CN108254773B - Real-time clock error resolving method of multiple GNSS - Google Patents
Real-time clock error resolving method of multiple GNSS Download PDFInfo
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- CN108254773B CN108254773B CN201711189608.3A CN201711189608A CN108254773B CN 108254773 B CN108254773 B CN 108254773B CN 201711189608 A CN201711189608 A CN 201711189608A CN 108254773 B CN108254773 B CN 108254773B
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S19/00—Satellite radio beacon positioning systems; Determining position, velocity or attitude using signals transmitted by such systems
- G01S19/38—Determining a navigation solution using signals transmitted by a satellite radio beacon positioning system
- G01S19/39—Determining a navigation solution using signals transmitted by a satellite radio beacon positioning system the satellite radio beacon positioning system transmitting time-stamped messages, e.g. GPS [Global Positioning System], GLONASS [Global Orbiting Navigation Satellite System] or GALILEO
- G01S19/42—Determining position
- G01S19/43—Determining position using carrier phase measurements, e.g. kinematic positioning; using long or short baseline interferometry
- G01S19/44—Carrier phase ambiguity resolution; Floating ambiguity; LAMBDA [Least-squares AMBiguity Decorrelation Adjustment] method
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- G—PHYSICS
- G04—HOROLOGY
- G04R—RADIO-CONTROLLED TIME-PIECES
- G04R20/00—Setting the time according to the time information carried or implied by the radio signal
- G04R20/02—Setting the time according to the time information carried or implied by the radio signal the radio signal being sent by a satellite, e.g. GPS
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Abstract
The invention provides a multi-GNSS real-time clock error resolving method, which comprises the following steps: s1, performing orbit determination calculation of multi-GNSS according to the global multi-GNSS observation data and multi-GNSS broadcast ephemeris data in the same time period, and estimating the deviation parameters among different types of satellite systems of the multi-GNSS; s2, constructing a navigation satellite system deviation model aiming at different types of stations, fitting different types of satellite system deviation parameters of the multi-GNSS corresponding to the different types of stations, and estimating the fitted multi-GNSS different types of satellite system deviation parameters; and S3, resolving satellite clock error parameters under the constraint of the deviation parameters between the fitted multi-GNSS satellite systems of different types based on the output precise orbit resolved by orbit determination and the real-time observation data flow. The method can effectively weaken or eliminate the influence of the deviation between systems on the real-time clock error estimation, improves the resolving efficiency of the real-time clock error, has stronger operability and is easy to popularize and realize.
Description
Technical Field
the invention relates to the technical field of satellite navigation, in particular to a multi-GNSS real-time clock error resolving method.
Background
At present, IGS can be used for providing an ultra-fast track and a clock error product, the timeliness and the precision of the ultra-fast track product can meet the precision requirement of real-time positioning, but the precision of the ultra-fast clock error product can only achieve about 3ns, and the high-precision positioning requirement cannot be met. Especially in the aspect of the real-time clock error product of the big dipper, a product which can meet the real-time precise single-point positioning cannot be provided temporarily.
In the real-time clock error calculation of GNSS, the satellite precision clock error of GNSS is currently calculated in two ways: one is that in the precise orbit determination process, the clock error parameter and the orbit parameter are estimated simultaneously, and the equation in the calculation process contains more calculation parameters including orbit, earth self-propagation parameter, troposphere, clock error, ambiguity, station coordinate and the like; the other method is to utilize the ultra-fast products of the GNSS, fix the satellite orbit, the coordinate of the survey station, the earth rotation parameter and the like.
the first method has relatively low resolving efficiency and cannot meet the requirement of real-time performance; the second method can greatly reduce the number of parameters of the equation, thereby improving the resolving efficiency and being used for real-time clock error estimation. However, for a multi-GNSS system, due to the existence of the deviation between different systems, the calculation efficiency of the real-time clock error is still affected to a certain extent.
