A phase bias generation and application method for multi-frequency PPP-AR

Abstract

The present application belongs to the global navigation positioning technology field, disclose a kind of phase deviation generation and application method for multi-frequency PPP-AR.For the problems of insufficient real-time stability, higher system implementation complexity and avoiding the influence of inter-frequency clock bias deviation by weakening the model strength in the process of inter-frequency clock bias deviation processing in existing multi-frequency precise point positioning ambiguity fixing technology, in the phase deviation generation stage of the server, the time-varying IFCB component in the third frequency is absorbed into the corresponding floating ambiguity state by parameterization, and on this basis, the multi-frequency uncalibrated phase delay parameter is estimated, which is further converted into the phase OSB product corresponding to each frequency for direct use by the user side. The method of the present application realizes the stable fixing of multi-frequency ambiguity and high-precision positioning without introducing additional independent IFCB product and without weakening the overall constraint ability of the user-side PPP-AR model.

Description

Technical Field

[0001] This invention belongs to the field of Global Navigation Satellite System (GNSS) positioning technology, and particularly relates to a method for generating and applying phase deviation for multi-frequency PPP-AR. Background Technology

[0002] Precise Point Positioning with Ambiguity Resolution (PPP-AR) technology generates phase deviation products from a reference network and applies them to the user end to restore the integer characteristics of carrier phase ambiguity, thereby accelerating PPP convergence and improving positioning accuracy.

[0003] As GNSS systems evolve from dual-frequency to multi-frequency, the introduction of third-frequency observations to construct multi-frequency ambiguity combinations such as Extra-Wide-Lane (EWL), Wide-Lane (WL), and Narrow-Lane (NL) is beneficial for enhancing ambiguity fixation capabilities. In multi-frequency PPP-AR technology, it is typically necessary to jointly estimate multi-frequency correlation parameters in the reference station network and generate corresponding phase OSB products for user-end use.

[0004] In multi-frequency GNSS observations, the satellite clock bias products used for different frequency signals are often generated based on a certain reference frequency, which manifests as inter-frequency clock bias (IFCB) on some satellites. This IFCB is more pronounced in the third-frequency signal and affects the generation of multi-frequency phase bias and ambiguity processing.

[0005] For the IFCB introduced by the third frequency signal, existing technologies have proposed some solutions.

[0006] Some solutions have proposed estimating the IFCB first and then introducing it as an independent correction into the multi-frequency PPP-AR processing flow. Theoretically, this solution can reduce the impact of the IFCB on the third-frequency ambiguity, but it has the following problems in practical applications:

[0007] On the one hand, IFCB exhibits significant time-varying characteristics on some satellites, with the variation amplitude reaching the level of carrier phase. In order to accurately estimate this deviation, it is often necessary to rely on data with a long time span or introduce additional constraints, making it difficult for IFCB to be stably estimated in real-time or near-real-time scenarios. If the IFCB estimation is unstable, its error will be directly transmitted to the phase deviation product, resulting in a decrease in the reliability of fixing the third frequency related ambiguity.

[0008] On the other hand, independent estimation of IFCB usually requires additional parameter modeling, product generation and broadcasting links; under multi-constellation and multi-frequency conditions, this approach increases system complexity and makes it more difficult to maintain product consistency; when there is a time or model inconsistency between IFCB products and phase deviation products, new systematic errors may be introduced.

[0009] In addition, some solutions choose to weaken the influence of IFCB at the user end by reducing the strength of the third-frequency ambiguity model, for example, by setting the third-frequency related ambiguity parameter to a random walk process; however, although such solutions can reduce the impact of IFCB on solution stability to a certain extent, they may also weaken the overall constraint capability of the PPP-AR model, prolong the ambiguity fixing time, and affect the reliability of ambiguity fixing when the observation conditions are poor.

[0010] In summary, existing multi-frequency PPP-AR technologies generally face the following engineering difficulties in IFCB processing:

[0011] 1) IFCB is difficult to estimate stably and reliably under real-time conditions; 2) Relying on independent IFCB products will increase the complexity of system implementation and maintenance; 3) By weakening the model strength to avoid IFCB, the efficiency and stability of ambiguity fixation will be sacrificed.

[0012] Therefore, how to reduce the impact of IFCB on multi-frequency phase deviation generation and ambiguity fixation without introducing additional independent IFCB products or weakening the strength of the PPP-AR model is a key problem that urgently needs to be solved in this field.

[0013] The statements in this section are merely background information related to the present invention and do not necessarily constitute prior art. Summary of the Invention

[0014] The purpose of this invention is to propose a phase deviation generation and application method for multi-frequency PPP-AR. By performing absorptive parameterization processing on the third-frequency IFCB at the server end and transmitting the processing result to the phase OSB product, the adverse effects of IFCB on the multi-frequency phase deviation generation and ambiguity fixing process can be reduced without generating and broadcasting the IFCB product separately or weakening the overall constraint capability of the user-end PPP-AR model.

[0015] To achieve the above objectives, the present invention adopts the following technical solution:

[0016] A method for generating and applying phase offset in multi-frequency PPP-AR includes the following steps:

[0017] Step 1. On the server side, establish a multi-frequency non-differential non-combined PPP-AR basic model, jointly process the multi-frequency pseudorange observations and carrier phase observations of the reference station, and estimate the clock error, tropospheric delay, ionospheric delay and floating-point ambiguity parameters related to the positioning solution;

[0018] Step 2. For the time-varying IFCB component contained in the third frequency carrier phase observation, an absorption parameterization processing strategy is adopted on the server side. Instead of estimating the IFCB as an independent state parameter, it is incorporated into the floating-point ambiguity state corresponding to the third frequency for unified modeling, and the multi-frequency floating-point ambiguity parameters are estimated.

