Method and system for estimating decimal cycle deviation by using continuous operation reference station network

Abstract

The invention relates to the technical field of satellite positioning, and provides a decimal cycle deviation estimation method and system using a continuous operation reference station network, and the method comprises the following steps: carrying out the signal receiving through the GNSS continuous operation reference station network; fixing the double-difference ambiguity between the base stations to obtain a double-difference observation value and the double-difference ambiguity of the GNSS continuous operation reference station network; constructing an FCB estimation model based on a carrier phase observation equation; and obtaining a receiver coordinate and a satellite coordinate in the GNSS continuous operation reference station network, calculating troposphere delay and ionosphere delay based on the double-difference observation value and the double-difference ambiguity, carrying out parameter recombination optimization on the FCB estimation model, and then solving to obtain a decimal cycle deviation estimation value of each frequency.

Description

Technical Field

[0001] The present invention relates to the field of satellite positioning technology, and more specifically, to a method and system for estimating fractional cycle deviations using a continuously operating reference station network. Background Art

[0002] Fractional Cycle Bias (FCB) is a key technical parameter in the field of Global Navigation Satellite System (GNSS). In GNSS positioning, the phase measurement value of the satellite signal usually contains an integer part (i.e., the number of complete cycles of signal propagation) and a fractional part (i.e., the incomplete cycle of signal propagation). The integer part is called ambiguity, and the fractional part is FCB. FCB is mainly generated by the hardware signal delay of the satellite and is included in the carrier phase observation. Among them, FCB destroys the integer cycle characteristics of ambiguity.

[0003] The estimation and correction of FCB are crucial to improving the accuracy of GNSS positioning. Due to the existence of satellite and receiver hardware delays, these delays will affect the phase measurement in the form of FCB, causing the ambiguity parameters to lose integer characteristics. Especially in precise single-point positioning, it is necessary to eliminate FCB to fix the integer ambiguity of the carrier phase, thereby improving positioning accuracy. At present, the estimation of FCB generally uses dual-frequency ionosphere-free combined observations. This combined observation is not affected by ionospheric delays, so the FCB can be decomposed into wide-lane FCB and narrow-lane FCB and then estimated separately. However, when new satellite frequencies are added, the existing FCB estimates need to be adjusted or recalibrated to adapt to the new signal characteristics and frequency combinations. In addition, for new frequency combinations, the FCB estimates of wide and narrow lanes may be difficult to remain stable in the short term, especially in narrow lane combinations, because the wavelength is shorter, it is difficult to remain stable for a long time, so more frequent FCB estimates may be required. Summary of the invention

[0004] In order to overcome the defect in the prior art that the FCB estimation value has poor stability when a new satellite frequency is added, resulting in reduced positioning accuracy, the present invention provides a method and system for estimating fractional cycle deviations using a continuous operating reference station network (Continuous Operational Reference System, CORS).

[0005] In order to solve the above technical problems, the technical solution of the present invention is as follows:

[0006] A method for estimating fractional week bias using a GNSS continuously operating reference station network comprises the following steps:

[0007] Utilize the GNSS continuously operating reference station network for signal reception;

[0008] The double-difference ambiguity between base stations is fixed to obtain the double-difference observation value and double-difference ambiguity of the GNSS continuously operating reference station network;

[0009] Construct the FCB estimation model based on the carrier phase observation equation;

[0010] The receiver coordinates and satellite coordinates in the GNSS continuously operating reference station network are obtained, and the tropospheric delay and ionospheric delay are calculated based on the double difference observations and double difference ambiguities, and the FCB estimation model is solved after parameter reorganization and optimization to obtain the fractional week deviation estimate of each frequency.

[0011] Furthermore, the present invention also proposes a system for estimating the decimal week deviation using a GNSS continuously operating reference station network, and applies the decimal week deviation estimation method proposed in the present invention. The system comprises:

[0012] A GNSS continuously operating reference station network, including several receivers for receiving GNSS satellite signals;

[0013] The first calculation unit is used to fix the double difference ambiguity between base stations to obtain the double difference observation value and double difference ambiguity of the GNSS continuously operating reference station network;

[0014] The second computing unit is equipped with an FCB estimation model, which is used to solve the FCB estimation model after parameter reorganization and optimization based on the receiver coordinates and satellite coordinates in the GNSS continuously operating reference station network, and the tropospheric delay and ionospheric delay calculated according to the double difference observations and the double difference ambiguity, so as to obtain the fractional week deviation estimation value of each frequency.

