CN109143273B - Multi-frequency multi-mode GNSS cycle slip and data interrupt repairing method - Google Patents
Multi-frequency multi-mode GNSS cycle slip and data interrupt repairing method Download PDFInfo
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Abstract
The invention provides a multi-frequency multi-mode GNSS cycle slip and data interrupt repairing method, which considers the influence of ionosphere variation among epochs on cycle slip detection, ensures more reliable cycle slip calculation, realizes the repair of data interrupt at a certain time interval besides normal cycle slip detection and repair, effectively solves the problem of reinitialization ambiguity caused by frequent cycle slip or data interrupt in practical application, reduces the complexity of data processing, and improves the availability and continuity of data and high-precision positioning.
Description
Technical Field
The invention relates to the field of GNSS precise positioning, in particular to a multi-frequency multi-mode GNSS cycle slip and a repairing method of data interruption.
Background
GPS starts from a military project in the United states in 1958, and when the system is fully operated in 1995, the system starts to send signals on L1 and L2 carriers, and by the year 2016, the space part of the GPS is totally 32 satellites, and part of the GPS simultaneously broadcasts L1, L2 and L5 signals. The importance of satellite-based navigation in military strategy, economy, society, science and the like is recognized very early, the European first conference approves the construction of a civil satellite navigation system Galileo of the European first conference in 2002, each satellite publicly broadcasts signals with 5 frequencies, 4 service modes are controlled by public, business, life safety and public safety, and each service mode adopts different signals. The Beidou (BDS) is a first Global Navigation Satellite System (GNSS) positioning system for broadcasting three-frequency signals, along with independent research and development of China, an independently operated Beidou navigation satellite system starts to cover the asia-Tai region, the appearance of Beidou breaks through the situation that a GPS is only occupied by the head in the satellite navigation field for a long time, and at present, beidou can provide high-quality independent navigation service in the asia-Tai region and can cover the whole world in the year 2020. In addition, various regional augmentation systems such as russian GLONASS and japan QZSS form a Global Navigation Satellite System (GNSS) today. The Multi-frequency Multi-mode (Multi-GNSS) combined navigation positioning mode is the development direction of the current and future GNSS, and has great advantages in improving positioning accuracy, positioning availability and reliability and enhancing the geometric strength of the observation satellite.
After the multi-frequency multi-mode operation mode appears, particularly, the Beidou whole system broadcasts three-frequency signals so that a user has more frequency observation data, and various combined observation values which are derived from the original observation data and meet different requirements and keep the integer characteristics of ambiguity are added with the application advantages of the GNSS. However, the complex observation environment causes frequent occlusion of the GNSS signals to generate cycle slips and signal interruption, which seriously affects the continuity and usability of the observation data, and even cycle slips of only 1 week bring about observation errors of 20-cm, so that the repair of cycle slips and data interruption is an important influencing factor for high-precision application of GNSS.
First, in one embodiment of a method of processing cycle slips, the ambiguity is reinitialized with cycle slips as part of the ambiguity. In another embodiment, cycle slips are detected and repaired. However, cycle slip frequently occurs in a complex environment in practical applications, and in the above embodiment, the ambiguity is reinitialized, which increases complexity of data processing, prolongs data processing time, and reduces availability of high-precision results. In contrast, in the above-described alternative embodiment, effective repair of cycle slip can significantly improve observed data, avoid frequent reinitialization of ambiguity, and improve availability of high-precision positioning results. Therefore, how to repair cycle slips in GNSS data is an important issue.
Secondly, in an embodiment of the short-time signal interrupt processing, the ambiguity needs to be reinitialized, and in the practical application, the ambiguity is reinitialized, so that the complexity of data processing is increased, the data processing time is prolonged, and the possibility of high-precision results is reduced. Therefore, how to handle signal interruption is also a problem to be solved.
Disclosure of Invention
The applicant has found that the processing of short-time (30 s-60 s) signal interrupts can be treated as a cycle slip detection and repair problem, and the interrupt data connection is realized through model and ionosphere changes. Further, there are various methods for repairing cycle slip in GNSS data, specifically, such as polynomial fitting, higher order difference, linear combination, and the like. Specifically, the turbo edit algorithm is the most widely used double-frequency cycle slip detection method at present, and is applied to a plurality of large-scale software such as Bemeese, PANDA and the like. When ionosphere changes smoothly, detection and repair are performed using, for example, kalman filtering, linear functions, etc., based on the rate of change of the geometrically non-linear combination of phases.
