CN109143273A - The restorative procedure of multi-frequency multi-mode GNSS cycle slip and data outage - Google Patents
The restorative procedure of multi-frequency multi-mode GNSS cycle slip and data outage Download PDFInfo
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S19/00—Satellite radio beacon positioning systems; Determining position, velocity or attitude using signals transmitted by such systems
- G01S19/01—Satellite radio beacon positioning systems transmitting time-stamped messages, e.g. GPS [Global Positioning System], GLONASS [Global Orbiting Navigation Satellite System] or GALILEO
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- G01S19/00—Satellite radio beacon positioning systems; Determining position, velocity or attitude using signals transmitted by such systems
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Abstract
The present invention provides the restorative procedures of a kind of multi-frequency multi-mode GNSS cycle slip and data outage, consider influence of the Ionospheric variability amount to Detection of Cycle-slip between epoch, cycle slip resolves more reliable, and the reparation of the data outage of certain time interval is also achieved outside normal detection and reparation for cycle slips, effectively solve in practical application frequently cycle slip or data outage and bring reinitializes ambiguity issue, the complexity of data processing is reduced, the availability and continuity of data and high accuracy positioning are improved.
Description
Technical field
The present invention relates to GNSS precision positioning field, especially a kind of reparation of multi-frequency multi-mode GNSS cycle slip and data outage
Method.
Background technique
GPS starts from military's project in the U.S. in 1958, and when nineteen ninety-five runs completely, system starts in L1 and L2 carrier wave
Upper transmission signal ends in January, 2016, and space segment shares 32 satellites, and part satellite broadcasts L1, L2 and L5 letter simultaneously
Number.Just recognize very early satellite-based navigation military strategy, economy, society and in terms of importance, the Eurosummit
The commercial satellite navigation system Galileo of oneself was approved the construction of in 2002, every satellite discloses the signal for broadcasting 5 frequencies,
Divide disclosure, commercial affairs, 4 kinds of life security, public safety control service modes, every kind of service mode all uses different signals.North
Bucket (BDS) is the GNSS positioning system that first total system broadcasts three frequency signals, with Chinese independent research, independently operated north
Bucket navigational satellite system starts to cover the Asian-Pacific area, and it is only in field of satellite navigation for a long time that GPS has been broken in the appearance of Beidou
The situation towered above the rest, currently, Beidou can provide the self-contained navigation service of high quality in the Asian-Pacific area, and will be in the year two thousand twenty or so
It is covering the whole world.In addition to this there are also various regional common group of system of the enhancings such as the GLONASS of Russia and the QZSS of Japan
At Global Navigation Satellite System (GNSS) now.The combination of multifrequency (Multi-frequency) multimode (Multi-GNSS)
Navigator fix mode is the developing direction of the current and following GNSS, is promoting positioning accuracy, is improving location availability and reliability
And have great advantage in terms of the geometry intensity of enhancing observation satellite.
After multi-frequency multi-mode work pattern occurs, especially Beidou total system, which broadcasts three frequency signals, makes user have more multi-frequency
Observe data, and by these original observed datas derive meet different demands, keep fuzziness integer characteristic it is various
Combination observation both increases the application advantage of GNSS.However, complicated observing environment blocks GNSS signal frequently
Existing cycle slip and signal interruption, seriously affect the continuity and availability of observation data, will bring 20 only 1 week cycle slip
The observation error of~cm, therefore the reparation of cycle slip and data outage is the important factor in order of GNSS high-precision applications.
Firstly, in an embodiment of the processing method to cycle slip, using cycle slip as a part of fuzziness, again initially
Change fuzziness.It in another embodiment, is detection and reparation cycle slip.But cycle slip is frequent in complex environment in practical applications
Occur, in an above-mentioned embodiment, reinitialize fuzziness, when increasing the complexity of data processing, extending data processing
Between, reduce the availability of high-precision result.Opposite, in above-mentioned another embodiment, effectively repairing cycle slip can be significantly improved
Data are observed, avoids frequently reinitializing fuzziness, improves the availability of high accuracy positioning result.Therefore, how right
The reparation of cycle slip in GNSS data is an important problem.
Secondly, need to reinitialize fuzziness in an embodiment of the processing of short time signal interruption, equally,
In practical applications, fuzziness is reinitialized, the complexity of data processing is increased, extends data processing time, reduces
A possibility that high-precision result.Therefore, how signal interruption is handled and is also a problem to be solved.