[0015] Furthermore, the present invention also proposes a positioning system, comprising:

[0016] An FCB estimation module, which uses the fractional cycle deviation estimation method proposed in the present invention to perform FCB estimation;

[0017] The positioning module is used to fix the PPP ambiguity according to the FCB estimation value output by the FCB estimation module to perform precise point positioning.

[0018] Furthermore, the present invention also proposes a device, including a memory and a processor, wherein the memory stores computer-readable instructions, wherein when the computer-readable instructions are executed by the processor, the processor executes all or part of the steps of the method for estimating decimal week deviations using a GNSS continuously operating reference station network as described in the present invention.

[0019] Furthermore, the present invention also proposes a storage medium having computer-readable instructions stored thereon, wherein the computer-readable instructions, when executed by a processor, implement all or part of the steps of the method for estimating decimal week deviations using a GNSS continuously operating reference station network as described in the present invention.

[0020] Compared with the prior art, the technical solution of the present invention has the following beneficial effects:

[0021] The present invention utilizes the GNSS CORS network to estimate the FCB of each frequency individually when the CORS network ambiguity is fixed, which is further used to fix the ambiguity of precise single-point positioning; when there is a new satellite frequency, there is no need to readjust all FCB estimation values, and the FCB estimation can be directly performed and increased for the new satellite frequency without combining the observation values. This is more convenient to process than the traditional ionosphere-free method, and effectively improves the stability of FCB estimation and positioning efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] Figure 1 The flowchart of a fractional cycle deviation estimation method according to an embodiment of the present invention is shown.

[0023] Figure 2 FIG. 4 is an architecture diagram of a fractional cycle deviation estimation system according to an embodiment of the present invention.

[0024] Figure 3 It is an architecture diagram of a positioning system according to an embodiment of the present invention. DETAILED DESCRIPTION

[0025] Exemplary embodiments will be described in detail herein, examples of which are shown in the accompanying drawings. When the following description refers to the drawings, the same numbers in different drawings represent the same or similar elements unless otherwise indicated. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present invention. Instead, they are merely examples of devices and methods consistent with some aspects of the present invention as detailed in the appended claims.

[0026] The terms used in the present invention are only for the purpose of describing specific embodiments and are not intended to limit the present invention. The singular forms "a", "the" and "the" used in the present invention and the appended claims are also intended to include plural forms unless the context clearly indicates other meanings. It should also be understood that the term "and / or" used herein refers to and includes any or all possible combinations of one or more associated listed items.

[0027] It should be understood that although the terms first, second, third, etc. may be used in the present invention to describe various information, these information should not be limited to these terms. These terms are only used to distinguish the same type of information from each other. For example, without departing from the scope of the present invention, the first information may also be referred to as the second information, and similarly, the second information may also be referred to as the first information. Depending on the context, the word "if" as used herein may be interpreted as "at the time of" or "when" or "in response to determining".

[0028] The present invention is described in detail below with reference to the accompanying drawings and specific embodiments.

[0029] Example 1

[0030] This embodiment proposes a method for estimating fractional cycle deviations using a continuously operating reference station network, such as Figure 1 As shown, it is a flow chart of the method for estimating fractional cycle deviations using a continuously operating reference station network according to this embodiment.

[0031] The method for estimating fractional cycle deviations using a continuously operating reference station network proposed in this embodiment includes the following steps:

[0032] Utilize the GNSS continuously operating reference station network for signal reception;

[0033] The double-difference ambiguity between base stations is fixed to obtain the double-difference observation value and double-difference ambiguity of the GNSS continuously operating reference station network;

[0034] Construct the FCB estimation model based on the carrier phase observation equation;

[0035] The receiver coordinates and satellite coordinates in the GNSS continuously operating reference station network are obtained, and the tropospheric delay and ionospheric delay are calculated based on the double difference observations and double difference ambiguities, and the FCB estimation model is solved after parameter reorganization and optimization to obtain the fractional week deviation estimate of each frequency.

