Wide-area GNSS time frequency transmission method, device and equipment based on crowdsourcing RTK technology

Abstract

The invention discloses a wide-area GNSS time-frequency transmission method, device and equipment based on crowdsourcing RTK technology, and the method comprises the steps: building an unbiased single-difference ionosphere and troposphere delay correction model at a server side through the atmospheric correction information generated by a reference station and a user side in a convergence region on the basis of a single-difference ionosphere weighted observation model, and transmitting the unbiased single-difference ionosphere and troposphere delay correction model to a server side; atmospheric correction prior information is broadcasted to a time-frequency transmission user, and a crowdsourcing RTK single-difference observation model is constructed by fixing integer ambiguity through the atmospheric correction prior information; on the basis of a crowdsourcing RTK single-difference observation model, pseudo-range hardware delay is separated from clock difference parameters, only phase hardware delay is absorbed, a phase clock model is formed, and a corresponding random model is constructed. According to the invention, the stability of zero short baseline condition magnitude is realized, and the precision upper limit of the existing GNSS time transfer technology is broken through. Meanwhile, the advantages of the crowdsourcing RTK technology in the aspect of ionosphere space transfer are utilized, and the capability is expanded to a wide area range.

Description

Technical Field

[0001] This invention relates to the field of GNSS time transfer technology, and in particular to a wide-area GNSS time and frequency transfer method, apparatus and equipment based on crowdsourced RTK technology. Background Technology

[0002] High-precision time and frequency transmission is a crucial foundation for modern time and frequency systems and navigation systems, playing a key role in metrology, GNSS spatiotemporal reference maintenance, and the fields of earth science and space technology. With the rapid development of research on relativistic geodesy, gravity difference determination, and reference frame maintenance based on optical atomic clocks and GNSS, time and frequency observation has become an important technical approach to improving the accuracy of geodetic references. With the breakthrough in short-term stability of optical atomic clocks... The lack of fiber optic links makes cross-site time comparison links a key bottleneck limiting the application of optical clocks on regional and even broader scales. Although fiber optic links can achieve time and frequency transmission with near-intrinsic stability to high-performance atomic clocks, their construction cost and geographical coverage limit their widespread deployment. In contrast, GNSS, with its wide coverage, low cost, and global deployment capabilities, is considered the most promising means of achieving large-scale time and frequency transmission. However, the performance of existing GNSS time and frequency transmission is limited... Scale, Breakthrough The scale remains challenging.

[0003] The main challenges lie in two aspects: integer ambiguity fixation capability and pseudorange hardware delay processing. On the one hand, numerous studies have shown that achieving integer ambiguity fixation in models such as IPPP, UDUC, and SDRTK can improve short-term frequency stability by approximately 30% to 60% compared to PPP without ambiguity fixation. However, due to the strong coupling between ionospheric delay and ambiguity in the observation equations, the success rate of ambiguity fixation highly depends on the fine-grained processing of ionospheric delay. Baseline-based models (such as SDRTK and UDUC RTK) typically need to switch between fixed, weighted, and floating-point strategies based on baseline length or regional ionospheric activity levels, lacking a unified constraint framework, resulting in significant performance variations across different regions and spatiotemporal conditions. While single-station models (such as PPP and IPPP) can mitigate the impact through ionospheric de-combination or ionospheric parameterization, integer ambiguity fixation still relies on external precision products and requires a lengthy filtering convergence process to reach steady state. On the other hand, existing GNSS time and frequency transfer models, whether baseline-based models (SDRTK and UDUC RTK) or single-station models (PPP and IPPP), inevitably absorb different forms of hardware delay in their clock bias parameters, including pseudorange hardware delay or hardware delay after frequency combination. Pseudorange hardware delay not only has a high noise level, but multi-frequency combination further amplifies the noise, both of which raise the noise floor of the clock bias sequence, thus limiting short-term frequency stability. Therefore, there is an urgent need to develop new algorithmic frameworks to further mitigate the effects of the ionosphere and pseudorange / phase hardware delay on time transfer, in order to achieve higher stability GNSS time and frequency transfer over a wide area. Summary of the Invention

[0004] The purpose of this invention is to overcome the aforementioned defects and problems in the prior art and provide a wide-area GNSS time-frequency transfer method, apparatus, and device based on crowdsourced RTK technology. This wide-area GNSS time-frequency transfer method achieves zero short baseline conditions. The stability of this technology breaks through the accuracy limit of existing GNSS time transfer technology. At the same time, it further utilizes the advantages of crowdsourced RTK technology in ionospheric space transfer and extends this capability to a wide area.

[0005] To achieve the above objectives, the technical solution of the present invention is:

[0006] In a first aspect, the present invention provides a wide-area GNSS time-frequency transfer method based on crowdsourced RTK technology, comprising:

[0007] Based on the single-difference ionospheric weighted observation model, by aggregating atmospheric correction information generated by reference stations and user terminals in the region, an unbiased single-difference ionospheric and tropospheric delay correction model is constructed on the server side, and atmospheric correction prior information is broadcast to users in time and frequency transmission. The single-difference observation model of crowdsourced RTK is constructed by fixing integer ambiguity with atmospheric correction prior information.

