PPP-RTK-based satellite-ground enhanced signal conversion method and model

Abstract

The invention discloses a satellite-ground enhanced signal conversion method and a satellite-ground enhanced signal conversion model based on PPP-RTK, which are used for solving the problems that conventional RTK in a ground-free network area fails, and satellite-based PPP-RTK depends on a special terminal and is slow in convergence. Generating an RTCM-OSR correction number by analyzing the SSR correction number, fusing the orbital clock correction, ionosphere and troposphere correction and performing weighted interpolation; the ambiguity is solved in combination with the observation value of the base station, dual-path verification and weighted fusion are carried out in combination with M-W and LAMBDA algorithms, and the wide lane ambiguity fixing reliability is improved; and when the ground-based signal is interrupted for more than 1 second, inheriting and maintaining Kalman filtering continuity by using the state vector and generating a switching identifier. The model integrates a signal processing module, a correction generation module, an ambiguity verification module and a switching control module, so that a conventional RTK terminal obtains centimeter-level positioning (level 2.3 cm and elevation 4.1 cm) without a public network, the interruption recovery is less than or equal to 1.5 seconds, and the scene upgrading cost of power inspection, power transmission monitoring, wind power plant monitoring and the like is reduced.

Description

Technical Field

[0001] This invention relates to the field of satellite navigation and positioning technology, and is particularly applicable to areas without terrestrial network coverage. Specifically, it relates to a method and data processing model for converting satellite-based PPP-RTK augmentation signals into ground-based RTK signals, which can be used in scenarios such as power equipment inspection and transmission line monitoring. Background Technology

[0002] In western regions without public power grids, such as the barren desert areas of Gansu, conventional RTK technology faces a failure dilemma due to the inability to deploy dense base stations, requiring a base station spacing of less than 50km. While satellite-based PPP-RTK technology can achieve wide-area coverage, it relies on dedicated terminals, making it incompatible with existing RTK equipment in the power industry. Furthermore, after satellite signals are interrupted due to blockage, the PPP reconvergence time exceeds 30 minutes, failing to meet the urgent real-time requirements of power grid inspections and severely restricting the operation and maintenance efficiency and security of the power grid in special geographical environments.

[0003] The shortcomings of current solutions include: the "space-ground integrated" service provided by companies such as Qianxun Location requires customized terminals, resulting in high transformation costs for the power industry; traditional seamless switching algorithms rely heavily on continuous ground-based signals and fail completely in areas without network coverage; existing RTCM protocol conversion technology only supports ground-based data conversion and fails to solve the core problem of accessing existing RTK terminals with satellite-based signals, thus failing to overcome the technical bottleneck in scenarios without public network access.

[0004] To address the aforementioned challenges, this invention proposes a method and model for converting satellite-based PPP-RTK signals to ground-based RTK signals. This method and model can be deployed in a data processing unit to analyze SSR corrections broadcast by satellites in real time. Combined with an atmospheric delay forecast model and an LSM spatial interpolation algorithm, it generates a standard RTCM protocol output, enabling conventional RTK terminals to directly obtain centimeter-level positioning capabilities even without a public network. Summary of the Invention

[0005] (a) Purpose of the invention

[0006] The purpose of this invention is to address the long-standing technological fragmentation in the field of high-precision positioning using Global Navigation Satellite Systems (GNSS). While traditional ground-based augmentation systems (RTK) can provide centimeter-level real-time positioning accuracy, their core bottleneck lies in their reliance on a densely distributed network of ground reference stations, typically spaced ≤50 kilometers apart, and a stable public mobile communication network. This architecture makes it impossible to establish effective coverage in vast, remote areas such as deserts, Gobi, oceans, and uninhabited plateaus, resulting in a long-standing lack of high-precision positioning support for critical operations requiring work in areas without infrastructure, such as power line inspection and geological disaster monitoring. Meanwhile, while satellite-based precise point positioning technologies (such as PPP) with global coverage can overcome the limitations of ground base stations, their convergence times, generally exceeding 30 minutes, make them unsuitable for the stringent real-time requirements of scenarios such as disaster emergency response and automated engineering machinery.