[0036] The GNSS Continuously Operating Reference Station Network (GNSS CORS Network) used in this embodiment is a network composed of multiple fixed-position Global Navigation Satellite System (GNSS) receivers. These receivers can provide different types of verified GNSS observation values, such as carrier phase, pseudorange, etc., as well as various correction numbers and status information to users of different types, different needs and different levels in real time, and provide high-precision positioning services.

[0037] This embodiment uses the GNSS CORS network to estimate the FCB of each frequency separately. When a new satellite frequency is added, it can be directly added without combining the observation values. This is more convenient to process than the traditional ionosphere-free method.

[0038] In an optional embodiment, the FCB estimation model is expressed as:

[0039]

[0040] in, represents the carrier phase observation value of satellite p by receiver k; represents the distance from satellite p to receiver k; receiver delay ζ k =c(dt k -d k ), where dt k is the clock error of receiver k, d k is the hardware delay of receiver k, c is the speed of light; satellite delay ζ p =c(d p +D p ), where d p is the clock error of satellite p, D p is the hardware delay of satellite p; λ is the wavelength, is the integer ambiguity of receiver k to satellite p, is the tropospheric delay from satellite p to receiver k, is the ionospheric delay from satellite p to receiver k; ε is the observation noise.

[0041] It should be noted that since the receiver coordinates and satellite coordinates can be directly obtained in the GNSS CORS network, the distance from satellite p to receiver k can be directly calculated. Then the FCB estimation model can be expressed as:

[0042]

[0043] Among them, due to p is what we require, and the unknowns also include receiver hardware delay, integer ambiguity, tropospheric delay, and ionospheric delay. It can be expressed as the zenith delay multiplied by the mapping function, while the other unknown parameters are linearly related and cannot be separated from each other. Therefore, the current FCB estimation model is rank-deficient and cannot be solved directly.

[0044] For rank-deficient equations, parameter reorganization or additional conditional constraints are generally used to solve them. Parameter reorganization is to recombine several linearly related parameters to establish new parameters.

[0045] Specifically, assume that the observation equation is: y = Ax + ε;

[0046] Among them, y is the observed value, x is the unknown, and A is the coefficient matrix, that is, the above observation equation is rank-deficient. Assume that y is a vector of size k, x is a vector of size l, the rank of matrix A is q, and q < l. Since A is rank-deficient, all parameters in x cannot be fully estimated, but some parameters can be estimated.

[0047] Assume that x can be decomposed as: x = x s + x v = Sα + Vβ;

[0048] Among them, α represents the parameters that can be estimated, with a size of q; β represents the parameters that cannot be estimated, with a size of l - q; the choice of matrix S determines which parameters can be estimated; V belongs to the null space of matrix A, that is, A·V = 0. When x is decomposed, the observation equation can be expressed as: y = A ′ α + ε;

[0049] Among them, the size of A ′ is k×q, and at this time the equation is full-rank and in a solvable state.

[0050] In this embodiment, an optimization method of parameter recombination is selected to perform parameter recombination optimization on the FCB estimation model and then solve it to obtain the fractional cycle bias estimation value of each frequency.

[0051] Furthermore, in an optional embodiment, based on the receiver coordinates, satellite coordinates in the GNSS continuous operation reference station network, and the double-difference tropospheric delay and double-difference ionospheric delay, performing parameter recombination optimization on the FCB estimation model and then solving it includes the following steps:

[0052] Obtain the receiver coordinates and satellite coordinates in the GNSS continuous operation reference station network, and update the FCB estimation model to:

[0053]

[0054] Perform parameter recombination on the FCB estimation model; among them, let the recombined satellite delay Then the FCB estimation model is expressed as:

[0055]

[0056] Among them, the recombined delay of receiver i Its subscript (·) i(i-1) =(·) i -(·) i-1 ; the recombined ambiguity of receiver i Its superscript (·) p1 =(·) p -(·)1 ;

[0057] The double-differenced tropospheric delay and double-differenced ionospheric delay are calculated using the double-differenced observations and double-differenced ambiguities of the GNSS continuously operating reference station network to determine the reconstructed ambiguities of the receiver Then the FCB estimation model is updated as:

[0058]

[0059] At this time, the updated FCB estimation model is in a solvable state, and the FCB estimation model is solved to determine the current reconstructed satellite delay and the reassembly delay of receiver i It is used to further calculate the fractional week deviation estimate; its expression is:

[0060]

[0061] Among them, FCB p is the estimated fractional week bias of satellite p; [·] indicates rounding.

