CN107421434A - More base station Multi GNSS Long baselines near real-time deformation monitoring methods - Google Patents
More base station Multi GNSS Long baselines near real-time deformation monitoring methods Download PDFInfo
- Publication number
- CN107421434A CN107421434A CN201710672999.8A CN201710672999A CN107421434A CN 107421434 A CN107421434 A CN 107421434A CN 201710672999 A CN201710672999 A CN 201710672999A CN 107421434 A CN107421434 A CN 107421434A
- Authority
- CN
- China
- Prior art keywords
- mover
- mrow
- ambiguity
- mtd
- station
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
- 238000012544 monitoring process Methods 0.000 title claims abstract description 69
- 238000000034 method Methods 0.000 title claims abstract description 43
- 238000012545 processing Methods 0.000 claims abstract description 89
- 238000012216 screening Methods 0.000 claims description 15
- 239000011159 matrix material Substances 0.000 claims description 13
- 230000002159 abnormal effect Effects 0.000 claims description 4
- 239000005436 troposphere Substances 0.000 claims description 4
- 238000004458 analytical method Methods 0.000 claims description 3
- 238000007499 fusion processing Methods 0.000 claims description 3
- 230000008569 process Effects 0.000 claims description 3
- 230000007704 transition Effects 0.000 claims description 3
- 238000004364 calculation method Methods 0.000 abstract description 8
- 238000012958 reprocessing Methods 0.000 abstract 1
- 238000011269 treatment regimen Methods 0.000 abstract 1
- XEEYBQQBJWHFJM-UHFFFAOYSA-N Iron Chemical compound [Fe] XEEYBQQBJWHFJM-UHFFFAOYSA-N 0.000 description 4
- 238000010586 diagram Methods 0.000 description 3
- 238000005516 engineering process Methods 0.000 description 3
- 229910052742 iron Inorganic materials 0.000 description 2
- NAWXUBYGYWOOIX-SFHVURJKSA-N (2s)-2-[[4-[2-(2,4-diaminoquinazolin-6-yl)ethyl]benzoyl]amino]-4-methylidenepentanedioic acid Chemical compound C1=CC2=NC(N)=NC(N)=C2C=C1CCC1=CC=C(C(=O)N[C@@H](CC(=C)C(O)=O)C(O)=O)C=C1 NAWXUBYGYWOOIX-SFHVURJKSA-N 0.000 description 1
- 101150064138 MAP1 gene Proteins 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- FFBHFFJDDLITSX-UHFFFAOYSA-N benzyl N-[2-hydroxy-4-(3-oxomorpholin-4-yl)phenyl]carbamate Chemical compound OC1=C(NC(=O)OCC2=CC=CC=C2)C=CC(=C1)N1CCOCC1=O FFBHFFJDDLITSX-UHFFFAOYSA-N 0.000 description 1
- 230000008859 change Effects 0.000 description 1
- 238000006243 chemical reaction Methods 0.000 description 1
- 238000012937 correction Methods 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 230000006870 function Effects 0.000 description 1
- 101150077939 mapA gene Proteins 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000005457 optimization Methods 0.000 description 1
- 230000000737 periodic effect Effects 0.000 description 1
Classifications
-
- 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
Landscapes
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Position Fixing By Use Of Radio Waves (AREA)
- Mobile Radio Communication Systems (AREA)
Abstract
The invention provides a kind of Multi GNSS Long baselines near real-time deformation monitoring methods based on more base stations, and in order to improve Long baselines calculation accuracy, the present invention reduces influence of the orbit error to Baselines using supper-fast Multi GNSS satellites track product；Using multiple posteriori residuals editor iteration, influence of the data exception to Baselines is proposed as far as possible；Utilize sliding window batch mode, it is possible to achieve near real-time high accuracy Baselines；Using more base station Combined Treatment patterns, the precision and stability of Baselines can be improved；Using the ambiguity resolution strategy of innovation, Baseline Processing Precision is further improved.In order to improve data-handling efficiency, the present invention utilizes normal equation superimposing technique, normal equation information before making full use of, evades resolving repeatability；Handled using common reference station, evade the reprocessing at same datum station；Using non-poor tupe, Distributed Calculation advantage can be made full use of, improves data-handling efficiency.
Description
Technical Field
The invention relates to the technical field of deformation monitoring, in particular to a Multi-GNSS long baseline near real-time deformation monitoring method based on multiple base stations.
Background
GNSS (Global navigation satellite system) deformation monitoring is an important technology concerning the safety of people's lives and properties, and has a great deal of applications in working conditions such as critical house monitoring, landslide monitoring, dam monitoring and the like. The traditional deformation monitoring is only developed for solving specific services, and along with the development of national economy, the requirements of large-scale deformation monitoring services are increasingly obvious, such as national critical house monitoring, long-distance railway line landslide monitoring, long-distance electric power iron tower settlement monitoring and the like.
The GNSS deformation monitoring needs to be provided with a large number of reference stations to support the national millimeter deformation monitoring service, and the base length of the traditional near real-time monitoring service is about 15 kilometers. In order to meet the requirements of large-area span near-real-time deformation monitoring business such as nationwide dangerous house monitoring, long-distance railway line landslide monitoring, long-distance electric power iron tower settlement monitoring and the like, the conventional medium and short base length algorithm cannot meet the requirements of the large-area business, and the conventional processing algorithm has low processing efficiency for the working conditions of tens of thousands of monitoring points. In addition, the traditional deformation monitoring is a service developed for specific requirements, and the user quantity is small.
Disclosure of Invention
The invention provides a method for monitoring the near-real-time millimeter-scale deformation by using a long base line, which improves the processing precision of the near-real-time long base line from the aspects of posterior residual editing iteration, sliding window processing, multi-base station combined processing and ambiguity fixing strategy processing; in order to improve the high-efficiency processing of massive users, in addition to distributed processing, optimization is also performed in the aspects of non-differential mode parallel processing, common reference station processing and normal equation superposition.
