CN109059751B - Deformation data monitoring method and system - Google Patents

Deformation data monitoring method and system Download PDF

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CN109059751B
CN109059751B CN201811049111.6A CN201811049111A CN109059751B CN 109059751 B CN109059751 B CN 109059751B CN 201811049111 A CN201811049111 A CN 201811049111A CN 109059751 B CN109059751 B CN 109059751B
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CN109059751A (en
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涂锐
卢晓春
张睿
张鹏飞
张兴刚
刘金海
黄小东
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National Time Service Center of CAS
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    • G01—MEASURING; TESTING
    • G01B—MEASURING LENGTH, THICKNESS OR SIMILAR LINEAR DIMENSIONS; MEASURING ANGLES; MEASURING AREAS; MEASURING IRREGULARITIES OF SURFACES OR CONTOURS
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Abstract

The invention discloses a deformation data monitoring method and a system, wherein the monitoring method comprises the steps of firstly, acquiring a phase/pseudo-range observation value between a reference station and a user station by utilizing a GNSS receiver; acquiring an acceleration observed value of the user station by using the acceleration; acquiring auxiliary parameters; secondly, preprocessing the phase/pseudo-range observation value, the acceleration observation value and the auxiliary parameter to obtain observation data; then determining a double-difference observation model and a state model according to the observation data; finally, based on the double-difference observation model and the state model, parameter resolving is carried out by adopting a Kalman filtering method to obtain a deformation monitoring result; according to the method, the high-precision low-frequency phase/pseudo range observation value is obtained by utilizing the GNSS technology, the high-frequency acceleration observation value is obtained by utilizing the accelerometer, and the high-frequency acceleration observation value and the low-frequency acceleration observation value are fused, so that the high-frequency information complementation and the low-frequency information complementation are realized, the GNSS noise is effectively inhibited, the solving strength is enhanced, and the accuracy and the convergence speed of the deformation monitoring result are further improved.

Description

Deformation data monitoring method and system
Technical Field
The invention relates to the technical field of data monitoring, in particular to a deformation data monitoring method and system.
Background
At present, a global satellite navigation GNSS real-time dynamic positioning RTK technology is mainly adopted for monitoring deformation data, but RTK can only provide low-frequency displacement information, and high-frequency speed and acceleration information of the RTK are seriously polluted due to signal noise, so that the deformation cannot be quickly and accurately monitored for slight change. Based on the above problems, how to quickly and accurately monitor the high-frequency deformation data becomes a problem which needs to be solved in the field.
Disclosure of Invention
The invention aims to provide a deformation data monitoring method and a system, so as to quickly and accurately determine a deformation monitoring result.
In order to achieve the above object, the present invention provides a deformation data monitoring method, including:
acquiring a phase/pseudo-range observation value between a reference station and a user station by using a GNSS receiver; acquiring an acceleration observed value of the user station by using the acceleration; acquiring auxiliary parameters;
preprocessing the phase/pseudo-range observation value, the acceleration observation value and the auxiliary parameter to obtain observation data;
determining a double-difference observation model and a state model according to the observation data;
based on the double-difference observation model and the state model, a Kalman filtering method is adopted for parameter resolving to obtain a deformation monitoring result;
and realizing deformation monitoring on the deformation body according to the deformation monitoring result.
Optionally, the auxiliary parameters include a broadcast ephemeris, a coordinate of a station, an antenna model, an antenna phase center correction file, and a rotation parameter of the earth.
Optionally, the preprocessing the phase/pseudo-range observation value, the acceleration observation value, and the auxiliary parameter to obtain observation data specifically includes:
carrying out data integrity check, gross error elimination and cycle slip detection on the phase/pseudo range observation value, the acceleration observation value and the auxiliary parameter;
correcting relativity, tide, antenna phase center, troposphere and earth rotation error of the processed data to obtain observation data; the observation data includes: and (4) preprocessing the double-difference phase observed value, the double-difference pseudo range observed value and the survey station acceleration.