[0062] As an exemplary description, this embodiment assumes that three CORS base stations participate in the solution, and illustrates the optimization solution process of the FCB estimation model.

[0063] According to the logic of parameter reorganization method, the FCB estimation model is updated as follows:

[0064]

[0065] The above formula is the observation equation of CORS base station receiver 1, where the subscript 1 represents CORS base station receiver 1 and the superscript p represents the satellite number; is the carrier phase observation value from satellite p to CORS base station receiver 1; is the geometric distance from satellite p to CORS base station receiver 1. Since both the satellite position and the receiver position are known, is known; p is the hardware delay of satellite p, and ζ1 is the hardware delay of CORS base station receiver 1. is the ambiguity of the carrier observation from satellite p to CORS base station receiver 1, and are the tropospheric and ionospheric delays from satellite p to CORS base station receiver 1, respectively. Then the observation equation of CORS base station receiver 1 can be expressed as:

[0066]

[0067] The observation equation of CORS base station receiver 2 is:

[0068]

[0069] Among them, the receiver delay after reorganization is Subscript 21 means: (·) 21 =(·)2-(·)1; the ambiguity of the reorganization The superscript p1 represents (·) p1 =(·) p -(·) 1 ;

[0070] By analogy, the observation equation of CORS base station receiver 3 is:

[0071] Since this embodiment uses CORS stations, the double-difference ambiguity between base stations can be fixed in advance, and the double-difference tropospheric and double-difference ionospheric delays can be calculated through double-difference observations and double-difference ambiguity, so It can be considered to be known. Then the observation equation of CORS base station receivers 1, 2, and 3 can be expressed as:

[0072]

[0073] Among them, the unknown number is the delay of the reorganized satellite hardware and the receiver delay after reassembly Assuming that the number of satellites observed by the three stations simultaneously is S, the number of observations is 3S, and the number of unknowns is S+2, that is, S satellite hardware delays and 2 receiver hardware delays. Therefore, the equation is solvable.

[0074] It should be noted that what is being solved at this time is not the original satellite hardware delay and receiver hardware delay, but the satellite hardware delay and receiver hardware delay after parameter reorganization, which can be used to further calculate the fractional week deviation estimate.

[0075] In an optional embodiment, fixing the double difference ambiguity between base stations includes fixing the double difference ambiguity between base stations using a differential positioning method.

[0076] Among them, the differential positioning method refers to directly subtracting the observation values ​​between the satellite and the receiver to eliminate certain error terms, including: receiver clock error, satellite clock error, orbit error, and when the distance is relatively short, tropospheric delay and ionospheric delay can also be eliminated.

[0077] Further optionally, when fixing the double difference ambiguity between base stations, the following steps are included:

[0078] Construct double-difference observation value model and double-difference pseudo-range observation model;

[0079] Estimate the tropospheric delay of the base station, express the tropospheric delay as the product of the zenith delay and the mapping function, and optimize and update the double difference observation value model and the double difference pseudorange observation model according to the base station coordinates and the satellite coordinates; the expression is:

[0080]

[0081] In the formula, subscripts k and l represent receivers, superscripts p and m represent satellites, and (·) kl =(·) k -(·) l , (·) pm =(·) p -(·) m ; Φ represents the carrier phase observation value from the satellite to the receiver, ρ represents the geometric distance from the satellite to the receiver; λ is the wavelength; is the double difference ambiguity, T k is the double-difference tropospheric delay, is the double difference ionospheric delay; ε is the observation noise; m pm represents the difference between the mapping functions of satellite p and satellite m;

[0082] Based on Kalman filtering, the double difference observation value model and the double difference pseudo-range observation model after optimization and update are solved to obtain the double difference ambiguity Double difference tropospheric delay T k and double difference ionospheric delay The floating point solution of ;

[0083] based on The floating-point solution is obtained by using the LAMBDA (Least-squares AMBiguity Decorrelation Adjustment) algorithm to fix the double-difference ambiguity between base stations.