[0007] To overcome the aforementioned limitations, this invention proposes a satellite-to-ground augmentation signal conversion method and model based on PPP-RTK. Its primary objective is to achieve centimeter-level real-time positioning capabilities globally, by integrating the wide-area coverage of satellite-based augmentation data with the low-latency characteristics of ground-based augmentation information, without the need for constructing new dense reference stations. The core challenge lies in the incompatibility between the State Domain Correction (SSR) data broadcast by PPP-RTK and the RTCM protocol, which only supports Observation Domain Correction (OSR) data in traditional RTK terminals. Existing RTK devices can only parse OSR data in the RTCM protocol format and cannot directly utilize satellite-broadcast SSR correction information. The method and model provided by this invention construct an efficient signal conversion mechanism to convert SSR correction information into OSR signals conforming to the RTCM protocol specification in real time. This allows traditional RTK terminals to directly utilize satellite-based augmentation services without any hardware modifications, significantly reducing the technical upgrade costs for industry users in acquiring high-precision real-time positioning capabilities.

[0008] (II) Technical Solution

[0009] To achieve the above objectives, the technical solution adopted by the present invention is as follows:

[0010] A satellite-to-ground signal enhancement conversion method based on PPP-RTK, the method comprising:

[0011] Step 1: Real-time parsing of SSR format precise positioning corrections via satellite communication link, while simultaneously acquiring OSR format regional differential data streams via terrestrial network.

[0012] Step 2: Establish a two-dimensional evaluation system. When the overall score of the satellite-based signal is better than that of the ground-based signal, activate the satellite-based conversion mode.

[0013] Step 3: Based on the original observations of the base station, integrate the orbital clock error correction, ionospheric model delay correction, and tropospheric zenith delay correction, and generate pseudorange correction values ​​conforming to the OSR format through a spatiotemporal weighted interpolation algorithm; combine the phase observations of the base station, introduce precise orbital deviation compensation and ionospheric delay model correction, and generate phase correction values ​​after solving the phase ambiguity through the inverse distance weighted method.

[0014] Step 4: The Melbourne-Wübbena combined observation algorithm and the LAMBDA search algorithm are used for dual-path verification. When the difference between the solutions of the two methods is less than half a cycle, a weighted fusion strategy is used to determine the optimal wide-lane ambiguity. Based on the fixed wide-lane ambiguity results, a set of constraint equations is constructed and the correlation reduction solution is achieved through the matrix orthogonal decomposition algorithm.

[0015] Step 5: When the base signal is interrupted and the switching mechanism is triggered, the satellite-based data pre-buffering mechanism is started and the state vector inheritance algorithm is used to maintain the continuity of Kalman filtering. At the same time, the working mode switching identifier is broadcast through a dedicated communication message.

[0016] In addition, this invention provides a satellite-to-ground signal enhancement conversion model based on PPP-RTK. The model is deployed in a data processing unit to achieve real-time conversion from satellite-based to ground-based signals. The model includes:

[0017] The signal input module is responsible for receiving SSR format state domain correction data from satellite broadcast and OSR format observation domain correction data streams from ground-based CORS networks;

[0018] The signal quality assessment module evaluates the quality indicators of satellite-based and ground-based signal sources in real time and decides whether to activate the satellite-based conversion mode based on preset thresholds.

[0019] The pseudorange correction generation module integrates multiple correction information to generate pseudorange correction values ​​that conform to the OSR standard.

[0020] The phase correction generation module combines phase observations to resolve ambiguity and generate phase correction values.

[0021] The ambiguity processing and verification module executes a dual-path verification and fusion strategy to ensure the reliability of wide-lane ambiguity fixation.

[0022] The seamless switching control module implements a pre-buffering and state inheritance mechanism to achieve smooth and fast switching when the ground signal is interrupted, and generates a switching identifier;

[0023] The signal output module outputs the generated RTCM format OSR corrected data stream and mode switching identifier for use by conventional RTK terminals.