[0008] Based on the single-difference observation model of crowdsourced RTK, a phase clock model is formed by separating the pseudorange hardware delay from the clock difference parameter and absorbing only the phase hardware delay, and a corresponding stochastic model is constructed.

[0009] Preferably, the single-difference observation model of the crowdsourced RTK is:

[0010] ;

[0011] ;

[0012] ;

[0013] ;

[0014] In the formula, For expectation operator; superscript Indicates satellite, ,use Distinguishing between different GNSS constellations; subscript and These represent the reference station and the user terminal, respectively. For frequency identification, ; and These are pseudorange and phase OC single-difference observations, respectively; Indicates the distance between a single-difference station; This is the tropospheric projection coefficient; The relative tropospheric delay between stations in the zenith direction; For unbiased single-difference ionospheric delay, For frequency-related terms; Wavelength; For double-difference ambiguity, superscript Indicates the reference satellite; and These are the prior values ​​for the ionosphere and troposphere, respectively. and These are pseudo-range clock error and phase clock error, respectively.

[0015] The pseudo-range clock difference and phase clock difference are:

[0016] ;

[0017] ;

[0018] In the formula, This represents the receiver's actual clock bias; For the pseudorange hardware delay of the inter-station receiver; For the phase hardware delay of the inter-station receiver; For ambiguity.

[0019] Preferably, the unbiased single-difference ionospheric delay is synchronously transmitted back to the server based on the user-side calculation results. and the relative tropospheric delay between stations in the zenith direction It is used to construct wide-area ionospheric and tropospheric delay correction models and to broadcast precise atmospheric correction prior information to users in time and frequency.

[0020] Preferably, the phase clock model is:

[0021] ;

[0022] ;

[0023] ;

[0024] ;

[0025] In the formula, For expectation operator; superscript Indicates satellite, ,use Distinguishing between different GNSS constellations; subscript and These represent the reference station and the user terminal, respectively. For frequency identification, ; and These are pseudorange and phase OC single-difference observations, respectively; This refers to the distance between a single-difference station and its satellite. For phase clock difference parameters; This is the tropospheric projection coefficient; The relative tropospheric delay between stations in the zenith direction; For frequency-related terms; For unbiased single-difference ionospheric delay; Hardware delay for pseudorange between receivers; For phase hardware delay between receivers at the second frequency and above; Wavelength; For double-difference ambiguity, superscript Indicates the reference satellite; and These are the prior values ​​for the ionosphere and troposphere, respectively.

[0026] The phase clock difference parameter is:

[0027] ;

[0028] In the formula, This represents the receiver's actual clock bias; The first frequency phase deviation between receivers; The wavelength is the first frequency. The first frequency is the inter-station single-difference ambiguity.

[0029] Preferably, a fixed reference satellite is used to ensure single-difference ambiguity terms. It is a fixed constant.

[0030] Preferably, there is a phase hardware delay between the second and higher frequency receivers. for:

[0031] ;

[0032] In the formula, for Hardware phase delay between receivers at the same frequency; The hardware delay between receiver phases at the first frequency; The wavelength is the first frequency. The inter-station single-difference reference satellite ambiguity is set for the first frequency. for Inter-station single-difference reference satellite ambiguity at frequency;

[0033] The inter-receiver pseudorange hardware delay for:

[0034] ;

[0035] In the formula, for Hardware delay of pseudorange between receivers at the frequency; The hardware delay between receiver phases at the first frequency; The first frequency is the inter-station single-difference reference satellite ambiguity.

[0036] Preferably, the random model is:

[0037] ;

[0038] ;

[0039] ; ; ;

[0040] ; ;

[0041] In the formula, and These are the variance-covariance matrices of the pseudorange and phase observations, respectively; This is the variance component matrix related to the receiver, used to characterize the relative differences in the observation noise levels of different receivers; This is the variance component matrix related to pseudorange observations, and its diagonal elements are the variances of pseudorange observations at each frequency. This is the variance component matrix related to carrier phase observations, with its diagonal elements being the variance of carrier phase observations at each frequency; This is the variance component matrix related to the satellite elevation angle, used to describe the characteristic that observation noise increases as the satellite elevation angle decreases; For Kronecker product; For the reference station variance component factor; For user-side variance component factors; The variance of the pseudorange observation at the first frequency; for Pseudorange observation variance at frequency; The variance of the phase observation at the first frequency; for Phase observation variance at frequency; For the first The high-angle correlation variance components corresponding to each satellite; The zenith direction variance is used to characterize the baseline noise level of GNSS observations under optimal observation geometry conditions. This is the satellite's elevation angle.