The invention solves the following technical problems:
1) the deployment quantity of the domestic reference stations can be effectively reduced, and the resources of the existing reference stations are fully utilized.
2) And a high-efficiency data processing strategy is provided, which comprises a short arc equation combination and non-differential distributed processing mode, so that the computing resources can be effectively reduced, near-real-time deformation monitoring service across the country is provided, and the service cost is reduced.
The technical scheme adopted by the invention is as follows:
a Multi-GNSS long baseline near real-time deformation monitoring method based on multiple base stations is characterized by comprising the following steps:
according to the coordinates of the monitoring points, a reference station analysis is combined, a reference station selection strategy is utilized, and a reference station list participating in resolving is given;
dividing subtasks according to monitoring point data participating in resolving, and executing cloud platform distributed processing;
the cloud platform distributed processing utilizes an ultra-fast Multi-GNSS satellite orbit product to reduce the influence of orbit errors on near-real-time long baseline solution of the Multi-GNSS.
Further, the reference station selection strategy specifically includes the following steps:
step 1, screening a reference station within a distance of 100km according to the coordinates of the monitoring points;
step 2, screening the reference station with the absolute coordinate point repetition rate less than 3mm horizontally and 6mm vertically according to the coordinate stability information of the reference station;
step 3, screening the reference station meeting the admission standard according to the data quality information of the reference station;
step 4, dividing the candidate reference stations into six areas according to the azimuth angles, and selecting the reference station closest to the monitoring point in each area;
and 5, giving a reference station list participating in resolving.
Further, the candidate reference stations are divided into six zones according to the following method:
zone 1, 0-60 °;
zone 2, 60-120 °;
zone 3, 120-180 °;
zone 4, 180-240 °;
zone 5, 240-300 °;
zone 6, 300-360 deg.
Further, the Multi-GNSS near real-time long baseline solution specifically includes the following steps:
performing non-differential PPP (Point-to-Point protocol) processing on a reference station and a monitoring point by using an ultra-fast Multi-GNSS satellite orbit product, performing posterior residual editing iteration, removing abnormal data, and generating normal equation information of a current arc section, wherein m-1 normal equations are arranged in front of the normal equation of the current arc section;
combining the normal equation of the current arc section and the previous m-1 normal equations into a long arc normal equation by utilizing sliding window batch processing;
adopting an ambiguity fixing strategy, adopting independent base line pairs for a plurality of base stations, adopting independent satellite pairs for satellite pairs on the base line pairs, and screening the ambiguity fixing probability of the wide lane/narrow lane of the satellite pairs on the base line pairs;
and (3) carrying out ambiguity fixing by adopting a wide lane and narrow lane combined mode, adding ambiguity fixing information into a long arc equation to update the long arc equation, and repeatedly carrying out ambiguity screening and ambiguity fixing treatment on the wide lane/narrow lane.
Further, the parameters to be estimated for non-differential PPP processing of the reference station and the monitoring point include: satellite clock error, receiver clock error, coordinates, zenith troposphere delay, inter-satellite system delay, and inter-satellite frequency delay.
Furthermore, the ambiguity fixing strategy adopts double-difference ambiguity constraint and adds a long arc equation to realize the joint processing of ambiguity fixing and multiple base stations.
Further, the double-difference ambiguity constraint is equivalent to adding constraint conditions to four non-difference ambiguities to define a virtual observation value V of the double-difference ambiguityB：
Wherein,double-difference ambiguity for fixed ionospheric-free combinations, PBThe weight value of the virtual observed value of the double-difference ambiguity is shown, D is a projection matrix of the non-difference ambiguity, and X is a parameter vector to be estimated;
wherein,andrepresenting two satellites, the two satellites are provided with a single satellite,which represents two of the receivers, is,is composed ofStation andthe degree of non-differential ambiguity between satellites,is composed ofStation andthe degree of non-differential ambiguity between satellites,is composed ofStation andthe degree of non-differential ambiguity between satellites,is composed ofStation andnon-differential ambiguity between satellites;
based on the constraints of equation (11) and equation (12), the form of the least squares solution is rewritten as equation (13), implementing a transition from non-differential to double-differential baseline processing:
where A is a coefficient matrix, P is a weight matrix, L is an observed value minus a model calculated value, NX ═ W, N is a normal equation, and X is a weight matrixnewFor X, N with double interpolation ambiguity constraintnewFor adding double-interpolation ambiguity constraint method N, WnewW with double-interpolated ambiguity constraints added.
Further, if double-difference ambiguity constraints of multiple baselines are applied, the multi-reference station combined fusion processing can be realized.
Further, the degree of blur needs to reach 95% in the degree of blur fixing process.
Further, the cloud platform distributed processing specifically includes the following steps:
setting parameter and non-difference PPP processing subtask division of n monitoring points, setting parameter and non-difference PPP processing subtask division of q reference stations, and setting parameter and non-difference PPP processing subtask division of p cloud servers;
processing tasks of the monitoring points and the reference station are divided to different cloud processing servers for parallel processing through a non-differential PPP processing subtask dividing module;
recovering and combining short arc equations of the monitoring points and the reference station, and dividing the equations to the cloud server for parallel processing;
and carrying out ambiguity fixing processing.
The invention has the following beneficial effects:
1) a near real-time long baseline resolving algorithm is provided, the problem of low near real-time processing precision of a long baseline is solved, and the method has the core innovation points that: the method has the advantages that the influence of orbit errors on baseline resolution is reduced by using an ultra-fast Multi-GNSS (Multi-Global navigation satellite system) satellite orbit product, baseline processing precision and stability can be improved by using multiple residual iterations, near-real-time high-precision baseline resolution can be realized by using a sliding window batch processing mode, baseline resolution precision and stability can be improved by using a Multi-reference station combined processing mode, and long baseline ambiguity fixing rate can be improved by using an innovative ambiguity processing strategy, so that the precision is further improved.