[0084] It should be noted that the non-difference observation equation is:

[0085] Then, after making a difference between satellites, we get the single difference observation value: The subscript kl represents (·) kl =(·) k -(·) l .

[0086] Further using the single difference to make differences between satellites, we can get the double difference observation value: The superscript pm means: (·) pm =(·) p -(·) m , is the double difference ambiguity, is the double-difference tropospheric delay, is the double difference ionospheric delay.

[0087] The double-difference pseudorange observation equation is: in, is the pseudorange observation value.

[0088] Since the coordinates of the CORS base station are known and the satellite position is also known, the distance from the satellite to the CORS base station is known, that is, is a known quantity, so the double difference observation value and double difference pseudorange observation equation can be expressed as:

[0089]

[0090] In differential processing, the relative delay of the troposphere is generally estimated, that is, the tropospheric delay of the CORS base station l is considered to be known, and the tropospheric delay is expressed as the product of the zenith delay and the mapping function, that is:

[0091]

[0092] Among them, T k is the zenith tropospheric delay of base station k, m pm is the difference between the mapping functions of satellite p and satellite m. The above equation is the observation equation of the carrier and pseudorange of a single frequency. If other frequencies are considered, it can be further expressed as:

[0093]

[0094] Where i represents frequency; f1 is the frequency of band 1, f i is the frequency of band i; is the ionospheric delay of frequency band 1. Applying Kalman filtering to the above equation, we can get the parameters T k , The floating-point solution of The floating-point solution of the double-difference ambiguity can be obtained by using the LAMBDA method.

[0095] It should be noted that the LAMBDA method is a technology used to quickly estimate integer ambiguities in global positioning systems. By constructing a reduced-correlation ambiguity transformation matrix, the original double-difference ambiguities are converted into new ambiguities. These new ambiguities have lower correlation and more uniform conditional variance spectra, thereby making the search space for integer ambiguities smaller to improve efficiency.

[0096] Furthermore, in an optional embodiment, the method further comprises the following steps:

[0097] The fractional cycle deviation estimate is sent to the user end for precise point positioning ambiguity fixation.

[0098] This embodiment performs FCB estimation when the CORS network ambiguity is fixed, and can estimate the FCB at each frequency, which is further used to fix the ambiguity of precise single-point positioning. In addition, during the positioning process, when there is a new satellite frequency, there is no need to readjust all FCB estimation values. The FCB estimation can be directly performed for the new satellite frequency and directly increased, which effectively improves the stability of the FCB estimation and the positioning efficiency.

[0099] Example 2

[0100] This embodiment proposes a fractional cycle deviation estimation system using a continuously running reference station network, and applies the fractional cycle deviation estimation method proposed in Embodiment 1. Figure 2 , which is a structural diagram of the fractional cycle deviation estimation system of this embodiment.

[0101] The fractional cycle deviation estimation system using a continuously operating reference station network proposed in this embodiment includes:

[0102] A GNSS continuously operating reference station network, including several receivers for receiving GNSS satellite signals;

[0103] The first calculation unit is used to fix the double difference ambiguity between base stations to obtain the double difference observation value and double difference ambiguity of the GNSS continuously operating reference station network;

[0104] The second computing unit is equipped with an FCB estimation model, which is used to solve the FCB estimation model after parameter reorganization and optimization based on the receiver coordinates and satellite coordinates in the GNSS continuously operating reference station network, and the tropospheric delay and ionospheric delay calculated according to the double difference observations and the double difference ambiguity, so as to obtain the fractional week deviation estimation value of each frequency.

[0105] It can be understood that the system of this embodiment corresponds to the method of the above-mentioned embodiment 1, and the options in the above-mentioned embodiment 1 are also applicable to this embodiment, so they will not be described repeatedly here.