[0042] Secondly, the present invention provides a wide-area GNSS time-frequency transmission device based on crowdsourced RTK technology, the device being used to implement the aforementioned wide-area GNSS time-frequency transmission method based on crowdsourced RTK technology, the device comprising:

[0043] The first model building module is used to build an unbiased single-difference ionospheric and tropospheric delay correction model on the server by aggregating atmospheric correction information generated by reference stations and user terminals in the region, based on the single-difference ionospheric weighted observation model, and to transmit atmospheric correction prior information to users in time and frequency, and to build a crowdsourced RTK single-difference observation model by fixing integer ambiguity with atmospheric correction prior information.

[0044] The second model building module is used to form a phase clock model based on the single-difference observation model of crowdsourced RTK by separating the pseudorange hardware delay in the clock difference parameters and absorbing only the phase hardware delay, and to build the corresponding stochastic model.

[0045] Thirdly, the present invention provides a wide-area GNSS time and frequency transmission device based on crowdsourced RTK technology, including a memory and a processor;

[0046] The memory is used to store computer program code and transmit the computer program code to the processor;

[0047] The processor is configured to execute the wide-area GNSS time-frequency transfer method based on crowdsourced RTK technology as described above, according to instructions in the computer program code.

[0048] Fourthly, the present invention provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the wide-area GNSS time-frequency transmission method based on crowdsourced RTK technology as described above.

[0049] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0050] This invention discloses a wide-area GNSS time-frequency transfer method, apparatus, and device using crowdsourced RTK technology. By separating pseudorange hardware delay from clock bias parameters and absorbing only the more stable phase hardware delay, the noise level of clock bias parameters is reduced, short-term frequency stability and the speed of filtering to steady state are improved, and zero short baseline conditions are achieved. The stability achieved is on a scale that surpasses the accuracy limit of existing GNSS time transfer technologies. Furthermore, leveraging the advantages of crowdsourced RTK technology in ionospheric space transfer, this capability is extended to a wide area. Attached Figure Description

[0051] Figure 1 This is a flowchart of a wide-area GNSS time-frequency transmission method based on crowdsourced RTK technology proposed in this invention.

[0052] Figure 2 This is a structural block diagram of a wide-area GNSS time and frequency transmission device based on crowdsourced RTK technology proposed in this invention.

[0053] Figure 3 This is a structural block diagram of a wide-area GNSS time and frequency transmission device based on crowdsourced RTK technology proposed in this invention. Detailed Implementation

[0054] The present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0055] See Figure 1 This invention provides a wide-area GNSS time-frequency transfer method based on crowdsourced RTK technology, comprising:

[0056] S1. Based on the single-difference ionospheric weighted observation model, by aggregating atmospheric correction information generated by reference stations and user terminals within the region, an unbiased single-difference ionospheric and tropospheric delay correction model is constructed on the server side, and atmospheric correction prior information is broadcast to users via time and frequency transmission. The single-difference observation model of crowdsourced RTK is constructed by fixing integer ambiguity with atmospheric correction prior information.

[0057] S2. Based on the single-difference observation model of crowdsourced RTK, a phase clock model is formed by separating the pseudorange hardware delay in the clock error parameter and absorbing only the phase hardware delay, and a corresponding stochastic model is constructed.

[0058] Based on the fact that the stability of the receiver carrier phase hardware delay is significantly better than that of the pseudorange delay, this invention proposes a novel GNSS time-frequency transfer method. By separating the pseudorange hardware delay from the clock error parameter and absorbing only the phase hardware delay, the zero short baseline condition is achieved. The stability achieved surpasses the accuracy limit of existing GNSS time-frequency transmission technologies. Furthermore, by leveraging the advantages of crowdsourced RTK technology in ionospheric space transmission, this capability is extended to a wider area.

[0059] Furthermore, the crowdsourced RTK solution framework is based on the classic single-difference ionospheric weighted (SD) observation model. By aggregating atmospheric correction information generated by reference stations and user terminals within the region, it constructs a spatiotemporally continuous and consistent unbiased single-difference ionospheric and tropospheric delay correction model on the server side and transmits the atmospheric correction prior information to users via time-frequency transmission. This atmospheric correction prior information provides key constraints for user-side integer ambiguity fixation, significantly improving the success rate and reliability of ambiguity fixation over a wide area, enabling long baseline solutions to possess the stability of short baselines, thereby achieving rapid and precise positioning at the centimeter level. The single-difference observation model of crowdsourced RTK is as follows:

[0060] ;

[0061] ;

[0062] ;

[0063] ;

[0064] In the formula, For expectation operator; superscript Indicates satellite, ,use Distinguishing between different GNSS constellations; subscript and These represent the reference station and the user terminal, respectively. For frequency identification, ; and These are pseudorange and phase OC single-difference observations, respectively; Indicates the distance between a single-difference station; This is the tropospheric projection coefficient; The relative tropospheric delay between stations in the zenith direction; For unbiased single-difference ionospheric delay, For frequency-related terms; Wavelength; For double-difference ambiguity, superscript Indicates the reference satellite; and These are prior values ​​for the ionosphere and troposphere, respectively, provided by a crowdsourced RTK server, the user's own historical data, or nearby users; and These are pseudo-range clock error and phase clock error, respectively.

