Beidou router enhanced positioning local difference keeping method and system

Abstract

The invention discloses a Beidou router enhanced positioning local difference keeping method and system. The method comprises the following steps: constructing a three-level cooperation system consisting of a cloud, edge nodes and terminal equipment; according to the invention, a Beidou router is used as a side end, and the availability of differential data of a server is judged through CRC verification and a local RTK positioning module; if the server-side differential data is available, directly forwarding the server-side differential data to a regional terminal user for use, and if the server-side differential data is unavailable, starting a local differential holding mode; in the local difference maintaining mode, an ionosphere weighting model is adopted, the influence of large ionosphere modeling residual errors is overcome, the accurate coordinates of the current router are solved, then the coordinates and observation data are coded into local difference data, the local difference data are broadcasted to a user through a LoRa local area network, and it is guaranteed that the user can obtain the real-time data of the router under the condition that the server side difference data are unavailable. And high-precision positioning can still be realized based on local differential data, and the direct bidirectional communication load between the server and a plurality of users is weakened.

Description

Technical Field

[0001] This invention belongs to the field of GNSS (Global Navigation Satellite System) positioning and navigation technology, and relates to a BeiDou high-precision enhanced positioning method, and in particular to a BeiDou router enhanced positioning local differential preservation method and system. Background Technology

[0002] Ground-based augmentation systems are a crucial component in improving the accuracy of satellite navigation and positioning. Their nationwide network of reference stations provides real-time differential data services to a massive number of positioning terminals. The current mainstream Network Dynamic Differential (NRTK) technology employs a "single-terminal independent communication" mode, requiring each terminal to have a 4G / 5G communication module and a system differential account. It obtains differential data from the server by reporting its own location.

[0003] This model suffers from problems such as wasted communication resources and high equipment costs. For certain industries, it also carries the risk of exposing their precise coordinate information. Meanwhile, the ground-based augmentation system server faces enormous pressure from concurrent access.

[0004] To address this, this invention proposes a local differential augmentation positioning method based on a BeiDou router, taking into account the significant spatial clustering and consistent demand characteristics of terminals in fixed scenarios and combining the concept of edge computing. This method enables the utilization of BeiDou ground-based augmentation differential information across multiple locations.

[0005] Specifically, conventional network RTK (Real Time Kinematic) systems are an advanced satellite positioning technology that utilizes a continuously operating network of reference stations to provide high-precision positioning services in real time. Existing technology terminals directly use server-side differential data, and their positioning models themselves do not contain ionospheric parameters. This corresponds to a fixed ionospheric model in the model. For example, invention patents with publication numbers CN119232229A and CN118965390A can demonstrate direct interaction with the user terminal through server-side differential data. However, no one has yet considered the problems with directly using server-side differential data, such as the potentially large ionospheric modeling errors that may exist in the differential data.

[0006] Correspondingly, there is PPP (Precise Point Position) or PPP-RTK technology. PPP-RTK technology is based on regional precise atmospheric augmentation PPP. Its service performance depends on the accuracy of regional atmospheric modeling. For example, the invention patent with publication number CN119758401A discloses a positioning method, storage medium and program product based on cloud interactive augmentation. It performs cloud interactive modeling based on the ionospheric delay of each satellite generated by the platform and the ionospheric delay of each satellite generated by the user. Based on the cloud interactive modeling, interactive ionospheric augmentation information is obtained. Specifically, a crowdsourcing method is used to improve the modeling accuracy of the ionosphere itself. However, the model and related applications are only applicable to PPP with additional atmospheric constraints. Summary of the Invention

[0007] Purpose of the invention: This invention proposes a local differential preservation method for BeiDou routers to enhance positioning. By setting up a local BeiDou router as an edge between the server and the user, the availability of differential data on the server is judged. The local differential preservation mode improves the positioning performance of the terminal, and the multi-application effect of the local router also reduces the communication load on the server.

[0008] Technical solution: According to a first aspect of the present invention, a method for enhancing local differential positioning of a BeiDou router is provided, the method comprising the following steps:

[0009] S1. A local Beidou router is set up as an edge terminal between the server and the user. The local Beidou router receives differential data from the server and determines the availability of the differential data by checking the CRC and the output result of the local RTK positioning module. If the determination result is available, proceed to step S2; otherwise, proceed to step S3.

[0010] The local Beidou router described in S2 broadcasts the server-side differential data to users in the area via LoRa.

[0011] S3 uses an ionospheric weighted model to calculate the precise coordinates of the current router, encodes the precise coordinates and observation data into local differential data, and broadcasts it to users in the area via the LoRa LAN;

[0012] S4 broadcasts the server-side differential data from step S2 or the local differential data from step S3 via LoRa. The user receives the differential data, thereby achieving high-precision enhanced positioning.