[0106] Example 3

[0107] This embodiment provides a positioning system, such as Figure 3 FIG. 1 is a diagram showing the architecture of the positioning system of this embodiment.

[0108] The positioning system proposed in this embodiment includes:

[0109] An FCB estimation module, applying the fractional cycle deviation estimation method proposed in Example 1 to perform FCB estimation;

[0110] The positioning module is used to fix the PPP ambiguity according to the FCB estimation value output by the FCB estimation module to perform precise point positioning.

[0111] It can be understood that the system of this embodiment applies the method of the above-mentioned embodiment 1, and the options in the above-mentioned embodiment 1 are also applicable to this embodiment, so they will not be described repeatedly here.

[0112] Example 4

[0113] This embodiment proposes a computer device, including a memory and a processor, wherein the memory stores computer-readable instructions, and when the computer-readable instructions are executed by the processor, the processor executes all or part of the steps of the fractional cycle deviation estimation method proposed in Example 1.

[0114] Example 5

[0115] This embodiment proposes a storage medium on which computer-readable instructions are stored, wherein the computer-readable instructions, when executed by a processor, implement all or part of the steps of the fractional cycle deviation estimation method proposed in Embodiment 1.

[0116] Exemplarily, the storage medium includes, but is not limited to, a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, and other media that can store program codes.

[0117] Exemplarily, the instructions, programs, code sets or instruction sets may be implemented using conventional programming languages.

[0118] Exemplarily, the processor includes but is not limited to a smart phone, a personal computer, a server, a network device, etc., and is used to execute all or part of the steps of the fractional cycle deviation estimation method described in Example 1.

[0119] Each embodiment of the present invention is described in a progressive manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the device embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment. The device embodiment described above is merely exemplary, in which the modules described as separate components may or may not be physically separated, and the functions of each module can be implemented in the same or one or more software and / or hardware when implementing the scheme of the present invention. It is also possible to select some or all of the modules according to actual needs to achieve the purpose of the scheme of this embodiment.

[0120] Obviously, the above embodiments of the present invention are merely examples for clearly illustrating the present invention, and are not intended to limit the embodiments of the present invention. For those skilled in the art, other different forms of changes or modifications can be made based on the above description. It is not necessary and impossible to list all the embodiments here. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the protection scope of the claims of the present invention.

Claims

1. A method for estimating fractional cycle deviations using a continuously operating reference station network, characterized in that: The following steps are involved: Utilize the GNSS continuously operating reference station network for signal reception; The double-difference ambiguity between base stations is fixed to obtain the double-difference observation value and double-difference ambiguity of the GNSS continuously operating reference station network; Construct the FCB estimation model based on the carrier phase observation equation; The receiver coordinates and satellite coordinates in the GNSS continuously operating reference station network are obtained, and the tropospheric delay and ionospheric delay are calculated based on the double difference observations and double difference ambiguities, and the FCB estimation model is solved after parameter reorganization and optimization to obtain the fractional week deviation estimate of each frequency.

2. The fractional cycle deviation estimation method according to claim 1, characterized in that: In constructing the FCB estimation model based on the carrier phase observation equation, the FCB estimation model includes: in, represents the carrier phase observation value of satellite p by receiver k; represents the distance from satellite p to receiver k; receiver delay ζ k =c(dt k -d k ), where dt k is the clock error of receiver k, d k is the hardware delay of receiver k, c is the speed of light; satellite delay ζ p =c(d p +D p ), where d p is the clock error of satellite p, D p is the hardware delay of satellite p; λ is the wavelength, is the integer ambiguity of receiver k to satellite p, is the tropospheric delay from satellite p to receiver k, is the ionospheric delay from satellite p to receiver k; ε is the observation noise.