[0065] Specifically, the pseudorange clock error and phase clock error are as follows:

[0066] ;

[0067] ;

[0068] In the formula, This represents the receiver's actual clock bias; For the pseudorange hardware delay of the inter-station receiver; For the phase hardware delay of the inter-station receiver; For ambiguity.

[0069] As can be seen from the above formula, the relative receiver pseudorange clock error parameter contains the true clock error. Based on this, the pseudorange hardware delay of the inter-station receiver was further absorbed. The relative receiver phase clock difference parameter also includes the actual inter-station clock difference and incorporates the inter-station receiver phase hardware delay. and ambiguity term The single-difference observation model of crowdsourced RTK, also known as the decoupled clock (DC) model, is characterized by different combined clock error parameters corresponding to pseudorange and phase observations. However, in multi-frequency decoupled clock models, each frequency requires estimation of an independent set of combined clock error terms, resulting in weak model constraints and high sensitivity to observation noise, making it difficult to directly meet the high stability requirements of time-frequency transfer. To improve the robustness of the model and the accuracy of clock error estimation, it is necessary to further reshape the clock error parameters.

[0070] Constructing unbiased ionospheric priors using crowdsourced GNSS data is a crucial technical approach to improving wide-area ambiguity fixing capabilities. By aggregating observations from a large number of users and reference stations, a continuous and consistent wide-area unbiased ionospheric map can be built, providing accurate ionospheric priors for users. The GNSS precise positioning framework centered on "crowdsourced atmosphere" is crowdsourced RTK. It extends the traditional single-difference ionospheric weighted RTK model, which is only applicable to regional areas, to a wide-area scale, effectively transforming long-baseline RTK into a short-baseline problem, significantly enhancing the success rate and stability of integer ambiguity fixing. This technology not only improves precise positioning performance but also lays the foundation for improving the stability of wide-area GNSS time transfer. The advantages of crowdsourced RTK in ionospheric parameter processing enable it to effectively suppress ionospheric residuals and naturally eliminate satellite phase deviations within the single-difference framework, thereby significantly improving ambiguity fixing efficiency and clock sequence smoothness. More importantly, this mechanism unifies the traditionally separate processing of short, medium, and long baseline time transfers under a single model system, extending GNSS time transfer capabilities from local links to wide-area scales. As the performance of external time references (such as optical clock-driven crystal oscillators) further improves and their applications become more widespread, the advantages of this solution will become more prominent, and it is expected to provide key technical support for future high-precision time and frequency transmission and real-time updates of gravity position.

[0071] Furthermore, based on the user-side calculation results, unbiased single-difference ionospheric delay is synchronously transmitted back to the server. and the relative tropospheric delay between stations in the zenith direction It is used to construct wide-area ionospheric and tropospheric delay correction models and to broadcast precise atmospheric correction prior information to users in time and frequency.

[0072] Furthermore, based on the decoupled clock model, its clock error parameters can be further simplified into a combined clock error term dominated solely by pseudorange bias, i.e., the pseudorange clock model (Code Clock, CC). This model is also a single-difference RTK model, widely used in baseline calculation and time-frequency transfer. Its observation model can be expressed as:

[0073] ;

[0074] ;

[0075] ;

[0076] In the formula, For expectation operators; and These are pseudorange and phase OC single-difference observations, respectively; This refers to the distance between a single-difference station and its satellite. For pseudo-range clock error parameters; This is the tropospheric projection coefficient; The relative tropospheric delay between stations in the zenith direction; For frequency-related terms; For single-difference unbiased ionospheric delay; For pseudorange hardware delay of receivers at the second frequency and above; For the phase hardware delay between receivers; Wavelength; It represents double-difference ambiguity.

[0077] The pseudorange hardware delay between receivers at the second frequency and above is:

[0078] ;

[0079] The phase hardware delay between receivers is:

[0080] ;

[0081] In the formula, For the phase hardware delay of the inter-station receiver; The hardware delay of pseudorange between receivers at the first frequency; For ambiguity.

[0082] Specifically, the pseudorange and phase observation equations both contain pseudorange clock error parameters, and their specific expressions are as follows:

[0083] ;

[0084] In the formula, This represents the receiver's actual clock bias.

[0085] As can be seen from the above equation, the relative receiver clock error parameter in the pseudorange clock model consists only of the actual clock error and the inter-station pseudorange hardware delay at the first frequency, and its structure is frequency-independent. Therefore, it has higher model strength compared to the DC model. However, the pseudorange hardware delay must be obtained from pseudorange observations, and its value usually has a high noise level, requiring a certain filtering process to reach a steady state. As a result, in actual observation links, the frequency stability of the CC model on short time scales is often limited, making it difficult to meet the requirements of high-precision time and frequency transmission.

