PPP-rtk positioning method considering quality information of PPP-b2b product

Abstract

The application belongs to the technical field of satellite navigation and positioning, and specifically discloses a PPP-RTK positioning method considering PPP-B2b product quality information. The method uses the post-checking residual error obtained by PPP solving of the reference station, and after height angle normalization, the residual error root mean square of the satellites in the sliding window is taken as the PPP-B2b product quality parameter, which is directly introduced into the observation value random model at the user end, so that the observation values of different satellites are differentially weighted according to the PPP-B2b product quality parameter, the influence of the residual error of the PPP-B2b orbit / clock difference product on the positioning calculation is effectively reduced, and the positioning precision at the user end is improved. Secondly, the user end can reasonably allocate the observation value weight according to the PPP-B2b product quality parameter, suppress the interference of the satellites with larger errors on the calculation, thereby shortening the time required for ambiguity fixing and position convergence, and accelerating the PPP-RTK convergence performance.

Description

Technical Field

[0001] This invention belongs to the field of satellite navigation and positioning technology, and specifically relates to a PPP-RTK positioning method that takes into account the quality information of PPP-B2b products. Background Technology

[0002] PPP-B2b service broadcasts correction information via GEO satellites. Users only need the ability to receive BeiDou B2b signals to obtain high-precision positioning services without any network communication, thus overcoming the limitations of network coverage. Although PPP-B2b service achieves high-precision positioning without a network, traditional PPP models inherently suffer from long convergence times and difficulty in fixing integer ambiguities. Furthermore, research shows that the positioning accuracy of traditional PPP-B2b floating-point solutions is typically at the decimeter level. While accuracy can be improved by extending observation time, the improvement is limited in dynamic environments or scenarios with constrained observation conditions.

[0003] Therefore, some scholars have proposed a PPP-RTK precise positioning method based on PPP-B2b products using a single base station. This method achieves rapid integer ambiguity resolution under the PPP-B2b service system, significantly improving real-time high-precision positioning service capabilities. Combining the wide-area products of PPP-B2b with the regional augmentation advantages of PPP-RTK has become an important research direction in the field of high-precision positioning. However, the accuracy of orbit and clock error products broadcast by PPP-B2b still lags behind that of precise products, and these errors can propagate further to the user positioning resolution process, thus affecting the ambiguity fixation performance, convergence speed, and positioning accuracy of PPP-RTK.

[0004] Currently, a common approach is to incorporate the parameters to be estimated for spatial ranging errors into the PPP observation equation to compensate for the residual errors of PPP-B2b products, thereby improving PPP convergence speed and positioning accuracy. However, research on the propagation mechanism, influence patterns, and compensation methods of PPP-B2b product errors in PPP-RTK is still lacking. Therefore, constructing corresponding error reduction and compensation mechanisms for PPP-B2b product errors is of great significance and engineering application value for improving the real-time high-precision positioning capability under the PPP-B2b enhancement system.

[0005] In summary, the current approach to addressing the impact of PPP-B2b product accuracy on positioning performance mainly employs product error parameterization modeling. This method utilizes the differences between PPP-B2b orbit and clock error products and high-precision reference products to construct a product error characterization model. Furthermore, the error parameters are incorporated into the PPP observation model for joint estimation, thereby mitigating the impact of PPP-B2b product errors on positioning results.

[0006] However, the accuracy of PPP-B2b products varies across different systems and satellite types, and this method does not fully consider this impact. Furthermore, while this method effectively improves positioning accuracy over long periods, the gain on convergence speed is limited after introducing the parameters to be estimated, making it unsuitable for rapid positioning. To accelerate convergence, some scholars have proposed a PPP-RTK precise positioning method based on PPP-B2b products, but this method does not fully consider the influence of residual errors in satellite orbits and clock bias products. Summary of the Invention

[0007] The purpose of this invention is to propose a PPP-RTK positioning method that takes into account the quality information of PPP-B2b products. This method, based on the technical characteristics of PPP-RTK, constructs PPP-B2b product quality parameters by statistically analyzing the root mean square value of the PPP verification residuals of reference stations, and introduces them into the observation value stochastic model to implement differentiated weighting for different satellite observation values. This reduces the impact of residual errors of PPP-B2b orbital clock products on positioning solutions, thereby improving positioning accuracy and PPP-RTK convergence performance.