Summary of the invention
Applicants have found that the processing to short time (30s~60s) signal interruption, can by its as Detection of Cycle-slip with
The problem of reparation, is handled, and interruption data connection is realized by model and Ionospheric variability.Further, to GNSS data
In cycle slip reparation there are many method, specifically, for example fitting of a polynomial, high order difference, linear combination and the methods of.Tool
Body, TurboEdit algorithm is a kind of double frequency Cycle Slips Detection being most widely used at present, many large softwares such as
It is applied in Bemese, PANDA etc..When Ionospheric variability is steady, the change rate using phase without geometry linear combination is base
Plinth, using such as Kalman filtering, linear function etc. is detected and is repaired.
Applicant further study show that, to the detection of the cycle slip in GNSS data and to repair include GF (Geometry-
Free, based on no geometry domain) built-up pattern and GB (Geometry-based is based on geometry domain) built-up pattern.Compared to GB group
The mold strength of molding type, GF built-up pattern is weak and treatment effect is lower, in the application that cycle slip and data terminal frequently occur,
Such as long range dynamic positioning etc., possibly it can not be securely fixed cycle slip.In addition, in Multiple system, GF built-up pattern by
In eliminating location parameter, the mutual auxiliary between multisystem has been abandoned completely, this is between system each in integrated navigation and location
A kind of waste connected each other.Further, current cycle-slip detection and repair method mostly merely ignore ionosphere effect or
Person substitutes only with previous ionosphere information, when ionosphere is active or the data outage time is longer, the reliability of method
It is affected.
To solve the above-mentioned problems, the purpose of the present invention is to provide a kind of multi-frequency multi-mode GNSS cycle slip and data outages
Restorative procedure and system consider the influence in ionosphere, are resolved and data terminal with being suitable for the real-time dynamic cycle slip of multi-frequency multi-mode
It repairs.
In order to achieve the above object, the present invention provides the reparation sides of a kind of multi-frequency multi-mode GNSS cycle slip and data outage
Method, comprising the following steps:
Judge whether that data outage occurs, if data outage does not occur, obtains Ionospheric variability amount priori value between epoch；It is no
Then, Ionospheric variability amount in the data outage time according to the Time Forecast of data outage；
According to Ionospheric variability amount between the epoch or in the data outage time, Ionospheric variability amount establishes the ionization of geometry domain
Layer weighted model, and cycle slip float-solution is obtained according to geometry domain ionosphere weighted model；
Cycle slip is attempted according to the cycle slip float-solution to fix, and when cycle slip is fixed, exports cycle slip integer solution, and obtain epoch
Between Ionospheric variability amount.
Preferably, in the restorative procedure of above-mentioned multi-frequency multi-mode GNSS cycle slip and data outage, judge whether that number occurs
It include: to judge whether the time interval between current epoch and previous epoch is equal with the sampling interval according to the step of interrupting, if equal,
Data outage does not occur then, if unequal, data outage occurs.
Preferably, in the restorative procedure of above-mentioned multi-frequency multi-mode GNSS cycle slip and data outage, data outage is occurring
When, judge whether data outage can be repaired, when the data outage can be repaired, ionosphere is established according to the time of data outage
The model of variable quantity, and forecast Ionospheric variability amount in the data outage time.
Preferably, in the restorative procedure of above-mentioned multi-frequency multi-mode GNSS cycle slip and data outage, according to data outage
The model that time establishes Ionospheric variability amount is obtained by following formula:
Wherein, d representative polynomial order, c indicate that c orders, s indicate s epoch, a0、acIndicate coefficient.
Preferably, in the restorative procedure of above-mentioned multi-frequency multi-mode GNSS cycle slip and data outage, judge that data outage is
No recoverable standard are as follows: whether the time of data outage is in recoverable time range.
Preferably, in the restorative procedure of above-mentioned multi-frequency multi-mode GNSS cycle slip and data outage, judging whether to occur
It is further comprising the steps of before data outage:
Obtain initial value, design matrix and the satellite clock correction correcting information of the current epoch of detection cycle slip.
Preferably, in the restorative procedure of above-mentioned multi-frequency multi-mode GNSS cycle slip and data outage, judging whether to occur
It is further comprising the steps of before data outage: difference between epoch being asked to carrier phase observable and Pseudo-range Observations, and corrects epoch difference
The satellite clock correction of carrier phase observable and Pseudo-range Observations afterwards.