[0086] Furthermore, given the inherent limitations of pseudorange clock models in terms of noise and stability, it is necessary to further renormalize the clock bias parameters to construct a more robust clock bias model. Phase hardware delay typically exhibits weak time-varying or approximately constant behavior over time. Therefore, constructing clock bias based on phase observations not only avoids the dominant effect of pseudorange observation noise but also provides a higher theoretical upper limit for improving frequency stability. Based on this, the phase clock bias can be defined as a combined clock bias term dominated by phase deviation, forming a phase clock (PC) model, whose expression is:

[0087] ;

[0088] ;

[0089] ;

[0090] ;

[0091] In the formula, For expectation operator; superscript Indicates satellite, ,use Distinguishing between different GNSS constellations; subscript and These represent the reference station and the user terminal, respectively. For frequency identification, ; and These are pseudorange and phase OC single-difference observations, respectively; This refers to the distance between a single-difference station and its satellite. For phase clock difference parameters; This is the tropospheric projection coefficient; The relative tropospheric delay between stations in the zenith direction; For frequency-related terms; For unbiased single-difference ionospheric delay; Hardware delay for pseudorange between receivers; For phase hardware delay between receivers at the second frequency and above; Wavelength; For double-difference ambiguity, superscript Indicates the reference satellite; and These are prior values ​​for the ionosphere and troposphere, respectively, provided by a crowdsourced RTK server, the user's own historical data, or nearby users.

[0092] Both the pseudorange and phase observation equations use phase clock error parameters. These parameters are introduced into the actual clock error by including the first frequency carrier phase hardware delay and integer ambiguity term, thus explicitly separating the unstable pseudorange hardware delay from the clock error parameters. Only the stable carrier phase-related hardware delay and ambiguity components are retained, improving the stability and solvability of the time-frequency transfer model. The specific expression is as follows:

[0093] ;

[0094] In the formula, This represents the receiver's actual clock bias; The first frequency phase deviation between receivers; The wavelength is the first frequency. The first frequency is the inter-station single-difference ambiguity.

[0095] As can be seen from the above equation, the inter-receiver phase clock bias consists of the receiver's actual clock bias, the inter-receiver phase deviation, and an ambiguity term, and its form is independent of frequency. By treating the phase deviation as a time-stable constant, the PC model significantly enhances the overall observability of the model under multi-frequency conditions, making the clock bias estimation smoother on short timescales and significantly improving the convergence speed of filtering. With the high-quality ionospheric prior support provided by crowdsourced RTK, the PC model can achieve high stability performance closer to theoretical values ​​in actual links, providing potential advantages for wide-area time transfer.

[0096] Specifically, the phase hardware delay between receivers at the second frequency and above. for:

[0097] ;

[0098] In the formula, for Hardware phase delay between receivers at the same frequency; The hardware delay between receiver phases at the first frequency; The wavelength is the first frequency. The inter-station single-difference reference satellite ambiguity is set for the first frequency. for Inter-station single-difference reference satellite ambiguity at frequency;

[0099] Hardware delay between receiver pseudorange for:

[0100] ;

[0101] In the formula, for Hardware delay of pseudorange between receivers at the frequency; The hardware delay between receiver phases at the first frequency; The first frequency is the inter-station single-difference reference satellite ambiguity.

[0102] Specifically, the phase clock difference parameter still includes the single-difference ambiguity term of the reference satellite. When the reference satellite is switched, the ambiguity term will change accordingly, causing abrupt changes in the phase clock parameter, which is extremely detrimental to the continuity and stability of time and frequency transmission. Therefore, the PC model needs to be combined with a strategy of never changing the reference satellite to ensure that the reference satellite remains consistent throughout the entire period. In this case, it can be considered a fixed constant that will not affect the accuracy or stability of time and frequency transmission; that is, the reference satellite is fixed to ensure the single-difference ambiguity term... It is a fixed constant.

[0103] The phase clock model (PC) absorbs only the phase hardware delay from single-frequency phase observations, exhibiting smooth time variation and low noise levels. Therefore, it can generate clock error sequences with even lower noise and higher frequency stability on short-term scales. In contrast, the pseudorange clock model (CC) absorbs the hardware delay from pseudorange observations. Due to the inherently large pseudorange noise, the CC model's clock error sequence exhibits higher noise and weaker short-term stability. Furthermore, in PPP / IPPP, the clock error parameters simultaneously absorb additional hardware biases from the ionospheric desmosphere combination, further amplifying noise and limiting frequency stability. The PC model avoids these noise sources, has a simple model structure, does not rely on external bias products, and maintains robust performance under single-frequency, dynamic, and unidirectional filtering conditions, while also possessing good real-time performance. Combined with high-precision ionospheric priors provided by crowdsourced RTK, the high-stability time-transfer capability of the PC model can be extended to wide-area scales.