[0024] (III) Beneficial Effects

[0025] This invention significantly improves the practical performance of high-precision positioning systems through a satellite-ground fusion mechanism. In terms of coverage, the method based on this model breaks through the dependence of traditional positioning technologies on dense reference station networks, achieving stable centimeter-level positioning output in large-scale uninhabited areas. Especially in open areas where the distance between reference stations is significantly greater than the conventional deployment standard, positioning accuracy in both horizontal and vertical directions remains superior to industry standards. Convergence efficiency has also achieved a breakthrough optimization. Static initialization time is reduced by orders of magnitude compared to traditional schemes, and the dynamic reacquisition process is shortened to a level far below the tolerance for interruptions in conventional operations. This efficiency improvement mainly stems from the prior information fusion mechanism and adaptive ambiguity fixing strategy adopted by the model, which greatly reduces the interference of environmental factors on the initialization process. Simultaneously, the protocol conversion process defined by this model has broad compatibility. The protocol data output by the model supports mainstream commercial terminal devices, and the conversion latency strictly meets the core indicator requirements of real-time dynamic positioning scenarios. Attached image description:

[0026] Figure 1 This is a flowchart of the model architecture of the present invention.

[0027] Figure 2 This is a flowchart of the signal conversion method of the present invention.

[0028] Figure 3 This is a schematic diagram of atmospheric error modeling of the present invention. Detailed implementation method:

[0029] The specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. The core of the present invention lies in providing a satellite-to-ground signal enhancement conversion method and model based on PPP-RTK. This model achieves real-time conversion from satellite-based signals to ground-based signals through a series of collaborative functional modules. The specific implementation process is as follows:

[0030] The model runs on a data processing unit. In a typical implementation scenario, this data processing unit is deployed in a field without a public network. The model performs the following functions through the data processing unit's interface: receiving SSR signals broadcast by satellite and receiving OSR data broadcast by ground-based CORS stations. The RTCM format OSR signal generated by the model is output through the data processing unit's output interface for reception by conventional RTK terminals.

[0031] The signal conversion method performs the following steps in sequence:

[0032] Step 1 is executed by the model's signal input module. It receives wide-area SSR format precise positioning correction data via satellite communication link, including orbital deviation correction, clock deviation correction, wide lane ambiguity deviation, and narrow lane ambiguity deviation. At the same time, it receives OSR format regional differential correction data broadcast by ground-based CORS stations via the ground network.

[0033] Step 2, executed by the model's signal quality assessment module, establishes a two-dimensional signal quality assessment system. Ground-based signal quality factors include comprehensive signal-to-noise ratio, transmission delay, and data integrity indicators; satellite-based signal quality factors include comprehensive signal availability and correction update timeliness indicators. When the assessment results indicate that the ground-based signal quality is better than a specific threshold for the satellite-based signal, the ground-based mode is used; otherwise, the satellite-based data conversion mode is activated.

[0034] Step 3: The pseudorange correction generation module and the phase correction generation module of the model work together to execute the pseudorange correction value. The pseudorange correction value needs to take into account three factors: orbital deviation correction, ionospheric delay compensation, and tropospheric delay correction. The conversion from the state domain to the observation domain is achieved through spatial weighting, specifically expressed as follows:

[0035] ΔP=α·ΔO orbit +β·I model +γ·T ZTD (1)

[0036] In the formula ΔO orbit It is the track deviation correction amount, I model It is the ionospheric delay correction, T ZTD It is the tropospheric zenith delay correction, where α, β, and γ are dynamic weighting coefficients.

[0037] To improve the spatiotemporal response capability of ionospheric delay modeling, a time derivative compensation term is introduced into the basic spatial surface model. The spatial surface parameters are solved using least-squares fitting of the reference station network, and the time derivative term is achieved through inter-epoch differencing. The specific representation of the ionospheric correction model is as follows:

[0038]

[0039] STEC=a0+a1dL+a2dB+a3dLdB+a4dL 2 +a5d 2 B (3)

[0040] In the formula, k1 is the static ionospheric weight, k2 is the dynamic ionospheric weight, a0 to a5 are the ionospheric model parameters, and dL and dB are the differences in latitude and longitude from the satellite puncture point to the center point, respectively. The center point is generally selected as the geometric center of all reference stations.

[0041] Accurate correction of tropospheric zenith delay is achieved using an elevation compensation model, specifically expressed as follows:

[0042] T ZTD =ZTD·mf (4)

[0043] ZTD=a0+a1dL+a2dB+a3dH (5)

[0044] In the formula, mf is the tropospheric mapping function.

[0045] After completing the ionosphere / troposphere modeling, the spatial correction information needs to be mapped to the phase observation domain. The specific expression for generating the phase correction value is as follows:

[0046]

[0047] In the formula φ i The original phase observation value of the i-th reference station is n, and the number of reference stations involved in the solution is w. i =||rr i || -p r is the spatial coordinate of the virtual reference station. i These are the spatial coordinates of the i-th reference station.