Optionally, the double-difference observation model is determined according to the observation data, and a specific formula is as follows:
Figure BDA0001794057330000021
Figure BDA0001794057330000022
Figure BDA0001794057330000023
wherein, the subscripts b and r are the reference station and the user station respectively, k represents the epoch serial number, i is the ith satellite, j is the jth satellite,
Figure BDA0001794057330000024
is a double-difference pseudo range observed value after preprocessing between k epoch i, j satellite b and r measuring stations,
Figure BDA0001794057330000025
is the inter-satellite difference of unit rotation vector between i and j satellites on k epoch survey station r, sr(k) For the displacement correction at the k epoch measurement station r,
Figure BDA0001794057330000026
is double difference ionosphere error between k epoch i, j satellite b and r survey station,
Figure BDA0001794057330000027
is double difference troposphere error between k epoch i, j satellite b and r survey station,
Figure BDA0001794057330000028
is the double-difference geometric distance between k epoch i, j satellite b and r survey station,p(k) for the k epoch pseudorange observation noise,
Figure BDA0001794057330000029
is a double-difference phase observed value after preprocessing between k epoch i, j satellite b and r measuring stations, lambda is the carrier wave wavelength,
Figure BDA00017940573300000210
is double-difference ambiguity between k epoch i, j satellite b and r survey station,φ(k) for the k epoch phase observation noise,
Figure BDA00017940573300000211
in order to observe the noise variance for the phase,
Figure BDA00017940573300000212
the noise variance is observed for the pseudoranges.
Optionally, the state model is determined according to the observation data, and a specific formula is as follows:
Figure BDA00017940573300000213
Figure BDA00017940573300000214
where s is the coordinate baseline vector, v is the velocity of the survey station, u is the baseline drift error, amb is all double-differenced ambiguities, τ is the sampling frequency of the GNSS, βkIs the dynamic noise of the kth epoch, a is the preprocessed station acceleration, QEFor dynamic state noise arrays, qaIs the variance of the acceleration, quIs the variance of the baseline drift.
The invention also provides a deformation data monitoring system, which comprises:
an acquisition module for acquiring a phase/pseudorange observation between a reference station and a subscriber station using a GNSS receiver; acquiring an acceleration observed value of the user station by using the acceleration; acquiring auxiliary parameters;
the preprocessing module is used for preprocessing the phase/pseudo-range observation value, the acceleration observation value and the auxiliary parameter to obtain observation data;
the model determining module is used for determining a double-difference observation model and a state model according to the observation data;
the deformation monitoring result determining module is used for performing parameter resolving by adopting a Kalman filtering method based on the double-difference observation model and the state model to obtain a deformation monitoring result;
and the deformation monitoring module is used for monitoring the deformation of the deformation body according to the deformation monitoring result.
Optionally, the auxiliary parameters include a broadcast ephemeris, a coordinate of a station, an antenna model, an antenna phase center correction file, and a rotation parameter of the earth.
Optionally, the preprocessing module specifically includes:
the first preprocessing unit is used for carrying out data integrity check, gross error elimination and cycle slip detection on the phase/pseudo-range observed value, the acceleration observed value and the auxiliary parameter;
the second preprocessing unit is used for correcting relativity, tide, an antenna phase center, a troposphere and earth rotation errors of the processed data to obtain observation data; the observation data includes: and (4) preprocessing the double-difference phase observed value, the double-difference pseudo range observed value and the survey station acceleration.
Optionally, the double-difference observation model is determined according to the observation data, and a specific formula is as follows:
Figure BDA0001794057330000031
Figure BDA0001794057330000032
Figure BDA0001794057330000033
wherein, the subscripts b and r are the reference station and the user station respectively, k represents the epoch serial number, i is the ith satellite, j is the jth satellite,
Figure BDA0001794057330000034
is a double-difference pseudo range observed value after preprocessing between k epoch i, j satellite b and r measuring stations,
Figure BDA0001794057330000035
is the inter-satellite difference of unit rotation vector between i and j satellites on k epoch survey station r, sr(k) For the displacement correction at the k epoch measurement station r,
Figure BDA0001794057330000036
is double difference ionosphere error between k epoch i, j satellite b and r survey station,
Figure BDA0001794057330000041
is double difference troposphere error between k epoch i, j satellite b and r survey station,
Figure BDA0001794057330000042
is the double-difference geometric distance between k epoch i, j satellite b and r survey station,p(k) for the k epoch pseudorange observation noise,
Figure BDA0001794057330000043
is a double-difference phase observed value after preprocessing between k epoch i, j satellite b and r measuring stations, lambda is the carrier wave wavelength,
Figure BDA0001794057330000048
is double-difference ambiguity between k epoch i, j satellite b and r survey station,φ(k) for the k epoch phase observation noise,
Figure BDA0001794057330000044
in order to observe the noise variance for the phase,
Figure BDA0001794057330000045
the noise variance is observed for the pseudoranges.