[0104] This invention explores the feasibility of separating hardware delay from clock bias parameters. Phase hardware delay is more stable, has lower noise, and weaker drift in the time domain, and theoretically interferes less with clock bias estimation compared to pseudorange hardware delay. Based on this characteristic, this invention explicitly separates the influence of pseudorange hardware delay within a crowdsourced RTK framework, proposing a single-difference phase clock model (PC) dominated by phase hardware delay. Under zero short baseline conditions, this model can ensure the theoretical stability of GNSS time transfer within the mean time constant. Reaching Scale. By further combining the product in the precision orbit with the high-quality ionospheric priors provided by crowdsourced RTK, the PC model achieves robust integer ambiguity fixing capabilities and can extend high-stability time-frequency transfer performance to wide-area links.

[0105] Furthermore, based on the parameterized modeling of the observation equation, a reasonable stochastic model is crucial to ensuring the accuracy of clock error estimation and the success rate of ambiguity fixation. The variance-covariance matrix of the observation equation can be expressed as:

[0106] ;

[0107] ;

[0108] ; ; ;

[0109] ; ;

[0110] In the formula, and These are the variance-covariance matrices of the pseudorange and phase observations, respectively; This is the receiver-related variance component matrix, used to characterize the relative differences in observation noise levels among different receivers. Differentiated processing is achieved by assigning different noise figures to different receivers, and when the observation accuracy of each receiver is consistent, it degenerates into a unit array; This is the variance component matrix related to pseudorange observations, where the diagonal elements are the variances of pseudorange observations at each frequency. ; This is the variance component matrix related to carrier phase observations, where the diagonal elements are the variances of carrier phase observations at each frequency. ; This is the variance component matrix related to the satellite elevation angle, used to describe the characteristic that observation noise increases as the satellite elevation angle decreases. ; For Kronecker product; For the reference station variance component factor; For user-side variance component factors; The variance of the pseudorange observation at the first frequency; for Pseudorange observation variance at frequency; The variance of the phase observation at the first frequency; for Phase observation variance at frequency; For the first The high-angle correlation variance components corresponding to each satellite; The zenith direction variance is used to characterize the baseline noise level of GNSS observations under optimal observation geometry conditions. This is the satellite's elevation angle.

[0111] To evaluate the performance of the time-frequency transfer method proposed in this invention under different application conditions, three types of experiments with progressively increasing complexity were designed, gradually expanding from the ideal co-clock environment to practical application scenarios such as dynamic and medium-to-long baseline environments. First, under co-clock conditions, the IPPP, pseudorange clock (CC), and phase clock (PC) models were compared through zero-baseline and ultra-short-baseline observations, focusing on analyzing the differences in short-to-long-time stability of the three models to determine their performance upper limits under ideal conditions. The co-clock zero-short-baseline experiment shows that the PC model solves the MDEV of the clock difference sequence in... It can be reached at this location In terms of magnitude, the PC model achieves approximately 18% and 42.5% improvements in frequency stability compared to the CC model and IPPP (CSRS-PPP Service), respectively, and demonstrates significant noise suppression across the entire power spectral density. In single-epoch mode, the PC model exhibits lower noise and significantly faster convergence speed than the CC model, achieving instantaneous convergence to sub-picosecond levels after ambiguity fixation, while the CC model still shows a noticeable filtering convergence process. Furthermore, in single-frequency and static-simulated dynamic experiments, the robustness and applicability of the PC model under frequency resource constraints and weakened geometric constraints are evaluated. In both single-frequency and static-simulated dynamic experiments, the PC model maintains performance similar to the dual-frequency static mode, with its short-time noise level and convergence speed superior to both the CC model and IPPP. Under simulated dynamic conditions, it continues to demonstrate stronger robustness and faster re-convergence capability. Finally, under medium-to-long baseline conditions, crowdsourced RTK technology was introduced, with intermediate stations simulating crowdsourced users and a segmented ionospheric delay processing strategy employed to analyze the impact of different ionospheric processing strategies on time-frequency stability. Experiments on medium-to-long baselines dominated by actual clock biases showed that the PC model improved average frequency stability by 8% compared to IPPP and supported single-frequency data processing. Compared to the classic single-difference ionospherically weighted RTK scheme, the average frequency stability was improved by 59.3%, and spatial coverage was significantly enhanced. These three types of experiments covered typical application scenarios from common clocks and short baselines to medium-to-long baselines, providing comprehensive experimental support for verifying the effectiveness of the proposed clock bias model.

