Method and system for precise displacement monitoring of GNSS single receiver enhanced by sparse reference station network
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2026-05-15
- Publication Date
- 2026-08-11
AI Technical Summary
[0003]现有GNSS精密位移监测技术主要包括三类：一是实时动态差分（RTK）技术，其依赖参考站与监测站间的差分处理消除公共误差，但精度随站间距离增加而显著衰减，需布设密集且空间分布合理的参考站网，建设运维成本高昂；二是精密单点定位（PPP）技术，其通过精密星历和钟差产品改正状态空间误差，不直接依赖参考站，但存在收敛速度慢、实时性不足的问题；三是历元差分单点位移监测引擎（VADASE）技术，其通过对载波相位观测值历元差分估计速度并积分得位移，无需解算模糊度，模型简单灵活，可实现瞬时高精度监测，但传统VADASE采用广播星历改正卫星轨道及钟差，受限于广播星历精度，位移误差随时间不断累积，难以满足长时段精密监测需求
本发明充分利用卫星轨道的平滑性以及卫星钟速的稳定性，仅需稀疏参考站网即可估计高精度卫星钟速，显著降低了参考站网的建设密度和运维成本；通过稀疏参考站网估计卫星钟速，替代传统VADASE中的广播星历改正，有效避免了位移误差随时间累积的问题，保障了长时段精密位移监测的精度；无需依赖北斗PPP-B2b、Galileo高精度服务或IGS实时服务等外部通讯链路，仅需超快速星历及稀疏参考站网数据，适用区域更广，可靠性更高；采用历元差分策略，无需解算载波相位模糊度，模型简单，能够实现瞬时速度估计，经数值积分及漂移改正后获得高精度位移，适用于地震、滑坡等突发灾害的实时监测预警。
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Abstract
Description
Technical Field
[0001] This invention relates to the field of GNSS single receiver precision displacement monitoring technology, and in particular to a GNSS single receiver precision displacement monitoring method and system enhanced by sparse reference station networks. Background Technology
[0002] Precision displacement is a direct reflection of the deformation and failure process and is a core parameter for earthquake early warning, geological disaster monitoring, and safety assessment of major projects. Global Navigation Satellite Systems (GNSS), with their advantages of high precision, all-weather operation, and continuous three-dimensional positioning, have been widely applied in the field of precision displacement monitoring.
[0003] Existing GNSS precision displacement monitoring technologies mainly include three categories: First, Real-time Dynamic Differential (RTK) technology, which relies on differential processing between reference stations and monitoring stations to eliminate common errors. However, its accuracy decreases significantly with increasing distance between stations, requiring a dense and spatially distributed reference station network, resulting in high construction and maintenance costs. Second, Precise Point Positioning (PPP) technology, which corrects state-space errors through precise ephemeris and clock bias products, without directly relying on reference stations. However, it suffers from slow convergence speed and insufficient real-time performance. Third, Epichronous Differential Single-Point Displacement Monitoring Engine (VADASE) technology, which estimates velocity and integrates it by epochal differential of carrier phase observations. It does not require ambiguity resolution, has a simple and flexible model, and can achieve instantaneous high-precision monitoring. However, traditional VADASE uses broadcast ephemeris to correct satellite orbits and clock biases. Limited by the accuracy of broadcast ephemeris, displacement errors accumulate over time, making it difficult to meet the needs of long-term precision monitoring.
[0004] To overcome the limitations of broadcast ephemeris errors, existing enhancement methods mainly include: using high-precision real-time ephemeris such as BeiDou PPP-B2b, Galileo high-precision service, or IGS real-time service, but these methods suffer from communication link dependence and limitations of satellite systems and service areas; or using a reference station network to estimate broadcast ephemeris errors, but this requires the deployment of a dense reference station network. Therefore, there is an urgent need in this field to propose a GNSS single-receiver precision displacement monitoring technology that balances accuracy requirements, communication needs, and construction costs. Summary of the Invention
[0005] The purpose of this invention is to provide a precise displacement monitoring method for a single GNSS receiver enhanced by a sparse reference station network.