Disclosure of Invention
In order to overcome the above problems or at least partially solve the above problems, the present invention provides a method for calculating a real-time clock offset of a multi-GNSS, so as to effectively reduce or eliminate an influence of an inter-system bias on a real-time clock offset estimation, thereby effectively improving a calculation efficiency of the real-time clock offset.
the invention provides a multi-GNSS real-time clock error resolving method, which comprises the following steps: s1, performing orbit determination calculation of the multi-GNSS according to the global multi-GNSS observation data and the multi-GNSS broadcast ephemeris data in the same time period with the multi-GNSS observation data, and estimating the deviation parameters among different types of satellite systems of the multi-GNSS; s2, constructing a navigation satellite system deviation model aiming at different types of stations, fitting different types of satellite system deviation parameters of the multi-GNSS corresponding to the different types of stations, and estimating the fitted multi-GNSS different types of satellite system deviation parameters; and S3, resolving satellite clock error parameters under the constraint of the deviation parameters between the fitted multi-GNSS satellite systems of different types based on the output precise orbit resolved by orbit determination and the real-time observation data flow.
wherein the step of S1 further comprises: s11, preprocessing the observation data of the global multi-GNSS, wherein the observation data of the global multi-GNSS at least comprises carrier phase observation data and pseudo-range observation data of two frequencies corresponding to each satellite navigation system; s12, performing broadcast ephemeris fitting on the multi-GNSS broadcast ephemeris data to acquire a reference orbit of the multi-GNSS satellite; s13, constructing a multi-GNSS observation equation containing deviation parameters among different types of satellite systems of the multi-GNSS based on the preprocessed carrier phase observation data and pseudo-range observation data; s14, based on the reference orbit, carrying out error correction and linearization processing on the multi-GNSS observation equation to obtain a first-order partial derivative equation of the multi-GNSS observation equation relative to the deviation parameter; and S15, solving the deviation parameters between the precise orbit and the different types of satellite systems of the multi-GNSS based on the first-order partial derivative equation.
wherein the step of S2 further comprises: s21, based on the types of the corresponding stations of different types, the GNSS stations are grouped according to the principle that the same station types are grouped into one group; s22, respectively constructing corresponding navigation satellite system deviation models for different types of satellite system deviation parameters of the multi-GNSS in different groups, and fitting the different types of satellite system deviation parameters of the multi-GNSS; and S23, estimating fitting model parameters of the navigation satellite system deviation model, and acquiring the fitted deviation parameters among the satellite systems of different types of the multi-GNSS.
Wherein the step of S3 further comprises: s31, acquiring real-time observation data streams of multiple GNSS, and preprocessing the data streams; s32, fixing the precise orbit by giving a preset level of weight, and setting the deviation parameters between the fitted multi-GNSS satellite systems of different types as constraint information; s33, based on the fixed precise orbit and the weight thereof and the preprocessed real-time observation data stream, under the constraint of the constraint information, adopting a non-differential phase and pseudo-range combined observation value without an ionosphere to establish a non-differential observation model; s34, estimating the satellite clock error parameter based on the non-difference observation model.
Wherein the pseudo-range observation data and the carrier phase observation data are further represented by the following formulas:
where the superscript s denotes a GNSS satellite, the subscript r denotes a station, the subscript j denotes a frequency bin, and pseudorange and carrier phase observations respectively representing observations of a j-th frequency of the satellite s by the station r, representing a geometric distance from the j-th frequency observation satellite s to the station r, c representing an optical velocity, dtr representing a station clock difference at a signal reception time, dts representing a satellite clock difference at a signal transmission time, representing a tropospheric delay, representing a first-order ionospheric amplification factor, representing a first-order ionospheric delay at f1 frequency, dr, j and ds, j representing a station and satellite pseudorange hardware delay at f1 frequency, respectively, and ζ r, j and ζ s, j representing a station and satellite carrier phase hardware delay at f1 frequency, and initial phase information, λ j representing a wavelength corresponding to the fj frequency, representing a non-differential integer ambiguity at the frequency fj, and representing additive errors in the pseudorange and phase observations, respectively.
Wherein the non-differential observation model is further represented by the following formula:
G, R, E, C denotes a GPS system, a GLONASS system, a GALILEO system, and a BDS system, respectively, r denotes a station, s denotes a satellite, t denotes a reference time, a pseudo-range observation value of the corresponding system at time t, and a phase observation value of the corresponding system at time t, respectively, c denotes a light velocity, denotes a geometric distance between the satellite s and the station r at time t, dtr denotes a station clock difference, dts denotes a satellite clock difference, and denotes a difference between the GLONASS system, the GALILEO system, and the BDS system with respect to the GPS system, denotes a tropospheric delay at time t, denotes an ionospheric delay at time t, ε G (t), ε R (t), E (t), and ε C (t) denote an observation noise and a residual error of the corresponding system at time t, and denote an ambiguity of the corresponding system, respectively.
wherein the step of S22 is to fit the inter-system bias parameters of different types of satellites of the multi-GNSS by the following ISB fitting model:
in the formula, Isbq represents the systematic bias among different types of satellites, a0, a1, a2, { bi } and { ci } represent the parameters of the ISB fitting model to be estimated, q is qi-q0, qi represents the ith epoch, q0 represents the starting epoch, n represents the number of period terms, and T represents the known period.
