (2) may be rewritten as:
；
specific, estimatable forms of the corresponding reorganization parameters are:
。
the method for constructing the atmosphere constraint baseline resolving model by adding the ionosphere pseudo-observed value and the single-difference troposphere delay information to the Beidou baseline resolving model of the single difference between stations specifically comprises the following steps:
The ionosphere pseudo-observation value constraint is added on the basis of a Beidou baseline solution model of single difference between stations to construct the following function model:
; in the/> The ionospheric pseudo-observation is in the following specific form:
；
In the method, in the process of the invention, Representing the situation when the frequency number is greater than 2, while the corresponding ionospheric stochastic model construction is inversely weighted/> according to distanceCorresponding variance-biasCan be expressed as:
， for the empirical distance, based on the actual monitoring area reference net distance selection, Height angle information corresponding to the base line;
Constructing a single difference troposphere information constraint model:
；
In the formula, a function model Corresponding fitting parameter informationRepresenting zenith troposphere polynomial coefficients, the coefficients being fit according to stable fiducial points; /(I)Respectively represent two-dimensional coordinate difference values from the measuring point to the reference measuring point,The geodetic height information of the measuring points is represented, and each measuring point is different due to geography and climate; in view of this, it is necessary to adaptively determine,/>, after the geographic environment test according to the baseline pair of each measuring pointNamely, an adjusting coefficient;
After the parameter recombination and the atmospheric delay constraint, the following atmospheric constraint baseline calculation model considering the atmospheric delay can be constructed:
。
In the fourth step, the quality control is performed on the baseline calculation data, and then an unbiased atmosphere constraint baseline calculation model is constructed specifically as follows: after the Kalman filtering parameter estimation is completed, performing DIA parameter quality control, and processing parameter deviation so as to construct an unbiased atmosphere constraint baseline solution model, wherein the specific process is as follows:
The parameters and variances of the Kalman filter estimation are expressed in the form of: Wherein, observe residual/> ；
Expressed as filter gain,ForVariance of the time state vector;
1. Detecting: checking whether the dynamic model has rough difference, and constructing statistics according to the observation value residual error and the weight matrix Setting significance level valueIts critical value isIfThe test amount is obvious, and errors exist in the observed value, so that an identification stage is required to be entered;
2. identification: based on the fact that the model errors exist in the model in the previous step, the step can be used for searching the model errors and finding problematic observation data, and at the moment, the model errors which possibly exist are checked one by one, namely error items are gradually added The following observation equation is constructed:
And constructs test statistics/> : IfThen considerNotably, the error parameters of the most remarkable error term are taken as function model correction terms or the term data is eliminated.
3. And (3) adjusting: after finding the model error based on the identification, correcting the influence of the parameter estimation, and if a plurality of errors exist in the model, repeating the step identification until all error items are not obvious; assume the firstAndIf there is a significant error in the observed value, the corresponding adjustment process can be expressed as:
。
In the step seven, in the process of checking the three-dimensional unconstrained adjustment calculation result, the passing result is based on the absolute value of the three-dimensional unconstrained adjustment baseline component correction The following requirements/>, should be satisfiedIn the above, the ratio ofBaseline accuracy specified for the corresponding level, ifAnd if the result does not meet the requirement, adopting a corresponding method to process, and repeating the steps five to six until the detection adjustment solution is not exceeded, and outputting a final result.
In the step seven, in the process of checking the three-dimensional unconstrained adjustment calculation result, the passing result is based on the absolute value of the three-dimensional unconstrained adjustment baseline component correctionThe following requirements/>, should be satisfiedIn the above, the ratio ofBaseline accuracy specified for the corresponding level, ifAnd if the result does not meet the requirement, adopting a corresponding method to process, and repeating the steps five to six until the detection adjustment solution is not exceeded, and outputting a final result.