[0006] To achieve the above objectives, the present invention is implemented according to the following technical solution: This invention includes the following steps: S1 uses observation data from a sparse reference station network to fix the reference station coordinates and satellite orbits, and estimates the satellite clock velocity parameters; the satellite orbits are predicted using ultra-fast ephemeris, and the reference station coordinates are post-processed weekly or celestial coordinates. S2 uses monitoring station observation data to calculate the satellite velocity reference value based on the satellite orbit, corrects the satellite clock speed, and estimates the receiver velocity parameters epoch by epoch. S3 obtains the precise displacement of the receiver using a numerical integration algorithm based on the receiver speed parameters.
[0007] Furthermore, the method for estimating satellite clock speed parameters using the fixed reference station coordinates and satellite orbit includes: An epoch-difference observation equation is constructed for the reference station. The receiver coordinate time differential is set to zero. The satellite velocity reference value is calculated using the satellite ephemeris. When using the ultra-fast satellite ephemeris, the error of the satellite velocity reference value is ignored, resulting in an observation equation containing only the satellite clock velocity, the reference station clock velocity, and the ionospheric delay rate. When the reference station uses dual-frequency data, the ionospheric delay is eliminated by using an anti-ionospheric combination, resulting in an observation equation that contains only the satellite clock velocity and the reference station clock velocity.
[0008] Furthermore, step S1 also includes: By introducing the zero-sum baseline constraint equation for satellite clock velocity, and combining it with the aforementioned observation equation, the elevation angle weight function is used, and the extended Kalman filter algorithm is employed to estimate the parameters, thereby obtaining the estimated values of satellite clock velocity and reference station clock velocity, as well as their variance-covariance matrix.
[0009] Furthermore, the method for estimating receiver velocity parameters epoch-by-epoch includes: An epoch-difference observation equation is constructed for the monitoring station to correct the satellite velocity reference value calculated from the ultra-fast satellite ephemeris and the satellite clock velocity estimated from the sparse reference station network, resulting in an observation equation containing receiver velocity, receiver clock velocity and ionospheric delay rate of change. When the monitoring station provides single-frequency data, the rate of change of ionospheric delay is ignored under the condition that the change of ionospheric delay is slow, resulting in an observation equation that includes receiver velocity and receiver clock speed. When the monitoring station provides dual-frequency data, the ionospheric delay is eliminated by using an anti-ionospheric combination, resulting in an observation equation that contains only receiver velocity and receiver clock speed.
[0010] Furthermore, step S2 also includes: Based on the monitoring station observation equation, the elevation angle weight function is used, and the extended Kalman filter algorithm is employed to estimate the parameters, thereby obtaining the estimated values of receiver velocity and receiver clock velocity, as well as their variance-covariance matrix.
[0011] Furthermore, the method for obtaining the precise displacement of the receiver through a numerical integration algorithm includes: In the time interval In the process, the estimated receiver velocity is numerically integrated to obtain the receiver displacement; linear fitting is performed using the integral data before the receiver displacement occurs, and drift correction is applied to the integral result when the displacement occurs.
[0012] Secondly, the GNSS single-receiver precision displacement monitoring system enhanced by sparse reference station networks includes: Satellite clock velocity estimation module: This module is used to estimate satellite clock velocity parameters by using observation data from a sparse reference station network, fixing the reference station coordinates and satellite orbits; the satellite orbits are predicted using ultra-fast ephemeris, and the reference station coordinates are post-processed weekly or celestial coordinates. Receiver velocity estimation module: Used to calculate the satellite velocity reference value based on the satellite orbit using the observation data from the monitoring station, correct the satellite clock speed, and estimate the receiver velocity parameters epoch by epoch; Precision displacement determination module: used to obtain the precise displacement of the receiver through a numerical integration algorithm based on the receiver velocity parameters; the precision displacement determination module also includes a drift correction unit, used to perform linear fitting on the integral data of the receiver before the displacement occurs, and to correct the drift of the integral result when the displacement occurs.
[0013] Thirdly, embodiments of this application also provide an electronic device, including: a processor, a communication interface, a memory, and a communication bus, wherein the processor, the communication interface, and the memory communicate with each other through the communication bus; the processor executes a sparse reference station network-enhanced GNSS single-receiver precision displacement monitoring method by calling logical instructions in the memory, wherein the executable instructions, when executed, cause the processor to perform the steps of the method described in the first aspect.
[0014] Fourthly, embodiments of this application also provide a computer-readable storage medium storing one or more programs that, when executed by an electronic device including multiple applications, cause the electronic device to perform the steps of the method described in the first aspect.