Preferably, in the restorative procedure of above-mentioned multi-frequency multi-mode GNSS cycle slip and data outage, the non-difference of GB model is single
Stand single epoch single-frequency GNSS carrier phase observable and Pseudo-range Observations equation it is as follows:
E(pj)=Gx+endtj-dtS, j+τ+μjι；
Wherein, j indicates j-th of frequency；φjIndicating phase observations vector, unit is rice,pjTable
Show pseudorange observation vector, unit is rice,N indicates the number of the satellite of observation simultaneously, the value of n for greater than
Natural number equal to 4；The design matrix of G indicates coordinate parameter X=[x, y, z]；δtjIndicate carrier phase observable receiver clock-offsets,
As unit of rice；dtjThe receiver clock-offsets for indicating Pseudo-range Observations, as unit of rice；δtS, jIndicate the satellite clock of n*1 dimension phase
Difference, as unit of rice, δ tS, j=[δ tS, j 1..., δ tS, j n]T；dtS, jThe satellite clock correction for indicating n*1 dimension pseudorange, as unit of rice,
dtS, j=[dtS, j 1..., dtS, j n]T: τ indicates that n*1 is tieed up to process delay vector, as unit of rice, τ=[τ1... τn]T；ι is indicated
N*1 in 1st frequency ties up ionosphere delay, as unit of rice, ι=[ι1..., ιn]T；μj=f1 2/fj 2；λjIndicate j-th of frequency
The wavelength of rate, as unit of rice/week；Indicate that n*1 ties up carrier phase observable ambiguity vector, wherein the
K element beFor integerFor satellite initial phase deviation, with
Week is unit,For receiver initial phase deviation, as unit of week, enIndicate n*1 dimensional vector, element is 1.
Preferably, in the restorative procedure of above-mentioned multi-frequency multi-mode GNSS cycle slip and data outage, the geometry domain ionization
Layer weighted model is obtained by following formula:
And
Wherein, H=[A, es] it is the design matrix merged after baseline parameter and receiver clock-offsets, should mutually there be the b after merging
=[b, δ t],It is the uncertainty for modeling single poor Ionospheric variability amount between epoch, τ=[τ1... τn]T；ι indicates the 1st
N*1 in a frequency ties up ionosphere delay, and as unit of rice, b is the baseline parameter between two epoch, and z indicates integer cycle slip, e2f
Indicate 2f*1 dimensional vector, element is the factor arrays that 1, υ indicates ionosphere delay variable quantity and frequency dependence, on different frequency
Ionosphere delay variable quantity and its frequency square be inversely proportional, IsIndicate that s ties up unit matrix, Γ=[Λ, 0]T, QsIndicate non-
Poor observation association's factor battle array relevant to elevation angle, διIndicate observation receiver clock-offsets, ι0Indicate the n*1 dimension in the 1st frequency
The priori value of ionosphere delay variable quantity.
In the restorative procedure of multi-frequency multi-mode GNSS cycle slip provided by the invention and data outage, first judge whether to send out first
Raw data outage obtains Ionospheric variability amount priori value between epoch when data outage occurs；When data outage does not occur,
Ionospheric variability amount in the data outage time according to the Time Forecast of data outage；According to Ionospheric variability between the epoch
Ionospheric variability amount establishes geometry domain ionosphere weighted model in amount or data outage time, and according to geometry domain ionosphere
Weighted model obtains cycle slip float-solution；It attempts cycle slip according to the cycle slip float-solution to fix, when cycle slip is fixed, output cycle slip is whole
Number solution, and obtain Ionospheric variability amount between epoch.Consider influence of the Ionospheric variability amount to Detection of Cycle-slip between epoch, cycle slip solution
It calculates more reliable, and also achieves outside normal detection and reparation for cycle slips the reparation of the data outage of certain time interval, have
Effect solves frequent cycle slip or data outage in practical application and bring reinitializes ambiguity issue, reduces data processing
Complexity improves the availability and continuity of data and high accuracy positioning.
Detailed description of the invention
Fig. 1 is the flow chart of the restorative procedure of multi-frequency multi-mode GNSS cycle slip and data outage in one embodiment of the invention.
Specific embodiment
A specific embodiment of the invention is described in more detail below in conjunction with schematic diagram.According to following description and
Claims, advantages and features of the invention will become apparent from.It should be noted that attached drawing is all made of very simplified form and
Using non-accurate ratio, only for the purpose of facilitating and clarifying the purpose of the embodiments of the invention.