[0013] Furthermore, including:

[0014] The determination of the availability of differential data by combining CRC checksum and the output of the local RTK positioning module includes:

[0015] For a complete frame of differential data, the CRC check value of the server-side differential data is first calculated using a lookup table method. The calculated CRC check value is then compared with the expected CRC check value carried by the differential data to check if they are consistent.

[0016] If the calculated CRC check value is inconsistent with the expected value, it indicates that the differential data of this frame is invalid, and we try to find and judge the next frame of complete differential data; otherwise, it indicates that the differential data of this frame is valid, and the frame data is transmitted to the RTK positioning module of the local Beidou router. The coordinate solution is calculated using the ionospheric fixed model. If the fixed solution of the local Beidou router is obtained, it is considered that the currently received server differential data is usable, and we continue to implement step S2; otherwise, we directly implement step S3.

[0017] Furthermore, including:

[0018] In step S3, the precise coordinates of the current router are calculated using an ionospheric weighted model, and the precise coordinates and observation data are encoded into local differential data, including:

[0019] S31 performs standard pseudorange single-point positioning and uses two dimensions—geometric precision factor and post-hoc residual—to preliminarily screen the quality of the observations received by the receiver from each satellite, eliminating abnormal observations and obtaining valid observations. The threshold for the geometric precision factor is set to 30, and the post-hoc residual is judged based on a chi-square distribution with a significance level of 0.001.

[0020] Furthermore, including:

[0021] In step S3, the precise coordinates of the current router are calculated using an ionospheric weighted model, and the precise coordinates and observation data are encoded into local differential data. This step also includes:

[0022] S32 constructs an ionospheric weighted function model, which includes a functional model and a stochastic model. The functional model is expressed as follows:

[0023]

[0024] in, It is a double difference operator. Indicates the sth m Effective carrier observations of a satellite at frequency f after initial station distance correction. Indicates the sth m The effective pseudorange observations of 1 satellite at frequency f after initial station distance correction, where subscripts 1, 2, ..., f represent different frequencies, and superscript s m Representing different satellites, m is the total number of satellites. Indicates the sth mThe cosines of each satellite in the x, y, and z directions, where x, y, and z represent the coordinates to be solved, and γ... f The ionospheric amplification factor at the f-th frequency is For the sth m The ionospheric delay of each satellite at the first frequency For the sth m spurious ionospheric observations from a single satellite at the first frequency; This corresponds to the floating-point fuzziness.

[0025] The stochastic model is used to determine the precision impact of each observation based on prior precision, and it is expressed as follows:

[0026]

[0027] Where E is the satellite elevation angle, and a and b are the prior precisions of elevation angle-independent and elevation angle-dependent observations, respectively;

[0028] After determining the function model, the stochastic model, and the corresponding constraint equations, S33 uses sequential least squares estimation or Kalman filtering to solve the floating-point solution of the parameter to be estimated X, and substitutes the corresponding floating-point solution back into the function model to calculate the residuals of each observation.

[0029] S34 uses the IGG III scheme to adjust the previous stochastic model based on the obtained residual vector, and then uses the adjusted stochastic model to recalculate the estimated parameter X.

[0030] The S35 iteration steps S32-S34 are repeated several times to obtain a high-precision floating-point solution corresponding to the parameter X to be estimated.

[0031] Furthermore, including:

[0032] In step S3, the precise coordinates of the current router are calculated using an ionospheric weighted model, and the precise coordinates and observation data are encoded into local differential data. This step also includes:

[0033] S36 constructs the ambiguity corresponding to different frequencies based on the obtained floating-point solution of the parameter to be estimated X, including ultra-wide alley, wide alley, and narrow alley combined floating-point ambiguity, expressed as:

[0034]

[0035] in, For the sth m The floating-point ambiguity corresponding to the f-th frequency of a satellite. For the sth m The floating-point ambiguity corresponding to the second frequency of each satellite For the sth m The floating-point ambiguity corresponding to the first frequency of each satellite Indicates the sth m Floating-point ambiguity of a satellite at the ultra-wide lane frequency (EWL) For the sth m Floating-point ambiguity of a satellite at the wide-lane frequency WL For the sth m Floating-point ambiguity of a satellite at narrow alleyway frequency NL;

[0036] Therefore, the floating-point solution X1 of the parameter to be estimated after transformation is expressed as:

[0037]

[0038] S37 employs an ambiguity fixing strategy, sequentially fixing the floating-point ambiguities of the ultra-wide alley, wide alley, and narrow alley to integers, thereby obtaining a fixed solution X2 with higher accuracy and reliability, expressed as:

[0039]

[0040] in, This refers to high-precision fixed-solution router coordinates.