Optionally, the state model is determined according to the observation data, and a specific formula is as follows:
Figure BDA0001794057330000046
Figure BDA0001794057330000047
where s is the coordinate baseline vector, v is the velocity of the survey station, u is the baseline drift error, amb is all double-differenced ambiguities, τ is the sampling frequency of the GNSS, βkIs the dynamic noise of the kth epoch, a is the preprocessed station acceleration, QEFor dynamic state noise arrays, qaIs the variance of the acceleration, quIs the variance of the baseline drift.
According to the specific embodiment provided by the invention, the invention discloses the following technical effects:
according to the method, the high-precision low-frequency phase/pseudo range observation value is obtained by utilizing the GNSS technology, the high-frequency acceleration observation value is obtained by utilizing the accelerometer, and the high-frequency acceleration observation value and the low-frequency acceleration observation value are fused, so that the high-frequency information complementation and the low-frequency information complementation are realized, the GNSS noise is effectively inhibited, the solving strength is enhanced, and the accuracy and the convergence speed of the deformation monitoring result are further improved.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings needed to be used in the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings without inventive exercise.
FIG. 1 is a flow chart of a deformation data monitoring method according to an embodiment of the present invention;
fig. 2 is a structural diagram of a deformation data monitoring system according to an embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The invention aims to provide a deformation data monitoring method and a system, so as to quickly and accurately determine a deformation monitoring result.
In order to make the aforementioned objects, features and advantages of the present invention comprehensible, embodiments accompanied with figures are described in further detail below.
Fig. 1 is a flowchart of a deformation data monitoring method according to an embodiment of the present invention, and as shown in fig. 1, the present invention provides a deformation data monitoring method, where the monitoring method includes:
step S1: acquiring a phase/pseudo-range observation value between a reference station and a user station by using a GNSS receiver; acquiring an acceleration observed value of the user station by using the acceleration; acquiring auxiliary parameters;
step S2: preprocessing the phase/pseudo-range observation value, the acceleration observation value and the auxiliary parameter to obtain observation data;
step S3: determining a double-difference observation model and a state model according to the observation data;
step S4: based on the double-difference observation model and the state model, a Kalman filtering method is adopted for parameter resolving to obtain a deformation monitoring result; the deformation monitoring result comprises displacement, speed and acceleration; the deformation monitoring result is a broadband deformation monitoring result, and the frequency bandwidth of the broadband deformation monitoring result is determined according to actual requirements;
step S5: realizing deformation monitoring of the deformation body according to the deformation monitoring result; the morphs include bridges, roads, buildings, disasters.
The following detailed analysis of each step was performed:
the auxiliary parameters comprise broadcast ephemeris, survey station coordinates, an antenna model, an antenna phase center correction file and earth rotation parameters; the earth rotation parameters are polar motion and daily change parameters.
Step S2: preprocessing the phase/pseudo-range observation value, the acceleration observation value and the auxiliary parameter to obtain observation data, which specifically comprises the following steps:
step S21: carrying out data integrity check, gross error elimination and cycle slip detection on the phase/pseudo range observation value, the acceleration observation value and the auxiliary parameter so as to ensure that the processed data are clean data with complete data types, no gross error and no cycle slip;
step S22: correcting relativity, tide, antenna phase center, troposphere and earth rotation error of the processed data to obtain observation data with modeling error deducted; the observation data includes: and (4) preprocessing the double-difference phase observed value, the double-difference pseudo range observed value and the survey station acceleration.