One embodiment of the invention provides the restorative procedure of a kind of multi-frequency multi-mode GNSS cycle slip and data outage, specifically,
As shown in FIG. 1, FIG. 1 is the flow chart of the restorative procedure of multi-frequency multi-mode GNSS cycle slip and data outage in one embodiment of the invention,
It the described method comprises the following steps: firstly, the fuzziness of intialization phase observation, shown in step S1 as shown in figure 1.
Next, SPP (Single Point Positioning, One-Point Location) is obtained in the current epoch of detection cycle slip
Initial value, design matrix and satellite clock correction correcting information.Step S2 as shown in figure 1.
Specifically, broadcast ephemeris data of the SPP when current epoch calculates co-ordinates of satellite and clock deviation should be with previous epoch phase
Together, to prevent the jump due to front and back epoch satellite clock caused by replacing broadcasting satellite almanac data.Meanwhile SPP
Satellite elevation mask should be set when calculating co-ordinates of satellite and clock deviation, and troposphere uses UNB3 model correction, and double frequency/tri- frequencies are seen
Measured data weakens ionosphere effect using no ionospheric combination.
Wherein, UNB3 model refers to the tropospheric delay correction mould of the research group research of new Brunswick university
Type.Further, no ionosphere (Ionosphere-free) combination is to constitute a kind of group using the Pseudo-range Observations of two frequencies
Observation is closed, to weaken the influence of this part of the ionosphere of atmosphere delay in Pseudo-range Observations.
Further, initial value acquired in SPP includes but is not limited between coordinate initial value, satellite and the earth of receiver
The initial value etc. of distance.
Step S3: difference between epoch is asked to carrier phase observable and Pseudo-range Observations, and corrects phase observations after epoch difference
The satellite clock correction of value and Pseudo-range Observations.
Specifically, the single station single epoch single-frequency GNSS carrier phase observable of non-difference of GB model and the equation difference of Pseudo-range Observations
It is as follows:
Wherein, j indicates j-th of frequency；φjIndicating phase observations vector, unit is rice,pjTable
Show pseudorange observation vector, unit is rice,N is indicated while the number of the satellite of observation, and the value of n is greater than 1
Natural number；The design matrix of G indicates coordinate parameter X=[x, y, z]；δtjIt indicates carrier phase observable receiver clock-offsets, is with rice
Unit；dtjThe receiver clock-offsets for indicating Pseudo-range Observations, as unit of rice；δtS, jIndicate the satellite clock correction of n*1 dimension phase, with
Rice is unit, δ tS, j=[δ tS, j 1..., δ tS, j n]T；dtS, jThe satellite clock correction for indicating n*1 dimension pseudorange, as unit of rice, dtS, j=
[dtS, j 1..., dtS, j n]T: τ indicates that n*1 is tieed up to process delay vector, as unit of rice, τ=[τ1... τn]T；ι indicates the 1st
N*1 in frequency ties up ionosphere delay, as unit of rice, ι=[ι1..., ιn]T；μj=f1 2/fj 2；λjIndicate j-th of frequency
Wavelength, as unit of rice/week；Indicate that n*1 ties up carrier phase observable ambiguity vector, wherein k-th
Element isFor integer,For satellite initial phase deviation, it is with week
Unit,For receiver initial phase deviation, as unit of week, enIndicate n*1 dimensional vector, element is 1.