2) Based on a non-differential processing mode, double-differential ambiguity constraint is applied in a virtual observation value mode, a multi-reference station processing technology can be flexibly realized, distributed subtask division can be performed in a finer granularity mode, the distributed computing advantages can be fully utilized, the subtask division is flexible, and the data processing efficiency is improved; the method for combining the short arc method equation into the long arc method equation is provided, so that redundant processing in batch processing of the sliding window can be avoided, and the resolving efficiency is further improved.
Drawings
FIG. 1 is a general flow chart of near real-time long baseline deformation monitoring of Multi-base-station Multi-GNSS of the present invention.
Fig. 2 is a flow chart of the reference station selection of the present invention.
Figure 3 is a schematic diagram of the reference station selection of the present invention.
FIG. 4 is a flow chart of the Multi-GNSS near real-time long baseline solution of the present invention.
FIG. 5 is a schematic diagram of the distributed solution principle of the present invention.
Detailed Description
In order to improve the stability and the precision of long baseline solution, the invention adopts a multi-reference station joint processing mode. Due to data interruption of a single reference station, periodic change of satellite observation geometry, different stability of the reference station, different data quality of the reference station, different geometric distance between the reference station and a monitoring point and different geometric distribution of the reference station relative to the monitoring point, the influence of selecting an improper reference station on the monitoring precision is large. The invention provides a selection strategy of multiple reference stations around a monitoring point, and the reference stations participating in resolving are automatically selected according to a certain threshold value, so that the stability and the precision of long-baseline resolving are preliminarily ensured.
In order to improve the long baseline resolving precision, the method utilizes an ultra-fast Multi-GNSS satellite orbit product to reduce the influence of orbit errors on baseline resolving; editing and iterating by utilizing a plurality of posterior residual errors, and putting forward the influence of data abnormity on baseline calculation as much as possible; by utilizing a sliding window batch processing mode, near-real-time high-precision baseline resolution can be realized; by utilizing a multi-reference station combined processing mode, the accuracy and stability of baseline calculation can be improved; and an innovative ambiguity processing strategy is adopted, so that the baseline resolving precision is further improved.
In order to improve the data processing efficiency, the method and the system utilize a method equation superposition technology, fully utilize the prior method equation information, and avoid the resolving repeatability; processing by utilizing a common reference station, and avoiding repeated processing of the same reference station; by utilizing the non-differential processing mode, the distributed computing advantages can be fully utilized, and the data processing efficiency is improved.
The invention is further illustrated below with reference to the figures and examples.
Fig. 1 is a general flow chart of Multi-base-station Multi-GNSS near real-time long baseline deformation monitoring of the present invention, which includes the following steps: according to the coordinates of the monitoring points, a reference station analysis is combined, a reference station selection strategy is utilized, and a reference station list participating in resolving is given; and performing subtask division according to the monitoring point data participating in resolving, executing cloud platform distributed processing, and improving processing efficiency.
Fig. 2 is a flow chart of reference station selection of the present invention, comprising the steps of: firstly, screening a reference station within a distance of 100km according to the coordinates of the monitoring points; secondly, screening the reference station with the absolute coordinate point repetition rate less than 3mm horizontally and 6mm vertically according to the coordinate stability information of the reference station; thirdly, screening the reference station meeting the admission standard according to the data quality information of the reference station; fourthly, dividing the candidate reference stations into six zones according to the azimuth angle, and selecting the reference station closest to the monitoring point in each zone as shown in figure 3 (zone 1, 0-60 degrees, zone 2, 60-120 degrees, zone 3, 120-180 degrees, zone 4, 180-240 degrees, zone 5, 240-300 degrees, zone 6, 300-360 degrees); and finally, giving a reference station list participating in the resolving.
The preferred embodiment of the invention: the monitoring precision is 2mm horizontally and 4mm vertically at the baseline of 70 kilometers, and the calculation updating frequency is 1 hour.
FIG. 4 is a flow chart of the Multi-GNSS near real-time long baseline solution of the present invention. Firstly, carrying out non-differential PPP processing on a reference station and a monitoring point by using an ultra-fast Multi-GNSS satellite orbit product, wherein the parameters are estimated as follows: satellite clock error, receiver clock error, coordinates, zenith troposphere delay, satellite inter-system delay (ISB) and satellite inter-frequency delay (IFB), and carrying out posterior residual editing iteration to remove abnormal data and generate normal equation information (formula, detailed description of matrix and detailed description of each important parameter) of the current arc segment:
wherein,is a GPS ionosphere-free carrier-phase observation,is an ionospheric-free carrier-phase observation for GLONASS (russian GLONASS satellite navigation system),for BDS (China Beidou satellite navigation System) ionosphere-free carrier phase observed value, G represents GPS, R represents GLONASS, C represents BDS, i represents a receiver, j represents a satellite number, rho is the geometric distance from the satellite to the receiver, and is the satellite position [ x ]jyjzj]TWith receiver coordinate [ x ]iyizi]TM is a troposphere model mapAs a function, T is the zenith tropospheric delay, c is the speed of light, λ is the wavelength,is the receiver GPS clock time-out, dtjIs the clock error of the satellite or the like,is the signal propagation time, ISB, between satellite j and receiver iR-GAnd ISBC-GIs the intersystem deviation,. ljIs the frequency label of the GLONASS satellite, IFBiIs the carrier phase inter-frequency offset of the receiver, biAnd bjRespectively, the fractional deviation of the phase of the receiver and the satellite, and B is an ambiguity parameter.
The invention adopts ultra-fast Multi-GNSS satellite orbit product pair [ x ] when non-differential PPP processing is carried outjyjzj]TFixing; fixing the ambiguity by using a double-difference constraint modeiAnd bjUnifying into the non-differential ambiguity parameter, the later mentioned ambiguity contains the hardware phase fractional deviation term. Parameters that need to be estimated for non-differential PPP processing include, the receiver coordinate [ x ]iyizi]TClock error of receiverSatellite clock difference dtkInter-system bias ISBR-GAnd ISBC-GInter-frequency offset IFBiZenith tropospheric delay T, and ambiguity parameter B.