Step S3: the double-difference observation model is determined according to the observation data, and the specific formula is as follows:
Figure BDA0001794057330000061
Figure BDA0001794057330000062
Figure BDA0001794057330000063
wherein, the subscripts b and r are the reference station and the user station respectively, k represents the epoch serial number, i is the ith satellite, j is the jth satellite,
Figure BDA0001794057330000064
is a double-difference pseudo range observed value after preprocessing between k epoch i, j satellite b and r measuring stations,
Figure BDA0001794057330000065
is the inter-satellite difference of unit rotation vector between i and j satellites on k epoch survey station r, sr(k) For the displacement correction at the k epoch measurement station r,
Figure BDA0001794057330000066
is double difference ionosphere error between k epoch i, j satellite b and r survey station,
Figure BDA0001794057330000067
is double difference troposphere error between k epoch i, j satellite b and r survey station,
Figure BDA0001794057330000068
is the double-difference geometric distance between k epoch i, j satellite b and r survey station,p(k) for the k epoch pseudorange observation noise,
Figure BDA0001794057330000069
is a double-difference phase observed value after preprocessing between k epoch i, j satellite b and r measuring stations, lambda is the carrier wave wavelength,
Figure BDA00017940573300000610
is double-difference ambiguity between k epoch i, j satellite b and r survey station,φ(k) for the k epoch phase observation noise,
Figure BDA00017940573300000611
in order to observe the noise variance for the phase,
Figure BDA00017940573300000612
the noise variance is observed for the pseudoranges.
Step S3: and determining a state model according to the observation data.
Because the effective range of RTKs is typically within a few kilometers, the ionospheric and tropospheric errors of double difference are negligible. Therefore, only displacement, velocity, acceleration, baseline drift, and carrier phase parameters are considered in the equation of state establishment.
In the standard RTK positioning, a second-order gaussian markov model is commonly used for the state equation, and the expression is as follows:
Figure BDA0001794057330000071
Figure BDA0001794057330000072
wherein s is a coordinate baseline vector, v is the velocity of the survey station, amb is all double-difference ambiguities, τ is the sampling frequency of the GNSS, a is the preprocessed acceleration of the survey station, QSFor dynamic noise arrays, qaAs variance of acceleration, αkIs the dynamic noise of the kth epoch.
After acceleration observation is added, because the acceleration after baseline drift correction can represent the real acceleration, only a first-order gaussian markov model needs to be used, and then a state model is determined according to the observation data, wherein a specific formula is as follows:
Figure BDA0001794057330000073
Figure BDA0001794057330000074
where s is the coordinate baseline vector, v is the velocity of the survey station, u is the baseline drift error, amb is all double-differenced ambiguities, τ is the sampling frequency of the GNSS, βkIs the dynamic noise of the kth epoch, a is the preprocessed station acceleration, QEFor dynamic state noise arrays, qaIs the variance of the acceleration, quIs the variance of the baseline drift.
The invention estimates the displacement and the speed one by one according to the dynamic noise for data resolving. The baseline drift error of the accelerometer is treated as a random walk process, the carrier phase ambiguity is treated as a constant in a continuous arc section, and the initialization is required when cycle slip occurs. It should be noted that, because the GNSS sampling frequency is usually 1Hz, and the accelerometer sampling frequency is 100Hz, the data calculation filter only predicts at each accelerometer sampling point, and only performs filtering processing at the GNSS sampling point.
The invention can obtain displacement, speed information and baseline drift error in real time after the filtering processing, and real acceleration information can be obtained after baseline drift is deducted from original acceleration observation.
Fig. 2 is a structural diagram of a deformation data monitoring system according to an embodiment of the present invention, and as shown in fig. 2, the present invention further provides a deformation data monitoring system, where the monitoring system includes:
an acquisition module 1, configured to acquire a phase/pseudo-range observation value between a reference station and a subscriber station by using a GNSS receiver; acquiring an acceleration observed value of the user station by using the acceleration; acquiring auxiliary parameters;
the preprocessing module 2 is configured to preprocess the phase/pseudo-range observation value, the acceleration observation value, and the auxiliary parameter to obtain observation data;
the model determining module 3 is used for determining a double-difference observation model and a state model according to the observation data;
the deformation monitoring result determining module 4 is used for performing parameter calculation by adopting a Kalman filtering method based on the double-difference observation model and the state model to obtain a deformation monitoring result;
and the deformation monitoring module 5 is used for monitoring the deformation of the deformation body according to the deformation monitoring result.
The auxiliary parameters comprise broadcast ephemeris, survey station coordinates, antenna models, antenna phase center correction files and earth rotation parameters.