[0015] The beneficial effects of this invention are: This invention relates to a method and system for precise displacement monitoring of a single GNSS receiver enhanced by a sparse reference station network. Compared with existing technologies, this invention has the following technical advantages: This invention fully utilizes the smoothness of satellite orbits and the stability of satellite clock velocities, requiring only a sparse reference station network to estimate high-precision satellite clock velocities, significantly reducing the construction density and operation and maintenance costs of the reference station network. By estimating satellite clock velocities through a sparse reference station network, replacing the broadcast ephemeris correction in traditional VADASE, it effectively avoids the problem of displacement error accumulation over time, ensuring the accuracy of long-term precision displacement monitoring. It does not rely on external communication links such as BeiDou PPP-B2b, Galileo high-precision services, or IGS real-time services, requiring only ultra-fast ephemeris and sparse reference station network data, thus having a wider applicable area and higher reliability. Employing an epoch difference strategy, it eliminates the need to resolve carrier phase ambiguity, resulting in a simple model that enables instantaneous velocity estimation. After numerical integration and drift correction, high-precision displacement is obtained, making it suitable for real-time monitoring and early warning of sudden disasters such as earthquakes and landslides. Attached Figure Description
[0016] Figure 1 This is a flowchart illustrating the steps of the GNSS single-receiver precision displacement monitoring method enhanced by sparse reference station network according to the present invention. Figure 2 This is a flowchart of GNSS single receiver precision displacement monitoring in the embodiments of this specification. Detailed Implementation
[0017] The present invention will be further described below through specific embodiments. The illustrative embodiments and descriptions herein are used to explain the present invention, but are not intended to limit the present invention.
[0018] The present invention provides a GNSS single-receiver precision displacement monitoring method and system enhanced by sparse reference station networks, comprising the following steps: like Figure 1 As shown, this embodiment includes the following steps: S1 uses observation data from a sparse reference station network to fix the reference station coordinates and satellite orbits, and estimates the satellite clock velocity parameters; the satellite orbits are predicted using ultra-fast ephemeris, and the reference station coordinates are post-processed weekly or celestial coordinates. S2 uses monitoring station observation data to calculate the satellite velocity reference value based on the satellite trajectory, corrects the satellite clock speed, and estimates the receiver velocity parameters epoch by epoch. S3 obtains the precise displacement of the receiver using a numerical integration algorithm based on the receiver speed parameters; In this embodiment, step S1; For receiver r and satellite s, the GNSS pseudorange and phase observation equations at frequency i can be expressed as follows: (1) in, and These represent pseudorange and phase observations after corrections for errors such as receiver antenna phase center, receiver solid tide / ocean tide load / polar tide, receiver zenith tropospheric dry delay, satellite antenna phase center, phase entanglement (only phase observations need to be considered), and relativistic effects. This is the geometric distance between the satellite's center of mass and the receiver reference point, calculated based on the receiver coordinates and the satellite's orbit. For receiver clock bias, For satellite clock bias, Let be the receiver's tropospheric wet delay projection function. For the receiver's wet delay to the zenith troposphere, For ionospheric delay, For phase ambiguity, due to the absorption of receiver and satellite hardware delays, it loses its integer properties; For pseudo-range, For phase residuals; The expression for geometric distance is: (2) in The receiver-satellite line-of-sight vector. For satellite orbit, For receiver coordinates; Differentiating both sides of equation (2) over time, we get: (3) because ,but: (4) Differentiating both sides of equation (1) over time, we get: (5) Combining formulas (4) and (5), we get: (6) When no cycle slip occurs, the time differential of the phase ambiguity is zero, the zenith tropospheric wet delay changes slowly and can be ignored, and neglecting the residual term, we obtain: (7) For the reference station, the receiver coordinates are fixed, i.e. ,get: (8) By calculating the satellite velocity reference value using satellite ephemeris, we obtain: (9) in This is a reference value for satellite velocity. This represents the error in the satellite velocity reference value. The satellite velocity reference value error has a transformation relationship between the orbital coordinate system and the Earth-fixed coordinate system, expressed as: (10) in This is the unit vector of the radial / tangential / normal directions of the satellite orbit in the Earth-fixed coordinate system; When using ultrafast satellite ephemeris, the error in the satellite velocity reference value can be ignored, resulting in: (11) When the reference station uses dual-frequency data, ionospheric delay is eliminated using an anti-ionospheric combination, resulting in: (12) For each receiver-satellite pair in a sparse reference station network, an observation equation can be constructed as shown in formula (11) or (12). For a sparse reference station network, due to the correlation between satellite clock velocity and reference station clock velocity, parameter estimation cannot be performed