Equation (2) is an error equation, a is a coefficient matrix, X is a parameter vector to be estimated, V is a residual vector,is a virtual observed value of the prior value, L is the observed value minus the calculated value of the model, P is the weight matrix,X0andis the prior value and value array of the parameter vector, the corresponding normal equation is, NX ═ W, n is the normal equation. In order to improve the precision and stability of the solution, the posterior residual error needs to be iteratively edited to remove abnormal data, the basic principle is as follows, the variation of the posterior residual error of the kth epoch relative to the residual error of the k-1 epoch can be expressed as,
Δvk＝v(k)-v(k-1) (3)
the residual mean and mean square error of each epoch within successive observation arcs can be expressed as,
after the maximum residual variation max (delta v) is removed, the mean value and mean square deviation of the residual variation of each epoch are calculated again,is the number of the observed values,
if the following conditions are satisfied,
＞k0 new
max(Δv)＞J0(6)
wherein k is0、J0If the current calendar is the set threshold value, cycle slip and gross error of epoch k can occur, and the (k + 1) th calendar is continuously checkedAnd (5) Yuan. If the k +1 th epoch also exceeds the limit and the residual variance also exceeds the limit, the k epoch is considered as gross error. Otherwise, the cycle slip is considered to have occurred at the kth epoch. And taking the kth epoch as the starting epoch of the new residual arc segment, and judging again.
The method comprises the steps of obtaining a clean normal equation N through multiple times of non-differential PPP (precision Point Positioning) processing and posterior residual editing, performing long arc section solution in order to enhance the stability of the solution, avoiding redundancy processing in order to improve the solution efficiency, performing batch processing by using a sliding window, merging the previous m-1 normal equations with the current normal equation, and combining the boundary constraint conditions of the two adjacent methods as shown in a formula (7), wherein V isk，k+1Is a virtual observation of a boundary constraint, XkAnd Xk+1Is the estimated parameter correction value of two arc segments, Pk，k+1Is the weight value of the virtual observation, Lk，k+1Is the observed value minus the model calculated value. The adjacent method equation combination is actually the adjustment problem with additional virtual observation value constraint, and the minimum variance estimation is adopted, so that the performance function is minimum, and the equation (8) is satisfied.
Vk，k+1＝Φ(tk+1，tk)Xk-Xk+1，Pk，k+1-Lk，k+1(7)
Wherein, tkIs the time of epoch k, tk+1Is the time of epoch k +1, Φ (t)k+1，tk) Is a state transition matrix, VkAnd PkIs k epoch residual vector and weight value, Vk+1And Pk+1Is the k +1 epoch residual vector and weight value, min represents the minimum value expressed by this formula.
According toIt is possible to obtain,
wherein N iskIs the normal equation for k epochs, Nk+1Is the normal equation for the k +1 epoch. The k epoch method equation, the k +1 epoch method equation and constraint information among epochs are considered, the merged method equation is shown in a formula (10), the merging method can fully utilize the stability of long arc resolving and can avoid redundant non-differential PPP processing to improve the processing efficiency.
In order to avoid the fault of a single reference station, the influence of the satellite geometry on the result is weakened, the stability of the result is enhanced, a multi-reference station mode is adopted, and the joint processing effect of ambiguity fixing and the multi-reference station is realized in a method equation added by double-difference ambiguity constraint. The double-difference ambiguity problem is equivalent to defining a virtual observation value V of double-difference ambiguity by adding proper constraint conditions to four non-difference ambiguitiesB，
Wherein,double-difference ambiguity for fixed ionospheric-free combinations, PBIs the weight of the virtual observations, D is the projection matrix of the non-differential ambiguities,
wherein,andrepresenting two satellites, the two satellites are provided with a single satellite,which represents two of the receivers, is,is composed ofStation andnon-differential ambiguity between satellites. In consideration of the above constraint, the form of the least square solution can be rewritten as formula (13), the conversion from non-difference to double-difference baseline processing is realized, if double-difference ambiguity constraint of a plurality of baselines is applied, the fusion processing mode of the multi-reference station can be realized, and the reliability of the processing result is enhanced.
Wherein, Xnew、Nnew、WnewX, N, W with double-interpolated ambiguity constraints.
Compared with other double-difference ambiguity fixing strategies, the fixed ambiguity can be eliminated from a normal equation by using the algorithm of the invention, and the calculation efficiency can be greatly improved.
The inventive ambiguity fixing strategy is adopted, the independent base line pair is adopted by the multi-base station, the independent satellite pair is also adopted by the satellite pair on the base line pair, the independent satellite pair is selected by adopting the similar strategy of selecting the independent base line, and the ambiguity fixing probability of the Wide Lane (WL)/Narrow Lane (NL) of the satellite pair on the base line pair is screened.
Carrying out ambiguity fixing by adopting a wide lane and narrow lane combined mode, and firstly fixingWidth-fixed lane ambiguity Bw，B1Is the ambiguity at the L1 frequency, B2Ambiguity at L2 frequency, wide-lane double-difference ambiguityIs composed of four non-differential ambiguities, as shown in formula (15), and wide lane double-differential ambiguity variance valueThe error propagation equation (16) yields. The fixed probability Prob of double-difference wide-lane ambiguity can be obtained according to the formula (17)wWhere B is the ambiguity parameter, σ is the estimated variance value,is the nearest integer value of B.