The preprocessing module 2 of the present invention specifically includes:
the first preprocessing unit is used for carrying out data integrity check, gross error elimination and cycle slip detection on the phase/pseudo-range observed value, the acceleration observed value and the auxiliary parameter;
the second preprocessing unit is used for correcting relativity, tide, an antenna phase center, a troposphere and earth rotation errors of the processed data to obtain observation data; the observation data includes: and (4) preprocessing the double-difference phase observed value, the double-difference pseudo range observed value and the survey station acceleration.
The invention discloses a double-difference observation model determined according to observation data, which comprises the following specific formula:
Figure BDA0001794057330000081
Figure BDA0001794057330000082
Figure BDA0001794057330000083
wherein, the subscripts b and r are the reference station and the user station respectively, k represents the epoch serial number, i is the ith satellite, j is the jth satellite,
Figure BDA0001794057330000091
is a double-difference pseudo range observed value after preprocessing between k epoch i, j satellite b and r measuring stations,
Figure BDA0001794057330000092
is the inter-satellite difference of unit rotation vector between i and j satellites on k epoch survey station r, sr(k) For the displacement correction at the k epoch measurement station r,
Figure BDA0001794057330000093
is double difference ionosphere error between k epoch i, j satellite b and r survey station,
Figure BDA0001794057330000094
is double difference troposphere error between k epoch i, j satellite b and r survey station,
Figure BDA0001794057330000095
is the double-difference geometric distance between k epoch i, j satellite b and r survey station,p(k) for the k epoch pseudorange observation noise,
Figure BDA0001794057330000096
is a double-difference phase observed value after preprocessing between k epoch i, j satellite b and r measuring stations, lambda is the carrier wave wavelength,
Figure BDA0001794057330000097
is double-difference ambiguity between k epoch i, j satellite b and r survey station,φ(k) for the k epoch phase observation noise,
Figure BDA0001794057330000098
in order to observe the noise variance for the phase,
Figure BDA0001794057330000099
the noise variance is observed for the pseudoranges.
The invention determines a state model according to the observation data, and the specific formula is as follows:
Figure BDA00017940573300000910
Figure BDA00017940573300000911
where s is the coordinate baseline vector, v is the velocity of the survey station, u is the baseline drift error, amb is all double-differenced ambiguities, τ is the sampling frequency of the GNSS, βkIs the dynamic noise of the kth epoch, a is the preprocessed station acceleration, QEFor dynamic state noise arrays, qaIs the variance of the acceleration, quIs the variance of the baseline drift.
The invention has the beneficial effects that:
firstly, the observation of the high-frequency accelerometer is increased, and the frequency of the result information is improved.
Because the frequency of the accelerometer is 100Hz or above and is hundreds of times of the GNSS sampling frequency, the invention integrates the GNSS and the high-frequency accelerometer for observation and fusion calculation, effectively makes up the defect that the GNSS can only acquire low-frequency information, and greatly improves the frequency of result information.
And secondly, through the fusion of the two technologies, the advantages are complemented, and the GNSS deformation monitoring result is enriched.
The GNSS technology is easy to obtain high-precision low-frequency displacement information, but the high-frequency information has noise pollution; accelerometers are prone to acquiring high frequency acceleration information, but have baseline drift errors. The high-frequency and low-frequency information complementation is realized through the fusion of the two, and a user can obtain displacement, speed and acceleration deformation information with high precision and wide frequency band in real time.
Thirdly, the GNSS positioning accuracy and the convergence speed are improved through the acceleration information constraint with high signal-to-noise ratio.
The high-frequency and high-signal-to-noise ratio acceleration information is integrated into GNSS positioning, GNSS noise can be effectively restrained, solving strength is enhanced, and then GNSS positioning accuracy and convergence speed can be improved.
The embodiments in the present description are described in a progressive manner, each embodiment focuses on differences from other embodiments, and the same and similar parts among the embodiments are referred to each other. For the system disclosed by the embodiment, the description is relatively simple because the system corresponds to the method disclosed by the embodiment, and the relevant points can be referred to the method part for description.
The principles and embodiments of the present invention have been described herein using specific examples, which are provided only to help understand the method and the core concept of the present invention; meanwhile, for a person skilled in the art, according to the idea of the present invention, the specific embodiments and the application range may be changed. In view of the above, the present disclosure should not be construed as limiting the invention.