The applicant has further studied to find that the detection and repair of cycle slips in GNSS data includes GF (Geometry-free based on no geometric domain) combined model and GB (Geometry-based on several He Yu) combined model. Compared with the GB combined model, the GF combined model has weak model strength and low processing effect, and in applications where cycle slip and data terminals frequently occur, such as long-distance dynamic positioning and the like, the cycle slip may not be reliably fixed. In addition, in the multi-system application, the GF combined model completely discards the mutual assistance among the multi-systems due to the elimination of the position parameters, which is a waste of the mutual connection among the systems in the combined navigation positioning. Furthermore, the current cycle slip detection and repair methods mostly simply ignore the ionosphere effect or only replace the ionosphere effect with the previous ionosphere information, and when the ionosphere is active or the data interrupt time is long, the reliability of the method is affected.
In order to solve the above problems, the present invention is directed to a method and a system for repairing multi-frequency multi-mode GNSS cycle slip and data interruption, which consider the effect of ionosphere, so as to be suitable for repairing multi-frequency multi-mode real-time dynamic cycle slip calculation and data terminals.
In order to achieve the above purpose, the present invention provides a method for repairing multi-frequency multi-mode GNSS cycle slip and data interruption, comprising the following steps:
judging whether data interruption occurs or not, and if the data interruption does not occur, acquiring a priori value of ionosphere variation among epochs; otherwise, forecasting the ionosphere variation in the data interruption time according to the data interruption time;
establishing a geometric domain ionosphere weighting model according to the ionosphere variation among the epochs or the ionosphere variation in the data interrupt time, and acquiring a cycle slip floating solution according to the geometric domain ionosphere weighting model;
and according to the cycle slip floating solution, cycle slip fixation is tried, when the cycle slip is fixed, a cycle slip integer solution is output, and the ionosphere variation among epochs is obtained.
Preferably, in the above method for repairing multi-frequency multi-mode GNSS cycle slip and data interruption, the step of determining whether the data interruption occurs includes: judging whether the time interval between the current epoch and the previous epoch is equal to the sampling interval, if so, not generating data interruption, and if not, generating data interruption.
Preferably, in the method for repairing multi-frequency multi-mode GNSS cycle slip and data interruption, when data interruption occurs, whether the data interruption is repairable is determined, and when the data interruption is repairable, a model of ionosphere variation is built according to the time of the data interruption, and the ionosphere variation is predicted within the time of the data interruption.
Preferably, in the above-mentioned repair method for multi-frequency multi-mode GNSS cycle slip and data interruption, the modeling of ionospheric change according to the time of the data interruption is obtained by the following formula:
wherein d represents the polynomial order, c represents the order of the c-th term, s represents the s-th epoch, a 0 、a c All represent coefficients.
Preferably, in the above method for repairing a multi-frequency multi-mode GNSS cycle slip and a data interrupt, the criterion for determining whether the data interrupt is repairable is: whether the time of the data interrupt is within a repairable time frame.
Preferably, in the above method for repairing multi-frequency multi-mode GNSS cycle slip and data interruption, before determining whether the data interruption occurs, the method further includes the following steps:
and acquiring initial values, design matrixes and satellite clock correction information of the current epoch of the detected cycle slip.
Preferably, in the above method for repairing multi-frequency multi-mode GNSS cycle slip and data interruption, before determining whether the data interruption occurs, the method further includes the following steps: and solving the inter-epoch difference between the phase observation value and the pseudo-range observation value, and correcting the satellite clock difference of the phase observation value and the pseudo-range observation value after the epoch difference.