[0019] Step 3. On the server side, based on the multi-frequency floating-point ambiguity parameters after IFCB absorption, construct an additional wide-lane EWL, wide-lane WL, and narrow-lane NL combination, and estimate the uncalibrated phase delay UPD parameter in the combination domain;

[0020] Step 4. Further map the estimated combined domain UPD parameters to the phase OSB products corresponding to each frequency, so that the phase OSB product corresponding to the third frequency contains the equivalent correction information after IFCB absorption.

[0021] Step 5. At the user end, the phase OSB product is used to correct the carrier phase observations of the corresponding frequency, and a cascaded ambiguity fixing strategy is used to perform multi-frequency PPP-AR solution.

[0022] Furthermore, based on the aforementioned method for generating and applying phase offsets in multi-frequency PPP-AR, this invention also proposes a corresponding system for generating and applying phase offsets in multi-frequency PPP-AR, the scheme of which is as follows:

[0023] A phase offset generation and application system for multi-frequency PPP-AR includes the following modules:

[0024] The multi-frequency PPP-AR basic model construction module is used to build a multi-frequency non-differential non-combined PPP-AR basic model on the server side, and to jointly process the multi-frequency pseudorange observations and carrier phase observations of the reference station to estimate the clock error, tropospheric delay, ionospheric delay and floating-point ambiguity parameters related to the positioning solution.

[0025] The third-frequency IFCB absorptive parameterization module is used to perform absorptive parameterization on the server side for the time-varying IFCB components contained in the third-frequency carrier phase observation. Instead of estimating the IFCB as an independent state parameter, it incorporates it into the floating-point ambiguity state corresponding to the third frequency for unified modeling and estimates the multi-frequency floating-point ambiguity parameters.

[0026] The module for constructing multi-frequency ambiguity combination and estimating multi-frequency UPD parameters is used to construct additional wide-lane EWL, wide-lane WL and narrow-lane NL combinations on the server based on the multi-frequency floating-point ambiguity parameters after IFCB absorption, and to estimate the uncalibrated phase delay UPD parameters in the combination domain.

[0027] The UPD to phase OSB conversion module is used to further map the estimated combined domain UPD parameters to phase OSB products corresponding to each frequency, so that the phase OSB product corresponding to the third frequency contains the equivalent correction information after IFCB absorption.

[0028] It also includes a multi-frequency PPP-AR solution module, which is used at the user end to correct the carrier phase observations of the corresponding frequency using the phase OSB product, and to perform multi-frequency PPP-AR solution using a cascaded ambiguity fixing strategy.

[0029] Furthermore, based on the aforementioned method for generating and applying phase deviations in multi-frequency PPP-AR, this invention also proposes a computer device comprising a memory and one or more processors.

[0030] Executable code is stored in memory. When the processor executes the executable code, it implements the steps of the aforementioned method for generating and applying phase offsets in multi-frequency PPP-AR.

[0031] Furthermore, based on the aforementioned method for generating and applying phase offsets for multi-frequency PPP-AR, this invention also proposes a computer-readable storage medium storing a program that, when executed by a processor, implements the steps of the aforementioned method for generating and applying phase offsets for multi-frequency PPP-AR.

[0032] The present invention has the following advantages:

[0033] As described above, this invention proposes a phase deviation generation and application method for multi-frequency PPP-AR. In the phase deviation generation stage at the server end, the time-varying IFCB component in the third frequency is absorbed into the corresponding floating-point ambiguity state through parameterization. Based on this, the uncalibrated phase delay parameters of the multiple frequencies are estimated and further converted into phase OSB products corresponding to each frequency for direct use by the user end. Since the IFCB is no longer estimated as an independent parameter in real time, its time variation on the phase deviation product is limited to the parameterization process of the reference station network. This helps reduce the impact of IFCB estimation instability on the generation of third-frequency phase deviation and the reliability of ambiguity fixation, and improves the stability of multi-frequency phase deviation products under real-time or near-real-time conditions. Furthermore, this invention avoids the need for modeling, generating, and broadcasting independent IFCB products, reducing the number of product links and the workload of maintaining consistency between products, thus reducing the complexity of implementing and maintaining PPP-AR service systems under multi-constellation and multi-frequency conditions. Furthermore, this invention absorbs IFCB during the phase deviation generation stage, rather than weakening the ambiguity model strength at the user end. This suppresses the influence of IFCB while avoiding prolonged ambiguity fixing time and decreased solution stability caused by weakening the user-end model. It also helps maintain the overall constraint capability of the PPP-AR model and improves ambiguity fixing efficiency. At the user end, only phase OSB correction needs to be applied to the corresponding frequency and conventional multi-frequency PPP-AR solution needs to be performed, without adding IFCB-related processing logic. This facilitates the deployment and application of this invention in existing receivers and service systems, and improves the engineering feasibility and application value of multi-frequency PPP-AR. Attached Figure Description

[0034] Figure 1 This is a flowchart of the phase deviation generation and application method for multi-frequency PPP-AR in an embodiment of the present invention. Detailed Implementation

[0035] The present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments:

[0036] Example 1

[0037] like Figure 1As shown, this invention addresses the problems of insufficient real-time stability, high system implementation complexity, and the need to mitigate the impact of inter-frequency clock bias in existing multi-frequency precise single-point positioning ambiguity fixing technologies during inter-frequency clock bias processing. It provides a phase bias generation and application method for multi-frequency PPP-AR. This method, under multi-frequency observation conditions, performs absorptive processing on the third frequency inter-frequency clock bias (IFCB) and generates an Observable-Specific Bias (OSB) product. This achieves stable fixing of multi-frequency ambiguities and high-precision positioning without introducing an independent IFCB product or weakening the overall constraint capability of the user-side PPP-AR model. The method follows a clear data processing chain, with clear input-output relationships and sequential dependencies between steps, facilitating implementation in real-time or near-real-time multi-frequency PPP-AR service systems.