[0048] Step 4, executed by the model's ambiguity processing and verification module, employs two independent algorithms simultaneously for verification to ensure the reliability of ambiguity fixation: the Melbourne-Wübbena combined method to eliminate geometric correlation errors, and the LAMBDA search method to optimize integer constraint solutions. When the difference between the two solutions is within a threshold range, a weighted fusion strategy is implemented. For the Melbourne-Wübbena combined method to eliminate geometric correlation errors, a wide-lane ambiguity estimate is constructed based on dual-frequency pseudorange and phase observations.

[0049]

[0050] In the formula, λ1 and λ2 are carrier wavelengths, φ1 and φ2 are carrier phase observations, P1 and P2 are pseudorange observations, and λ w The wavelength is the wide-lane wavelength.

[0051] To optimize integer constraint solving using the LAMBDA search method, a fuzzy search optimization problem is constructed.

[0052]

[0053] In the formula Let Z be the floating-point ambiguity vector, and Z be the decorrelation transformation matrix. Let z be the ambiguity variance-covariance matrix, and z be the integer ambiguity candidate vector.

[0054] When the difference in the two-path solution meets the reliability condition

[0055] |ΔN|=|N MW -N LAMBDA | < 0.5 weeks (9)

[0056] Perform fuzzy fusion

[0057] N final =α·NMW +β·N LAMBDA (10)

[0058] A set of constraint equations is constructed based on the fixed width ambiguity.

[0059]

[0060] In the formula, A is the wide-lane ambiguity constraint coefficient matrix, B is the original double-difference observation equation, and I a To fix the ambiguity value of the wide alley, I b This represents the carrier phase residual vector. QR matrix decomposition is used to eliminate parameter correlation, significantly improving fixing efficiency and success rate.

[0061] Step 5: Executed collaboratively by the model's seamless switching control module and signal output module, the model immediately triggers the seamless switching process when the base signal interruption lasts for more than 1 second. First, the satellite-based data pre-buffering mechanism is activated, using cached results from historical SSR data to extrapolate orbital parameters, maintaining the continuity of positioning calculations. Then, the state vector inheritance algorithm is executed, fully inheriting the 15-dimensional state parameter vector x from the Kalman filter. k =[X,Y,Z,dt,N1,N2,...,N 12 ] T [X,Y,Z] represents the three-dimensional position in the ECEF coordinate system, dt is the receiver clock error, and N1~N 12 These are fixed unambiguity parameters. Finally, the model broadcasts a mode switching identifier to the terminal via a dedicated communication message.

[0062] A 72-hour continuous test conducted at a wind farm in the desert region of Gansu Province demonstrated that the signal processing unit running the model described in this invention successfully converted the satellite-based PPP-RTK signal into an OSR signal in RTCM format in real time and connected it to a conventional RTK terminal. Test data shows that the method and model proposed in this invention can achieve a stable horizontal positioning accuracy of ≤2.3cm (RMS) and an elevation direction accuracy of ≤4.1cm (RMS). When the satellite signal is interrupted, it switches to satellite-based mode and restores positioning within ≤1.5 seconds. Satellite-based single-mode can continuously provide centimeter-level positioning for up to 31 minutes. The ambiguity fixation success rate reaches 98.3% under a multi-system 12-satellite configuration. The test environment temperature covers extreme conditions from -25℃ to +55℃, fully meeting the stringent requirements of power line inspection.

[0063] The above embodiments are preferred embodiments of the present invention, but the scope of patent protection is not limited thereto. Any equivalent structural transformations made based on the inventive concept and the description and drawings, or direct or indirect applications in other related fields such as power line inspection, geological disaster monitoring, and marine engineering, are all within the scope of patent protection of the present invention.