[0008] To achieve the above objectives, the present invention adopts the following technical solution: A PPP-RTK positioning method that takes into account PPP-B2b product quality information includes the following steps: Step 1. At one or more reference stations with known coordinates, the reference station continuously receives the orbit correction and clock error correction broadcast by PPP-B2b, and simultaneously collects observation data including pseudorange and carrier phase. With fixed reference station coordinates, PPP calculation is performed at each reference station to obtain the post-hoc residual for each satellite at each epoch. Select a sliding window with a preset window length, normalize the post-verification residuals of each satellite within the window according to its elevation angle, and calculate its root mean square value as the PPP-B2b product quality parameter for that satellite within the current window. Step 2. The reference station broadcasts the PPP-B2b product quality parameters of the current window to the user terminal in real time; Step 3. In the PPP-RTK solution at the user end, the PPP-B2b product quality parameters are introduced into the observation value stochastic model. The user end performs Kalman filter estimation based on the observation value stochastic model to complete the PPP-RTK positioning.

[0009] The present invention has the following advantages: As described above, this invention proposes a PPP-RTK positioning method that considers PPP-B2b product quality information. First, this method constructs PPP-B2b product quality parameters based on PPP post-hoc residuals. Specifically, the post-hoc residuals obtained from the PPP calculation at the reference station are normalized by elevation angle, and the root mean square of the satellite residuals within the sliding window is used as the PPP-B2b product quality parameters. Second, this invention broadcasts the PPP-B2b product quality parameters from the reference station to the user terminal, and directly incorporates these parameters into the observation stochastic model (variance or weight calculation) at the user terminal. This allows for differentiated weighting of observations from different satellites based on the PPP-B2b product quality parameters. By constructing and broadcasting PPP-B2b product quality parameters and incorporating them into the user terminal's observation stochastic model, this invention effectively reduces the impact of residual errors in PPP-B2b orbit / clock errors on positioning calculations, especially in scenarios with large PPP-B2b product errors, thus improving the positioning accuracy at the user terminal. Secondly, since the PPP-B2b product quality parameters reflect the residual error level of each satellite in the current time period, the user end can reasonably allocate the weight of the observations based on this (since the product quality parameters extracted by the reference station for each satellite are different, the product quality parameters are used as the basis for weighting the observations), which suppresses the interference of satellites with large errors on the solution, thereby shortening the time required for ambiguity fixing and position convergence, thus accelerating the convergence performance of PPP-RTK. Attached Figure Description

[0010] Figure 1 This is a flowchart of a PPP-RTK positioning method that takes into account PPP-B2b product quality information in an embodiment of the present invention. Detailed Implementation

[0011] The present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments: Blewitt (1998) first theoretically proved the equivalence of functional models and stochastic models. During adjustment, adding additional parameters to be estimated as extensions of the functional model or incorporating them into the observation noise to transform the corresponding stochastic model yields the same effect. Based on this, this invention provides an effective and easily implemented enhancement strategy for PPP-RTK real-time positioning based on PPP-B2b products. This enhancement strategy introduces PPP-B2b product quality parameters into the stochastic model of user-end observations, combines it with post-record residual statistical analysis of reference stations, and employs a differentiated weighting method to construct the stochastic model of observations. This aims to suppress the impact of residual satellite orbit and clock errors in PPP-B2b products on positioning solutions, effectively improving the positioning accuracy and convergence performance of user-end systems based on PPP-B2b products.

[0012] The method of this invention mainly includes steps such as obtaining and preprocessing post-verification residuals at the reference station, generating PPP-B2b product quality parameters, constructing a stochastic model of differentiated observations, and parameter estimation. The overall process is as follows: Figure 1 As shown.

[0013] The PPP-RTK positioning method in this embodiment, which takes into account the quality information of PPP-B2b products, includes the following steps: Step 1. Obtain and preprocess the residuals after verification at the reference station.

[0014] This invention continuously receives orbital corrections and clock corrections broadcast by PPP-B2b at one or more reference stations with known coordinates, and simultaneously collects observation data, including pseudorange and carrier phase.

[0015] With fixed reference station coordinates, PPP calculations are performed at each reference station to obtain the post-hoc residual for each satellite at each epoch.

[0016] Select a sliding window with a preset window length, normalize the post-verification residuals of each satellite within the window according to its elevation angle, and calculate its root mean square value as the PPP-B2b product quality parameter for that satellite within the current window.

[0017] Step 1.1. Data collection.

[0018] At one or more reference stations with known coordinates, continuously receive orbit corrections and clock corrections broadcast by PPP-B2b, and synchronously collect observation data, including pseudorange and carrier phase.