Preferably, in the above-mentioned repair method for multi-frequency multi-mode GNSS cycle slip and data interruption, the equation of the non-differential single-station single-epoch single-frequency GNSS phase observation value and the pseudo-range observation value of the GB model is as follows:
E(p j )＝Gx+e n dt j -dt s，j +τ+μ j ι；
wherein j represents the j-th frequency; phi (phi) j Representing the phase observation vector, in meters,
p j representing pseudo-range observation vector in meters +.>
n represents the number of satellites observed simultaneously, and the value of n is a natural number greater than or equal to 4; g represents the coordinate parameter x= [ X, y, z]Is a design matrix of (a); δt j Representing the receiver clock difference of the phase observation value, and taking meters as a unit; dt (dt) j Receiver clock differences representing pseudorange observations in meters; δt s，j The satellite clock difference, representing the n 1-dimensional phase, in meters,δt s，j ＝[δt s，j 1 ，…，δt s，j n ] T ；dt s，j satellite clock difference representing n 1-dimensional pseudoranges in meters, dt s，j ＝[dt s，j 1 ，…，dt s，j n ] T : τ represents n×1-dimensional p-flow delay vector, τ= [ τ ] in meters 1 ，…τ n ] T The method comprises the steps of carrying out a first treatment on the surface of the Iota represents the n 1-dimensional ionospheric delay at frequency 1, in meters, iota= [ iota ] 1 ，…，ι n ] T ；μ j ＝f 1 2 /f j 2 ；λ j A wavelength representing the jth frequency in meters per week; />
Represents an n 1-dimensional phase observation ambiguity vector, wherein the kth element is +.>
Is an integer>
For the initial phase deviation of the satellite, +.>
E, in units of weeks, for initial phase deviation of the receiver n Represents an n 1-dimensional vector, and the elements are all 1.
Preferably, in the above multi-frequency multi-mode GNSS cycle slip and data interrupt repair method, the geometric domain ionosphere weighting model is obtained by the following formula:
Wherein, H=[A，e s ]Is a design matrix after combining the baseline parameters and the receiver clock difference, and correspondingly has b= [ b, δt after combination]，
Is used for modeling uncertainty of single difference ionosphere variation among epochs, τ= [ τ ] 1 ，…τ n ] T The method comprises the steps of carrying out a first treatment on the surface of the Iota represents the n 1 dimension ionospheric delay in meters at frequency 1, b is the baseline parameter between two epochs, z represents an integer cycle slip, e 2f Representing a 2f x 1 dimension vector, wherein the elements are 1, v represents a coefficient array of ionospheric delay variation and frequency correlation, the ionospheric delay variation at different frequencies is inversely proportional to the square of the frequency, I s Represents an s-dimensional identity matrix, Γ= [ Λ,0] T ，Q s A co-factor matrix representing the correlation of non-differential observations with altitude angle, delta ι Indicating observed receiver clock difference, iota 0 A priori values representing the n 1-dimensional ionospheric delay variation over the 1 st frequency.
In the multi-frequency multi-mode GNSS cycle slip and data interrupt repairing method provided by the invention, firstly, whether data interrupt occurs is judged, and when the data interrupt occurs, the priori value of the ionosphere variation among epochs is obtained; when no data interruption occurs, forecasting the ionosphere variation in the data interruption time according to the data interruption time; establishing a geometric domain ionosphere weighting model according to the ionosphere variation among the epochs or the ionosphere variation in the data interrupt time, and acquiring a cycle slip floating solution according to the geometric domain ionosphere weighting model; and according to the cycle slip floating solution, cycle slip fixation is tried, when the cycle slip is fixed, a cycle slip integer solution is output, and the ionosphere variation among epochs is obtained. The influence of ionosphere variation among epochs on cycle slip detection is considered, cycle slip calculation is more reliable, and data interruption at a certain time interval is repaired in addition to normal cycle slip detection and repair, so that the problem of reinitialization ambiguity caused by frequent cycle slip or data interruption in practical application is effectively solved, the complexity of data processing is reduced, and the availability and continuity of data and high-precision positioning are improved.
Drawings
FIG. 1 is a flowchart of a method for repairing multi-frequency multi-mode GNSS cycle slips and data interrupts according to an embodiment of the present invention.
Detailed Description
Specific embodiments of the present invention will be described in more detail below with reference to the drawings. Advantages and features of the invention will become more apparent from the following description and claims. It should be noted that the drawings are in a very simplified form and are all to a non-precise scale, merely for convenience and clarity in aiding in the description of embodiments of the invention.
An embodiment of the present invention provides a method for repairing a multi-frequency multi-mode GNSS cycle slip and a data interrupt, specifically, as shown in fig. 1, fig. 1 is a flowchart of a method for repairing a multi-frequency multi-mode GNSS cycle slip and a data interrupt in an embodiment of the present invention, where the method includes the following steps: first, the ambiguity of the phase observations is initialized as shown in step S1 in fig. 1.