[0038] like Figure 1 As shown, the phase offset generation and application method for multi-frequency PPP-AR in this embodiment includes the following steps:

[0039] Step 1. On the server side, establish a multi-frequency non-differential non-combined PPP-AR basic model, jointly process the multi-frequency pseudorange observations and carrier phase observations of the reference station, and estimate the clock error, tropospheric delay, ionospheric delay and floating-point ambiguity parameters related to the positioning solution.

[0040] On the server side, based on the multi-frequency non-differential non-combined PPP-AR fundamental model, joint parameter estimation is performed on multi-frequency pseudorange observations and carrier phase observations. For any reference station, any satellite, and any frequency observation, corresponding observation equations can be established to characterize the relationship between geometric distance, receiver clock bias, satellite clock bias, tropospheric delay, ionospheric delay, ambiguity parameters, and hardware biases between the receiver and satellite ends. The formulas are as follows:

[0041] (1)

[0042] superscript and subscript and These represent a specific satellite, receiver, and frequency. These are pseudorange observations. These are carrier phase observations; For satellite to receiver geometric distance; and These are receiver clock bias and satellite clock bias, respectively. For oblique path tropospheric delay; The slant path ionospheric delay at the first frequency point, The frequency correlation coefficient, Indicates the first frequency of the satellite. Indicates the satellite's second frequency; Wavelength; For integer phase ambiguity; and These are the code hardware delay deviations at the receiver and satellite ends, respectively. and These are the phase hardware delay deviations at the receiver and satellite ends, respectively. and These are code and phase observation noises, respectively, which include multipath effects.

[0043] Step 2. For the time-varying IFCB component contained in the third frequency carrier phase observation, an absorption parameterization processing strategy is adopted on the server side. Instead of estimating the IFCB as an independent state parameter, it is incorporated into the floating-point ambiguity state corresponding to the third frequency for unified modeling, and the multi-frequency floating-point ambiguity parameters are estimated.

[0044] After incorporating satellite orbit and clock bias products, as well as model correction terms, equation (1) is reparameterized into the following form:

[0045] (2)

[0046] in, and These are the observed values ​​minus the calculated values ​​for pseudorange and phase observations, respectively. Represents the line-of-sight vector. This represents the position increment relative to prior coordinates; The mapping function represents the wetted zenith delay in the troposphere, and is: .

[0047] The remaining reparameterized parameters are as follows:

[0048] (3)

[0049] in, To incorporate receiver clock bias after incorporating receiver endcode hardware delay deviation; To incorporate the slant path ionospheric delay after merging the hardware delay bias between the receiver and satellite endcodes; floating-point ambiguity. It absorbs a combination of code and phase hardware delay bias, thus losing its integer property; This represents the correlation coefficient of the second frequency of the ionosphere. This indicates the hardware delay deviation of the first frequency code at the receiver end. This indicates the hardware delay deviation of the second frequency code at the receiver end. This indicates the hardware delay deviation of the first frequency code at the satellite end. This indicates the hardware delay deviation of the second frequency code at the satellite end. This indicates the time-varying portion of the multi-frequency phase delay bias at the satellite end. This indicates the frequency deviation introduced by multiple frequencies. Inter-frequency clock bias (IFCB) introduced by multiple frequencies.

[0050] Unlike the dual-frequency scenario, since the ionospheric delay parameter cannot completely absorb the code hardware delay deviation at the third frequency, a corresponding inter-frequency deviation parameter needs to be introduced during multi-frequency modeling. Preferably, an externally provided code OSB product can be used to correct the satellite-end code hardware delay deviation, and the remaining receiver-end related deviation can be estimated as a system constant parameter. Furthermore, under the condition of traditional satellite clock bias products, the satellite phase hardware delay deviation at the third frequency can be decomposed into a time-invariant part and a time-varying part, where the time-varying part is the IFCB. For third-frequency signals with significant IFCB effects, a uniform processing strategy using an absorptive ambiguity parameter modeling approach is adopted; for third-frequency signals with insignificant IFCB effects, the same modeling framework can be used for compatible processing. Through the above methods, it is possible to effectively avoid estimating and broadcasting the IFCB as an independent product, while ensuring the stability and consistency of subsequent combined domain UPD estimation and frequency domain phase OSB generation.

[0051] Step 3. Based on the multi-frequency floating-point ambiguity parameters after IFCB absorption, construct an additional wide-lane EWL, wide-lane WL, and narrow-lane NL combination, and estimate the uncalibrated phase delay UPD parameter in the combination domain.

[0052] Based on the multi-frequency floating-point ambiguity parameters estimated by the reference station network, additional wide-lane EWL, wide-lane WL, and narrow-lane NL ambiguity combinations are further constructed to achieve step-by-step processing from long-wavelength combinations to short-wavelength combinations.