Further, there are two characteristics for GNSS cycle slip tool: integer and continuity.Specifically, it is different from rough error, GNSS weeks
Jumping has integer characteristic, and will occur epoch from cycle slip and continue backward.Therefore, the detection of cycle slip must be set up in observation data
Between the epoch of front and back on the basis of difference, specifically, single differential mode type is respectively by such as between carrier phase observable and the epoch of Pseudo-range Observations
Following (formulas 3) and (formula 4) indicate:
Wherein, Δ indicates difference operator, specifically, Δ (*)=(*)k+1-(*)k。
It is singly inserted in model between above-mentioned epoch, the initial phase deviation of receiver and satellite all completely eliminates, and two are gone through
Differentiated integer Δ Z between memberjIt is defined as cycle slip.Strictly speaking, differentiated carrier phase observable receiver clock-offsets Δ δ tjWith
Pseudo-range Observations receiver clock-offsets Δ dtjBecause between inter-frequency deviation and observation type deviation there are due to it is different, but due between frequency
Deviation is highly stable whithin a period of time between deviation and observation type, therefore has Δ δ tj=Δ dtj≡Δδt。
Wherein, Δ δ tj=δ tj(k+1)-δtj(k), Δ dtj=dtj(k+1)-dtj(k)。
Since tropospheric propagation path is close between the epoch of front and back for signal, the tropospheric delay Δ τ of difference is non-between epoch
It is often small, it can be ignored and will not influence cycle slip resolving.Satellite clock correction can be obtained from ephemeris file, be accordingly regarded as known
, single poor geometry item follows following derivation between epoch:
Wherein, b is the baseline parameter between two epoch, and b=xk+1-xk。
Difference operator between epoch is omitted to express simplicity, to form carrier phase observable and puppet after correction satellite clock correction
Single differential mode type is indicated by such as following (formulas 6) and (formula 7) respectively between epoch away from observation:
E(pj+δts)=Ab+enδt-μjι.(formula 7)
Further, the carrier phase observable and Pseudo-range Observations gathered in all f frequencies are indicated by following formula respectively:
Wherein,Indicate that carrier phase observable subtracts the vector after calculated value,Table
Show the vector after Pseudo-range Observations subtract calculated value, if μ=[μ1..., μf]T,Λ=diag (λ1...,
λf), then above-mentioned (formula 8) and (formula 9) can write a Chinese character in simplified form are as follows:
Wherein, y=[φT, pT]T, v=[- μT, μT]T, Γ=[Λ, 0]T。
Further, the variance matrix of single epoch un-differenced observation may be expressed as:
Wherein, QsIt is un-differenced observation association's factor battle array relevant to elevation angle, Qf=blkdiag (Qφ, Qp) indicate specific frequency
The precision of rate,Wherein σφAnd σpRespectively indicate in j-th of frequency of un-differenced observation phase and
Pseudorange accuracy.
Step S4: calculating current epoch and whether the time interval between previous epoch is equal to the sampling interval, if equal,
It is judged as normal observation state, enters step S8, otherwise, is then judged as data outage, enters step S5.
Specifically, in step s 4, when judging current epoch is normal observation state or data outage state, ionization
The processing of layer is influenced by factors such as the length at data sampling interval, ionosphere active state and data outage time.Specifically
, the sampling interval is big, then needs historical data window (time needed) longer；Ionosphere is more active, the order of model
It is higher.
Step S5, judge to observe data outage time whether can in repair coverage, if can in repair coverage, into
Enter step S7, otherwise, then enters step S6.
Specifically, be directed to different receiving stations, it is described can repair coverage it is also different, for example, Shanghai CORS stands 2014 years
Beidou data can repair 30-60s.
Step S6, if the time of observation data outage beyond it is described can repair coverage, show repairing failure in data, directly
Return step S1 is met, the fuzziness of carrier phase observable is reinitialized.
Step S7 selects certain epoch window to establish ionosphere using historical data according to the size of the time of data outage
The model of variable quantity, and forecast data interrupt during Ionospheric variability amount priori value.
Specifically, obtaining the priori of Ionospheric variability amount between data outage two epoch of front and back under data outage state
Value.In practical applications, since the sampling interval is smaller, the time of the data outage observed is very short, and two go through before and after data outage
Ionospheric variability amount very little between member, so that can be ignored；When the time of the data outage observed is longer, ionosphere
Variable quantity increases with the increase of break period, will affect the float-solution of cycle slip parameter, cannot be ignored, at this time using elder generation
The preceding data repaired without cycle slip or cycle slip seek the ionosphere information of previous epoch, when further according to the data outage observed
Between size, model Ionospheric variability using certain epoch window, and forecast data interrupts ionosphere between two epoch of front and back
Variable quantity, while determining according to the forecast precision of Ionospheric variability the weight in ionosphere.Further, the epoch window is big
Small to be determined according to the size in the sampling interval and the size of data outage time, specific embodiment needs to combine practical
Observing environment is determined.
Firstly, the ionosphere information of previous epoch is sought using the data previously repaired without cycle slip or cycle slip, tool
Body, can be derived according to above-mentioned (formula 6), in the case where no cycle slip, Ionospheric variability amount ι between two epoch can by with
Lower formula obtains:
Wherein, subscript i and j respectively indicates i-th of frequency and j-th of frequency,WithIt respectively indicates and is gone through in two frequencies
Carrier phase observable after difference, μ between memberI, j=fi 2/fj 2。
Secondly, Ionospheric variability amount shows very strong temporal correlation in a short time between epoch, therefore in a few minutes
It is interior to be denoted as a function about the time, i.e., certain epoch window is selected according to the size of the time of data outage
It is obtained using the Ionospheric variability amount that historical data obtains by following formula:
Wherein, d representative polynomial order, c indicate that c orders, s indicate s epoch, a0、acIndicate coefficient.