Next, the SPP (Single Point Positioning ) obtains initial values, design matrices, and satellite clock correction information at the current epoch of the detected cycle slip. As in step S2 in fig. 1.
Specifically, the broadcast ephemeris data of the SPP when calculating the satellite coordinates and clock differences of the current epoch should be the same as the previous epoch to prevent the jump of the satellite clock of the previous and subsequent epochs due to the replacement of the broadcast satellite ephemeris data. Meanwhile, the SPP should set a satellite cut-off altitude when calculating satellite coordinates and clock errors, the troposphere is corrected by adopting a UNB3 model, and the dual-frequency/three-frequency observation data adopts ionosphere-free combination to weaken the ionosphere influence.
The UNB3 model refers to a tropospheric delay correction model studied by a research group of university of New Brinswack. Further, ionosphere-free (Ionosphere-free) combining is to use pseudorange observations of two frequencies to form a combined observation to attenuate the effect of the atmospheric delayed Ionosphere portion of the pseudorange observations.
Further, the initial values acquired by the SPP include, but are not limited to, initial values of coordinates of the receiver, initial values of distances between the satellite and the earth, and the like.
Step S3: and solving the inter-epoch difference between the phase observation value and the pseudo-range observation value, and correcting the satellite clock difference of the phase observation value and the pseudo-range observation value after the epoch difference.
Specifically, equations of the non-differential single-station single-epoch single-frequency GNSS phase observation value and the pseudo-range observation value of the GB model are as follows:
wherein j represents the j-th frequency; phi (phi) j Representing the phase observation vector, in meters,
p j representing pseudo-range observation vector in meters +.>
n represents the number of satellites observed simultaneously, and the value of n is a natural number greater than 1; g represents the coordinate parameter x= [ X, y, z]Is a design matrix of (a); δt j Representing the receiver clock difference of the phase observation value, and taking meters as a unit; dt (dt) j Receiver clock differences representing pseudorange observations in meters; δt s，j Satellite clock difference representing n 1-dimensional phase, δt in meters s，j ＝[δt s，j 1 ，…，δt s，j n ] T ；dt s，j Satellite clock difference representing n 1-dimensional pseudoranges in meters, dt s，j ＝[dt s，j 1 ，…，dt s，j n ] T : τ represents n×1-dimensional p-flow delay vector, τ= [ τ ] in meters 1 ，…τ n ] T The method comprises the steps of carrying out a first treatment on the surface of the Iota represents the n 1-dimensional ionospheric delay at frequency 1, in meters, iota= [ iota ] 1 ，…，ι n ] T ；μ j ＝f 1 2 /f j 2 ；λ j A wavelength representing the jth frequency in meters per week; />
Represents an n 1-dimensional phase observation ambiguity vector, wherein the kth element is +.>
Is an integer>
For the initial phase deviation of the satellite, +.>
E, in units of weeks, for initial phase deviation of the receiver n Represents an n 1-dimensional vector, and the elements are all 1.
Further, GNSS cycle slip has two characteristics: integer and continuous. Specifically, unlike gross errors, GNSS hops have integer characteristics and continue backward from the cycle-slip occurrence epoch. Therefore, the cycle slip detection must be based on the inter-epoch difference between the front and rear epochs of the observed data, and specifically, the inter-epoch single difference model of the phase observed value and the pseudo-range observed value is represented by the following (formula 3) and (formula 4), respectively:
where Δ represents the difference operator, in particular, Δ (= (+) () k+1 -(*) k 。
In the single-insertion model between epochs, the initial phase bias of the receiver and the satellite is completely eliminated, and the integer DeltaZ is the difference between the two epochs j I.e. defined as cycle slip. Strictly speaking, the phase observations after the difference are connectedClock difference delta t of receiver j Receiver clock difference delta dt from pseudo-range observation j The difference is due to the existence of the inter-frequency deviation and the inter-observation type deviation, but the inter-frequency deviation and the inter-observation type deviation are very stable in a period of time, and thus have delta t j ＝Δdt j ≡Δδt。
Wherein Δδt j ＝δt j (k+1)-δt j (k)，Δdt j ＝dt j (k+1)-dt j (k)。
Because the propagation paths of signals in the troposphere between the front epoch and the back epoch are very close, the troposphere delay delta tau of the inter-epoch difference is very small, and the delay delta tau can be ignored without influencing cycle slip calculation. The satellite clock differences can be obtained from the ephemeris file, and thus considered as known terms, the geometric term of the single difference between epochs follows the following derivation:
where b is the baseline parameter between two epochs and b=x k+1 -x k 。