In the seventh step, the two-dimensional plane coordinate system is changed, and the conversion process is carried out according to the following requirements:
，。
in the step eight, in the step of checking the two-dimensional constraint adjustment solution result, the absolute value of the same baseline of the passing result unconstrained adjustment with poor corresponding correction should meet the following requirements: in the above, the ratio of/> If the result does not meet the requirement, the known coordinates and the like as constraints are considered to have some larger-error values, and the larger-error constraint values are deleted and steps seven to eight are repeated until the requirement is met.
The principle of the invention is explained as follows:
Considering the restriction of the non-stationary characteristic of the meteorological condition of the vertical gradient under the condition of large height difference on the estimation precision of the atmospheric delay, by adding the single-difference ionosphere pseudo-observation value and ionosphere constraint information, taking the baseline length and the height difference between stations as independent variables, constructing a function model and a random model to reasonably constrain the single-difference atmospheric delay between stations, solving the restriction of inaccurate atmospheric delay estimation on the calculation precision of the baseline, and providing reliable technical support for high-precision safety monitoring of a high-rise dam.
Example 1:
the big Dipper high-precision safety monitoring algorithm based on the atmospheric delay constraint comprises the following steps:
Firstly, acquiring satellite observation data of monitoring points and reference station coordinates, preprocessing the observation data, detecting integrity, removing abnormal observation data, and constructing a Beidou base line calculation model of single difference between stations from a non-difference model by using the residual normal observation values;
Step two, adding an ionospheric pseudo-observation value to the Beidou baseline resolving model of the single difference between the stations and establishing an atmosphere constraint baseline resolving model by single difference troposphere delay information;
Step three, carrying out baseline calculation through a baseline calculation model of the atmospheric constraint information to obtain a baseline calculation value and a variance-covariance matrix thereof, and constructing a variance calculation random model according to the variance-covariance matrix;
Step four, quality control is carried out on the baseline solution data, then an unbiased atmosphere constraint baseline solution model is constructed, and then partial ambiguity fixed parameter estimation is carried out on the unbiased atmosphere constraint baseline solution model to obtain a corresponding baseline vector;
Step five, carrying out quality evaluation on the baseline vector calculated by the baseline, if the baseline vector is qualified, carrying out step six, and if the baseline vector is not qualified, returning to step four to restart until the baseline vector is qualified;
Step six, firstly substituting the qualified baseline vector into a network adjustment solution function model to construct a three-dimensional unconstrained adjustment solution model, and then substituting the reference station coordinate into the three-dimensional unconstrained adjustment solution model to perform three-dimensional unconstrained adjustment solution to obtain a three-dimensional unconstrained adjustment solution result;
Step seven, checking a three-dimensional unconstrained adjustment solution result, and if the three-dimensional unconstrained adjustment solution result is checked to be qualified, converting qualified three-dimensional coordinates into a two-dimensional plane coordinate system by combining a variance-covariance matrix; detecting and eliminating rough differences in turn, carrying out three-dimensional unconstrained adjustment on the eliminated result to obtain three-dimensional coordinates after adjustment, then converting the three-dimensional coordinates after adjustment into a two-dimensional plane coordinate system by combining variance-covariance matrix, and carrying out two-dimensional constraint adjustment solution on the two-dimensional plane coordinate system to obtain a two-dimensional constraint adjustment solution result;
Step eight, checking a two-dimensional constraint adjustment solution result, if the two-dimensional constraint adjustment solution result is qualified, namely the coordinate information of the monitoring point, and ending the whole process; if the detection is unqualified, deleting the two-dimensional baseline observation value with larger error, then carrying out two-dimensional constraint adjustment calculation on the rest baseline observation value again, then carrying out detection again on the new two-dimensional constraint adjustment calculation result, and sequentially cycling until the detection is qualified, so as to obtain the coordinate information of the monitoring point, and ending the whole process.