[0112] GNSS-based time and frequency transfer technology has received widespread attention and research, but its long-term stability accuracy remains limited. The magnitude is significant. Therefore, based on the characteristic that the receiver carrier phase hardware delay stability is significantly better than the pseudorange delay, this invention proposes a novel time transfer method. By separating the pseudorange hardware delay from the clock difference parameter and absorbing only the phase hardware delay, a zero short baseline condition is achieved. The stability achieved is on a scale that surpasses the accuracy limits of existing GNSS time transfer technologies. Furthermore, by leveraging the advantages of crowdsourced RTK technology in ionospheric space transfer, this capability is extended to a wider area. The method described in this invention can provide key technical support for the construction of wide-area centimeter-level gravity and elevation benchmarks, as well as for research tasks with extremely high time transfer accuracy requirements; simultaneously, these significant needs are also expected to further promote the ubiquitous application of satellite precise positioning technology through crowdsourced RTK technology.

[0113] See Figure 2 The present invention also provides a wide-area GNSS time-frequency transmission device based on crowdsourced RTK technology. The device is used to implement the aforementioned wide-area GNSS time-frequency transmission method based on crowdsourced RTK technology. The device includes:

[0114] The first model building module is used to build an unbiased single-difference ionospheric and tropospheric delay correction model on the server by aggregating atmospheric correction information generated by reference stations and user terminals in the region, based on the single-difference ionospheric weighted observation model, and to transmit atmospheric correction prior information to users in time and frequency, and to build a crowdsourced RTK single-difference observation model by fixing integer ambiguity with atmospheric correction prior information.

[0115] The second model building module is used to form a phase clock model based on the single-difference observation model of crowdsourced RTK by separating the pseudorange hardware delay in the clock difference parameters and absorbing only the phase hardware delay, and to build the corresponding stochastic model.

[0116] See Figure 3 The present invention also provides a wide-area GNSS time and frequency transmission device based on crowdsourced RTK technology, including a memory and a processor;

[0117] The memory is used to store computer program code and transmit the computer program code to the processor;

[0118] The processor is configured to execute the wide-area GNSS time-frequency transfer method based on crowdsourced RTK technology as described above, according to instructions in the computer program code.

[0119] The present invention also provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the wide-area GNSS time-frequency transmission method based on crowdsourced RTK technology as described above.

[0120] Generally, the computer instructions for implementing the method of the present invention can be carried on any combination of one or more computer-readable storage media. Non-transitory computer-readable storage media can include any computer-readable medium except for the signal itself, which is temporarily propagating.

[0121] Computer-readable storage media can be, for example, but not limited to, electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatuses, or any combination thereof. More specific examples (a non-exhaustive list) of computer-readable storage media include: electrical connections having one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EKROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof. In this invention, a computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device.

[0122] Computer program code for performing the operations of this invention can be written in one or more programming languages ​​or a combination thereof. These programming languages ​​include object-oriented programming languages—such as Java, Smalltalk, and C++—as well as conventional procedural programming languages—such as the "C" language or similar programming languages. In particular, Python, suitable for neural network computation, and platform frameworks such as TensorFlow and PyTorch can be used. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer or to an external computer (e.g., via the Internet using an Internet service provider) through any type of network, including a local area network (LAN) or a wide area network (WAN).

[0123] For details regarding the aforementioned equipment and non-transitory computer-readable storage media, please refer to the specific description of a wide-area GNSS time-frequency transfer method based on crowdsourced RTK technology and its beneficial effects, which will not be repeated here.

[0124] Although embodiments of the present invention have been shown and described above, it should be understood that the above embodiments are exemplary and should not be construed as limiting the present invention. Those skilled in the art can make changes, modifications, substitutions and variations to the above embodiments within the scope of the present invention.

Claims

1. A wide-area GNSS time-frequency transfer method based on crowdsourced RTK technology, characterized in that, include: Based on the single-difference ionospheric weighted observation model, by aggregating atmospheric correction information generated by reference stations and user terminals in the region, an unbiased single-difference ionospheric and tropospheric delay correction model is constructed on the server side, and atmospheric correction prior information is broadcast to users in time and frequency transmission. The single-difference observation model of crowdsourced RTK is constructed by fixing integer ambiguity with atmospheric correction prior information. Based on the single-difference observation model of crowdsourced RTK, a phase clock model is formed by separating the pseudorange hardware delay from the clock difference parameter and absorbing only the phase hardware delay, and a corresponding stochastic model is constructed.

2. The wide-area GNSS time-frequency transfer method based on crowdsourced RTK technology according to claim 1, characterized in that, The single-difference observation model for crowdsourced RTK is as follows: ； ； ； ； In the formula, For expectation operator; superscript Indicates satellite, ,use Distinguishing between different GNSS constellations; subscript and These represent the reference station and the user terminal, respectively. For frequency identification, ; and These are pseudorange and phase OC single-difference observations, respectively; Indicates the distance between a single-difference station; This is the tropospheric projection coefficient; The relative tropospheric delay between stations in the zenith direction; For unbiased single-difference ionospheric delay, For frequency-related terms; Wavelength; For double-difference ambiguity, superscript Indicates the reference satellite; and These are the prior values ​​for the ionosphere and troposphere, respectively. and These are pseudo-range clock error and phase clock error, respectively. The pseudo-range clock difference and phase clock difference are: ； ； In the formula, This represents the receiver's actual clock bias; For the pseudorange hardware delay of the inter-station receiver; For the phase hardware delay of the inter-station receiver; For ambiguity.