The inter-epoch differential operator is omitted for simplicity of expression, so that an inter-epoch single difference model of the phase observation value and the pseudo-range observation value after correcting the satellite clock difference is formed, and is represented by the following (formula 6) and (formula 7), respectively:
E(p j +δt s )＝Ab+e n δt-μ j iota. (7)
Further, the phase observations and the pseudo-range observations over all f frequencies are collected, respectively, as represented by the following formulas:
wherein, the liquid crystal display device comprises a liquid crystal display device,
vector representing phase observations minus calculated values, +.>
A vector obtained by subtracting the calculated value from the pseudo-range observed value is represented, and if μ= [ μ ] 1 ，…，μ f ] T ，/>
Λ＝diag(λ 1 ，…，λ f ) The above (formula 8) and (formula 9) can be abbreviated as:
wherein y= [ phi ] T ，p T ] T ，v＝[-μ T ，μ T ] T ，Γ＝[Λ，0] T 。
Further, the variance matrix of single epoch non-difference observations can be expressed as:
wherein Q is s Is a correlation matrix of non-difference observed values and altitude angles, Q f ＝blkdiag(Q φ ，Q p ) Representing the accuracy of the particular frequency,
wherein sigma φ Sum sigma p The phase and pseudo-range accuracy on the jth frequency of the non-difference observations are represented, respectively.
Step S4: and (5) calculating whether the time interval between the current epoch and the previous epoch is equal to the sampling interval, if so, judging that the current epoch is in a normal observation state, entering step S8, otherwise, judging that the data is interrupted, and entering step S5.
Specifically, in step S4, when determining whether the current epoch is in the normal observation state or the data interrupt state, the ionosphere processing is affected by factors such as the data sampling interval, the ionosphere active state, and the length of the data interrupt time. Specifically, the larger the sampling interval, the longer the history data window (i.e., the time required) is required; the more active the ionosphere, the higher the order of the model.
Step S5, judging whether the time of the interruption of the observation data is in a repairable range, if so, entering step S7, otherwise, entering step S6.
Specifically, the repairable range is different for different receiving stations, for example, the Beidou data of the Shanghai CORS station 2014 can be repaired for 30-60s.
And S6, if the time of the interruption of the observed data exceeds the repairable range, indicating that the repair in the data fails, directly returning to the step S1, and reinitializing the ambiguity of the phase observation value.
And S7, selecting a certain epoch window according to the time of data interruption, establishing a model of the ionosphere variation by using historical data, and forecasting the priori value of the ionosphere variation during the data interruption.
Specifically, under the data interrupt state, the priori value of the ionosphere variation between two epochs before and after the data interrupt is obtained. In practical application, because the sampling interval is smaller, the time of the observed data interruption is very short, and the ionospheric change quantity between two epochs before and after the data interruption is very small, so that the ionospheric change quantity can be ignored; when the observed time of data interruption is longer, the ionosphere variation is increased along with the increase of the interruption time, the floating solution of cycle slip parameters can be influenced and cannot be ignored, the ionosphere information of the previous epochs can be obtained by using the data which have not been cycle slip or cycle slip repaired, then the ionosphere variation is modeled by adopting a certain epoch window according to the observed data interruption time, the ionosphere variation between two epochs before and after the data interruption is predicted, and meanwhile, the weight of the ionosphere is determined according to the prediction precision of the ionosphere variation. Further, the size of the epoch window is determined according to the size of the sampling interval and the size of the data interrupt time, and the specific implementation needs to be determined in combination with the actual observation environment.
First, the ionospheric information of the previous epoch is obtained using the data that has not been previously cycle-slip or cycle-slip repaired, specifically, it can be deduced from the above equation (6) that, in the absence of cycle-slip, the ionospheric change amount iota between two epochs can be obtained by the following equation:
wherein indices i and j represent the ith and jth frequencies, respectively,
and->
Respectively represent the phase observed value, mu after the difference between the two epochs at the frequency i，j ＝f i 2 /f j 2 。
Secondly, the ionospheric change amount between epochs shows a strong time correlation in a short time, so that the ionospheric change amount can be expressed as a function of time in a few minutes, namely, the ionospheric change amount obtained by selecting a certain epoch window according to the time of data interruption and using historical data is obtained by the following formula:
wherein d represents the polynomial order, c represents the order of the c-th term, s represents the s-th epoch, a 0 、a c All represent coefficients.