[0041] Furthermore, including:

[0042] In step S3, the precise coordinates of the current router are calculated using an ionospheric weighted model, and the precise coordinates and observation data are encoded into local differential data. This step also includes:

[0043] S38 encodes the high-precision fixed solution router coordinates and local observation data according to the RTCM protocol to generate local differential data, which is then broadcast to the positioning terminals in the area via LoRa to achieve the effect of local differential preservation when the server differential data is unavailable.

[0044] On the other hand, the present invention also provides a BeiDou router enhanced positioning local differential preservation system, the system comprising:

[0045] The differential data availability determination module is used to establish a local Beidou router as an edge between the server and the user. The local Beidou router receives differential data from the server and determines the availability of the differential data through CRC check and the output result of the local RTK positioning module. If the determination result is available, it enters the first broadcast module; otherwise, it enters the second broadcast module.

[0046] The first broadcast module is used to broadcast server differential data to users in the area via LoRa according to the local Beidou router;

[0047] The second broadcast module is used to calculate the precise coordinates of the current router using an ionospheric weighted model, encode the precise coordinates and observation data into local differential data, and broadcast them to users in the area via the LoRa local area network;

[0048] The positioning module is used to broadcast differential data from the server or local data via LoRa. Users receive the differential data to achieve high-precision enhanced positioning.

[0049] Beneficial effects: Compared with the prior art, the present invention has the following advantages:

[0050] This invention proposes a local differential retention method for enhanced positioning of BeiDou routers. By leveraging the relay of differential data from the edge nodes of the BeiDou router, it achieves a multi-application effect, greatly improving resource utilization and reducing the bidirectional communication load between the server and multiple users. At the same time, through the local differential retention function, it ensures that the user end can still achieve high-precision positioning based on local differential data even when the differential data on the server is unavailable.

[0051] Specifically, this invention addresses a problem with conventional RTK models: while it's commonly believed that ionospheric errors have been eliminated through region modeling, this isn't always the case; server-side differential data still contains errors, particularly in the ionosphere. Therefore, to compensate for the impact of server-side ionospheric modeling errors, a router is used as the connection platform between the server and the terminal. The router first determines whether the fixed ionospheric model is available (i.e., server-side differential data is unavailable). In this case, an ionospheric weighted model is constructed to generate local differential data. Thus, when server-side differential data is unavailable, the router provides local differential enhancement capabilities. In other words, this application primarily addresses the problem of unavailable server-side differential data by proposing a local enhancement approach or application model. The router and server form complementary enhancement capabilities, ultimately ensuring the continuity of the terminal's enhanced positioning service.

[0052] The ionospheric weighted model of this invention does not directly add ionospheric constraints, but changes the original fixed ionospheric model in network RTK. This model calculates router coordinates and iteratively adjusts the constraint strength of ionospheric pseudo-observations during the process, and adds some quality control measures to improve the estimation accuracy of parameters such as coordinates. Attached Figure Description

[0053] Figure 1 This is a flowchart illustrating the implementation of a local differential preservation method for enhanced positioning of a Beidou router as described in this invention.

[0054] Figure 2 This is a schematic diagram of the local differential maintenance working principle of the Beidou differential router in the cloud-edge-device three-level collaborative system proposed in this invention;

[0055] Figure 3 This is a diagram showing the positioning effect of the fixed ionosphere model during the active period of the ionosphere according to the present invention.

[0056] Figure 4 This is a localization effect diagram of the ionospheric weighted model during the active period of the ionosphere in this invention;

[0057] Figure 5 This is a verification scenario diagram of the invention proposed in this invention;

[0058] Figure 6 These are the test results of the working range of the Beidou router using the method proposed in this invention. Detailed Implementation

[0059] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0060] Example 1: The embodiment of the present invention discloses step 1, server differential data availability judgment: the local Beidou router receives differential data from the server, and judges the availability of differential data through CRC check and the output result of the local RTK positioning module; based on the availability result, it is decided to proceed to step 2 or step 3. If available, step 2 is performed; if unavailable, step 3 is performed.

[0061] Step 2, Server-side differential data forwarding: The local Beidou router broadcasts the server-side differential data to users in the area via LoRa.

[0062] Step 3, Local Differential Preservation Mode: The precise coordinates of the current router are calculated using an ionospheric weighted model. These coordinates and observation data are then encoded into local differential data and broadcast to users in the area via the LoRa LAN.

[0063] Step 4, LoRa multi-user high-precision positioning: The server differential data or local differential data from Step 2 or Step 3 are broadcast via LoRa. Users receive the differential data to achieve high-precision enhanced positioning.

[0064] The specific steps are as follows:

[0065] Step 1. Server-side differential data availability determination: The local Beidou router receives differential data from the server. For a complete frame of differential data, it first uses a lookup table method to calculate the CRC check value of the server-side differential data, and compares the calculated value with the expected CRC check value carried by the differential data to check whether they are consistent.