Example 2:
example 2 is substantially the same as example 1 except that:
The big dipper high accuracy safety monitoring algorithm based on atmospheric delay constraint, the preliminary treatment in step one adopts cycle slip to survey specifically to be:
because the MW combination observed quantity inter-epoch variation is smaller under the condition that no rough difference and cycle slip occur, the patent adopts a multi-epoch smoothing mode to judge whether cycle slip exists or not, and the smoothing mode is as follows: in the/> For the frontIndividual epochAverage value of combined observables,ForCombination of the first and second aspectsCalendar combination observational quantity,ForIs a variance of (2); cycle slip detection can be judged according to the smoothed value of MW combined observed values:
No rough difference and cycle slip;
And/> A coarse difference exists;
And/> There is a cycle slip;
when the adjacent epochs have no rough difference and cycle slip, the ionospheric delay correction information has stable short-time change, so that for geometric independence (GF) combination, the cycle slip can be judged by adopting the difference between the adjacent epochs, and the corresponding judgment formula can be expressed as follows: In/> AndCarrier phase observations and ionosphere parameters, respectively,Is carrier phase wavelength,Representing the threshold, detecting the satellite with cycle slip by Wei Xingtan and eliminating the data of the satellite.
The autonomous integrity monitoring specifically comprises: the method for rapidly monitoring satellite faults by using redundant observables is called autonomous integrity monitoring of a dam receiver, and is based on the pseudorange observables in GNSS if in epochThe satellite is synchronously observed, and the error equation is as follows: /(I)；
In the method, in the process of the invention,ForSubtracting the guard range calculated value vector from the dimension pseudo-range observed value; /(I)ForDesigning a matrix in order; /(I)A 4-dimensional parameter vector to be estimated, which is formed by the position of the receiver and the clock error of the receiver; /(I)RepresentationA residual vector of the pseudo-range observation value is maintained;
on the premise that the observation residual is in the premise, the observation noise of the receiver obeys Gaussian distribution, Obeying the zero-mean integral Gaussian distribution, and setting the variance of the components asResidual isAccording to the theory of probability statisticsSubject to degrees of freedom ofChi-square distribution of (C); When there is a coarse difference in the observed quantity,Non-central chi-square distribution obeying degree of freedom m-4Wherein, the method comprises the steps of, wherein,Representation ofNon-central parameters of distribution, and therefore, can be based onThe distribution characteristics of the satellite are used for detecting the fault satellite.
In the absence of faults, probability density functionsIndicating that no pseudo-range fault occurs when the system is in a normal state, and if the system sends out a monitoring Alarm, the system is a False Alarm (FA), and the False Alarm rateCan be expressed as: in the above, the ratio of/> AndThe quantiles representing the distribution, the values of which are defined byAnd degrees of freedomDetermining that the due formula holds: /(I)；
When there is a fault, the probability density isWhen pseudo-range fault occurs, if the system does not generate monitoring alarm, probabilityExpressed as: /(I)In the presence of faults,By、AndDetermining that the application satisfiesIn the application process, the solution valueAnd monitoring thresholdBy comparing, the probability value is combined to judge whether the fault exists, and the satellite-by-satellite judgment can be realized in the process, so that the specific satellite in which the fault exists can be determined.
Example 3:
example 3 is substantially the same as example 1 except that:
the big Dipper high-precision safety monitoring algorithm based on the atmospheric delay constraint is characterized in that the big Dipper baseline resolving model for constructing the inter-station single difference by combining the non-difference model is specifically as follows: hypothetical subscript AndRespectively representing the mobile station and the reference station, and obtaining the single difference Beidou observation equation between stations:
; wherein/> Representing epoch number,Differential values between stations representing differential observations between GNSS pseudo-ranges and carrier phase stations minus geometrical distances between satellites to receivers, respectively,Representing mathematical expectations; /(I)An inter-station differential value representing a geometric distance between the satellite and the receiver; /(I)Representing station star pair linearization vectors,Respectively representing the clock differences of the receiver of the inter-station difference component; /(I)Representing inter-station differential component troposphere zenith wet delay; /(I)A projection function (dimensionless) representing tropospheric wet retardation; /(I)A first order ionospheric bias delay at a first frequency representing an inter-station differential; /(I)Representing ionospheric delay conversion coefficients (dimensionless) between different frequencies; /(I)Respectively representing inter-station difference amounts of pseudo-range bias of a receiver end; /(I)A receiver end phase deviation representing the inter-station difference; /(I)Represents theCarrier wavelength of frequency; /(I)The unit of the whole-cycle ambiguity (unit: cycle) representing the inter-station difference is meter, unless otherwise specified, among the above parameters.