directly. It is necessary to introduce constraint equations, such as zero-sum reference constraints for satellite clock velocity. For a sparse reference station network consisting of n satellites, the zero-sum reference constraint equations for satellite clock velocities are: (13) Using the joint observation equation (8) or (9) and the baseline constraint equation (10), with the elevation angle weight function and the extended Kalman filter algorithm, the parameter estimates and their variance-covariance matrix can be obtained as follows: (14) in To predict the state vector, To predict the variance-covariance matrix, Here is the filter gain matrix. To design the matrix, The prior residuals of the observed values; In this embodiment, step S2: For the monitoring station, based on pseudorange and phase observations, the satellite velocity calculated from the ultrafast satellite ephemeris is corrected, the satellite clock velocity estimated from the sparse reference network is corrected, and the receiver velocity, receiver clock velocity, and ionospheric delay rate of change are estimated, resulting in the observation equation: (15) When the monitoring station provides single-frequency data, under the condition that the ionospheric delay changes slowly, ignoring the rate of change of the ionospheric delay, we obtain: (16) When the monitoring station provides dual-frequency data, the ionospheric delay is eliminated using an anti-ionospheric combination, resulting in: (17) Based on the monitoring station's observation equation, using the elevation angle weight function and the extended Kalman filter algorithm, the parameter estimates and their variance-covariance matrix are obtained as follows: (18) In this embodiment, step S3: In the time interval In the process, the estimated receiver velocity is numerically integrated to obtain the high-precision receiver displacement, expressed as: (19) Due to residual errors, the receiver displacement obtained by the integration in the above formula may drift. To solve the drift problem, linear fitting is performed using the integral data before the receiver displacement, and then the drift correction is applied to the integral result when the receiver displacement occurs, thereby further improving the displacement monitoring accuracy.
[0019] Secondly, the GNSS single-receiver precision displacement monitoring system enhanced by sparse reference station networks includes: Satellite clock velocity estimation module: This module is used to estimate satellite clock velocity parameters by using observation data from a sparse reference station network, fixing the reference station coordinates and satellite orbits; the satellite orbits are predicted using ultra-fast ephemeris, and the reference station coordinates are post-processed weekly or celestial coordinates. Receiver velocity estimation module: Used to calculate the satellite velocity reference value based on the satellite orbit using the observation data from the monitoring station, correct the satellite clock speed, and estimate the receiver velocity parameters epoch by epoch; Precision displacement determination module: used to obtain the precise displacement of the receiver through a numerical integration algorithm based on the receiver velocity parameters; the precision displacement determination module also includes a drift correction unit, used to perform linear fitting on the integral data of the receiver before the displacement occurs, and to correct the drift of the integral result when the displacement occurs.
[0020] Thirdly, the electronic equipment includes a processor, a communication interface, memory, and a communication bus. The processor, communication interface, and memory communicate with each other via the communication bus. The processor can call logical instructions from the memory to execute the sparse reference station network-enhanced GNSS single-receiver precision displacement monitoring method, which mainly includes the software processing part mentioned above.
[0021] When logical instructions in a memory can be implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this invention, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.
[0022] Fourthly, embodiments of the present invention also provide a computer program product, the computer program product including a computer program, the computer program being stored on a non-transitory computer-readable storage medium, and when the computer program is executed by a processor, the computer is able to execute the software processing portion of the sparse reference station network-enhanced GNSS single-receiver precision displacement monitoring method provided by the above methods.
[0023] This invention also provides a non-transitory computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the software processing portion of the sparse reference station network-enhanced GNSS single-receiver precision displacement monitoring method provided by the above methods.
[0024] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.
Claims
1. A method for precise displacement monitoring of a single GNSS receiver enhanced by a sparse reference station network, characterized in that, Includes the following steps: S1 uses observation data from a sparse reference station network to fix the reference station coordinates and satellite orbits, and estimates the satellite clock velocity parameters; the satellite orbits are predicted using ultra-fast ephemeris, and the reference station coordinates are post-processed weekly or celestial coordinates. S2 uses monitoring station observation data to calculate the satellite velocity reference value based on the satellite orbit, corrects the satellite clock speed, and estimates the receiver velocity parameters epoch by epoch. S3 obtains the precise displacement of the receiver using a numerical integration algorithm based on the receiver speed parameters.