[0053] This combination is beneficial in two ways: firstly, it improves the stability of ambiguity fixation; secondly, it helps to reflect the influence of the third-frequency IFCB absorption in the combined domain parameters. Its combined form is as follows:

[0054] (4)

[0055] in Indicates the floating-point ambiguity of EWL. Indicates the floating-point ambiguity of WL. Indicates the floating-point ambiguity of NL. The floating-point ambiguity representing the first frequency of the satellite. The floating-point ambiguity representing the second frequency of the satellite. The floating-point ambiguity representing the third frequency of the satellite, This represents the integer ambiguity of EWL. This represents the integer ambiguity of WL. Indicates the integer ambiguity of NL. This indicates the receiver-side hardware delay of EWL. This indicates the receiver-side hardware delay of WL. This indicates the receiver-side hardware delay of NL. This indicates the satellite-side hardware latency of EWL. This indicates the satellite-side hardware latency of WL. This indicates the satellite-end hardware latency of NL. This indicates the phase hardware delay at the third frequency end of the satellite. The first frequency wavelength of the satellite, The second frequency wavelength of the satellite, This is the third frequency wavelength for the satellite.

[0056] The EWL combination is preferably composed of the second and third frequency ambiguity parameters, the WL combination is preferably composed of the first and second frequency ambiguity parameters, and the NL combination is preferably constructed by further constructing the aforementioned frequency combination relationship.

[0057] Since the EWL, WL and NL combination is constructed linearly from the floating-point ambiguities of each frequency, and the third-frequency floating-point ambiguity parameter has already absorbed the time-varying component of IFCB during the modeling stage, the combination containing the third-frequency term naturally carries the corresponding IFCB absorption result, thus constraining the IFCB influence within the server-side processing flow.

[0058] In a reference station network consisting of multiple reference stations and multiple satellites, a pseudo-observation equation for UPD estimation is constructed based on the fractional-week part of the combined domain floating-point ambiguity parameter, and the receiver-side UPD parameters and satellite-side UPD parameters are jointly estimated.

[0059] UPD estimation is performed independently within the EWL, WL, and NL combined domains, or sequentially from EWL to WL and then to NL; for the UPD estimation... Observed by each station For each satellite, establish the corresponding network UPD pseudo-observation equation:

[0060] (5)

[0061] in, This represents the floor operator; Indicates the first The number of satellites observed by each station; This represents the total number of false observations. This represents the total number of UPD parameters for both the receiver and the satellite. It refers to EWL, WL, or NL; This represents the floating-point ambiguity of the first satellite at the first station. Indicates the first station The floating-point ambiguity of a satellite; This indicates the floating-point ambiguity of the first satellite at the second station; Indicates the second station The floating-point ambiguity of a satellite; Indicates the first The floating-point ambiguity of the first satellite at each station; Indicates the first The first monitoring station The floating-point ambiguity of a satellite; This indicates the hardware latency of the first testing station; This indicates the hardware delay at the second testing station; Indicates the first Hardware latency at each testing station; This indicates the hardware delay of the first satellite; Indicates the first Hardware delays for each satellite.

[0062] To eliminate the rank deficiency problem in parameter solving, this invention applies zero-sum constraints or equivalent baseline constraints to the UPD parameters at the satellite end, so that the UPD parameters in the reference station network have a unique solution.

[0063] Subsequently, the receiver-side and satellite-side UPD parameters can be obtained using the weighted least squares method, the sequential Kalman filter method, or other equivalent estimation methods. To ensure the consistency of the network solution results, before formally estimating the UPD parameters, it is preferable to select a common reference satellite to perform integer alignment processing on the floating-point ambiguities of the corresponding combined domain for each reference station.

[0064] Step 4. Further map the estimated combined domain UPD parameters to the phase OSB products corresponding to each frequency, so that the phase OSB product corresponding to the third frequency contains the equivalent correction information after IFCB absorption.

[0065] After estimating the combined domain UPD parameters on the server side, the combined domain UPD parameters are further converted into frequency-defined phase OSB products, rather than directly providing the combined domain UPD parameters to the user side.

[0066] The reason for adopting this approach is that if the user continues to use the third-frequency random walk ambiguity model, it may weaken the overall constraint capability of the PPP-AR solution model and have an adverse effect on the convergence speed and ambiguity fixing efficiency. By completing the conversion from UPD to phase OSB on the server side, the results that have absorbed the influence of IFCB can be uniformly encapsulated into the frequency domain product.

[0067] The formula for converting UPD to phase OSB is as follows:

[0068] (6)

[0069] in, The phase OSB value is expressed in meters. .

[0070] Through the above transformation, the IFCB influence absorbed in the third-frequency related combination is further transferred to the corresponding third-frequency phase OSB product, which includes the equivalent correction information after IFCB absorption. Thus, this invention realizes a processing chain of "server-side IFCB absorption, combination domain estimation of UPD, and frequency domain output of phase OSB," eliminating the need for the user end to additionally receive and process independent IFCB products, and eliminating the need to track the time-varying influence of IFCB in the third frequency using a random walk model.

[0071] Step 5. At the user end, the phase OSB product is used to correct the carrier phase observations of the corresponding frequency, and a cascaded ambiguity fixing strategy is used to perform multi-frequency PPP-AR solution.

[0072] At the user end, after receiving the phase OSB product, it is directly applied to the carrier phase observation value of the corresponding frequency to eliminate the influence of satellite phase deviation on the integer nature of ambiguity. After correction, the time-varying effects originally caused by ICB in the third frequency observation have been equivalently corrected by the third frequency phase OSB product generated by the server. Therefore, the third frequency ambiguity parameters no longer need to be processed by the random walk model at the user end, but are estimated using a constant value model, just like the ambiguity parameters of the first and second frequencies.

[0073] Based on this, the user terminal performs multi-frequency PPP-AR solution according to the cascaded ambiguity fixing strategy. It is preferred to first fix the EWL combined ambiguity of the long wavelength, then fix the WL combined ambiguity under the constraint of the fixed EWL, and finally construct the NL ambiguity vector and perform integer search.