Bw＝B1-B2(14)
By using the formulas (19) and (20), the estimated value of the narrow-lane double-difference ambiguity can be obtainedAnd estimating the varianceWhereinAndthe fixed probability Prob of the narrow lane double-difference ambiguity can be obtained by a formula (17) through the ionosphere-free combined double-difference ambiguity value and the estimated variance valuen，
Probw＞Prob0，Probn＞Prob0(21)
Ambiguity fixing for satellite pairs passing the screening formula (21), where Prob0And if the ambiguity fixed probability threshold is the ambiguity fixed probability threshold, adding the ambiguity fixed information into the normal equation to update the normal equation, and repeatedly performing ambiguity screening and fixing until 95% of ambiguities are fixed.
FIG. 5 is a schematic diagram of the distributed solution principle of the present invention. As shown in FIG. 5, monitor points Mon participating in a certain process are set1，Mon2，...，MonnA total of n reference stations Ref participating in the process1，Ref2，...，RefqQ total cloud server Ser participating in processing1，Ser2，...，SerPThe invention adopts a non-differential PPP processing mode, so that the processing tasks of the monitoring points and the reference station can be divided to different cloud processing servers for parallel processing through a non-differential PPP processing subtask dividing module, short arc equations of the monitoring points and the reference station are combined and can be divided to the cloud processing servers for parallel processing, and double differences can be applied according to the equations (11) to (13) at the stage of ambiguity fixingAmbiguity constraint realizes multi-reference station joint coordinate calculation of a single monitoring point, and by the above strategies, distributed calculation advantages can be fully utilized, and the method is more suitable for scenes of mass data processing.
The core processing code of the invention utilizes C/C + +, and the automated control utilizes Python language.
Although the present invention has been described with reference to the preferred embodiments, it is not intended to limit the present invention, and those skilled in the art can make variations and modifications of the present invention without departing from the spirit and scope of the present invention by using the methods and technical contents disclosed above.
Claims (10)
1. A Multi-GNSS long baseline near real-time deformation monitoring method based on multiple base stations is characterized by comprising the following steps:
according to the coordinates of the monitoring points, a reference station analysis is combined, a reference station selection strategy is utilized, and a reference station list participating in resolving is given;
dividing subtasks according to monitoring point data participating in resolving, and executing cloud platform distributed processing;
the cloud platform distributed processing utilizes an ultra-fast Multi-GNSS satellite orbit product to reduce the influence of orbit errors on near-real-time long baseline solution of the Multi-GNSS.
2. The Multi-base-station-based Multi-GNSS long-baseline near-real-time deformation monitoring method according to claim 1, wherein the base station selection strategy specifically includes the following steps:
step 1, screening a reference station within a distance of 100km according to the coordinates of the monitoring points;
step 2, screening the reference station with the absolute coordinate point repetition rate less than 3mm horizontally and 6mm vertically according to the coordinate stability information of the reference station;
step 3, screening the reference station meeting the admission standard according to the data quality information of the reference station;
step 4, dividing the candidate reference stations into six areas according to the azimuth angles, and selecting the reference station closest to the monitoring point in each area;
and 5, giving a reference station list participating in resolving.
3. The Multi-base-station-based Multi-GNSS long-baseline near-real-time deformation monitoring method according to claim 2, wherein the candidate base stations are divided into six regions according to the following method:
zone 1, 0-60 °;
zone 2, 60-120 °;
zone 3, 120-180 °;
zone 4, 180-240 °;
zone 5, 240-300 °;
zone 6, 300-360 deg.
4. The Multi-base-station-based Multi-GNSS long-baseline near real-time deformation monitoring method according to claim 2, wherein the Multi-GNSS near real-time long-baseline solution specifically includes the following steps:
performing non-differential PPP (Point-to-Point protocol) processing on a reference station and a monitoring point by using an ultra-fast Multi-GNSS satellite orbit product, performing posterior residual editing iteration, removing abnormal data, and generating normal equation information of a current arc section, wherein m-1 normal equations are arranged in front of the normal equation of the current arc section;
combining the normal equation of the current arc section and the previous m-1 normal equations into a long arc normal equation by utilizing sliding window batch processing;
adopting an ambiguity fixing strategy, adopting independent base line pairs for a plurality of base stations, adopting independent satellite pairs for satellite pairs on the base line pairs, and screening the ambiguity fixing probability of the wide lane/narrow lane of the satellite pairs on the base line pairs;
and (3) carrying out ambiguity fixing by adopting a wide lane and narrow lane combined mode, adding ambiguity fixing information into a long arc equation to update the long arc equation, and repeatedly carrying out ambiguity screening and ambiguity fixing treatment on the wide lane/narrow lane.
5. The Multi-base-station-based Multi-GNSS long-baseline near-real-time deformation monitoring method according to claim 4, wherein the parameters to be estimated for the non-differential PPP processing of the reference station and the monitoring point comprise: satellite clock error, receiver clock error, coordinates, zenith troposphere delay, inter-satellite system delay, and inter-satellite frequency delay.
6. The Multi-base-station-based Multi-GNSS long-baseline near-real-time deformation monitoring method according to claim 4, wherein the ambiguity fixing strategy adopts a double-difference ambiguity constraint and adds a long arc equation to realize ambiguity fixing and Multi-base-station joint processing.