2. The GNSS single-receiver precision displacement monitoring method enhanced by sparse reference station network according to claim 1, characterized in that, The method for estimating satellite clock speed parameters based on the fixed reference station coordinates and satellite orbit includes: An epoch-difference observation equation is constructed for the reference station. The receiver coordinate time differential is set to zero. The satellite velocity reference value is calculated using the satellite ephemeris. When using the ultra-fast satellite ephemeris, the error of the satellite velocity reference value is ignored, resulting in an observation equation containing only the satellite clock velocity, the reference station clock velocity, and the ionospheric delay rate. When the reference station uses dual-frequency data, the ionospheric delay is eliminated by using an anti-ionospheric combination, resulting in an observation equation that contains only the satellite clock velocity and the reference station clock velocity.
3. The GNSS single-receiver precision displacement monitoring method enhanced by sparse reference station network according to claim 1, characterized in that, Step S1 also includes: By introducing the zero-sum baseline constraint equation for satellite clock velocity, and combining it with the aforementioned observation equation, the elevation angle weight function is used, and the extended Kalman filter algorithm is employed to estimate the parameters, thereby obtaining the estimated values of satellite clock velocity and reference station clock velocity, as well as their variance-covariance matrix.
4. The GNSS single-receiver precision displacement monitoring method enhanced by sparse reference station network according to claim 1, characterized in that, The method for estimating receiver velocity parameters epoch-by-epoch includes: An epoch-difference observation equation is constructed for the monitoring station to correct the satellite velocity reference value calculated from the ultra-fast satellite ephemeris and the satellite clock velocity estimated from the sparse reference station network, resulting in an observation equation containing receiver velocity, receiver clock velocity and ionospheric delay rate of change. When the monitoring station provides single-frequency data, the rate of change of ionospheric delay is ignored under the condition that the change of ionospheric delay is slow, resulting in an observation equation that includes receiver velocity and receiver clock speed. When the monitoring station provides dual-frequency data, the ionospheric delay is eliminated by using an anti-ionospheric combination, resulting in an observation equation that contains only the receiver velocity and the receiver clock speed.
5. The GNSS single-receiver precision displacement monitoring method enhanced by sparse reference station network according to claim 1, characterized in that, Step S2 also includes: Based on the monitoring station observation equation, the elevation angle weight function is used, and the extended Kalman filter algorithm is employed to estimate the parameters, thereby obtaining the estimated values of receiver velocity and receiver clock velocity, as well as their variance-covariance matrix.
6. The GNSS single-receiver precision displacement monitoring method enhanced by sparse reference station network according to claim 1, characterized in that, The method for obtaining the precise displacement of the receiver using a numerical integration algorithm includes: In the time interval In the process, the estimated receiver velocity is numerically integrated to obtain the receiver displacement; linear fitting is performed using the integral data before the receiver displacement occurs, and drift correction is applied to the integral result when the displacement occurs.
7. A GNSS single-receiver precision displacement monitoring system enhanced by a sparse reference station network, for performing the method according to any one of claims 1-6, characterized in that, include: Satellite clock velocity estimation module: used to estimate satellite clock velocity parameters by using observation data from a sparse reference station network, fixing the reference station coordinates and satellite orbits; The satellite orbit is predicted using ultra-fast ephemeris, and the reference station coordinates are post-processed weekly or celestial coordinates. Receiver velocity estimation module: used to calculate the satellite velocity reference value based on the satellite trajectory using the observation data of the monitoring station, correct the satellite clock speed, and estimate the receiver velocity parameters epoch by epoch; Precision displacement determination module: used to obtain the precise displacement of the receiver through a numerical integration algorithm based on the receiver velocity parameters; the precision displacement determination module also includes a drift correction unit, used to perform linear fitting on the integral data of the receiver before the displacement occurs, and to correct the drift of the integral result when the displacement occurs.
8. An electronic device, comprising a processor, a communication interface, a memory, and a communication bus; wherein the memory is used to store a computer program; wherein the processor, the communication interface, and the memory communicate with each other via the communication bus; and the processor is used to execute the computer program stored in the memory to cause the electronic device to perform the method as described in any one of claims 1 to 6.
9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the method as described in any one of claims 1 to 6.