According to the inter-station differential observation equation, after inter-station differential, only satellite related deviation, such as satellite clock error and satellite phase deviation, can be eliminated, and at the moment, a corresponding GNSS inter-station single-difference model cannot be directly and independently estimated due to model rank deficiency caused by linear correlation among parameters, therefore, a full-value observation equation is required to be constructed through parameter recombination, and the equation is as follows:
Receiver clock error Receiver code biasPhase deviation from receiverRank deficiency between, selecting receiver code bias/>, in the form of ionosphere combinationFor this purpose, the differential observation equation at this time can be rewritten as:
；
In the belt The superscript parameters represent the reorganized parameters,Represented is a ionosphere combination;
for receiver phase deviation And ambiguityRank deficiency caused by linear correlation between the two, selecting one satellite/>, of each systemAmbiguitySelecting as a reference, the differential observation equation between the stations can be further rewritten as:
; the corresponding parameters at this time can be expressed as: /(I) ；
Receiver code biasReceiver phase offsetAnd ionospheric delayRank deficiency caused by linear correlation between the two, and receiver code deviation/>, of geometric independent combination, is selectedFor reference, eliminate reference frequencyThe pseudo-range hardware delay of (2) may be rewritten as:
；
specific, estimatable forms of the corresponding reorganization parameters are:
whereby rank deficiency between corresponding parameters is eliminated.
Example 4:
example 4 is substantially the same as example 1 except that:
the big dipper high accuracy safety monitoring algorithm based on atmospheric delay constraint, the big dipper baseline solution model of single difference between the stations adds ionosphere pseudo-observed value and single difference troposphere delay information to construct the atmosphere constraint baseline solution model specifically comprises:
The length of a base line between a corresponding monitoring point and a reference point of an actual safety monitoring area is generally within 5km, so that the geometric configuration of the measuring point of the area where a station is located is basically consistent, the corresponding space atmosphere correlation is stronger, and therefore, the ionosphere pseudo-observation value constraint can be added on the basis of a Beidou base line calculation model of single difference between stations to construct the following function model:
; in the/> The ionospheric pseudo-observation is in the following specific form:
；
In the method, in the process of the invention, Representing the situation when the frequency number is greater than 2, while the corresponding ionospheric stochastic model construction is inversely weighted/> according to distanceCorresponding variance-biasCan be expressed as: /(I)
，For the empirical distance, based on the actual monitoring area reference net distance selection,The ionosphere parameter can be reasonably calculated by the constraint of the function model and the random model for the altitude angle information corresponding to the base line;
Aiming at safety monitoring in the high mountain gorge valley region, the weather condition of the vertical gradient corresponding to the large altitude difference has non-stationary characteristics, the troposphere delay difference is obvious, the conventional observation model for neglecting the troposphere influence by the short base line is not applicable any more, the single-difference troposphere information constraint model is built, the gradient change, the nonlinear change and the elevation change characteristics of the troposphere in different directions are fully considered, and the single-difference troposphere information constraint model is built:
；
In the formula, a function model Corresponding fitting parameter informationRepresenting zenith troposphere polynomial coefficients, the coefficients being fit according to stable fiducial points; /(I)Respectively represent two-dimensional coordinate difference values from the measuring point to the reference measuring point,The geodetic height information of the measuring points is represented, and each measuring point is different due to geography and climate; in view of this, it is necessary to adaptively determine,/>, after the geographic environment test according to the baseline pair of each measuring pointNamely, the adjustment coefficients are evaluated according to the two adjustment coefficients, so as to determine the most suitable fitting model, and corresponding fitting parameter information/>, in the fitting processThe method is obtained according to stable datum point fitting, in the process, attention is paid to construction of a random model, and variance information is assisted according to a Gaussian function:
modeling, corresponding parameters/> To correspond to the distance of the monitoring point from the reference point,As an empirical tropospheric area accuracy factor;
After the parameter recombination and the atmospheric delay constraint, the following atmospheric constraint baseline calculation model considering the atmospheric delay can be constructed:
。
Example 5:
Example 5 is substantially the same as example 1 except that:
After the Kalman filtering parameter estimation is completed, the DIA parameter quality control is carried out, the parameter deviation is processed so as to construct an atmospheric constraint baseline solution model without deviation, and the DIA quality control comprises detection, identification and adjustment and comprises the following specific processes:
The parameters and variances of the Kalman filter estimation are expressed in the form of: Wherein, observe residual/> ；/>
Expressed as filter gain,ForVariance of the time state vector;
1. Detecting: checking whether the dynamic model has rough difference, and constructing statistics according to the observation value residual error and the weight matrix Setting significance level valueIts critical value isIfThe test amount is obvious, and errors exist in the observed value, so that an identification stage is required to be entered;
2. identification: based on the fact that the model errors exist in the model in the previous step, the step can be used for searching the model errors and finding problematic observation data, and at the moment, the model errors which possibly exist are checked one by one, namely error items are gradually added The following observation equation is constructed:
And constructs test statistics/> : IfThen considerNotably, taking the error parameter of the most remarkable error term as a function model correction term or eliminating the term data;
3. and (3) adjusting: after the model error is found based on identification, correcting the influence of the parameter estimation, and if a plurality of errors exist in the model, repeating identification until all error items are not obvious; assume the first AndIf there is a significant error in the observed value, the corresponding adjustment process can be expressed as:
。
the above description is merely of preferred embodiments of the present invention, and the scope of the present invention is not limited to the above embodiments, but all equivalent modifications or variations according to the present disclosure will be within the scope of the claims.
Claims (10)
1. The big dipper high accuracy safety monitoring algorithm based on atmospheric delay constraint, its characterized in that: the big dipper high-precision safety monitoring algorithm based on the atmospheric delay constraint comprises the following steps:
Firstly, acquiring satellite observation data of monitoring points and reference station coordinates, preprocessing the observation data, detecting integrity, removing abnormal observation data, and constructing a Beidou base line calculation model of single difference between stations from a non-difference model by using the residual normal observation values;
Step two, adding an ionospheric pseudo-observation value to the Beidou baseline resolving model of the single difference between the stations and establishing an atmosphere constraint baseline resolving model by single difference troposphere delay information;
Step three, carrying out baseline calculation through a baseline calculation model of the atmospheric constraint information to obtain a baseline calculation value and a variance-covariance matrix thereof, and constructing a variance calculation random model according to the variance-covariance matrix;
Step four, quality control is carried out on the baseline solution data, then an unbiased atmosphere constraint baseline solution model is constructed, and then partial ambiguity fixed parameter estimation is carried out on the unbiased atmosphere constraint baseline solution model to obtain a corresponding baseline vector;
Step five, carrying out quality evaluation on the baseline vector calculated by the baseline, if the baseline vector is qualified, carrying out step six, and if the baseline vector is not qualified, returning to step four to restart until the baseline vector is qualified;
Step six, firstly substituting the qualified baseline vector into a network adjustment solution function model to construct a three-dimensional unconstrained adjustment solution model, and then substituting the reference station coordinate into the three-dimensional unconstrained adjustment solution model to perform three-dimensional unconstrained adjustment solution to obtain a three-dimensional unconstrained adjustment solution result;
Step seven, checking a three-dimensional unconstrained adjustment solution result, and if the three-dimensional unconstrained adjustment solution result is checked to be qualified, converting qualified three-dimensional coordinates into a two-dimensional plane coordinate system by combining a variance-covariance matrix; detecting and eliminating rough differences in turn, carrying out three-dimensional unconstrained adjustment on the eliminated result to obtain three-dimensional coordinates after adjustment, then converting the three-dimensional coordinates after adjustment into a two-dimensional plane coordinate system by combining variance-covariance matrix, and carrying out two-dimensional constraint adjustment solution on the two-dimensional plane coordinate system to obtain a two-dimensional constraint adjustment solution result;
Step eight, checking a two-dimensional constraint adjustment solution result, if the two-dimensional constraint adjustment solution result is qualified, namely the coordinate information of the monitoring point, and ending the whole process; if the detection is unqualified, deleting the two-dimensional baseline observation value with larger error, then carrying out two-dimensional constraint adjustment calculation on the rest baseline observation value again, then carrying out detection again on the new two-dimensional constraint adjustment calculation result, and sequentially cycling until the detection is qualified, so as to obtain the coordinate information of the monitoring point, and ending the whole process.