7. The Multi-base-station-based Multi-GNSS long-baseline near-real-time deformation monitoring method according to claim 6, wherein the double-difference ambiguity constraint is equivalent to adding a constraint condition to four non-difference ambiguities to define a virtual observation value V of the double-difference ambiguityB：
<mrow> <msub> <mi>V</mi> <mi>B</mi> </msub> <mo>=</mo> <mi>D</mi> <mi>X</mi> <mo>-</mo> <msup> <mover> <mi>B</mi> <mo>&OverBar;</mo> </mover> <mi>d</mi> </msup> <mo>,</mo> <msub> <mi>P</mi> <mi>B</mi> </msub> <mo>-</mo> <mo>-</mo> <mo>-</mo> <mrow> <mo>(</mo> <mn>11</mn> <mo>)</mo> </mrow> </mrow>
Wherein,double-difference ambiguity for fixed ionospheric-free combinations, PBThe weight value of the virtual observed value of the double-difference ambiguity is shown, D is a projection matrix of the non-difference ambiguity, and X is a parameter vector to be estimated;
<mrow> <mi>D</mi> <msup> <mfenced open = "[" close = "]"> <mtable> <mtr> <mtd> <msubsup> <mi>B</mi> <mover> <mi>k</mi> <mo>&OverBar;</mo> </mover> <mover> <mi>i</mi> <mo>&OverBar;</mo> </mover> </msubsup> </mtd> <mtd> <msubsup> <mi>B</mi> <mover> <mi>k</mi> <mo>&OverBar;</mo> </mover> <mover> <mi>j</mi> <mo>&OverBar;</mo> </mover> </msubsup> </mtd> <mtd> <msubsup> <mi>B</mi> <mover> <mi>l</mi> <mo>&OverBar;</mo> </mover> <mover> <mi>i</mi> <mo>&OverBar;</mo> </mover> </msubsup> </mtd> <mtd> <msubsup> <mi>B</mi> <mover> <mi>l</mi> <mo>&OverBar;</mo> </mover> <mover> <mi>j</mi> <mo>&OverBar;</mo> </mover> </msubsup> </mtd> </mtr> </mtable> </mfenced> <mi>T</mi> </msup> <mo>=</mo> <mo>&lsqb;</mo> <mtable> <mtr> <mtd> <mn>1</mn> </mtd> <mtd> <mrow> <mo>-</mo> <mn>1</mn> </mrow> </mtd> <mtd> <mrow> <mo>-</mo> <mn>1</mn> </mrow> </mtd> <mtd> <mn>1</mn> </mtd> </mtr> </mtable> <mo>&rsqb;</mo> <msup> <mfenced open = "[" close = "]"> <mtable> <mtr> <mtd> <msubsup> <mi>B</mi> <mover> <mi>k</mi> <mo>&OverBar;</mo> </mover> <mover> <mi>i</mi> <mo>&OverBar;</mo> </mover> </msubsup> </mtd> <mtd> <msubsup> <mi>B</mi> <mover> <mi>k</mi> <mo>&OverBar;</mo> </mover> <mover> <mi>j</mi> <mo>&OverBar;</mo> </mover> </msubsup> </mtd> <mtd> <msubsup> <mi>B</mi> <mover> <mi>l</mi> <mo>&OverBar;</mo> </mover> <mover> <mi>i</mi> <mo>&OverBar;</mo> </mover> </msubsup> </mtd> <mtd> <msubsup> <mi>B</mi> <mover> <mi>l</mi> <mo>&OverBar;</mo> </mover> <mover> <mi>j</mi> <mo>&OverBar;</mo> </mover> </msubsup> </mtd> </mtr> </mtable> </mfenced> <mi>T</mi> </msup> <mo>-</mo> <mo>-</mo> <mo>-</mo> <mrow> <mo>(</mo> <mn>12</mn> <mo>)</mo> </mrow> </mrow>
wherein,andrepresenting two satellites, the two satellites are provided with a single satellite,which represents two of the receivers, is,is composed ofStation andthe degree of non-differential ambiguity between satellites,is composed ofStation andthe degree of non-differential ambiguity between satellites,is composed ofStation andthe degree of non-differential ambiguity between satellites,is composed ofStation andnon-differential ambiguity between satellites;
based on the constraints of equation (11) and equation (12), the form of the least squares solution is rewritten as equation (13), implementing a transition from non-differential to double-differential baseline processing:
<mrow> <msub> <mi>X</mi> <mrow> <mi>n</mi> <mi>e</mi> <mi>w</mi> </mrow> </msub> <mo>=</mo> <msup> <mrow> <mo>(</mo> <msup> <mi>A</mi> <mi>T</mi> </msup> <mi>P</mi> <mi>A</mi> <mo>+</mo> <msup> <mi>D</mi> <mi>T</mi> </msup> <msub> <mi>P</mi> <mi>B</mi> </msub> <mi>D</mi> <mo>)</mo> </mrow> <mrow> <mo>-</mo> <mn>1</mn> </mrow> </msup> <mrow> <mo>(</mo> <msup> <mi>A</mi> <mi>T</mi> </msup> <mi>P</mi> <mi>L</mi> <mo>+</mo> <msup> <mi>D</mi> <mi>T</mi> </msup> <msub> <mi>P</mi> <mi>B</mi> </msub> <msup> <mover> <mi>B</mi> <mo>&OverBar;</mo> </mover> <mi>d</mi> </msup> <mo>)</mo> </mrow> <mo>=</mo> <msubsup> <mi>N</mi> <mrow> <mi>n</mi> <mi>e</mi> <mi>w</mi> </mrow> <mrow> <mo>-</mo> <mn>1</mn> </mrow> </msubsup> <msub> <mi>W</mi> <mrow> <mi>n</mi> <mi>e</mi> <mi>w</mi> </mrow> </msub> <mo>-</mo> <mo>-</mo> <mo>-</mo> <mrow> <mo>(</mo> <mn>13</mn> <mo>)</mo> </mrow> </mrow>
where A is a coefficient matrix, P is a weight matrix, L is an observed value minus a model calculated value, NX ═ W, N is a normal equation, and X is a weight matrixnewFor X, N with double interpolation ambiguity constraintnewFor adding double-interpolation ambiguity constraint method N, WnewW with double-interpolated ambiguity constraints added.
8. The Multi-base-station-based Multi-GNSS long-baseline near-real-time deformation monitoring method according to claim 7, wherein if double-difference ambiguity constraints of multiple baselines are applied, Multi-base-station joint fusion processing can be realized.
9. The Multi-base-station-based Multi-GNSS long-baseline near-real-time deformation monitoring method according to claim 8, wherein the ambiguity needs to reach 95% in the ambiguity fixing process.