[0019] Step 1.2. PPP solution and post-verification residual extraction.

[0020] With fixed reference station coordinates, precise single-point positioning (PPP) calculations are performed at each reference station to obtain the phase observation residuals for each satellite at each epoch, which are then used as post-hoc residuals. .

[0021] in superscript Represents satellite, subscript Represents the receiver.

[0022] It should be noted that the PPP solution process is fairly standard and is not an innovation of this invention, so it will not be described in detail here.

[0023] To ensure the accuracy of the obtained PPP-B2b product quality parameters, the PPP calculation at each reference station continues for a preset period of time, such as half an hour; until the PPP calculation status stabilizes, the PPP-B2b product quality parameters are calculated through steps 1.2 to 1.4.

[0024] Step 1.3. Elevation angle normalization.

[0025] Select a sliding window of preset length, and normalize the post-verification residuals of each satellite within the window according to its elevation angle to obtain the normalized post-verification residuals. To eliminate the systematic influence of the elevation angle on the magnitude of the post-test residual, the formula is as follows: (1) in For the satellite elevation angle, This is a commonly used PPP elevation angle stochastic model.

[0026] In this embodiment, the preset window length is, for example, but not limited to, 30 epochs.

[0027] In addition, to ensure the accuracy of the obtained PPP-B2b product quality parameters, this embodiment selects post-hoc residuals with satellite elevation angles greater than 10 degrees for calculation, and removes observations that are greatly affected by atmospheric residues and multipath effects.

[0028] This approach ensures that the post-test residuals included in the statistics primarily reflect the track / clock error of the PPP-B2b product itself.

[0029] Step 1.4. Calculation of PPP-B2b product quality parameters.

[0030] Normalized residuals for all satellites within the window Calculate the root mean square (RMS) residual value for each satellite, which will be used as the PPP-B2b product quality parameter for that satellite within the current window. The formula is as follows: (2) In the formula, Representative satellite In the current window ( The PPP-B2b product quality parameters within the scope of this document are as follows: These represent the start and end times of the current window, respectively. Representing the current moment, This represents the number of reference stations, which is equivalent to the number of receivers.

[0031] Step 2. Obtain the quality parameters of the PPP-B2b product on the user side.

[0032] The normalized residual RMS value of each satellite obtained in step 1 is used as the PPP-B2b product quality parameter. The reference station broadcasts the PPP-B2b product quality parameter of the current window to the user terminal in real time through the communication link.

[0033] To ensure that users obtain the current PPP-B2b product quality information, the PPP-B2b product quality parameters need to be updated in real time as the window moves. The specific update process is calculated according to steps 1.2 to 1.4 above.

[0034] The reference station will broadcast the latest PPP-B2b product quality parameters of the satellite to the user terminal in real time via the communication link.

[0035] The communication link in this embodiment includes, for example, a 4G network or a 5G network.

[0036] Step 3. Construct a stochastic model for PPP-B2b product quality parameters on the user side.

[0037] In the PPP-RTK solution at the user end, the PPP-B2b product quality parameters are introduced into the observation value stochastic model. The user end performs Kalman filter estimation based on the observation value stochastic model to complete the PPP-RTK positioning.

[0038] Step 3.1. Construct a stochastic model of differential observations.

[0039] The difference in the random model is reflected in the fact that the product quality parameters extracted from each satellite at the reference station are different.

[0040] In PPP-RTK computation at the user end, traditional stochastic observation models are typically based solely on elevation angle or signal-to-noise ratio. Compared to traditional stochastic observation models, this invention improves the phase observation variance to: (3) In the formula The variance of the phase observations, Represents the accuracy of satellite observations. This is a PPP elevation angle stochastic model. (In this embodiment...) This represents the impact of satellite elevation angle and orbital clock error product accuracy on positioning calculations.

[0041] In PPP, the variance of phase observations and the variance of pseudorange observations for each satellite together constitute the stochastic model of observations. Due to the low accuracy of pseudorange observations, a traditional stochastic model is still used.

[0042] The aforementioned product quality parameters are broadcast to the user terminal in real time and directly incorporated into the observation value stochastic model (variance or weight calculation), and differentiated weighting is applied to the observation values ​​of different satellites based on the PPP-B2b product quality parameters.

[0043] Step 3.2. PPP solution.

[0044] Based on the above stochastic model, the user terminal performs Kalman filtering estimation to obtain high-precision parameters such as position and clock error.