3. The wide-area GNSS time-frequency transfer method based on crowdsourced RTK technology according to claim 2, characterized in that, Based on the user-side calculation results, the unbiased single-difference ionospheric delay is synchronously transmitted back to the server. and the relative tropospheric delay between stations in the zenith direction It is used to construct wide-area ionospheric and tropospheric delay correction models and to broadcast precise atmospheric correction prior information to users in time and frequency.

4. The wide-area GNSS time-frequency transfer method based on crowdsourced RTK technology according to claim 1, characterized in that, The phase clock model is as follows: ； ； ； ； In the formula, For expectation operator; superscript Indicates satellite, ,use Distinguishing between different GNSS constellations; subscript and These represent the reference station and the user terminal, respectively. For frequency identification, ; and These are pseudorange and phase OC single-difference observations, respectively; This refers to the distance between a single-difference station and its satellite. For phase clock difference parameters; This is the tropospheric projection coefficient; The relative tropospheric delay between stations in the zenith direction; For frequency-related terms; For unbiased single-difference ionospheric delay; Hardware delay for pseudorange between receivers; For phase hardware delay between receivers at the second frequency and above; Wavelength; For double-difference ambiguity, superscript Indicates the reference satellite; and These are the prior values ​​for the ionosphere and troposphere, respectively. The phase clock difference parameter is: ； In the formula, This represents the receiver's actual clock bias; The first frequency phase deviation between receivers; The wavelength is the first frequency. The first frequency is the inter-station single-difference ambiguity.

5. A wide-area GNSS time-frequency transfer method based on crowdsourced RTK technology according to claim 4, characterized in that, A fixed reference satellite is used to ensure single-difference ambiguity terms. It is a fixed constant.

6. A wide-area GNSS time-frequency transfer method based on crowdsourced RTK technology according to claim 4, characterized in that, Phase hardware delay between receivers at the second and above frequencies for: ； In the formula, for Hardware phase delay between receivers at the frequency; The hardware delay between receiver phases at the first frequency; The wavelength is the first frequency. The inter-station single-difference reference satellite ambiguity is set for the first frequency. for Inter-station single-difference reference satellite ambiguity at frequency; The inter-receiver pseudorange hardware delay for: ； In the formula, for Hardware delay of pseudorange between receivers at the frequency; The hardware delay between receiver phases at the first frequency; The first frequency is the inter-station single-difference reference satellite ambiguity.

7. A wide-area GNSS time-frequency transfer method based on crowdsourced RTK technology according to claim 4, characterized in that, The stochastic model is: ； ； ； ； ； ； ； In the formula, and These are the variance-covariance matrices of the pseudorange and phase observations, respectively; This is the variance component matrix related to the receiver, used to characterize the relative differences in the observation noise levels of different receivers; This is the variance component matrix related to pseudorange observations, and its diagonal elements are the variances of pseudorange observations at each frequency. This is the variance component matrix related to carrier phase observations, with its diagonal elements being the variance of carrier phase observations at each frequency; This is the variance component matrix related to the satellite elevation angle, used to describe the characteristic that observation noise increases as the satellite elevation angle decreases; For Kronecker product; For the reference station variance component factor; For user-side variance component factors; The variance of the pseudorange observation at the first frequency; for Pseudorange observation variance at frequency; The variance of the phase observation at the first frequency; for Phase observation variance at frequency; For the first The high-angle correlation variance components corresponding to each satellite; The zenith direction variance is used to characterize the baseline noise level of GNSS observations under optimal observation geometry conditions. This is the satellite's elevation angle.

8. A wide-area GNSS time-frequency transmission device based on crowdsourced RTK technology, characterized in that, The apparatus is used to implement the method according to any one of claims 1 to 7, the apparatus comprising: The first model building module is used to build an unbiased single-difference ionospheric and tropospheric delay correction model on the server by aggregating atmospheric correction information generated by reference stations and user terminals in the region, based on the single-difference ionospheric weighted observation model, and to transmit atmospheric correction prior information to users in time and frequency, and to build a crowdsourced RTK single-difference observation model by fixing integer ambiguity with atmospheric correction prior information. The second model building module is used to form a phase clock model based on the single-difference observation model of crowdsourced RTK by separating the pseudorange hardware delay in the clock difference parameters and absorbing only the phase hardware delay, and to build the corresponding stochastic model.

9. A wide-area GNSS time and frequency transmission device based on crowdsourced RTK technology, characterized in that, Including memory and processor; The memory is used to store computer program code and to transmit the computer program code to the processor; The processor is configured to execute the method as described in any one of claims 1 to 7 according to instructions in the computer program code.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, implements the method as described in any one of claims 1 to 7.

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