Suitable epoch window is selected according to the size of data outage time, has correctly been fixed by no cycle slip or cycle slip
Historical data obtain Ionospheric variability amount sequence, the coefficient of Ionospheric variability flow function model is acquired under criterion of least squares
(a in i.e. above-mentioned formula 130And am), then bring required epoch-making moment into, the priori of Ionospheric variability amount in forecast data interruption
Value.
It should be noted that situation difference, the size of data outage time are enlivened in the ionosphere of different sizes in sampling interval
The order of difference, the historical data window size needed and the Ionospheric variability amount model established is also different, according to experiment table
Bright, function of first order model can be obtained fitting and prediction effect well.
Step S8 passes through Ionospheric variability amount priori value of the list between historical data acquisition epoch after poor.
Specifically, under normal observation state, obtain current epoch and between previous epoch Ionospheric variability amount priori value.
In practical applications, the ionosphere delay variation when sampling interval very little or gentle ionosphere activity, after list is poor between epoch
Amount is very small, can be ignored at this time, when the sampling interval is gradually increased or ionosphere activity aggravates, ionosphere delay between epoch
Variable quantity cannot ignore, at this time using the previously ionosphere that had been previously utilizing without the data acquisition that cycle slip or cycle slip have been surveyed
Information is forecast according to the size in sampling interval and the difference of ionosphere active state using different size of sliding window
Current epoch Ionospheric variability amount obtains priori value, weakens influence of the Ionospheric variability amount to Detection of Cycle-slip between epoch, is sampling
It is spaced in the movable violent situation in larger or ionosphere, is influenced by Ionospheric variability forecast precision, only with ionospheric forecast
Value corrects ionosphere item influence of the Ionospheric variability amount to cycle slip float-solution can not completely eliminating epoch, needs at this time
It will be according to ionospheric forecast precision additional weight.The specific acquisition methods and above-mentioned steps of Ionospheric variability amount priori value between epoch
Acquisition methods in S7 are consistent.
Step S9, according to Ionospheric variability amount information establishes GB-IW model between the epoch of acquisition or in the data outage time
(geometry domain ionosphere weighted model), using the observational equation (above-mentioned (formula 1) and above-mentioned (formula 2)) to real time data or afterwards
Data are calculated, to obtain location parameter float-solution and cycle slip float-solution.
Specifically, using the priori value of the current epoch or the Ionospheric variability amount in the data outage phase that obtain, above-mentioned
Ionosphere imitation observation equation, and precision additional weight according to weather report are introduced on the basis of (formula 10), are established GB-IW model, are built
Vertical GB-IW (geometry domain ionosphere weighted model) model is as follows:
Wherein, H=[A, es] it is the design matrix merged after baseline parameter and receiver clock-offsets, should mutually there be the b after merging
=[b, δ t],It is the uncertainty for modeling single poor Ionospheric variability amount between epoch, z indicates integer cycle slip, e2fIt indicates
2f*1 dimensional vector, element is that 1, υ indicates that this is a coefficient with frequency dependence, the reason is that the electricity on different frequency
Absciss layer amount of delay and its frequency square are inversely proportional, and Is indicates that s ties up unit matrix, Γ=[Λ, 0]T, the non-difference sight of Qs expression
Measured value association's factor battle array relevant to elevation angle, διIndicate observation receiver clock-offsets, ι0Indicate the n*1 dimension ionization in the 1st frequency
The priori value of layer amount of delay.
The least square solution that parameter is acquired according to above-mentioned (formula 14) and (formula 15), the cycle slip float-solution that will be acquiredAnd association
Variance matrixFixed integer cycle slip is attempted by LAMBDA method.
Step S10 carries out trial fixation to the floating-point cycle slip in above-mentioned steps S9, judges whether fixation succeeds, if fixed
Success, then enter step S11；If fixed failure, returns to above-mentioned steps S1.
Specifically, exporting fixed cycle slip integer solution, and calculate between current epoch and previous epoch if cycle slip is fixed successfully
Ionospheric variability amount, step S11 as shown in figure 1, then enters back into above-mentioned steps S2.If the fixed failure of cycle slip, cycle slip, which resolves, to be lost
It loses, then returns to above-mentioned steps S1, reinitialize the fuzziness of carrier phase observable.