[0066] If the calculated value does not match the expected value, it indicates that the differential data for that frame is invalid, and an attempt is made to find and determine the next complete frame of differential data. If the calculated value matches the expected value, it indicates that the differential data for that frame is valid, and the data for that frame is transmitted to the router's local RTK positioning module for coordinate calculation. It is generally believed that atmospheric modeling errors such as ionospheric errors have been well eliminated in the differential data provided by the server, and the terminal coordinate calculation is often based on short baseline or ultra-short baseline mode, that is, the influence of atmospheric errors is not considered.

[0067] Therefore, by using the local BeiDou router as an edge device, the ionospheric fixed model commonly used by terminals is employed to assess the coordinate calculation performance before differential data forwarding.

[0068]

[0069] In the formula, It is a double difference operator. p is the carrier phase observation value at frequency i. i Let λ be the pseudorange observation value at frequency i. i Let N be the wavelength of the i-th frequency. i Let be the ambiguity of the i-th frequency, A be the direction cosine coefficient matrix, and δX be the position parameter to be estimated.

[0070] If the local Beidou router can successfully achieve a fixed solution based on the model described in formula (1), then the effectiveness of the terminal ionospheric fixed model is verified, and the currently received server differential data is considered to be available. Continue to implement the subsequent step 2. If a fixed solution cannot be achieved, then it means that the current server differential data is unavailable, and directly implement the subsequent step 3.

[0071] Step 2. Server-side differential data forwarding: If the differential data on the server is available, the local Beidou router directly writes it into the actual physical serial port of the LoRa module and broadcasts it to users in the area;

[0072] Step 3. Local Differential Preservation Mode: During periods of atmospheric activity, server-side differential data may contain significant atmospheric modeling errors, particularly ionospheric errors. Using a conventional fixed ionospheric model will not yield high-precision positioning results. In this case, local differential data needs to be generated at the BeiDou router. This local differential data consists of the precise coordinates of the BeiDou router and the observed values.

[0073] First, standard pseudorange single-point positioning is performed, and the quality of the observations received by the receiver from each satellite is initially screened by judging the geometric precision factor and post-hoc residuals. Abnormal observations are eliminated. The threshold for the geometric precision factor is set to 30, and the post-hoc residuals are judged based on the chi-square distribution with a significance level of 0.001. The observations are satellite observation data received by the receiver in real time.

[0074] In this embodiment, standard pseudorange single-point positioning is the most basic positioning method. If standard pseudorange single-point positioning fails, then the subsequent ionospheric weighting will definitely have problems as well, and is used to initially judge the quality of the observations.

[0075] After initial screening of the quality of the observations, the influence of atmospheric activity is reduced by adding ionospheric pseudo-observation constraints. The precise coordinates of the router are calculated based on the ionospheric weighted function model shown in the following formula. The main method is to use the least squares or Kalman filtering method based on the constructed function model and stochastic model to solve for the unknown X.

[0076]

[0077] In the formula, This is a double-difference operator, where the subscript f represents different frequencies and the superscript s represents different frequencies. m Representing different satellites, m is the total number of satellites. and These represent the carrier and pseudorange observations after initial station distance correction, respectively. Let x, y, and z represent the cosines in the x, y, and z directions, respectively, where x, y, and z represent the coordinates to be solved, and γ represents the y-z direction. f This is the ionospheric amplification factor at the corresponding frequency f. For the ionospheric delay at the first frequency, These are pseudo-observations of the ionosphere. That is the corresponding floating-point fuzziness.

[0078] Based on the above function model, for ease of description, it is simplified as follows:

[0079] V = B·XL(3)

[0080] Where V represents the observed residuals, and the specific forms of B, X, L are as follows:

[0081]

[0082] Based on the above function model, in actual calculations, the accuracy of different observations varies, and the impact of the accuracy of each observation needs to be considered, i.e., a stochastic model. For the measured observations of each satellite, their prior accuracy can usually be calculated using a model related to the elevation angle, as shown in the following formula:

[0083]

[0084] Where E is the satellite elevation angle, a and b are the prior accuracy of the elevation angle independent and elevation angle dependent observations, respectively. For pseudorange, it is usually taken as 0.3m, and for carrier, it is usually taken as 0.003m.

[0085] For spurious ionospheric observations, their prior accuracy is related to the order of magnitude of the residual ionospheric error, and the corresponding constraint equations are as follows.

[0086]

[0087] In the formula, This refers to the accuracy of pseudo-observations of the ionosphere, which can be empirically set based on the level of ionospheric activity. For the BeiDou ground-based augmentation system, the residual ionospheric error after modeling is generally in the range of centimeters or tens of centimeters. Therefore, we first take... The prior accuracy is 0.15m. In this embodiment, the prior accuracy of the ionosphere, or the part of the ionosphere in the stochastic model, is not infinitely high because there are errors in the pseudo-observations of the ionosphere, and therefore cannot be set to 0.