Wherein the step of estimating fitting model parameters of the navigation satellite system bias model in step S23 further comprises: estimating the fitting model parameters by using a least squares estimator or an robust Kalman filter.
further, after the step of S15, the method further includes: calculating an observation residual error, and judging whether the observation residual error is smaller than a set threshold value; if so, updating the orbit, performing ambiguity fixing calculation, replacing the fixed ambiguity parameters back to the multi-GNSS observation equation, determining satellite kinetic parameters, further updating the orbit, and outputting an orbit determination result; if not, updating the orbit parameter, the clock error parameter and the troposphere parameter, and reestablishing an observation equation for parameter estimation based on the updated observation data.
Further, between the steps of S21 and S22, the method further comprises: and removing the packets containing the GNSS stations, wherein the number of the GNSS stations is less than the set number.
According to the real-time clock error calculation method of the multi-GNSS, provided by the invention, while the multi-GNSS is precisely orbit-fixed calculated, the deviation parameters among different types of satellite systems of the multi-GNSS are estimated, and the real-time precise clock error calculation is carried out through the analysis of the deviation parameters among the different types of satellite systems of the multi-GNSS, so that the influence of the deviation among the systems on the real-time clock error estimation can be effectively weakened or eliminated, the number of parameters to be estimated in a real-time clock error observation equation is reduced, the calculation efficiency of the real-time clock error is effectively improved, the operability is stronger, and the method is easy to popularize and implement.
Drawings
FIG. 1 is a flowchart illustrating a method for real-time clock error calculation of multiple GNSS in accordance with an embodiment of the present invention;
FIG. 2 is a flowchart illustrating another method for multi-GNSS real-time clock error resolution according to an embodiment of the present invention;
FIG. 3 is a flowchart illustrating an orbit determination solution process of a multi-GNSS in accordance with an embodiment of the present invention;
FIG. 4 is a flowchart illustrating an alternative multi-GNSS orbit determination solution process according to an embodiment of the present invention;
FIG. 5 is a flowchart illustrating a process of modeling and analyzing bias parameters between multiple GNSS systems according to an embodiment of the present invention;
FIG. 6 is a flowchart illustrating a real-time clock error estimation process based on bias parameters and orbit parameters between multiple GNSS systems according to an embodiment of the present invention;
FIG. 7 is a flowchart illustrating an alternative embodiment of a real-time clock error estimation process based on bias parameters and orbit parameters of multiple GNSS systems.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention clearer, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, but not all, embodiments of the present invention. All other embodiments, which can be obtained by a person skilled in the art without any inventive step based on the embodiments of the present invention, are within the scope of the present invention.
as an embodiment of the present invention, the embodiment provides a method for calculating a real-time clock offset of a multi-GNSS, and referring to fig. 1, the method is a flowchart of the method for calculating the real-time clock offset of the multi-GNSS according to the embodiment of the present invention, and includes:
and S1, performing multi-GNSS orbit determination calculation according to the global multi-GNSS observation data and the multi-GNSS broadcast ephemeris data in the same time period as the multi-GNSS observation data, and estimating the deviation parameters among different types of satellite systems of the multi-GNSS.
It can be understood that before the estimation of the satellite clock error parameters, the precise orbit determination solution of the multi-GNSS is performed by using the observation data of the multi-mode GNSS stations which are approximately uniformly distributed in the world and the broadcast ephemeris data. And acquiring a precise orbit comprising satellite orbit parameters, survey station coordinate parameters, earth rotation ERP parameters and the like.
and when the precise orbit determination calculation is carried out, the influence of the inter-system deviation of the multi-GNSS on the clock difference calculation is considered, and the inter-system deviation parameters of different types of satellites of the multi-GNSS are estimated.
for example, firstly, 80 to 120 pieces of multi-GNSS original observation data of global measuring stations approximately uniformly distributed are obtained from IGS or MGEX websites, the observation arc period is not less than 24 hours, and meanwhile, multi-GNSS broadcast ephemeris data in the same time period are obtained, and the data file formats are all Rinex formats. Each satellite navigation system contains pseudorange and carrier phase observations at two frequencies, and downloads observations for at least one week. And then, based on the acquired data, performing multi-GNSS precise orbit determination calculation considering the deviation parameters among different types of satellite systems of the multi-GNSS.