Claims

1. A satellite-to-ground signal enhancement conversion method based on PPP-RTK, characterized in that, Includes the following steps: Step 1: Receive and parse the precise positioning correction data in SSR format in real time through the satellite communication link, and at the same time obtain the regional differential data stream in OSR format through the terrestrial network; Step 2: Establish a two-dimensional signal quality assessment system. Based on the scoring results of the ground-based signal-to-noise ratio, transmission delay, data integrity, satellite-based signal availability, and correction update timeliness, activate the satellite-based conversion mode. Step 3: Based on the original observations of the base station, the OSR format pseudorange correction values ​​are generated by integrating orbital clock error correction, ionospheric model delay correction and tropospheric zenith delay correction through a spatiotemporal weighted interpolation algorithm. Step 4: Combining the phase observations from the base station, precise orbital deviation compensation and ionospheric delay model correction are introduced. After solving the phase ambiguity using the inverse distance weighting method, the phase correction value is generated. Step 5: Combine the Melbourne-Wübbena combined observation algorithm with the LAMBDA search algorithm to perform dual-path ambiguity verification. When the difference between the two solutions is less than 0.5 weeks, a weighted fusion strategy is adopted to determine the optimal wide-lane ambiguity. Step 6: When the ground-based signal is interrupted for more than 1 second, the satellite-based data pre-buffering mechanism is activated and the state vector inheritance algorithm is used to maintain the continuity of Kalman filtering and synchronize the broadcast mode switching indicator.

2. The method according to claim 1, characterized in that, The formula for generating the pseudorange correction value is as follows: ΔP=α·ΔO orbit +β·I model +γ·T ZTD (1) STEC=a0+a1dL+a2dB+a3dLdB+a4dL 2 +a5d 2 B (1.2) T ZTD = ZTD mf (1.3).

3. The method according to claim 2, characterized in that, The ionospheric delay correction model introduces time derivative term compensation and dynamically adjusts the weight coefficient k2 to enhance spatiotemporal response capability.

4. The method according to claim 1, characterized in that, The formula for generating the phase correction value is as follows: weight w i =||rr i || -p Where r is the spatial coordinate of the virtual reference station, r i These are the spatial coordinates of the i-th reference station.

5. The method according to claim 1, characterized in that, The state vector inheritance algorithm inherits the 15-dimensional parameter vector x of the Kalman filter. k =[X,Y,Z,dt,N1,N2,...,N 12 ] T [X,Y,Z] is the three-dimensional position in the ECEF coordinate system, dt is the receiver clock error, and N1~N 12 It is a fixed unambiguity parameter.

6. A satellite-to-ground signal enhancement conversion model based on PPP-RTK, characterized in that, The model includes: a signal input module for receiving SSR format precise positioning corrections from a satellite communication link and OSR format regional differential data streams from a terrestrial network in real time; a signal quality assessment module configured to establish a two-dimensional signal quality assessment system, outputting mode switching commands based on the scoring results of ground-based signal-to-noise ratio, transmission delay, data integrity, satellite-based signal availability, and correction update timeliness; a pseudorange correction generation module configured to generate OSR format pseudorange correction values ​​based on the original observations from the reference station, integrating orbital clock error correction, ionospheric model delay correction, and tropospheric zenith delay correction, using a spatiotemporal weighted interpolation algorithm; and a phase correction generation module configured to combine the phase observations from the reference station. The system incorporates precise orbital deviation compensation and ionospheric delay model correction. Phase ambiguity is resolved using an inverse distance weighted method to generate phase correction values. An ambiguity processing and verification module is configured to perform dual-path ambiguity verification using a combination of the Melbourne-Wübbena combined observation algorithm and the LAMBDA search algorithm. When the difference between the two solutions is less than 0.5 cycles, a weighted fusion strategy is used to determine the optimal wide-lane ambiguity. A seamless switching control module is configured to activate a satellite-based data pre-buffering mechanism and use a state vector inheritance algorithm to maintain Kalman filter continuity when the base-based signal interruption exceeds 1 second, simultaneously generating a mode switching identifier. A signal output module is used to output the generated RTCM format OSR correction values ​​and mode switching identifier.

7. The model according to claim 6, characterized in that, In the pseudorange correction generation module, the pseudorange correction value is generated using the formula described in claim 2; in the ionospheric delay correction module, the time derivative term compensation and dynamic weight adjustment mechanism described in claim 3 is introduced; in the phase correction generation module, the phase correction value is generated using the formula and weight allocation method described in claim 4; in the seamless switching control module, the state vector inheritance algorithm inherits the 15-dimensional parameter vector of the Kalman filter described in claim 5.

8. A computer-readable storage medium storing a computer program, characterized in that, When the program is executed by a processor, it implements the steps of the method as described in any one of claims 1-5, or runs the model as described in any one of claims 6-7.

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