[0088] After determining the function model and the stochastic model, sequential least squares or Kalman filtering methods can be used to solve for the floating-point solution of the parameter X to be estimated.

[0089]

[0090] In the formula, the subscripts k and k-1 represent the current epoch and the adjacent previous epoch, respectively, X k-1 and P k-1 F represents the parameters to be estimated at epoch k-1 and the corresponding variance-covariance matrix. k,k-1 Represents the state transition matrix. and Q represents the predicted value of the parameter to be estimated at the current epoch and the corresponding variance-covariance matrix. k-1 Let K be the noise matrix. k Let R be the gain matrix. k Let X be the stochastic model matrix formed by formulas (5) and (6). k and P k I represents the estimated value of the parameter to be estimated at the current epoch and the corresponding variance-covariance matrix, where I is the identity matrix.

[0091] After obtaining the estimated value X of the parameter to be estimated k Then, the residuals of each observation are calculated by back substitution (4). Based on the obtained residual vector, the previous stochastic model is adjusted using the classic IGG III scheme. Then, the parameters to be estimated are recalculated using the adjusted stochastic model. Reasonably determining the accuracy of ionospheric pseudo-observations is the key to improving parameter accuracy. In order to avoid problems such as ambiguity fixing errors caused by excessively strong pseudo-observation constraints and insignificant performance gains caused by excessively weak constraints, only the prior accuracy of the ionosphere is used in the first filtering. In the subsequent process, the stochastic model corresponding to the ionospheric pseudo-observations is gradually adjusted and refined through multiple iterations, thereby improving the parameter estimation performance.

[0092] After 3-5 iterations, a relatively accurate floating-point solution for the estimated parameter X can be obtained. Furthermore, during the above iteration process, pre- and post-residual checks can be employed to further identify potential gross errors in the observations. Specifically, the pre-residual detection threshold is set to 30m, and the post-residual check uses four times the prior accuracy σ of the observation as the threshold. Finally, based on the floating-point solution for the estimated parameter X, leveraging the advantages of BeiDou multi-frequency data, ambiguities corresponding to different frequencies are constructed, including ultra-wide lane, wide lane, and narrow lane combined floating-point ambiguities.

[0093]

[0094] In the formula, the subscripts EWL, WL, and NL represent extra-wide lane, wide lane, and narrow lane, respectively.

[0095] The transformed floating-point solution X1 can be expressed in the form of coordinates, ionosphere, narrow-lane ambiguity, wide-lane ambiguity, and ultra-wide-lane ambiguity.

[0096]

[0097] A phased and partial ambiguity fixing strategy is adopted simultaneously. Based on the least squares correlation reduction algorithm, the floating-point ambiguities of the ultra-wide lane, wide lane, and narrow lane are fixed to integers sequentially. Considering that the original ambiguities are sensitive to residual errors and that the fixing effect of high-dimensional ambiguities is poor and may not pass the test of indicators such as Ratio, the advantages of Beidou multi-frequency ultra-wide lane and wide lane ambiguities, which have longer wavelengths and are easier to fix, are leveraged. Firstly, this reduces the ambiguity dimension fixed each time. Secondly, the already fixed ultra-wide lane / wide lane ambiguities can be used to add integer constraints to the unfixed narrow lane ambiguities, thereby improving the accuracy and fixing effect of the narrow lane ambiguities and increasing the success rate of narrow lane ambiguity fixing, resulting in a fixed solution X2 with higher accuracy and reliability.

[0098] Among them, fixing the ambiguity is actually mapping the ambiguity from floating-point numbers to integers. There are different algorithms, but the most commonly used one is the least squares decorrelation algorithm.

[0099] In this embodiment, based on the above least squares reduction algorithm, some adjustments can be made to the fixing strategy by taking advantage of the multi-frequency advantage: taking advantage of the multi-frequency advantage, a strategy of converting the original ambiguity into ultra-wide lane / wide lane / narrow lane ambiguity and fixing it step by step is adopted. This is mainly because the original ambiguity has a short wavelength and is sensitive to residual error. In addition, the original ambiguity has a large dimension, and the direct fixing effect is poor because the fixing is wrong or the verification indicators such as Ratio cannot pass.

[0100] This embodiment first fixes ambiguities such as ultra-wide lane / wide lane, which have longer wavelengths and high resistance to residual errors. This reduces the number of ambiguity dimensions fixed each time and allows the fixed ultra-wide lane / wide lane ambiguities to be used to impose integer constraints on the unfixed narrow lane ambiguities, thereby improving the accuracy and fixing effect of the narrow lane ambiguities.

[0101]

[0102] in, This refers to high-precision fixed-solution router coordinates.

[0103] In this embodiment, based on the ionospheric weighted model, relevant quality control and experimental strategy optimization were performed (such as preliminary screening of single-point positioning quality, pre- / post-test residual verification, iterative adjustment of the stochastic model, and step-by-step / partial ambiguity fixing strategy) to ensure the correctness of coordinate solution. All the previous content is part of coordinate solution.