Compared with prior art, the reparation side of multi-frequency multi-mode GNSS cycle slip and data outage provided by the embodiment of the present invention
Method at least has the advantages that
First, the restorative procedure of multi-frequency multi-mode GNSS cycle slip and data outage provided by the embodiment of the present invention is based on geometry
Domain (Geometry-based, hereinafter referred to as GB) model makes full use of and sees by each frequency of tie of the location information of receiver
Connecting each other between measured data and each satellite system.
Second, the application side of the restorative procedure of multi-frequency multi-mode GNSS cycle slip and data outage provided by the embodiment of the present invention
Formula, field and environment have diversity, while being suitable for the observation model of single-frequency, double frequency and optional frequency, static and dynamic
The application scenarios such as the integrated mode of application model, single system and multisystem have very strong compatibility.
Third, current cycle-slip detection and repair method all simply ignore the influence of the Ionospheric variability amount between epoch, when
When data sampling interval is big, the data outage time is long or ionosphere is enlivened, the reliability of current cycle-slip detection and repair method by
To seriously affecting, and the restorative procedure of multi-frequency multi-mode GNSS cycle slip provided by the present invention and data outage considers electricity between epoch
Influence of the absciss layer variable quantity to Detection of Cycle-slip, cycle slip resolving is more reliable, and there is no sensitive cycle slip combinations.
4th, current detection and reparation for cycle slips method is not directed to the reparation of data outage, and multifrequency provided by the invention is more
The restorative procedure of mould GNSS cycle slip and data outage also achieves in certain time interval data other than normal Detection of Cycle-slip
Disconnected reparation, can effectively solve frequent cycle slip or data outage in practical application and bring reinitializes ambiguity issue,
The complexity of data processing is reduced, the availability and continuity of data and high accuracy positioning are improved.
To sum up, in the restorative procedure of multi-frequency multi-mode GNSS cycle slip provided in an embodiment of the present invention and data outage, first
First judge whether that data outage occurs, when data outage occurs, obtains Ionospheric variability amount priori value between epoch；When not occurring
When data outage, Ionospheric variability amount in the data outage time according to the Time Forecast of data outage；According to the epoch
Between Ionospheric variability amount or Ionospheric variability amount establishes geometry domain ionosphere weighted model in the data outage time, and according to described
Geometry domain ionosphere weighted model obtains cycle slip float-solution；It attempts cycle slip according to the cycle slip float-solution to fix, when cycle slip is fixed
When, cycle slip integer solution is exported, and obtain Ionospheric variability amount between epoch.Ionospheric variability amount is to Detection of Cycle-slip between considering epoch
Influence, cycle slip resolves more reliable, and the data of certain time interval is also achieved outside normal detection and reparation for cycle slips
The reparation of interruption, effectively solves frequent cycle slip or data outage in practical application and bring reinitializes ambiguity issue,
The complexity of data processing is reduced, the availability and continuity of data and high accuracy positioning are improved.
The above is only a preferred embodiment of the present invention, does not play the role of any restrictions to the present invention.Belonging to any
Those skilled in the art, in the range of not departing from technical solution of the present invention, to the invention discloses technical solution and
Technology contents make the variation such as any type of equivalent replacement or modification, belong to the content without departing from technical solution of the present invention, still
Within belonging to the scope of protection of the present invention.
Claims (9)
1. a kind of restorative procedure of multi-frequency multi-mode GNSS cycle slip and data outage, which comprises the following steps:
Judge whether that data outage occurs, if data outage does not occur, obtains Ionospheric variability amount priori value between epoch；Otherwise,
Ionospheric variability amount in the data outage time according to the Time Forecast of data outage；
Ionospheric variability amount is established geometry domain ionosphere and is added according to Ionospheric variability amount between the epoch or in the data outage time
Model is weighed, and cycle slip float-solution is obtained according to geometry domain ionosphere weighted model；
Cycle slip is attempted according to the cycle slip float-solution to fix, when cycle slip is fixed, exports cycle slip integer solution, and obtains electricity between epoch
Absciss layer variable quantity.
2. the restorative procedure of multi-frequency multi-mode GNSS cycle slip and data outage as described in claim 1, which is characterized in that judgement is
No the step of data outage occurs include: judge time interval between current epoch and previous epoch whether with sampling interval phase
Deng if equal, data outage does not occur, if unequal, data outage occurs.