10. The Multi-base-station-based Multi-GNSS long-baseline near-real-time deformation monitoring method according to claim 8, wherein the cloud platform distributed processing specifically includes the steps of:
setting parameter and non-difference PPP processing subtask division of n monitoring points, setting parameter and non-difference PPP processing subtask division of q reference stations, and setting parameter and non-difference PPP processing subtask division of p cloud servers;
processing tasks of the monitoring points and the reference station are divided to different cloud processing servers for parallel processing through a non-differential PPP processing subtask dividing module;
recovering and combining short arc equations of the monitoring points and the reference station, and dividing the equations to the cloud server for parallel processing;
and carrying out ambiguity fixing processing.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201710672999.8A CN107421434B (en) | 2017-08-08 | 2017-08-08 | More base station Multi-GNSS Long baselines near real-time deformation monitoring methods |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201710672999.8A CN107421434B (en) | 2017-08-08 | 2017-08-08 | More base station Multi-GNSS Long baselines near real-time deformation monitoring methods |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN107421434A true CN107421434A (en) | 2017-12-01 |
| CN107421434B CN107421434B (en) | 2019-09-24 |
Family
ID=60437565
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201710672999.8A Active CN107421434B (en) | 2017-08-08 | 2017-08-08 | More base station Multi-GNSS Long baselines near real-time deformation monitoring methods |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN107421434B (en) |
Cited By (13)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108196282A (en) * | 2017-12-06 | 2018-06-22 | 千寻位置网络有限公司 | The execution method and system of GNSS positioning, positioning device, service terminal, memory |
| CN109540095A (en) * | 2018-12-29 | 2019-03-29 | 北方信息控制研究院集团有限公司 | Roadbed settlement monitoring method based on satellite navigation and least square |
| CN111045042A (en) * | 2019-12-20 | 2020-04-21 | 西安空间无线电技术研究所 | PPP-RTK enhancement method and system based on 'cloud-end' framework |
| CN111220065A (en) * | 2019-12-11 | 2020-06-02 | 湖南联智科技股份有限公司 | Multi-mode combined Beidou deformation monitoring data processing system and method |
| CN111258999A (en) * | 2020-01-16 | 2020-06-09 | 山东大学 | SLR-based BDS satellite orbit near-real-time checking service system |
| CN111610308A (en) * | 2020-04-27 | 2020-09-01 | 杭州电子科技大学 | Double-layer landslide monitoring system and method based on RTK technology |
| CN112013756A (en) * | 2020-08-27 | 2020-12-01 | 桂林电子科技大学 | Double-layer baseline slope deformation monitoring method |
| CN112381309A (en) * | 2020-11-23 | 2021-02-19 | 珠江水利委员会珠江水利科学研究院 | Reservoir dam safety monitoring and early warning method, device and system and storage medium |
| CN112444187A (en) * | 2019-08-28 | 2021-03-05 | 千寻位置网络有限公司 | Deformation monitoring method and device |
| CN112444832A (en) * | 2020-12-03 | 2021-03-05 | 深圳大学 | Cycle slip repairing method for one-machine multi-antenna receiver |
| CN113670252A (en) * | 2020-05-13 | 2021-11-19 | 千寻位置网络有限公司 | Self-adaptive arc length deformation monitoring method and device based on multi-source information fusion |
| CN117232380A (en) * | 2023-11-13 | 2023-12-15 | 武汉大学 | Method, device and equipment for monitoring deformation of strip engineering and readable storage medium |
| CN117630988A (en) * | 2023-11-14 | 2024-03-01 | 中交一公局厦门工程有限公司 | Beidou measurement data processing cloud platform with dynamic adjustment and optimization functions |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2014065664A1 (en) * | 2012-10-25 | 2014-05-01 | Fugro N.V. | Ppp-rtk method and system for gnss signal based position determination |
| CN105158780A (en) * | 2015-07-24 | 2015-12-16 | 北京跟踪与通信技术研究所 | Navigation positioning method based on interchangeability of multiple navigation satellites |
| CN106371119A (en) * | 2016-08-23 | 2017-02-01 | 武汉羲和科技有限公司 | Cloud positioning based GNSS global highly precise and enhanced positioning method |
| CN106871776A (en) * | 2017-02-14 | 2017-06-20 | 千寻位置网络有限公司 | A kind of real-time deformation monitoring system based on GNSS |
-
2017
- 2017-08-08 CN CN201710672999.8A patent/CN107421434B/en active Active
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2014065664A1 (en) * | 2012-10-25 | 2014-05-01 | Fugro N.V. | Ppp-rtk method and system for gnss signal based position determination |
| CN105158780A (en) * | 2015-07-24 | 2015-12-16 | 北京跟踪与通信技术研究所 | Navigation positioning method based on interchangeability of multiple navigation satellites |
| CN106371119A (en) * | 2016-08-23 | 2017-02-01 | 武汉羲和科技有限公司 | Cloud positioning based GNSS global highly precise and enhanced positioning method |
| CN106871776A (en) * | 2017-02-14 | 2017-06-20 | 千寻位置网络有限公司 | A kind of real-time deformation monitoring system based on GNSS |
Non-Patent Citations (1)
| Title |
|---|
| 陈正生 等: "大规模GNSS数据的分布式处理与实现", 《武汉大学学报（信息科学版）》 * |
Cited By (19)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108196282A (en) * | 2017-12-06 | 2018-06-22 | 千寻位置网络有限公司 | The execution method and system of GNSS positioning, positioning device, service terminal, memory |
| CN109540095A (en) * | 2018-12-29 | 2019-03-29 | 北方信息控制研究院集团有限公司 | Roadbed settlement monitoring method based on satellite navigation and least square |
| CN112444187A (en) * | 2019-08-28 | 2021-03-05 | 千寻位置网络有限公司 | Deformation monitoring method and device |