[0104] Then, according to the RTCM protocol, the coordinates are encoded with local observation data to generate local differential data, which is then broadcast to positioning terminals in the area via LoRa to achieve local differential preservation when server-side differential data is unavailable.

[0105] Step 4. The LoRa one-band multi-user high-precision positioning: Using a Beidou router as the edge device, differential data from the server or local device is broadcast to multiple users in the area via LoRa broadcasting, achieving the effect of one-band multi-application. In this way, the server only needs to communicate with the Beidou router, reducing the communication load caused by multiple users communicating bidirectionally with the server simultaneously;

[0106] Considering that the maximum total length of data sent by the selected LoRa module in a single transmission is 512 bytes, if the differential data length of the current frame exceeds 512 bytes, the frame data needs to be divided into multiple packets with a maximum length of 512 bytes each, and written to the actual physical serial port of the LoRa module in a loop. Otherwise, some data will be lost. Therefore, after processing the data, the serial port buffer needs to be cleared to avoid some "outdated" data blocking the serial port.

[0107] After the user receives the server-side or local differential data forwarded by the Beidou router, a CRC check must be performed first. After the check passes, the coordinates are calculated using the ionospheric fixed model to achieve high-precision positioning.

[0108] Example 2: Figure 1 As shown in the figure, this embodiment discloses a local differential preservation method for enhanced positioning of Beidou routers, and the specific steps are as follows:

[0109] Step 1: Server-side differential data availability determination. The local Beidou router receives differential data from the server. For a complete frame of differential data, the CRC check value of the server-side differential data is first calculated using a lookup table method. The calculated value is then compared with the expected CRC check value carried by the differential data to check whether they are consistent.

[0110] If the calculated value does not match the expected value, it indicates that the differential data for that frame is invalid, and an attempt is made to find and determine the next complete frame of differential data. If the calculated value matches the expected value, it indicates that the differential data for that frame is valid, and the data for that frame is transmitted to the router's local RTK positioning module for coordinate calculation. It is generally believed that atmospheric modeling errors, such as ionospheric errors, have been largely eliminated in the differential data provided by the server, and terminal coordinate calculations are often based on short baseline or ultra-short baseline modes, i.e., atmospheric error effects are not considered. Therefore, using the BeiDou router as an edge device, before forwarding the differential data, a commonly used fixed ionospheric model is employed to assess the coordinate calculation performance.

[0111]

[0112] In the formula, It is a double difference operator. p is the carrier phase observation value at frequency i. i Let λ be the pseudorange observation value at frequency i. i Let N be the wavelength of the i-th frequency. i Let be the ambiguity of the i-th frequency, A be the direction cosine coefficient matrix, and δX be the position parameter to be estimated.

[0113] If the local router can successfully achieve a fixed solution based on the model described in formula (1), then the effectiveness of the terminal ionosphere fixed model is verified, and the currently received server differential data is considered to be available. Continue to implement the subsequent step 2. If a fixed solution cannot be achieved, then it means that the current server differential data is unavailable, and directly implement the subsequent step 3.

[0114] Step 2: Server-side differential data forwarding. If the differential data on the server is available, the local Beidou router directly writes it into the actual physical serial port of the LoRa module and broadcasts it to users in the area.

[0115] Step 3, Local Differential Preservation Mode: During periods of atmospheric activity, server-side differential data may contain significant atmospheric modeling errors, particularly ionospheric errors. Using a conventional fixed ionospheric model will not yield high-precision positioning results. In this case, local differential data needs to be generated at the BeiDou router. First, an ionospheric weighted model is used to calculate the precise coordinates of the current router. The ionospheric weighted model can be expressed as follows:

[0116]

[0117] In the formula, η i Let i be the ionospheric amplification factor at the i-th frequency. The parameter to be estimated is the ionospheric delay. These are ionospheric pseudo-observations. The measurement noise variance constraint for ionospheric pseudo-observations is related to the order of magnitude of the ionospheric residual error, and the corresponding constraint equation is as follows.

[0118]

[0119] In the formula, δ Δ▽I This refers to the standard deviation of the noise from ionospheric spurious observations, which can be empirically set based on the level of ionospheric activity. For the BeiDou ground-based augmentation system, the residual ionospheric error after modeling is generally in the range of centimeters or tens of centimeters; therefore, δ is taken as... Δ▽I =0.15.

[0120] After successfully calculating the local coordinates of the BeiDou router using an ionospheric weighted model, these coordinates are encoded with local observation data to generate local differential data. This data is then broadcast to positioning terminals within the area via LoRa, thus achieving local differential data preservation when server-side differential data is unavailable. Figure 2 As shown.