[0074] After correcting satellite phase bias using network-generated OSB phase products, EWL and WL ambiguities are still affected by receiver-side phase bias. Receiver-related biases are eliminated by constructing inter-satellite single differences to restore the integer characteristics of EWL and WL floating-point ambiguities. Then, the LAMBDA method is used to search for integer ambiguities. Once the EWL ambiguities are successfully fixed, they are introduced as virtual observations into the PPP function model to constrain the parameter estimation process. By introducing EWL constraints, the correlation between ambiguity parameters and other parameters to be estimated is reduced, thereby improving the accuracy of residual floating-point ambiguity estimation.

[0075] Using fixed EWL ambiguities as constraints on the virtual observation equation, the PPP function model is expressed as follows:

[0076] (7)

[0077] in express satellites and Inter-satellite single-difference EWL virtual observation residuals express satellites and Inter-satellite single-difference EWL integer ambiguity express satellites and UPD products for inter-satellite single-difference EWL. express satellites and Inter-satellite single-difference EWL floating-point ambiguity, This represents the inter-satellite single difference operator.

[0078] Introducing fixed EWL ambiguities as constraints into the PPP adjustment can reduce the correlation between ambiguity parameters and other parameters to be estimated. This improves the estimation accuracy of the remaining floating-point ambiguities, thus benefiting the subsequent solution of WL ambiguities. After fixing the WL ambiguities using the same strategy as with EWL ambiguities, the fixed WL ambiguities are also introduced into the adjustment as virtual observations; the corresponding WL ambiguity virtual observation equation is expressed as:

[0079] (8)

[0080] in express satellites and Inter-satellite single-difference WL virtual observation residuals express satellites and Inter-satellite single-difference WL integer ambiguity express satellites and UPD products for inter-satellite single-difference WL (Wide-Loop) satellites. express satellites and Inter-satellite single-difference WL floating-point ambiguity, This represents the inter-satellite single difference operator.

[0081] By introducing the aforementioned virtual observations into the PPP solution, the correlation between the ambiguity parameter and other parameters to be estimated is further weakened, which is beneficial to improving the estimation accuracy of unknown parameters, significantly reducing the search space, and improving the fixing efficiency of NL ambiguity.

[0082] After the WL ambiguity is fixed, the ambiguity combination relationship is used to construct the NL floating-point ambiguity. Similar to EWL and WL ambiguities, the same strategy is also used to solve the NL ambiguity. Once the NL ambiguity is successfully fixed, it is also introduced into the PPP adjustment as a virtual observation to further constrain the parameter estimation process.

[0083] The corresponding NL ambiguity virtual observation equation can be written in the following form:

[0084] (9)

[0085] in express satellites and Inter-satellite single-difference NL virtual observation residuals express satellites and Inter-satellite single-difference integer ambiguity, express satellites and UPD products for inter-satellite single-difference NL. express satellites and Inter-satellite single-difference NL floating-point ambiguity, This represents the inter-satellite single difference operator.

[0086] By introducing fixed NL ambiguities into PPP adjustment, integer constraints are fully applied to the carrier phase ambiguities; thus, the PPP-AR ambiguity-fixed solution is obtained, i.e., the PPP-AR solution. Compared with the floating-point PPP solution, the PPP-AR solution can significantly improve positioning accuracy and significantly accelerate the convergence speed of the PPP solution, especially in the initial convergence phase. Through the above user-end application method, the user end does not need to process IFCB-related parameters separately, thereby achieving transparency of IFCB to the user-end processing flow. That is, the user end only needs to execute the phase OSB correction and ambiguity fixing process consistent with conventional multi-frequency PPP-AR, without adding additional processing logic such as independent IFCB estimation, interpolation, broadcast matching, or consistency judgment.

[0087] The above steps restrict IFCB processing to the server-side product generation stage. The user end does not need to be aware of the IFCB's existence, nor does it require an independent IFCB estimation, matching, or correction process, thus simplifying the implementation chain of multi-frequency PPP-AR on the user end. Compared to existing multi-frequency PPP-AR technologies, this invention has improvements in at least the following four aspects:

[0088] 1. This invention proposes an IFCB absorptive server-side generation framework for real-time multi-frequency PPP-AR. Instead of explicitly estimating, modeling, and broadcasting IFCBs as independent error sources, this invention processes them within the server-side phase offset generation link. Through third-frequency absorptive parameterization design, the time-varying IFCBs are digested within the reference station network. The system can output phase offset products directly usable in PPP-AR without additional IFCB products, thereby reducing dependence on independent IFCB product links and lowering the complexity of product generation and maintenance in multi-frequency PPP-AR service systems.

[0089] 2. This invention constructs a differentiated modeling framework for third-frequency IFCB absorption processing. Unlike existing technologies that directly weaken the strength of the third-frequency ambiguity model at the user end, this invention uniformly represents the time-varying impact of IFCB through third-frequency absorption ambiguity states at the server end, while the first and second frequency ambiguities continue to be modeled with constant values. At the user end, since the third-frequency phase OSB already includes the equivalent IFCB correction, the third-frequency ambiguity is still estimated using a constant value model. Through the above-mentioned server-side absorption and user-side constant value processing method, IFCB tracking capability, solution stability, and ambiguity fixation efficiency are balanced.