S2, constructing a navigation satellite system deviation model aiming at different types of stations, fitting the deviation parameters between different types of satellite systems of the multi-GNSS corresponding to the different types of stations, and estimating the fitted deviation parameters between the satellite systems of the multi-GNSS and different types.
The method can be understood as that the survey station is a receiver, according to the processing flow and the processing result of the steps, deviation parameters among different types of satellite systems of the multi-GNSS corresponding to different types of survey stations are extracted, the change rule of the deviation parameters is analyzed, data fitting is carried out on the deviation parameters among the different types of satellite systems of the multi-GNSS corresponding to the different types of survey stations, a navigation satellite system deviation model for each different type of survey station is constructed, and the fitted deviation parameters among the different types of satellite systems of the multi-GNSS are estimated according to the deviation model.
And S3, resolving satellite clock error parameters under the constraint of the deviation parameters between the fitted multi-GNSS satellite systems of different types based on the output precise orbit resolved by orbit determination and the real-time observation data flow.
It can be understood that, first, strong constraints are given to the precise orbits obtained in step S1, including satellite orbit parameters, survey station coordinate parameters, earth rotation ERP parameters, and the like, and the weight is amplified; then, the deviation parameters between the fitted multi-GNSS satellite systems of different types obtained in the step S2 are used as prior information and are constrained to establish an observation model; and finally, performing clock error parameter estimation according to the observation model, wherein the clock error parameters comprise satellite clock error parameters, station clock error parameters, real number non-ionosphere combination ambiguity parameters, troposphere delay error parameters and the like.
to further illustrate the embodiment, referring to fig. 2, a flowchart of another multi-GNSS real-time clock error calculation method according to the embodiment of the present invention includes: firstly, according to the acquired multi-GNSS original observation data and the broadcast ephemeris data, multi-GNSS precise orbit determination calculation considering the deviation among multi-GNSS systems is carried out, and multi-GNSS orbit parameters are acquired. And then, performing time sequence analysis according to the inter-multi-GNSS system deviation parameters involved in the multi-GNSS precise orbit determination resolving process, and performing inter-multi-GNSS system deviation modeling. And finally, adding prior constraint of the deviation parameters among the multiple GNSS systems according to the acquired multiple GNSS orbit parameters, and performing multi-GNSS real-time clock error calculation.
According to the real-time clock error calculation method of the multi-GNSS provided by the embodiment of the invention, while the multi-GNSS is precisely orbit-fixed calculated, the deviation parameters among different types of satellite systems of the multi-GNSS are estimated, and the real-time precise clock error calculation is carried out through the analysis of the deviation parameters among the different types of satellite systems of the multi-GNSS, so that the influence of the deviation among the systems on the real-time clock error estimation can be effectively weakened or eliminated, the number of parameters to be estimated in a real-time clock error observation equation is reduced, the calculation efficiency of the real-time clock error is effectively improved, the operability is stronger, and the method is easy to popularize and implement.
optionally, referring to fig. 3, the further processing step of S1 is a flowchart of a multi-GNSS orbit determination solution process according to an embodiment of the present invention, and includes:
S11, preprocessing the global multi-GNSS observation data, wherein the global multi-GNSS observation data at least comprises carrier phase observation data and pseudo-range observation data of two frequencies corresponding to each satellite navigation system.
it is understood that in the acquired global multi-GNSS observations, each satellite navigation system comprises at least two frequencies of carrier-phase observations and pseudorange observations for different satellite navigation systems. Before the acquired observation data of the global multi-GNSS is used for precise orbit determination calculation, the data is preprocessed, including preprocessing of carrier phase observation data and pseudo-range observation data. In one embodiment, the observation data of the global multi-GNSS is preprocessed by cycle slip detection and gross error rejection. Specifically, a TurboEdit method is adopted to calibrate cycle slip in multi-GNSS observation data, and gross errors are removed to obtain 'pure' observation data.