[0121] Step 4: The LoRa multi-user high-precision positioning uses a BeiDou router as an edge device. Differential data from the server or local device is broadcast to multiple users in the area via LoRa broadcast, achieving a multi-application effect within a single area. In this way, the server only needs to communicate with the BeiDou router, reducing the communication load caused by multiple users simultaneously communicating bidirectionally with the server.

[0122] Considering that the maximum total length of data sent by the selected LoRa module in a single transmission is 512 bytes, if the differential data length of the current frame exceeds 512 bytes, the frame data needs to be divided into multiple packets with a maximum length of 512 bytes each, and written to the actual physical serial port of the LoRa module in a loop. Otherwise, some data will be lost. Therefore, after processing the data, the serial port buffer needs to be cleared to avoid some "outdated" data blocking the serial port.

[0123] After the user receives the server-side or local differential data forwarded by the Beidou router, a CRC check must be performed first. After the check passes, the coordinates are calculated using the ionospheric fixed model to achieve high-precision positioning.

[0124] Figure 3 and Figure 4The images show the positioning performance during periods of ionospheric activity, using a fixed ionospheric model, and a weighted ionospheric model. It can be seen that during periods of high ionospheric activity, the conventional fixed ionospheric model is affected by ionospheric delay disturbances, making it difficult to maintain a stable position for extended periods. The overall stability rate is only 25.5%, and the positioning accuracy in the N, E, and U directions is only at the decimeter level, clearly indicating a significant error in the differential data. Switching to the weighted ionospheric model significantly improves the stability rate to 98.2%, and the positioning accuracy recovers to the centimeter level, further demonstrating that the main influencing factor in the differential data is ionospheric error. After successfully calculating the local coordinates using the weighted ionospheric model, these coordinates can be encoded with local observation data and broadcast as local differential data to positioning terminals within the area, thus achieving local differential data preservation when server-side differential data is unavailable. Experiments show that using the ionospheric weighted model can effectively solve the problem that the traditional ionospheric fixed model is difficult to fix for a long time due to ionospheric delay perturbation, thus enabling more stable local differential data broadcasting in the local differential preservation mode.

[0125] Figure 5 and Figure 6 The working range of the Beidou router was verified on the Southeast University campus. The LoRa module's air speed was set to 9600kbps. The receiver was fixed at the west gate of the gymnasium, and the transmitter moved along the road from the west gate of the gymnasium towards the west gate of the campus. Every 100 meters moved, the transmitter paused for 30 seconds to observe whether the receiver could stably obtain a fixed RTK solution. If it could, the above steps were repeated; if not, another 30 seconds were observed. If a fixed RTK solution could not be obtained, it could be determined that the current distance had exceeded the normal operating range. In the experiment, the location sharing and ranging functions of Gaode Maps were used to roughly measure the moving distance. Figure 5 As shown. By Figure 6 The results show that within a short range of 800m, the receiver can stably obtain the RTK fixed solution; at 850m, the receiver can no longer stably obtain the RTK fixed solution; and at 900m, the receiver can hardly obtain the fixed solution. Based on the above analysis, we can conclude that the effective working distance of the BeiDou router local differential preservation enhancement method proposed in this invention is approximately 800m.

[0126] Example 3: The present invention also provides a BeiDou router enhanced positioning local differential preservation system, the system comprising:

[0127] The differential data availability determination module is used to establish a local Beidou router as an edge between the server and the user. The local Beidou router receives differential data from the server and determines the availability of the differential data through CRC check and the output result of the local RTK positioning module. If the determination result is available, it enters the first broadcast module; otherwise, it enters the second broadcast module.

[0128] The first broadcast module is used to broadcast server differential data to users in the area via LoRa according to the local Beidou router;

[0129] The second broadcast module is used to calculate the precise coordinates of the current router using an ionospheric weighted model, encode the precise coordinates and observation data into local differential data, and broadcast them to users in the area via the LoRa local area network;

[0130] The positioning module is used to broadcast differential data from the server or local data via LoRa. Users receive the differential data to achieve high-precision enhanced positioning.

[0131] Other technical features of the BeiDou router enhanced positioning local differential preservation system described in this embodiment are similar to those of the corresponding BeiDou router enhanced positioning local differential preservation method, and will not be repeated here.

[0132] In the description of this invention, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. "A plurality of" means two or more, unless otherwise explicitly specified.

[0133] In this invention, unless otherwise explicitly specified and limited, the terms "installation," "connection," "linking," and "fixing," etc., should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral part; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; they can refer to the internal communication of two components or the interaction between two components. Those skilled in the art can understand the specific meaning of the above terms in this invention according to the specific circumstances.