[0045] The method of this invention selects an appropriate sliding window, normalizes the elevation angle of the post-test residuals of each satellite at each reference station to obtain the normalized post-test residuals, and then calculates all the normalized post-test residuals of each satellite within the window. These are sent to the user terminal (i.e., the rover terminal) as PPP-B2b product quality parameters. The user terminal incorporates these parameters into the observation stochastic model, thereby reducing the impact of residual errors of PPP-B2b orbit clock products on user positioning solutions and improving positioning accuracy and convergence performance.

[0046] 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 PPP-RTK positioning method that takes into account PPP-B2b product quality information, characterized in that, The steps include the following: Step 1. At one or more reference stations with known coordinates, the reference station continuously receives the orbit correction and clock error correction broadcast by PPP-B2b, and simultaneously collects observation data including pseudorange and carrier phase. With fixed reference station coordinates, PPP calculation is performed at each reference station to obtain the post-hoc residual for each satellite at each epoch. Select a sliding window with a preset window length, normalize the post-verification residuals of each satellite within the window according to its elevation angle, and calculate its root mean square value as the PPP-B2b product quality parameter for that satellite within the current window. Step 2. The reference station broadcasts the PPP-B2b product quality parameters of the current window to the user terminal in real time; Step 3. In the PPP-RTK solution at the user end, the PPP-B2b product quality parameters are introduced into the observation value stochastic model. The user end performs Kalman filter estimation based on the observation value stochastic model to complete the PPP-RTK positioning.

2. The PPP-RTK positioning method considering PPP-B2b product quality information according to claim 1, characterized in that, Step 1 specifically involves: Step 1.

1. Data Acquisition; At one or more reference stations with known coordinates, continuously receive the orbit correction and clock error correction broadcast by PPP-B2b, and synchronously collect observation data, including pseudorange and carrier phase. Step 1.

2. PPP solution and post-verification residual extraction; With fixed reference station coordinates, precise single-point positioning (PPP) calculations are performed at each reference station to obtain the phase observation residuals for each satellite at each epoch, which are then used as post-hoc residuals. , Middle and upper bids Represents satellite, subscript Represents the receiver; Step 1.

3. Elevation angle normalization; Select a sliding window of preset length, and normalize the post-verification residuals of each satellite within the window according to its elevation angle to obtain the normalized post-verification residuals. The normalization formula is as follows: （1） in For the satellite elevation angle, For PPP elevation angle stochastic model; Step 1.

4. Calculation of PPP-B2b product quality parameters; Based on the normalized residuals of all satellites within the sliding window Calculate the root mean square value of the post-hoc residual for each satellite, and use it as the PPP-B2b product quality parameter for that satellite within the current window. The formula is as follows: （2） In the formula, Representative satellite In the current window ( The PPP-B2b product quality parameters within the scope of this document are as follows: These represent the start and end times of the current window, respectively. Representing the current moment, This represents the number of reference stations, which is equivalent to the number of receivers.

3. The PPP-RTK positioning method considering PPP-B2b product quality information according to claim 2, characterized in that, In step 1.2, the PPP solution for each reference station is performed for a preset period of time before the post-verification residual extraction; until the PPP solution status is stable, the PPP-B2b product quality parameters are calculated through steps 1.2 to 1.

4.

4. The PPP-RTK positioning method considering PPP-B2b product quality information according to claim 2, characterized in that, In step 1.3, the preset window length is set to 30 epochs.

5. The PPP-RTK positioning method considering PPP-B2b product quality information according to claim 2, characterized in that, In step 1.3, post-hoc residuals with satellite elevation angles greater than 10 degrees are selected for calculation.

6. The PPP-RTK positioning method considering PPP-B2b product quality information according to claim 1, characterized in that, In step 2, the PPP-B2b product quality parameters are updated in real time as the window moves. The reference station will broadcast the latest PPP-B2b product quality parameters of the satellite to the user terminal in real time via the communication link.

7. The PPP-RTK positioning method considering PPP-B2b product quality information according to claim 1, characterized in that, In step 2, the communication link includes a 4G network or a 5G network.

8. The PPP-RTK positioning method considering PPP-B2b product quality information according to claim 2, characterized in that, In step 3, the process of constructing the stochastic model of differentiated observations is as follows: In the PPP-RTK solution on the user side, the variance of the phase observations is improved as follows: （3） In the formula The variance of the phase observations, Represents the accuracy of satellite observations. This is a random model for PPP elevation angle.

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