3. the restorative procedure of multi-frequency multi-mode GNSS cycle slip and data outage as described in claim 1, which is characterized in that occurring
When data outage, judge whether data outage can be repaired, when the data outage can be repaired, is built according to the time of data outage
The model of vertical Ionospheric variability amount, and forecast Ionospheric variability amount in the data outage time.
4. the restorative procedure of multi-frequency multi-mode GNSS cycle slip and data outage as claimed in claim 3, which is characterized in that according to number
It is obtained according to the model that the time of interruption establishes Ionospheric variability amount by following formula:
Wherein, d representative polynomial order, c indicate that c orders, s indicate s epoch, a0、acIndicate coefficient.
5. the restorative procedure of multi-frequency multi-mode GNSS cycle slip and data outage as claimed in claim 3, which is characterized in that judge number
According to interrupt whether recoverable standard are as follows: whether the time of data outage in recoverable time range.
6. the restorative procedure of multi-frequency multi-mode GNSS cycle slip and data outage as described in claim 1, which is characterized in that judging
It is further comprising the steps of before whether data outage occurring:
Obtain initial value, design matrix and the satellite clock correction correcting information of the current epoch of detection cycle slip.
7. the restorative procedure of multi-frequency multi-mode GNSS cycle slip and data outage as claimed in claim 6, which is characterized in that judging
It is further comprising the steps of before whether data outage occurring: difference between epoch being asked to carrier phase observable and Pseudo-range Observations, and is corrected
The satellite clock correction of carrier phase observable and Pseudo-range Observations after epoch difference.
8. the restorative procedure of multi-frequency multi-mode GNSS cycle slip and data outage as described in claim 1, which is characterized in that GB model
The single station single epoch single-frequency GNSS carrier phase observable of non-difference and Pseudo-range Observations equation it is as follows:
E(pj)=Gx+endtj-dtS, j+τ+μjι；
Wherein, j indicates j-th of frequency；φjIndicating phase observations vector, unit is rice,pjIndicate pseudo-
Away from observation vector, unit is rice,N is indicated while the number of the satellite of observation, and the value of n is more than or equal to 4
Natural number；The design matrix of G indicates coordinate parameter X=[x, y, z]；δtjIt indicates carrier phase observable receiver clock-offsets, is with rice
Unit；dtjThe receiver clock-offsets for indicating Pseudo-range Observations, as unit of rice；δtS, jIndicate the satellite clock correction of n*1 dimension phase, with
Rice is unit, δ tS, j=[δ tS, j 1..., δ tS, j n]T；dtS, jThe satellite clock correction for indicating n*1 dimension pseudorange, as unit of rice, dtS, j=
[dtS, j 1..., dtS, j n]T: τ indicates that n*1 is tieed up to process delay vector, as unit of rice, τ=[τ1... τn]T；ι indicates the 1st
N*1 in frequency ties up ionosphere delay, as unit of rice, ι=[ι1..., ιn]T；μj=f1 2/fj 2；λjIndicate j-th of frequency
Wavelength, as unit of rice/week；Indicate that n*1 ties up carrier phase observable ambiguity vector, wherein k-th
Element is For integer,For satellite initial phase deviation, with Zhou Weidan
Position,For receiver initial phase deviation, as unit of week, enIndicate n*1 dimensional vector, element is 1.
9. the restorative procedure of multi-frequency multi-mode GNSS cycle slip and data outage as described in claim 1, which is characterized in that described several
What domain ionosphere weighted model is obtained by following formula:
And
Wherein, H=[A, es] it is the design matrix merged after baseline parameter and receiver clock-offsets, should mutually there are b=[b, δ after merging
T],It is the uncertainty for modeling single poor Ionospheric variability amount between epoch, τ=[τ1... τn]T；ι indicates the 1st frequency
N*1 in rate ties up ionosphere delay, and as unit of rice, b is the baseline parameter between two epoch, and z indicates integer cycle slip, e2fIt indicates
2f*1 dimensional vector, element are the factor arrays that 1, υ indicates ionosphere delay variable quantity and frequency dependence, the electricity on different frequency
Absciss layer amount of delay and its frequency square are inversely proportional, IsIndicate that s ties up unit matrix, Γ=[Λ, 0]T, QsIndicate that non-difference is seen
Measured value association's factor battle array relevant to elevation angle, διIndicate observation receiver clock-offsets, ι0Indicate the n*1 dimension ionization in the 1st frequency
The priori value of layer amount of delay.
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