Optionally, the pseudo-range observation data and the carrier phase observation data are further represented by the following formulas:
where the superscript s denotes a GNSS satellite, the subscript r denotes a station, the subscript j denotes a frequency bin, and pseudorange and carrier phase observations respectively representing observations of a j-th frequency of the satellite s by the station r, representing a geometric distance from the j-th frequency observation satellite s to the station r, c representing an optical velocity, dtr representing a station clock difference at a signal reception time, dts representing a satellite clock difference at a signal transmission time, representing a tropospheric delay, representing a first-order ionospheric amplification factor, representing a first-order ionospheric delay at f1 frequency, dr, j and ds, j representing a station and satellite pseudorange hardware delay at f1 frequency, respectively, and ζ r, j and ζ s, j representing a station and satellite carrier phase hardware delay at f1 frequency, and initial phase information, λ j representing a wavelength corresponding to the fj frequency, representing a non-differential integer ambiguity at the frequency fj, and representing additive errors in the pseudorange and phase observations, respectively.
And S12, performing broadcast ephemeris fitting on the multi-GNSS broadcast ephemeris data to acquire a reference orbit of the multi-GNSS satellite.
It can be understood that the initial orbit is first calculated and the initial orbit parameters are fitted using the broadcast ephemeris; and then integrating the initial orbit parameters to obtain the initial reference orbit of the multi-GNSS satellite.
S13, constructing a multi-GNSS observation equation containing deviation parameters among different types of satellite systems of the multi-GNSS based on the preprocessed carrier phase observation data and the preprocessed pseudo-range observation data.
it is to be understood that after pre-processing the carrier-phase observation data and the pseudorange observation data according to the above steps, a multi-GNSS observation equation is constructed for the processed data, taking into account the inter-multi-GNSS system bias, the multi-GNSS observation equation relating to the inter-multi-GNSS different types of satellite system bias parameters.
and S14, based on the reference orbit, carrying out error correction and linearization processing on the multi-GNSS observation equation, and acquiring a first-order partial derivative equation of the multi-GNSS observation equation relative to the deviation parameter.
It can be understood that after the multi-GNSS observation equation is constructed based on the above steps, the multi-GNSS observation equation may have a bias, and the linearization performance is poor, which is not favorable for calculation. Therefore, according to the reference specification determined in the step, the error correction and linearization processing are carried out on the constructed multi-GNSS observation equation, and the first-order partial derivative equation of the multi-GNSS observation equation relative to the deviation parameters is calculated.
And S15, solving the deviation parameters between the precise orbit and the different types of satellite systems of the multi-GNSS based on the first-order partial derivative equation.
it is understood that after the design matrix of the multi-GNSS observation equations is calculated in the above-mentioned step, a linear multi-GNSS observation equation may be determined from the design matrix. And estimating the deviation parameters between different types of satellite systems of the precise orbit and the multi-GNSS by solving a linear multi-GNSS observation equation. In one embodiment, the linear multi-GNSS observation equation is solved by using a least square method, and the relevant parameters are estimated.
The related parameters comprise satellite orbit parameters, survey station coordinate parameters, earth rotation ERP parameters, troposphere delay error parameters, satellite clock error parameters, survey station clock error parameters, ambiguity parameters and bias ISB parameters among multiple GNSS systems. Meanwhile, the deviation parameters among the multiple GNSS systems are saved for subsequent analysis.
Further, after the step of S15, the method further includes:
Calculating an observation residual error, and judging whether the observation residual error is smaller than a set threshold value;
If so, updating the orbit, performing ambiguity fixing calculation, replacing the fixed ambiguity parameters back to the multi-GNSS observation equation, determining satellite kinetic parameters, further updating the orbit, and outputting an orbit determination result;
If not, updating the orbit parameter, the clock error parameter and the troposphere parameter, and reestablishing an observation equation for parameter estimation based on the updated observation data.
it should be understood that, referring to fig. 4, which is another multi-GNSS orbit determination solution processing flowchart according to an embodiment of the present invention, data preprocessing is performed according to observation data of a multi-GNSS, and multi-GNSS observation model building is performed according to the preprocessed data. Meanwhile, initial orbit fitting is carried out according to the broadcast ephemeris data, and orbit integration is determined. And then, according to the established multi-GNSS observation model and the orbit integral, performing parameter estimation by using a least square method. And then calculating an observation residual error, and judging whether the observation residual error is within a set threshold value range.