Claims (6)

1. A deformation data monitoring method is characterized by comprising the following steps:
acquiring a phase/pseudo-range observation value between a reference station and a user station by using a GNSS receiver; acquiring an acceleration observed value of the user station by using the acceleration; acquiring auxiliary parameters;
preprocessing the phase/pseudo-range observation value, the acceleration observation value and the auxiliary parameter to obtain observation data;
determining a double-difference observation model and a state model according to the observation data;
based on the double-difference observation model and the state model, a Kalman filtering method is adopted for parameter resolving to obtain a deformation monitoring result;
realizing deformation monitoring of the deformation body according to the deformation monitoring result;
the preprocessing the phase/pseudo-range observation value, the acceleration observation value and the auxiliary parameter to obtain observation data specifically includes:
carrying out data integrity check, gross error elimination and cycle slip detection on the phase/pseudo range observation value, the acceleration observation value and the auxiliary parameter;
correcting relativity, tide, antenna phase center, troposphere and earth rotation error of the processed data to obtain observation data; the observation data includes: the preprocessed double-difference phase observation value, the double-difference pseudo range observation value and the observation station acceleration are obtained;
the double-difference observation model is determined according to the observation data, and the specific formula is as follows:
Figure FDA0002510853500000011
Figure FDA0002510853500000012
Figure FDA0002510853500000013
wherein, the subscripts b and r are the reference station and the user station respectively, k represents the epoch serial number, i is the ith satellite, j is the jth satellite,
Figure FDA0002510853500000014
is a double-difference pseudo range observed value after preprocessing between k epoch i, j satellite b and r measuring stations,
Figure FDA0002510853500000015
is the inter-satellite difference of unit rotation vector between i and j satellites on k epoch survey station r, sr(k) For the displacement correction at the k epoch measurement station r,
Figure FDA0002510853500000016
is double difference ionosphere error between k epoch i, j satellite b and r survey station,
Figure FDA0002510853500000017
is double difference troposphere error between k epoch i, j satellite b and r survey station,
Figure FDA0002510853500000018
is the double-difference geometric distance between k epoch i, j satellite b and r survey station,p(k) is composed ofThe k epoch pseudorange observation noise is reduced,
Figure FDA0002510853500000019
is a double-difference phase observed value after preprocessing between k epoch i, j satellite b and r measuring stations, lambda is the carrier wave wavelength,
Figure FDA00025108535000000110
is double-difference ambiguity between k epoch i, j satellite b and r survey station,φ(k) for the k epoch phase observation noise,
Figure FDA00025108535000000111
in order to observe the noise variance for the phase,
Figure FDA00025108535000000112
the noise variance is observed for the pseudoranges.
2. A method for deformation data monitoring as defined in claim 1, wherein the auxiliary parameters include broadcast ephemeris, station coordinates, antenna model, antenna phase center correction files, and earth rotation parameters.
3. A deformation data monitoring method according to claim 1, characterized in that the state model is determined from the observation data by the following formula:
Figure FDA0002510853500000021
Figure FDA0002510853500000022
where s is the coordinate baseline vector, v is the velocity of the survey station, u is the baseline drift error, amb is all double-differenced ambiguities, τ is the sampling frequency of the GNSS, βkIs the dynamic noise of the kth epoch, a is the preprocessed station acceleration, QEFor dynamic state noise arrays, qaTo addVelocity variance, quIs the variance of the baseline drift.