Selecting proper epoch window according to the size of data interrupt time, acquiring ionosphere variation sequence by using history data without cycle slip or cycle slip which is correctly fixed, and obtaining the ionosphere variation sequence in least square standardThen the coefficients of the ionospheric change amount function model are obtained (i.e., a in the above formula 13 0 A) m ) And then carrying the calculated epoch time to forecast the priori value of the ionosphere variation in the data interruption period.
It should be noted that, the size of the sampling interval is different, the ionosphere active condition is different, the size of the data interrupt time is different, the size of the needed historical data window and the order of the established ionosphere variable quantity model are also different, and according to the experiment, the first-order function model can obtain good fitting and predicting effects.
And S8, acquiring the priori value of the ionosphere variation after single difference between epochs through historical data.
Specifically, under a normal observation state, a priori value of the ionosphere variation between the current epoch and the previous epoch is obtained. In practical application, when the sampling interval is very small or the ionosphere activity is gentle, the ionosphere delay variation after single difference between epochs is very small, at this time, the ionosphere delay variation between epochs can be ignored, when the sampling interval is gradually increased or the ionosphere activity is aggravated, at this time, the ionosphere information which is utilized before can be obtained by using the data which has not been measured before by cycle slip or cycle slip, the current ionosphere variation of epochs is forecasted by adopting sliding windows with different sizes according to the size of the sampling interval and the difference of the ionosphere active state, the priori value is obtained, the influence of the ionosphere variation between epochs on cycle slip detection is weakened, and under the condition that the sampling interval is large or the ionosphere activity is severe, the influence of the ionosphere variation forecasting precision is influenced by the ionosphere variation, the influence of the ionosphere variation between epochs on the cycle slip can not be completely eliminated by correcting the ionosphere item only by adopting the ionosphere forecasting value, and at this time, the weight is required to be added according to the ionosphere forecasting precision. The specific acquisition method of the ionosphere change amount priori value among epochs is consistent with the acquisition method in the step S7.
And S9, establishing a GB-IW model (geometric domain ionosphere weighting model) according to the acquired ionosphere variation information in epoch or data interrupt time, and calculating real-time data or post-event data by adopting the observation equation (the above (1) and the above (2)) so as to obtain a position parameter floating point solution and a cycle slip floating point solution.
Specifically, using the obtained prior value of the ionosphere variation in the current epoch or the data interrupt period, introducing an ionosphere pseudo-observation equation based on the above formula 10, and building a GB-IW model according to the additional weight of the prediction accuracy, wherein the built GB-IW (geometric domain ionosphere weighting model) model is as follows:
wherein H= [ A, e ] s ]Is a design matrix after combining the baseline parameters and the receiver clock difference, and correspondingly has b= [ b, δt after combination]，
Is used for modeling uncertainty of single difference ionosphere variation among epochs, z represents integer cycle slip, e 2f Represents a 2f x 1-dimensional vector, the elements of which are all 1, v represents a coefficient related to frequency, because the ionospheric delay variation at different frequencies Is inversely proportional to the square of its frequency, is represents an s-dimensional identity matrix, Γ= [ Λ,0] T Qs represents the co-factor matrix of the non-differential observations with respect to the altitude angle, delta ι Indicating observed receiver clock difference, iota 0 A priori values representing the n 1-dimensional ionospheric delay variation over the 1 st frequency.
Obtaining a least squares solution of the parameters according to the above (formula 14) and (formula 15), and solving the obtained cycle slip floating point solution
A covariance matrix +.>
Fixed integer cycle slips were tried by the LAMBDA method.
Step S10, fixing the floating point cycle slip in the step S9 in an attempt mode, judging whether the fixing is successful or not, and if the fixing is successful, entering a step S11; if the fixation fails, the process returns to the step S1.