And if the observation residual is smaller than a set threshold, updating the orbit, performing ambiguity fixing calculation, replacing the fixed ambiguity parameters with the multi-GNSS observation equation, determining satellite kinetic parameters, further updating the orbit, outputting an orbit determination result, and extracting ISB parameters. And if the observation residual is larger than or equal to the set threshold, updating the current orbit parameter, clock error parameter and troposphere parameter, and reestablishing an observation equation based on the updated observation data for parameter estimation.
optionally, referring to fig. 5, the step of further processing of S2 is a flowchart of modeling, analyzing and processing a bias parameter between multiple GNSS systems according to an embodiment of the present invention, where the flowchart includes:
And S21, grouping the GNSS survey stations according to the same survey station type into a group based on the corresponding different survey station types.
it can be understood that according to the multi-GNSS precision orbit determination solution involving the inter-system bias in the above steps, the inter-multi-GNSS system bias parameters are extracted from the solution result parameters, and a group is established according to the types of stations of different stations, and the inter-multi-GNSS system bias parameters corresponding to the same station type are grouped into one group.
in one embodiment, after grouping the GNSS stations, the method further comprises: and removing the packets containing the GNSS stations, wherein the number of the GNSS stations is less than the set number.
It is to be understood that, after the GNSS stations are grouped according to the above embodiment, the number of GNSS stations included in each group is counted, and the number of stations included in each group is compared with the set number, and if the number of stations in each group is smaller than the set number, the group is removed. For example, if the number is set to 5, all packets containing GNSS stations with a number less than 5 are removed.
S22, corresponding to the different types of satellite system deviation parameters of the multiple GNSS in different groups, respectively constructing corresponding navigation satellite system deviation models, and fitting the different types of satellite system deviation parameters of the multiple GNSS.
It can be understood that, since the ISB difference between different types of stations may exceed 100ns at most, corresponding time series models are respectively constructed for the inter-multi-GNSS system bias parameters of different types of stations, and the inter-multi-GNSS system bias is fitted.
Optionally, the inter-system bias parameters of different types of satellites of the multi-GNSS are fitted by the following ISB fitting model:
In the formula, Isbq represents the systematic bias among different types of satellites, a0, a1, a2, { bi } and { ci } represent the parameters of the ISB fitting model to be estimated, q is qi-q0, qi represents the ith epoch, q0 represents the starting epoch, n represents the number of period terms, and T represents the known period.
and S23, estimating fitting model parameters of the navigation satellite system deviation model, and acquiring the fitted deviation parameters among the satellite systems of different types of the multi-GNSS.
the method can be understood as that model parameter estimation is carried out according to the navigation satellite system deviation model obtained in the above steps, and deviation parameters between the fitted multiple GNSS different types of satellite systems are obtained.
optionally, the step of estimating fitting model parameters of the navigation satellite system bias model further includes: estimating the fitting model parameters by using a least squares estimator or an robust Kalman filter.
It will be appreciated that the fitted model parameters of the ISB may be estimated using a least squares estimator from the navigation satellite system bias model. Considering that the least square estimator does not have robust performance and only fits ISB parameters in a period of time, in order to improve the robust capability in the operation process, the robust least square estimator or a robust Kalman (Kalman) filter and the like are adopted to estimate parameters of a fitting model.
Optionally, referring to fig. 6, the step of processing S3 is a flowchart of a real-time clock offset estimation process based on bias parameters and orbit parameters between multiple GNSS systems according to an embodiment of the present invention, where the process includes:
And S31, acquiring the real-time observation data stream of the multiple GNSS and preprocessing the data stream.
It can be understood that, in order to implement the real-time clock error calculation, the real-time observation data streams of multiple GNSS devices need to be acquired first, and the acquired real-time observation data streams are preprocessed by the same preprocessing steps as those in the above embodiments.
and S32, fixing the precise orbit by giving a preset level of weight, and setting the deviation parameters between the fitted multi-GNSS satellite systems of different types as constraint information.
it is understood that the precise orbits acquired according to the above-described embodiments, including the coordinate of the survey station, the satellite orbit of the multi-GNSS, the earth rotation parameter, etc., are fixed by giving them strong constraints and amplifying the weights. Meanwhile, the deviation parameters between the fitted multi-GNSS satellite systems of different types, which are obtained according to the embodiment, are used as prior information and are constrained.
and S33, based on the fixed precise orbit and the weight thereof and the preprocessed real-time observation data stream, under the constraint of the constraint information, establishing a non-differential observation model by adopting a non-differential phase and pseudo-range combined observation value without an ionosphere.