3. The fractional cycle deviation estimation method according to claim 2, characterized in that: The method of solving the FCB estimation model after parameter reorganization and optimization based on the receiver coordinates, satellite coordinates, and the double-difference tropospheric delay and the double-difference ionospheric delay in the GNSS continuously operating reference station network includes: The receiver coordinates and satellite coordinates in the GNSS continuously operating reference station network are obtained, and the FCB estimation model is updated as follows: The parameters of the FCB estimation model are reorganized; wherein the reorganized satellite delay Then the FCB estimation model is expressed as: where the reconstructed delay of receiver i is Its subscript (·) i(i-1) =(·) i -(·) i-1 ; The reconstructed ambiguity at receiver i Its superscript (·) p1 =(·) p -(·) 1 ; Calculation of double-difference tropospheric data using double-difference observations and double-difference ambiguities from a GNSS continuously operating reference station network Delay and double-difference ionospheric delay to determine the reconstructed ambiguity at the receiver Then the FCB estimation model is updated as: At this time, the updated FCB estimation model is in a solvable state, and the FCB estimation model is solved to determine the current reconstructed satellite delay and the reassembly delay of receiver i It is used to further calculate the fractional week deviation estimate; its expression is: Among them, FCB p is the estimated fractional week bias of satellite p; [·] indicates rounding.

4. The fractional cycle deviation estimation method according to claim 1, characterized in that: The fixing of the double difference ambiguity between base stations includes fixing the double difference ambiguity between base stations using a differential positioning method.

5. The fractional cycle deviation estimation method according to claim 4, characterized in that: The fixing of the double difference ambiguity between base stations comprises the following steps: Construct double-difference observation value model and double-difference pseudo-range observation model; Estimate the tropospheric delay of the base station, express the tropospheric delay as the product of the zenith delay and the mapping function, and optimize and update the double difference observation value model and the double difference pseudorange observation model according to the base station coordinates and the satellite coordinates; the expression is: In the formula, subscripts k and l represent receivers, superscripts p and m represent satellites, and (·) kl =(·) k -(·) l , (·) pm =(·) p -(·) m ; Φ represents the carrier phase observation value from the satellite to the receiver, ρ represents the geometric distance from the satellite to the receiver; λ is the wavelength; is the double difference ambiguity, T k is the double-difference tropospheric delay, is the double difference ionospheric delay; ε is the observation noise; m pm represents the difference between the mapping functions of satellite p and satellite m; Based on Kalman filtering, the double difference observation value model and the double difference pseudo-range observation model after optimization and update are solved to obtain the double difference ambiguity Double difference tropospheric delay T k and double difference ionospheric delay The floating point solution of ; based on The LAMBDA algorithm is used to fix the double difference ambiguity between base stations.

6. The fractional cycle deviation estimation method according to any one of claims 1 to 5, characterized in that: The following steps are also included: The fractional cycle deviation estimate is sent to the user end for precise point positioning ambiguity fixation.

7. A system for estimating fractional cycle deviations using a continuously operating reference station network, applied to the method for estimating fractional cycle deviations according to any one of claims 1 to 6, characterized in that: include: A GNSS continuously operating reference station network, including several receivers for receiving GNSS satellite signals; The first calculation unit is used to fix the double difference ambiguity between base stations to obtain the double difference observation value and double difference ambiguity of the GNSS continuously operating reference station network; The second computing unit is equipped with an FCB estimation model, which is used to solve the FCB estimation model after parameter reorganization and optimization based on the receiver coordinates and satellite coordinates in the GNSS continuously operating reference station network, and the tropospheric delay and ionospheric delay calculated according to the double difference observations and the double difference ambiguity, so as to obtain the fractional week deviation estimation value of each frequency.

8. A positioning system, characterized in that: include: An FCB estimation module, applying the fractional cycle deviation estimation method according to any one of claims 1 to 5 to perform FCB estimation; The positioning module is used to fix the PPP ambiguity according to the FCB estimation value output by the FCB estimation module to perform precise point positioning.

9. A device comprising a memory and a processor, wherein the memory stores computer-readable instructions, characterized in that: When the computer-readable instructions are executed by the processor, the processor executes all or part of the steps of the fractional cycle deviation estimation method according to any one of claims 1 to 6.

10. A storage medium having computer-readable instructions stored thereon, characterized in that: When the computer-readable instructions are executed by a processor, all or part of the steps of the fractional cycle deviation estimation method according to any one of claims 1 to 6 are implemented.

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