Specifically, if the cycle slip is successfully fixed, a fixed cycle slip integer solution is output, and the ionospheric change between the current epoch and the previous epoch is calculated, as shown in step S11 in fig. 1, and then the above step S2 is entered. If the cycle slip fixing fails and the cycle slip resolving fails, the step S1 is returned to, and the ambiguity of the phase observation value is reinitialized.
Compared with the prior art, the multi-frequency multi-mode GNSS cycle slip and data interrupt repairing method provided by the embodiment of the invention has at least the following beneficial effects:
first, the method for repairing multi-frequency multi-mode GNSS cycle slip and data interruption provided by the embodiments of the present invention is based on a few He Yu (Geometry-based, abbreviated as GB) model, and fully uses the correlation between each frequency observation data and each satellite system using the position information of the receiver as a link.
Secondly, the application mode, the field and the environment of the multi-frequency multi-mode GNSS cycle slip and data interrupt repairing method provided by the embodiment of the invention are diversified, and the method is applicable to single-frequency, double-frequency and arbitrary-frequency observation models, static and dynamic application modes, single-system and multi-system combination modes and other application scenes, and has strong compatibility.
Third, the present cycle slip detection and repair method simply ignores the influence of ionosphere variation among epochs, when the data sampling interval is large, the data interrupt time is long or the ionosphere is active, the reliability of the present cycle slip detection and repair method is seriously influenced, and the multi-frequency multi-mode GNSS cycle slip and data interrupt repair method provided by the invention considers the influence of the ionosphere variation among epochs on cycle slip detection, the cycle slip solution is more reliable, and no sensitive cycle slip combination exists.
Fourth, the current cycle slip detection and repair method does not relate to repair of data interruption, and the repair method for multi-frequency multi-mode GNSS cycle slips and data interruption provided by the invention not only realizes repair of data interruption at a certain time interval except for normal cycle slip detection, but also can effectively solve the problem of reinitialization ambiguity caused by frequent cycle slips or data interruption in practical application, reduce complexity of data processing, and improve availability and continuity of data and high-precision positioning.
In summary, in the method for repairing multi-frequency multi-mode GNSS cycle slip and data interruption provided by the embodiment of the invention, firstly, whether data interruption occurs is judged, and when the data interruption occurs, the priori value of the ionosphere variation among epochs is obtained; when no data interruption occurs, forecasting the ionosphere variation in the data interruption time according to the data interruption time; establishing a geometric domain ionosphere weighting model according to the ionosphere variation among the epochs or the ionosphere variation in the data interrupt time, and acquiring a cycle slip floating solution according to the geometric domain ionosphere weighting model; and according to the cycle slip floating solution, cycle slip fixation is tried, when the cycle slip is fixed, a cycle slip integer solution is output, and the ionosphere variation among epochs is obtained. The influence of ionosphere variation among epochs on cycle slip detection is considered, cycle slip calculation is more reliable, and data interruption at a certain time interval is repaired in addition to normal cycle slip detection and repair, so that the problem of reinitialization ambiguity caused by frequent cycle slip or data interruption in practical application is effectively solved, the complexity of data processing is reduced, and the availability and continuity of data and high-precision positioning are improved.
The foregoing is merely a preferred embodiment of the present invention and is not intended to limit the present invention in any way. Any person skilled in the art will make any equivalent substitution or modification to the technical solution and technical content disclosed in the invention without departing from the scope of the technical solution of the invention, and the technical solution of the invention is not departing from the scope of the invention.
Claims (8)
1. A multi-frequency multi-mode GNSS cycle slip and data interrupt repairing method is characterized by comprising the following steps:
judging whether data interruption occurs or not, and if the data interruption does not occur, acquiring a priori value of ionosphere variation among epochs; otherwise, forecasting the ionosphere variation in the data interruption time according to the data interruption time;
establishing a geometric domain ionosphere weighting model according to the ionosphere variation among the epochs or the ionosphere variation in the data interrupt time, and acquiring a cycle slip floating solution according to the geometric domain ionosphere weighting model;
according to the cycle slip floating solution, cycle slip fixation is tried, when cycle slip is fixed, cycle slip integer solution is output, and inter-epoch ionosphere variation is obtained;
the geometrical domain ionosphere weighting model is obtained from the following formula:
Wherein H= [ A, e ] s ]Is a design matrix after combining the baseline parameters and the receiver clock difference, and correspondingly has b= [ b, δt after combination]，
Is used for modeling uncertainty of single difference ionosphere variation among epochs, τ= [ τ ] 1 ,...,τ n ] T The method comprises the steps of carrying out a first treatment on the surface of the Iota represents the n 1 dimension ionospheric delay in meters at frequency 1, b is the baseline parameter between two epochs, z represents an integer cycle slip, e 2f Representing a 2f x 1 dimension vector, wherein the elements are 1, v represents a coefficient array of ionospheric delay variation and frequency correlation, the ionospheric delay variation at different frequencies is inversely proportional to the square of the frequency, I s Represents an s-dimensional identity matrix, Γ= [ A,0] T ，Q s A co-factor matrix representing the correlation of non-differential observations with altitude angle iota 0 A priori values representing the n 1-dimensional ionospheric delay variation over the 1 st frequency.