It can be understood that a fixed precise orbit and the weight thereof are comprehensively considered according to the preprocessed real-time observation data stream, the deviation parameters between the fitted multi-GNSS satellite systems of different types are taken as constraint conditions, and a non-differential observation model is established by adopting the non-differential phase and pseudo-range combined observation value without an ionosphere.
Optionally, the non-difference observation model is further represented by the following formula:
g, R, E, C denotes a GPS system, a GLONASS system, a GALILEO system, and a BDS system, respectively, r denotes a station, s denotes a satellite, t denotes a reference time, a pseudo-range observation value of the corresponding system at time t, and a phase observation value of the corresponding system at time t, respectively, c denotes a light velocity, denotes a geometric distance between the satellite s and the station r at time t, dtr denotes a station clock difference, dts denotes a satellite clock difference, and denotes a difference between the GLONASS system, the GALILEO system, and the BDS system with respect to the GPS system, denotes a tropospheric delay at time t, denotes an ionospheric delay at time t, ε G (t), ε R (t), E (t), and ε C (t) denote an observation noise and a residual error of the corresponding system at time t, and denote an ambiguity of the corresponding system, respectively.
s34, estimating the satellite clock error parameter based on the non-difference observation model.
The method can be understood as that the target parameters are solved according to the non-differential observation model constructed in the steps, and the value of the satellite clock error parameter is estimated. The target parameters include satellite clock error parameters, station clock error parameters, real number non-ionospheric combination ambiguity parameters, tropospheric delay error parameters, and the like.
In one embodiment, the satellite clock error parameters are estimated using a square root filtering algorithm.
To further illustrate the embodiment, referring to fig. 7, another real-time clock error estimation processing flow chart based on bias parameters and orbit parameters between multiple GNSS systems according to the embodiment of the present invention is shown, in the flow chart, firstly, data preprocessing is performed on real-time observation data by coarse error elimination, and then, according to the preprocessed data, non-differential observation model construction is performed by combining fixed orbit parameters, coordinate parameters of a survey station, earth rotation ERP parameters, and multiple GNSS orbits, so as to obtain a non-differential observation model. On the basis, the ISB parameters of the multi-GNSS are used as prior constraints to carry out parameter estimation of square root filtering, and the multi-GNSS real-time clock error parameters are obtained.
finally, it should be noted that: the above examples are only intended to illustrate the technical solution of the present invention, but not to limit it; although the invention has been described in detail with reference to the foregoing embodiments, it will be understood by those skilled in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions of the embodiments of the present invention.
Claims (9)
1. A multi-GNSS real-time clock error resolving method is characterized by comprising the following steps:
S1, performing orbit determination calculation of the multi-GNSS according to the global multi-GNSS observation data and the multi-GNSS broadcast ephemeris data in the same time period with the multi-GNSS observation data, and estimating the deviation parameters among different types of satellite systems of the multi-GNSS;
s2, constructing a navigation satellite system deviation model aiming at different types of stations, fitting different types of satellite system deviation parameters of the multi-GNSS corresponding to the different types of stations, and estimating the fitted multi-GNSS different types of satellite system deviation parameters;
S3, resolving satellite clock error parameters under the constraint of the deviation parameters among the fitted multi-GNSS satellite systems of different types based on the output precise orbit resolved by orbit determination and the real-time observation data flow;
Wherein the step of S2 further comprises:
s21, based on the types of the corresponding stations of different types, the GNSS stations are grouped according to the principle that the same station types are grouped into one group;
S22, respectively constructing corresponding navigation satellite system deviation models for different types of satellite system deviation parameters of the multi-GNSS in different groups, and fitting the different types of satellite system deviation parameters of the multi-GNSS;
and S23, estimating fitting model parameters of the navigation satellite system deviation model, and acquiring the fitted deviation parameters among the satellite systems of different types of the multi-GNSS.
2. The method of claim 1, wherein the step of S1 further comprises:
S11, preprocessing the observation data of the global multi-GNSS, wherein the observation data of the global multi-GNSS at least comprises carrier phase observation data and pseudo-range observation data of two frequencies corresponding to each satellite navigation system;
S12, performing broadcast ephemeris fitting on the multi-GNSS broadcast ephemeris data to acquire a reference orbit of the multi-GNSS satellite;
S13, constructing a multi-GNSS observation equation containing deviation parameters among different types of satellite systems of the multi-GNSS based on the preprocessed carrier phase observation data and pseudo-range observation data;
S14, based on the reference orbit, carrying out error correction and linearization processing on the multi-GNSS observation equation to obtain a first-order partial derivative equation of the multi-GNSS observation equation relative to the deviation parameter;
And S15, solving the deviation parameters between the precise orbit and the different types of satellite systems of the multi-GNSS based on the first-order partial derivative equation.