[0090] 3. This invention establishes a unified transmission mechanism from the combined domain UPD to the frequency domain phase OSB. This invention establishes a mapping relationship from the combined domain UPD parameters of EWL, WL, and NL to each frequency phase OSB product, enabling the combined domain information, which absorbs the influence of IFCB, to be stably transmitted to the third-frequency phase OSB product, thus realizing the unified transmission of server-side processing results to user-side application results.

[0091] 4. This invention achieves a transparent and scalable engineering deployment method for IFCB to the user end. The IFCB absorptive phase offset generation method proposed in this invention does not depend on a specific constellation or frequency configuration. It can be adapted to multi-constellation scenarios such as GPS, Galileo, and BeiDou, and can also be extended to more frequency combinations. At the same time, since the user end only needs to apply OSB and perform conventional multi-frequency PPP-AR calculation, no additional IFCB processing logic needs to be developed. Therefore, it is suitable for multi-constellation and multi-frequency PPP-AR application scenarios in real-time service systems, terminal software, and product broadcasting platforms.

[0092] Example 2

[0093] This embodiment 2 describes a phase deviation generation and application system for multi-frequency PPP-AR, which is based on the same inventive concept as the phase deviation generation and application method for multi-frequency PPP-AR in embodiment 1.

[0094] The phase deviation generation and application system for multi-frequency PPP-AR in this embodiment includes the following modules:

[0095] The multi-frequency PPP-AR basic model construction module is used to build a multi-frequency non-differential non-combined PPP-AR basic model on the server side, and to jointly process the multi-frequency pseudorange observations and carrier phase observations of the reference station to estimate the clock error, tropospheric delay, ionospheric delay and floating-point ambiguity parameters related to the positioning solution.

[0096] The third-frequency IFCB absorptive parameterization module is used to perform absorptive parameterization on the server side for the time-varying IFCB components contained in the third-frequency carrier phase observation. Instead of estimating the IFCB as an independent state parameter, it incorporates it into the floating-point ambiguity state corresponding to the third frequency for unified modeling and estimates the multi-frequency floating-point ambiguity parameters.

[0097] The module for constructing multi-frequency ambiguity combination and estimating multi-frequency UPD parameters is used to construct additional wide-lane EWL, wide-lane WL and narrow-lane NL combinations on the server based on the multi-frequency floating-point ambiguity parameters after IFCB absorption, and to estimate the uncalibrated phase delay UPD parameters in the combination domain.

[0098] The UPD to phase OSB conversion module is used to further map the estimated combined domain UPD parameters to phase OSB products corresponding to each frequency, so that the phase OSB product corresponding to the third frequency contains the equivalent correction information after IFCB absorption.

[0099] It also includes a multi-frequency PPP-AR solution module, which is used at the user end to correct the carrier phase observations of the corresponding frequency using the phase OSB product, and to perform multi-frequency PPP-AR solution using a cascaded ambiguity fixing strategy.

[0100] It should be noted that any content not mentioned in the above-described functional modules of the system described in Embodiment 2 can be referred to the step description of the corresponding method in Embodiment 1 above, and will not be repeated in detail here.

[0101] Example 3

[0102] This embodiment 3 describes a computer device including a memory and one or more processors. Executable code is stored in the memory. When the processor executes the executable code, it implements the steps of the phase offset generation and application method for multi-frequency PPP-AR described in embodiment 1 above.

[0103] In this embodiment, the computer device can be any device or apparatus with data processing capabilities, and will not be described in detail here.

[0104] Example 4

[0105] This embodiment 4 describes a computer-readable storage medium storing a program that, when executed by a processor, is used to implement the steps of the phase deviation generation and application method for multi-frequency PPP-AR in embodiment 1 above.

[0106] The computer-readable storage medium can be an internal storage unit of any device or apparatus with data processing capabilities, such as a hard disk or memory, or an external storage device of any device with data processing capabilities, such as a plug-in hard disk, smart media card (SMC), SD card, flash card, etc.

[0107] Of course, the above description is only a preferred embodiment of the present invention. The present invention is not limited to the above-described embodiments. It should be noted that any equivalent substitutions or obvious modifications made by those skilled in the art under the guidance of this specification fall within the scope of this specification and should be protected by the present invention.