2. The Beidou high-precision safety monitoring algorithm based on the atmospheric delay constraint is characterized in that: the Beidou baseline resolving model for constructing the inter-station single difference by combining the observed values with the non-difference model comprises the following concrete steps:
Hypothetical subscript AndRespectively representing the mobile station and the reference station, and obtaining the single difference Beidou observation equation between stations: ; wherein/> The number of the epoch is represented,Differential values between stations representing differential observations between GNSS pseudo-ranges and carrier phase stations minus geometrical distances between satellites to receivers, respectively,Representing mathematical expectations; /(I)An inter-station differential value representing a geometric distance between the satellite and the receiver; /(I)Representing station star pair linearization vectors,Respectively representing the clock differences of the receiver of the inter-station difference component; /(I)Representing inter-station differential component troposphere zenith wet delay; /(I)A projection function representing tropospheric wet delay; /(I)A first order ionospheric bias delay at a first frequency representing an inter-station differential; /(I)Representing ionospheric delay conversion coefficients between different frequencies; /(I)Respectively representing inter-station difference amounts of pseudo-range bias of a receiver end; /(I)A receiver end phase deviation representing the inter-station difference; /(I)Represents theCarrier wavelength of frequency; /(I)Indicating the integer ambiguity of the inter-station difference.
3. The Beidou high-precision safety monitoring algorithm based on the atmospheric delay constraint as claimed in claim 2, wherein the algorithm is characterized in that: according to the inter-station single difference Beidou observation equation, after inter-station difference, the deviation related to satellites only, such as satellite clock difference and satellite phase deviation, can be eliminated, and at the moment, the corresponding GNSS inter-station single difference model cannot be directly and independently estimated due to model rank deficiency caused by linear correlation among parameters, therefore, a full-value observation equation is constructed through parameter recombination, and the equation is as follows:
Receiver clock error Receiver code biasPhase deviation from receiverRank deficiency between, selecting receiver code bias/>, in the form of ionosphere combinationFor this purpose, the differential observation equation at this time can be rewritten as:
；
In the belt The superscript parameters represent the reorganized parameters,Represented is a ionosphere combination;
at this time, for the receiver phase deviation And ambiguityRank deficiency caused by linear correlation between the two, selecting one satellite/>, of each systemAmbiguitySelecting as a reference, the differential observation equation between the stations can be further rewritten as:
; the corresponding parameters at this time can be expressed as: ；
then, the receiver code bias Receiver phase offsetAnd ionospheric delayRank deficiency caused by linear correlation between the two, and receiver code deviation/>, of geometric independent combination, is selectedFor reference, eliminate reference frequencyThe pseudo-range hardware delay of (2) may be rewritten as:
；
specific, estimatable forms of the corresponding reorganization parameters are:
。
4. The Beidou high-precision safety monitoring algorithm based on the atmospheric delay constraint is characterized in that: the method for constructing the atmosphere constraint baseline resolving model by adding the ionosphere pseudo-observed value and the single-difference troposphere delay information to the Beidou baseline resolving model of the single difference between the stations specifically comprises the following steps:
The ionosphere pseudo-observation value constraint is added on the basis of a Beidou baseline solution model of single difference between stations to construct the following function model:
; in the/> The ionospheric pseudo-observation is in the following specific form:
；
In the method, in the process of the invention, Representing the situation when the frequency number is greater than 2, while the corresponding ionospheric stochastic model construction is inversely weighted/> according to distanceCorresponding variance-biasCan be expressed as:
， For the empirical distance, according to the actual monitoring area reference net distance selection,/> And the height angle information corresponding to the base line.