3. the method of claim 1, wherein the step of S3 further comprises:
S31, acquiring real-time observation data streams of multiple GNSS, and preprocessing the data streams;
S32, fixing the precise orbit by giving a preset level of weight, and setting the deviation parameters between the fitted multi-GNSS satellite systems of different types as constraint information;
S33, based on the fixed precise orbit and the weight thereof and the preprocessed real-time observation data stream, under the constraint of the constraint information, adopting a non-differential phase and pseudo-range combined observation value without an ionosphere to establish a non-differential observation model;
S34, estimating the satellite clock error parameter based on the non-difference observation model.
4. the method of claim 2, wherein the pseudorange observations and carrier-phase observations are further represented by:
Where the superscript s denotes a GNSS satellite, the subscript r denotes a station, the subscript j denotes a frequency bin, and pseudorange and carrier phase observations respectively representing observations of a j-th frequency of the satellite s by the station r, representing a geometric distance from the j-th frequency observation satellite s to the station r, c representing an optical velocity, dtr representing a station clock difference at a signal reception time, dts representing a satellite clock difference at a signal transmission time, representing a tropospheric delay, representing a first-order ionospheric amplification factor, representing a first-order ionospheric delay at f1 frequency, dr, j and ds, j representing a station and satellite pseudorange hardware delay at f1 frequency, respectively, and ζ r, j and ζ s, j representing a station and satellite carrier phase hardware delay at f1 frequency, and initial phase information, λ j representing a wavelength corresponding to the fj frequency, representing a non-differential integer ambiguity at the frequency fj, and representing additive errors in the pseudorange and phase observations, respectively.
5. the method of claim 3, wherein the non-differential observation model is further represented using the following equation:
G, R, E, C denotes a GPS system, a GLONASS system, a GALILEO system, and a BDS system, respectively, r denotes a station, s denotes a satellite, t denotes a reference time, a pseudo-range observation value of the corresponding system at time t, and a phase observation value of the corresponding system at time t, respectively, c denotes a light velocity, denotes a geometric distance between the satellite s and the station r at time t, dtr denotes a station clock difference, dts denotes a satellite clock difference, and denotes a difference between the GLONASS system, the GALILEO system, and the BDS system with respect to the GPS system, denotes a tropospheric delay at time t, denotes an ionospheric delay at time t, ε G (t), ε R (t), E (t), and ε C (t) denote an observation noise and a residual error of the corresponding system at time t, and denote an ambiguity of the corresponding system, respectively.
6. The method according to claim 1, wherein the step of S22 is performed by fitting the inter-system bias parameters of different types of satellites of the multi-GNSS by the following ISB fitting model:
In the formula, Isbq represents the systematic bias among different types of satellites, a0, a1, a2, { bi } and { ci } represent the parameters of the ISB fitting model to be estimated, q is qi-q0, qi represents the ith epoch, q0 represents the starting epoch, n represents the number of period terms, and T represents the known period.
7. the method of claim 6, wherein the step of estimating fitting model parameters of the navigation satellite system bias model in step S23 further comprises:
estimating the fitting model parameters by using a least squares estimator or an robust Kalman filter.
8. The method of claim 2, further comprising, after the step of S15:
Calculating an observation residual error, and judging whether the observation residual error is smaller than a set threshold value;
If so, updating the orbit, performing ambiguity fixing calculation, replacing the fixed ambiguity parameters back to the multi-GNSS observation equation, determining satellite kinetic parameters, further updating the orbit, and outputting an orbit determination result;
if not, updating the orbit parameter, the clock error parameter and the troposphere parameter, and reestablishing an observation equation for parameter estimation based on the updated observation data.
9. The method of claim 1, further comprising, between the steps of S21 and S22:
And removing the packets containing the GNSS stations, wherein the number of the GNSS stations is less than the set number.
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| CN110059361B (en) * | 2019-03-22 | 2021-01-15 | 中国科学院测量与地球物理研究所 | Real-time regional troposphere modeling method based on robust Kalman filtering algorithm |
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| CN110988932B (en) * | 2019-12-11 | 2022-02-01 | 武汉大学 | Method for improving real-time clock error resolving precision of satellite-borne GPS receiver |
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