4. A deformation data monitoring system, the monitoring system comprising:
an acquisition module for acquiring a phase/pseudorange observation between a reference station and a subscriber station using a GNSS receiver; acquiring an acceleration observed value of the user station by using the acceleration; acquiring auxiliary parameters;
the preprocessing module is used for preprocessing the phase/pseudo-range observation value, the acceleration observation value and the auxiliary parameter to obtain observation data;
the model determining module is used for determining a double-difference observation model and a state model according to the observation data;
the deformation monitoring result determining module is used for performing parameter resolving by adopting a Kalman filtering method based on the double-difference observation model and the state model to obtain a deformation monitoring result;
the deformation monitoring module is used for monitoring the deformation of the deformation body according to the deformation monitoring result;
the preprocessing module specifically comprises:
the first preprocessing unit is used for carrying out data integrity check, gross error elimination and cycle slip detection on the phase/pseudo-range observed value, the acceleration observed value and the auxiliary parameter;
the second preprocessing unit is used for correcting relativity, tide, an antenna phase center, a troposphere and earth rotation errors of the processed data to obtain observation data; the observation data includes: the preprocessed double-difference phase observation value, the double-difference pseudo range observation value and the observation station acceleration are obtained;
the double-difference observation model is determined according to the observation data, and the specific formula is as follows:
Figure FDA0002510853500000031
Figure FDA0002510853500000032
Figure FDA0002510853500000033
wherein, the subscripts b and r are the reference station and the user station respectively, k represents the epoch serial number, i is the ith satellite, j is the jth satellite,
Figure FDA0002510853500000034
is a double-difference pseudo range observed value after preprocessing between k epoch i, j satellite b and r measuring stations,
Figure FDA0002510853500000035
is the inter-satellite difference of unit rotation vector between i and j satellites on k epoch survey station r, sr(k) For the displacement correction at the k epoch measurement station r,
Figure FDA0002510853500000036
is double difference ionosphere error between k epoch i, j satellite b and r survey station,
Figure FDA0002510853500000037
is double difference troposphere error between k epoch i, j satellite b and r survey station,
Figure FDA0002510853500000038
is the double-difference geometric distance between k epoch i, j satellite b and r survey station,p(k) for the k epoch pseudorange observation noise,
Figure FDA0002510853500000039
is a double-difference phase observed value after preprocessing between k epoch i, j satellite b and r measuring stations, lambda is the carrier wave wavelength,
Figure FDA00025108535000000310
is double-difference ambiguity between k epoch i, j satellite b and r survey station,φ(k) for the k epoch phase observation noise,
Figure FDA00025108535000000311
in order to observe the noise variance for the phase,
Figure FDA00025108535000000312
the noise variance is observed for the pseudoranges.
5. A deformation data monitoring system according to claim 4, wherein the auxiliary parameters include broadcast ephemeris, station coordinates, antenna model, antenna phase center correction files, and Earth rotation parameters.
6. A deformation data monitoring system according to claim 4, wherein the state model is determined from the observation data by the following formula:
Figure FDA00025108535000000313
Figure FDA00025108535000000314
where s is the coordinate baseline vector, v is the velocity of the survey station, u is the baseline drift error, amb is all double-differenced ambiguities, τ is the sampling frequency of the GNSS, βkIs the dynamic noise of the kth epoch, a is the preprocessed station acceleration, QEFor dynamic state noise arrays, qaIs the variance of the acceleration, quIs the variance of the baseline drift.
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CN111443366B (en) * 2020-04-28 2022-04-29 武汉大学 Method and system for detecting abnormal point in GNSS area network
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Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN102721398A (en) * 2012-02-29 2012-10-10 武汉苍穹数码仪器有限公司 Multimode GNSS high-precision real-time deformation monitoring system
CN103760594A (en) * 2014-01-21 2014-04-30 武汉大学 Integrated system of GNSS receiver and seismometer
CN108195283A (en) * 2017-12-04 2018-06-22 千寻位置网络有限公司 GNSS real-time deformations monitoring method and system based on seamless transitions
CN207622735U (en) * 2017-12-22 2018-07-17 交通运输部科学研究院 A kind of bridge deformation monitoring system based on Big Dipper GNSS

Family Cites Families (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2017131838A2 (en) * 2015-11-13 2017-08-03 Flir Systems, Inc. Sonar sensor fusion and model based virtual and augmented reality systems and methods

Patent Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN102721398A (en) * 2012-02-29 2012-10-10 武汉苍穹数码仪器有限公司 Multimode GNSS high-precision real-time deformation monitoring system
CN103760594A (en) * 2014-01-21 2014-04-30 武汉大学 Integrated system of GNSS receiver and seismometer
CN108195283A (en) * 2017-12-04 2018-06-22 千寻位置网络有限公司 GNSS real-time deformations monitoring method and system based on seamless transitions
CN207622735U (en) * 2017-12-22 2018-07-17 交通运输部科学研究院 A kind of bridge deformation monitoring system based on Big Dipper GNSS

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