| CN112444187B (en) * | 2019-08-28 | 2022-09-06 | 千寻位置网络有限公司 | Deformation monitoring method and device |
| CN111220065A (en) * | 2019-12-11 | 2020-06-02 | 湖南联智科技股份有限公司 | Multi-mode combined Beidou deformation monitoring data processing system and method |
| CN111220065B (en) * | 2019-12-11 | 2020-10-09 | 湖南联智科技股份有限公司 | Multi-mode combined Beidou deformation monitoring data processing system and method |
| CN111045042A (en) * | 2019-12-20 | 2020-04-21 | 西安空间无线电技术研究所 | PPP-RTK enhancement method and system based on 'cloud-end' framework |
| CN111045042B (en) * | 2019-12-20 | 2022-03-04 | 西安空间无线电技术研究所 | PPP-RTK enhancement method and system based on 'cloud-end' framework |
| CN111258999A (en) * | 2020-01-16 | 2020-06-09 | 山东大学 | SLR-based BDS satellite orbit near-real-time checking service system |
| CN111610308A (en) * | 2020-04-27 | 2020-09-01 | 杭州电子科技大学 | Double-layer landslide monitoring system and method based on RTK technology |
| CN113670252A (en) * | 2020-05-13 | 2021-11-19 | 千寻位置网络有限公司 | Self-adaptive arc length deformation monitoring method and device based on multi-source information fusion |
| CN113670252B (en) * | 2020-05-13 | 2024-01-26 | 千寻位置网络有限公司 | Self-adaptive arc length deformation monitoring method and device based on multisource information fusion |
| CN112013756A (en) * | 2020-08-27 | 2020-12-01 | 桂林电子科技大学 | Double-layer baseline slope deformation monitoring method |
| CN112013756B (en) * | 2020-08-27 | 2022-05-17 | 桂林电子科技大学 | Double-layer baseline slope deformation monitoring method |
| CN112381309A (en) * | 2020-11-23 | 2021-02-19 | 珠江水利委员会珠江水利科学研究院 | Reservoir dam safety monitoring and early warning method, device and system and storage medium |
| CN112444832A (en) * | 2020-12-03 | 2021-03-05 | 深圳大学 | Cycle slip repairing method for one-machine multi-antenna receiver |
| CN117232380A (en) * | 2023-11-13 | 2023-12-15 | 武汉大学 | Method, device and equipment for monitoring deformation of strip engineering and readable storage medium |
| CN117232380B (en) * | 2023-11-13 | 2024-01-30 | 武汉大学 | Method, device and equipment for monitoring deformation of strip engineering and readable storage medium |
| CN117630988A (en) * | 2023-11-14 | 2024-03-01 | 中交一公局厦门工程有限公司 | Beidou measurement data processing cloud platform with dynamic adjustment and optimization functions |
Also Published As
| Publication number | Publication date |
|---|---|
| CN107421434B (en) | 2019-09-24 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN107421434B (en) | More base station Multi-GNSS Long baselines near real-time deformation monitoring methods | |
| CN110231037B (en) | GNSS maneuvering satellite orbit determination method with additional clock error model constraint | |
| CN110007320B (en) | Network RTK resolving method | |
| WO2019228439A1 (en) | Gnss-rtk-based positioning method | |
| CN103221839B (en) | GNSS signal processing with regional augmentation message | |
| Li et al. | Review of PPP–RTK: Achievements, challenges, and opportunities | |
| CN108549095B (en) | Non-differential parallel enhancement method and system for regional CORS network | |
| CN105699999A (en) | Method for fixing narrow lane ambiguity of Beidou ground based augmentation system base station | |
| CN107064981B (en) | Differential positioning method and system based on GNSS, service terminal | |
| CN110109158A (en) | Subsequent supper-fast RTK location algorithm based on GPS, GLONASS and BDS multisystem | |
| CN107942346B (en) | A kind of high-precision GNSS ionized layer TEC observation extracting method | |
| CN103698790B (en) | Difference combined method between the Big Dipper and GPS dual system wide lane carrier phase mixing star | |
| CN105158780A (en) | Navigation positioning method based on interchangeability of multiple navigation satellites | |
| CN115933356B (en) | High-precision time synchronization system and method for virtual atomic clock | |
| Xia et al. | Observation of BDS-2 IGSO/MEOs yaw-attitude behavior during eclipse seasons | |
| CN106125113A (en) | A kind of high accuracy Baselines method utilizing multisystem GNSS observation | |
| CN116299586B (en) | Precise single-point positioning method, receiver, equipment and medium based on broadcast ephemeris | |
| Yu et al. | Performance assessment of RTPPP positioning with SSR corrections and PPP-AR positioning with FCB for multi-GNSS from MADOCA products | |
| Liu et al. | Initial evaluation and analysis of NRTK positioning performance with new BDS-3 signals | |
| CN117665871A (en) | GNSS non-difference fixed solution precision clock difference determination method under carrier distance support | |
| Li et al. | Robust Kalman filter for RTK positioning under signal-degraded scenarios | |
| CN117421933A (en) | Multi-frequency multi-system GNSS satellite phase deviation estimation method | |
| CN115639582B (en) | GeO satellite orbit maneuver period orbit determination method based on co-view time service | |
| CN115236706A (en) | Method and system for processing unidirectional ranging observation data of inter-satellite link | |
| CN115561793A (en) | Real-time Beidou phase decimal deviation rapid estimation method based on parallel computation |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant | ||
| TR01 | Transfer of patent right |
Effective date of registration: 20211026 Address after: 200438 9 / F, 10 / F, 11 / F, 12 / F, 38 Lane 1688, Guoquan North Road, Yangpu District, Shanghai Patentee after: QIANXUN SPATIAL INTELLIGENCE Inc. Patentee after: QIANXUN SI NETWORK (ZHEJIANG) Co.,Ltd. Address before: Room j165, 1st floor, building 64, 1436 Jungong Road, Yangpu District, Shanghai, 200433 Patentee before: QIANXUN SPATIAL INTELLIGENCE Inc. |
|
| TR01 | Transfer of patent right |