Claims

1. A method for generating and applying phase offset in multi-frequency PPP-AR, characterized in that, Includes the following steps: Step 1. On the server side, establish a multi-frequency non-differential non-combined PPP-AR basic model, and jointly process the multi-frequency pseudorange observations and carrier phase observations of the reference station to estimate the position coordinates, receiver clock error, tropospheric delay, ionospheric delay, and floating-point ambiguity parameters. Step 2. For the time-varying IFCB component contained in the third frequency carrier phase observation, an absorption parameterization processing strategy is adopted on the server side. Instead of estimating the IFCB as an independent state parameter, it is incorporated into the floating-point ambiguity state corresponding to the third frequency for unified estimation. Step 3. On the server side, based on the multi-frequency floating-point ambiguity parameters after IFCB absorption, construct an additional wide-lane EWL, wide-lane WL, and narrow-lane NL combination, and estimate the uncalibrated phase delay UPD parameter in the combination domain; Step 4. Further convert the estimated combined domain UPD parameters into phase OSB products corresponding to each frequency, wherein the phase OSB product corresponding to the third frequency includes the equivalent correction information after absorbing IFCB; Step 5. At the user end, the carrier phase observations of the corresponding frequency are corrected using the phase OSB product, and a cascaded ambiguity fixing strategy is used to perform multi-frequency PPP-AR solution; In step 1, the construction process of the multi-frequency non-difference non-combination PPP-AR basic model is as follows: For any reference station, any satellite, and any frequency observation, a corresponding observation equation can be established to characterize the relationship between geometric distance, receiver clock error, satellite clock error, tropospheric delay, ionospheric delay, ambiguity parameters, and hardware deviations at the receiver and satellite ends. The formula is as follows: (1) superscript and subscript and These represent a specific satellite, receiver, and frequency. These are pseudorange observations. These are carrier phase observations; For satellite to receiver geometric distance; and These are receiver clock bias and satellite clock bias, respectively. For oblique path tropospheric delay; The slant path ionospheric delay is the first frequency point; The frequency correlation coefficient, Indicates the first frequency of the satellite. Indicates the satellite's second frequency; Wavelength; For integer phase ambiguity; and These are the code hardware delay deviations at the receiver and satellite ends, respectively. and These are the phase hardware delay deviations at the receiver and satellite ends, respectively. and These are code and phase observation noises, respectively, which include multipath effects; In step 2, the process of the absorption parameterization strategy is as follows: After incorporating satellite orbit and clock bias products, as well as model correction terms, equation (1) is reparameterized into the following form: (2) in, and These are the observed values ​​minus the calculated values ​​for pseudorange and phase observations, respectively. Represents the line-of-sight vector. This represents the position increment relative to prior coordinates; The mapping function represents the wetted zenith delay in the troposphere, and is: ; The remaining reparameterized parameters are as follows: (3) in, To incorporate receiver clock bias after incorporating receiver endcode hardware delay deviation; To incorporate the slant path ionospheric delay after merging the hardware delay bias between the receiver and satellite endcodes; floating-point ambiguity. As a combination of code and phase hardware delay biases, it loses its integer property; This represents the correlation coefficient of the second frequency of the ionosphere. This indicates the hardware delay deviation of the first frequency code at the receiver end. This indicates the hardware delay deviation of the second frequency code at the receiver end. This indicates the hardware delay deviation of the first frequency code at the satellite end. This indicates the hardware delay deviation of the second frequency code at the satellite end. This indicates the time-varying portion of the multi-frequency phase delay bias at the satellite end. This indicates the frequency deviation introduced by multiple frequencies. This represents the inter-frequency clock bias (IFCB) introduced by multiple frequencies.

2. The method for generating and applying phase deviation for multi-frequency PPP-AR according to claim 1, characterized in that, In step 3, additional wide-lane EWL, wide-lane WL, and narrow-lane NL ambiguity combinations are constructed based on the multi-frequency floating-point ambiguity parameters estimated by the reference station network to achieve step-by-step processing from long-wavelength combinations to short-wavelength combinations. The combination form is as follows: (4) in Indicates the floating-point ambiguity of EWL. Indicates the floating-point ambiguity of WL. Indicates the floating-point ambiguity of NL. The floating-point ambiguity representing the first frequency of the satellite. The floating-point ambiguity representing the second frequency of the satellite. The floating-point ambiguity representing the third frequency of the satellite, This represents the integer ambiguity of EWL. This represents the integer ambiguity of WL. Indicates the integer ambiguity of NL. This indicates the receiver-side hardware delay of EWL. This indicates the receiver-side hardware delay of WL. This indicates the receiver-side hardware delay of NL. This indicates the satellite-side hardware latency of EWL. This indicates the satellite-side hardware latency of WL. For NL satellite-side hardware latency, For the phase hardware delay at the third frequency end of the satellite, The first frequency wavelength of the satellite, The second frequency wavelength of the satellite, This is the third frequency wavelength for the satellite.

3. The method for generating and applying phase deviation for multi-frequency PPP-AR according to claim 2, characterized in that, In step 3, the process of estimating the server-side UPD parameters is as follows: In a reference station network consisting of multiple reference stations and multiple satellites, a pseudo-observation equation for UPD estimation is constructed based on the fractional-week part of the floating-point ambiguity parameters in the combined domain, and the receiver-side UPD parameters and satellite-side UPD parameters are jointly estimated. UPD estimation is performed independently in the EWL, WL, and NL combined domains, or sequentially in the order from EWL to WL and then to NL. Observed by each station For each satellite, establish the corresponding network UPD pseudo-observation equation: (5) in, This represents the floor operator; Indicates the first The number of satellites observed by each station; This represents the total number of false observations. This represents the total number of UPD parameters for both the receiver and the satellite. It refers to EWL, WL, or NL; This represents the floating-point ambiguity of the first satellite at the first station. Indicates the first station The floating-point ambiguity of a satellite; This indicates the floating-point ambiguity of the first satellite at the second station; Indicates the second station The floating-point ambiguity of a satellite; Indicates the first The floating-point ambiguity of the first satellite at each station; Indicates the first The first monitoring station The floating-point ambiguity of a satellite; This indicates the hardware latency of the first testing station; This indicates the hardware delay at the second testing station; Indicates the first Hardware latency at each testing station; This indicates the hardware delay of the first satellite; Indicates the first Hardware delays for individual satellites; To eliminate the rank deficiency problem in parameter solving, a reference constraint condition is applied to the UPD parameters at the satellite end, so that the UPD parameters in the reference station network have a unique solution; then the UPD parameters at the receiver end and the satellite end are obtained.

4. The method for generating and applying phase deviation for multi-frequency PPP-AR according to claim 3, characterized in that, In step 4, the formula for converting UPD to phase OSB is as follows: (6) in, The phase OSB value is expressed in meters. ; Through the above conversion, the IFCB effect absorbed in the third frequency related combination is transferred to the phase OSB product corresponding to the third frequency, and the third frequency phase OSB product includes the equivalent correction information after IFCB absorption.