2. The method for repairing a multi-frequency multi-mode GNSS cycle slip and data outage of claim 1, wherein the step of determining whether the data outage has occurred comprises: judging whether the time interval between the current epoch and the previous epoch is equal to the sampling interval, if so, not generating data interruption, and if not, generating data interruption.
3. The method for repairing a multi-frequency multimode GNSS cycle slip and a data interrupt according to claim 1, wherein when a data interrupt occurs, it is determined whether the data interrupt is repairable, and when the data interrupt is repairable, a model of an ionospheric change amount is built according to a time of the data interrupt, and the ionospheric change amount is predicted in the time of the data interrupt.
4. The method of repairing multi-frequency multimode GNSS cycle slips and data interrupts according to claim 3, wherein modeling ionospheric changes based on the time of the data interrupts is obtained by the following formula:
wherein d represents the polynomial order, c represents the order of the c-th term, s represents the s-th epoch, a 0 、a c All represent coefficients.
5. The method for repairing a multi-frequency multimode GNSS cycle slip and data interrupt of claim 3, wherein the criteria for determining whether the data interrupt is repairable are: whether the time of the data interrupt is within a repairable time frame.
6. The method for repairing a multi-frequency multi-mode GNSS cycle slip and data disruption of claim 1, further comprising the steps of, prior to determining whether a data disruption has occurred:
and acquiring initial values, design matrixes and satellite clock correction information of the current epoch of the detected cycle slip.
7. The method for repairing a multi-frequency multi-mode GNSS cycle slip and data disruption of claim 6, further comprising the steps of, prior to determining whether a data disruption has occurred: and solving the inter-epoch difference between the phase observation value and the pseudo-range observation value, and correcting the satellite clock difference of the phase observation value and the pseudo-range observation value after the epoch difference.
8. The method of multi-frequency multi-mode GNSS cycle slip and data outage repair of claim 1, wherein the equation for the non-differential single station single epoch single frequency GNSS phase observations and pseudorange observations for the GB model is as follows:
E(p j )＝Gx+e n dt j -dt s，j +τ+μ j ι；
wherein j represents the j-th frequency; phi (phi) j Representing the phase observation vector, in meters,
p j representing pseudo-range observation vector in meters +.>
n represents the number of satellites observed simultaneously, and the value of n is a natural number greater than or equal to 4; g represents the coordinate parameter x= [ X, y, z]Is a design matrix of (a); δt j Representing the receiver clock difference of the phase observation value, and taking meters as a unit; dt (dt) j Receiver clock differences representing pseudorange observations in meters; δt s,j Satellite clock difference representing n 1-dimensional phase, δt in meters s，j ＝[δt s，j 1 ，…，δt s，j n ] T ；dt s,j Satellite clock difference representing n 1-dimensional pseudoranges in meters, dt s，j ＝[dt s，j 1 ，…，dt s，j n ] T : τ represents n 1 dimensionsFor the flow delay vector, τ= [ τ ] in meters 1 ,...,τ n ] T The method comprises the steps of carrying out a first treatment on the surface of the Iota represents the n 1-dimensional ionospheric delay at frequency 1, in meters, iota= [ iota ] 1 ,...,ι n ] T ；μ j ＝f 1 2 /f j 2 ；λ j A wavelength representing the jth frequency in meters per week; />
Represents an n 1-dimensional phase observation ambiguity vector, wherein the kth element is +.>
Is an integer>
For the initial phase deviation of the satellite, +.>
E, in units of weeks, for initial phase deviation of the receiver n Represents an n 1-dimensional vector, and the elements are all 1./>
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