5. The Beidou high-precision safety monitoring algorithm based on the atmospheric delay constraint is characterized in that: the single difference troposphere information constraint model is constructed according to the single difference troposphere delay information, and the model is as follows:
；
In the formula, a function model Corresponding fitting parameter informationRepresenting zenith troposphere polynomial coefficients, the coefficients being fit according to stable fiducial points; /(I)Respectively representing two-dimensional coordinate differences from the measuring point to the reference measuring point,The geodetic height information of the measuring points is represented, and each measuring point is different due to geography and climate; in view of this, it is necessary to adaptively determine,/>, after the geographic environment test according to the baseline pair of each measuring pointThe adjustment coefficient is obtained.
6. The Beidou high-precision safety monitoring algorithm based on the atmospheric delay constraint of claim 5 is characterized in that: after the parameter recombination and the atmospheric delay constraint, the following atmospheric constraint baseline calculation model considering the atmospheric delay can be constructed:
。
7. The Beidou high-precision safety monitoring algorithm based on the atmospheric delay constraint is characterized in that: in the fourth step, the quality control is performed on the baseline solution data, and then an unbiased atmosphere constraint baseline solution model is constructed specifically as follows: after the Kalman filtering parameter estimation is completed, performing DIA parameter quality control, and processing parameter deviation to construct an unbiased atmosphere constraint baseline solution model, wherein the DIA quality control comprises detection, identification and adjustment and comprises the following specific processes:
The parameters and variances of the Kalman filter estimation are expressed in the form of: wherein, observe the residual error ；
Expressed as filter gain,ForVariance of the time state vector;
1. Detecting: checking whether the dynamic model has rough difference, and constructing statistics according to the observation value residual error and the weight matrix Setting significance level valueIts critical value isIfThe test amount is obvious, and errors exist in the observed value, so that an identification stage is required to be entered;
2. identification: based on the fact that the model errors exist in the model in the previous step, the step can be used for searching the model errors and finding problematic observation data, and at the moment, the model errors which possibly exist are checked one by one, namely error items are gradually added The following observation equation is constructed:
And constructs test statistics/> : IfThen considerNotably, taking the error parameter of the most remarkable error term as a function model correction term or eliminating the term data;
3. and (3) adjusting: after the model error is found based on identification, correcting the influence of the parameter estimation, and if a plurality of errors exist in the model, repeating identification until all error items are not obvious; assume the first AndIf there is a significant error in the observed value, the corresponding adjustment process can be expressed as: /(I)。
8. The Beidou high-precision safety monitoring algorithm based on the atmospheric delay constraint is characterized in that: in the step seven, in the process of checking the three-dimensional unconstrained adjustment calculation result, the passing result is based on the absolute value of the three-dimensional unconstrained adjustment baseline component correctionThe following requirements/>, should be satisfiedIn the above, the ratio ofBaseline accuracy specified for the corresponding level, ifAnd if the result does not meet the requirement, adopting a corresponding method to process, and repeating the steps five to six until the detection adjustment solution is not exceeded, and outputting a final result.
9. The Beidou high-precision safety monitoring algorithm based on the atmospheric delay constraint is characterized in that: in the seventh step, the two-dimensional plane coordinate system is changed, and the conversion process is carried out according to the following requirements:
，。
10. the Beidou high-precision safety monitoring algorithm based on the atmospheric delay constraint is characterized in that: in the step eight, in the step of checking the two-dimensional constraint adjustment solution result, the absolute value of the same baseline of the passing result unconstrained adjustment with poor corresponding correction should meet the following requirements: in the above, the ratio of/> If the result does not meet the requirement, the known coordinates and the like as constraints are considered to have some larger-error values, and the larger-error constraint values are deleted and steps seven to eight are repeated until the requirement is met.
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