5. The method for generating and applying phase deviation for multi-frequency PPP-AR according to claim 4, characterized in that, Step 5 specifically involves: At the user end, after receiving the phase OSB product, it is directly applied to the carrier phase observation value of the corresponding frequency to eliminate the influence of the satellite end phase deviation on the integer nature of the ambiguity. After correction, the time-varying influence originally caused by IFCB in the third frequency observation has been equivalently corrected by the third frequency phase OSB product generated by the server. Therefore, the third frequency ambiguity parameters do not need to be processed by the random walk model at the user end, but are estimated by the constant value model in the same way as the ambiguity parameters of the first and second frequencies. Based on this, the user terminal performs multi-frequency PPP-AR solution according to the cascaded ambiguity fixing strategy. First, the EWL combined ambiguity of the long wavelength is fixed, then the WL combined ambiguity is fixed under the constraint of the fixed EWL, and finally the NL ambiguity vector is constructed and integer search is performed. After correcting satellite phase deviation using the phase OSB product generated by the network, EWL and WL ambiguities are still affected by receiver-side phase deviation. Receiver-related deviations are eliminated by constructing inter-satellite single differences to restore the integer characteristics of EWL and WL floating-point ambiguities. Then, the LAMBDA method is used to search for integer ambiguities. Once the EWL ambiguity is successfully fixed, it is introduced as a virtual observation into the PPP function model to constrain the parameter estimation process; by introducing EWL constraints, the correlation between the ambiguity parameter and other parameters to be estimated is reduced. Using fixed EWL ambiguities as constraints on the virtual observation equation, the PPP function model is expressed as follows: (7) in express satellites and Inter-satellite single-difference EWL virtual observation residuals express satellites and Inter-satellite single-difference EWL integer ambiguity express satellites and UPD products for inter-satellite single-difference EWL. express satellites and Inter-satellite single-difference EWL floating-point ambiguity, This represents the inter-satellite single difference operator; Since the EWL ambiguities have been fixed as integer values, they are considered known quantities. After introducing the EWL constraints into the PPP adjustment and updating the parameters to be estimated again, the WL ambiguities are fixed using the same strategy as for the EWL ambiguities. These fixed WL ambiguities are then introduced into the adjustment as virtual observations. The corresponding virtual observation equation is expressed as: (8) in express satellites and Inter-satellite single-difference WL virtual observation residuals express satellites and Inter-satellite single-difference WL integer ambiguity express satellites and UPD products for inter-satellite single-difference WL (Wide-Loop) satellites. express satellites and Inter-satellite single-difference WL floating-point ambiguity, This represents the inter-satellite single difference operator; After fixing the WL ambiguity, the ambiguity combination relationship is used to construct the NL floating-point ambiguity; the solution of the NL ambiguity adopts the same strategy as EWL and WL ambiguities; once the NL ambiguity is successfully fixed, it is introduced as a virtual observation into the PPP adjustment to further constrain the parameter estimation process; the corresponding NL ambiguity virtual observation equation is written in the following form: (9) in express satellites and Inter-satellite single-difference NL virtual observation residuals express satellites and Inter-satellite single-difference integer ambiguity, express satellites and UPD products for inter-satellite single-difference NL. express satellites and Inter-satellite single-difference NL floating-point ambiguity, This represents the inter-satellite single difference operator; By introducing fixed NL ambiguities into PPP adjustment, integer constraints are fully applied to the carrier phase ambiguities; thus, the ambiguity-fixed solution of PPP-AR is obtained, i.e., the PPP-AR solution.

6. A phase offset generation and application system for multi-frequency PPP-AR, used to implement the phase offset generation and application method for multi-frequency PPP-AR as described in claim 1, characterized in that, The phase deviation generation and application system for multi-frequency PPP-AR includes the following modules: The multi-frequency PPP-AR basic model construction module is used on the server side to establish a multi-frequency non-differential non-combined PPP-AR basic model. It performs joint processing on the multi-frequency pseudorange observations and carrier phase observations of the reference station to estimate the position coordinates, receiver clock error, tropospheric delay, ionospheric delay, and floating-point ambiguity parameters. The third-frequency IFCB absorptive parameterization module is used to perform absorptive parameterization on the server side for the time-varying IFCB component contained in the third-frequency carrier phase observation. Instead of estimating the IFCB as an independent state parameter, it incorporates it into the floating-point ambiguity state corresponding to the third frequency for unified modeling, and estimates the multi-frequency floating-point ambiguity parameters that absorb the IFCB. The module for constructing multi-frequency ambiguity combination and estimating multi-frequency UPD parameters is used to construct additional wide-lane EWL, wide-lane WL and narrow-lane NL combinations on the server based on the multi-frequency floating-point ambiguity parameters after IFCB absorption, and to estimate the uncalibrated phase delay UPD parameters in the combination domain. The UPD to phase OSB conversion module is used to further convert the estimated combined domain UPD parameters into phase OSB products corresponding to each frequency, so that the phase OSB product corresponding to the third frequency includes the equivalent correction information after absorbing the IFCB. The module also includes a multi-frequency PPP-AR solution module. At the user end, the phase OSB product is used to correct the carrier phase observations of the corresponding frequency, and a cascaded ambiguity fixing strategy is adopted for multi-frequency PPP-AR solution.

7. A computer device comprising a memory and one or more processors; executable code is stored in the memory; characterized in that, When the processor executes the executable code, it implements the steps of the phase deviation generation and application method for multi-frequency PPP-AR as described in claim 1.

8. A computer-readable storage medium having a program stored thereon, characterized in that, When executed by the processor, the program is used to implement the steps of the phase deviation generation and application method for multi-frequency PPP-AR as described in claim 1.

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