[0134] In this invention, unless otherwise explicitly specified and limited, "above" or "below" the second feature can mean that the first feature is in direct contact with the second feature, or that the first feature is in indirect contact with the second feature through an intermediate medium. Furthermore, "above," "over," and "on top" of the second feature can mean that the first feature is directly above or diagonally above the second feature, or simply that the first feature is at a higher horizontal level than the second feature. "Below," "below," and "under" the second feature can mean that the first feature is directly below or diagonally below the second feature, or simply that the first feature is at a lower horizontal level than the second feature.

[0135] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the present invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Furthermore, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0136] Any process or method description in the flowchart or otherwise herein can be understood as representing a module, segment, or portion of code comprising one or more executable instructions for implementing a particular logical function or process, and the scope of the preferred embodiments of the invention includes additional implementations in which functions may be performed not in the order shown or discussed, including substantially simultaneously or in reverse order depending on the functions involved, as will be understood by those skilled in the art to which embodiments of the invention pertain.

[0137] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-included system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of computer-readable media include: an electrical connection having one or more wires (electronic device), a portable computer disk drive (magnetic device), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Alternatively, the computer-readable medium may be paper or other suitable media on which the program can be printed, since the program can be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, interpreting, or otherwise processing as necessary, and then stored in a computer memory.

[0138] It should be understood that various parts of the present invention can be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0139] Those skilled in the art will understand that all or part of the steps of the methods in the above embodiments can be implemented by a program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, the program includes one or a combination of the steps of the method embodiments.

[0140] Furthermore, the functional units in the various embodiments of the present invention can be integrated into a processing module, or each unit can exist physically separately, or two or more units can be integrated into a module. The integrated module can be implemented in hardware or as a software functional module. If the integrated module is implemented as a software functional module and sold or used as an independent product, it can also be stored in a computer-readable storage medium.

[0141] Although embodiments of the present invention have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present invention. Those skilled in the art can make changes, modifications, substitutions and variations to the above embodiments within the scope of the present invention.

Claims

1. A method for enhancing local differential positioning in a BeiDou router, characterized in that, The method includes the following steps: S1. A local Beidou router is set up as an edge terminal between the server and the user. The local Beidou router receives differential data from the server and determines the availability of the differential data by checking the CRC and the output result of the local RTK positioning module. If the determination result is available, proceed to step S2; otherwise, proceed to step S3. The local Beidou router described in S2 broadcasts the server-side differential data to users in the area via LoRa. S3 uses an ionospheric weighted model to calculate the precise coordinates of the current router, encodes the precise coordinates and observation data into local differential data, and broadcasts it to users in the area via the LoRa LAN; S4 broadcasts the server-side differential data from step S2 or the local differential data from step S3 via LoRa. The user receives the differential data, thereby achieving high-precision enhanced positioning.

2. The BeiDou router enhanced positioning local differential preservation method according to claim 1, characterized in that, The determination of the availability of differential data by combining CRC checksum and the output of the local RTK positioning module includes: For a complete frame of differential data, the CRC check value of the server-side differential data is first calculated using a lookup table method. The calculated CRC check value is then compared with the expected CRC check value carried by the differential data to check if they are consistent. If the calculated CRC check value is inconsistent with the expected value, it indicates that the differential data of this frame is invalid, and we try to find and judge the next frame of complete differential data; otherwise, it indicates that the differential data of this frame is valid, and the frame data is transmitted to the RTK positioning module of the local Beidou router. The coordinate solution is calculated using the ionospheric fixed model. If the fixed solution of the local Beidou router is obtained, it is considered that the currently received server differential data is usable, and we continue to implement step S2; otherwise, we directly implement step S3.

3. The BeiDou router enhanced positioning local differential preservation method according to claim 2, characterized in that, In step S3, the precise coordinates of the current router are calculated using an ionospheric weighted model, and the precise coordinates and observation data are encoded into local differential data, including: S31 performs standard pseudorange single-point positioning and uses two dimensions—geometric precision factor and post-hoc residual—to preliminarily screen the quality of the observations received by the receiver from each satellite, eliminating abnormal observations and obtaining valid observations. The threshold for the geometric precision factor is set to 30, and the post-hoc residual is judged based on a chi-square distribution with a significance level of 0.

001.

4. The BeiDou router enhanced positioning local differential preservation method according to claim 3, characterized in that, In step S3, the precise coordinates of the current router are calculated using an ionospheric weighted model, and the precise coordinates and observation data are encoded into local differential data. This step also includes: S32 constructs an ionospheric weighted function model, which includes a functional model and a stochastic model. The functional model is expressed as follows: in, It is a double difference operator. Indicates the sth m Effective carrier observations of a satellite at frequency f after initial station distance correction. Indicates the sth m The effective pseudorange observations of 1 satellite at frequency f after initial station distance correction, where subscripts 1, 2, ..., f represent different frequencies, and superscript s m Representing different satellites, m is the total number of satellites. Indicates the sth m The cosines of each satellite in the x, y, and z directions, where x, y, and z represent the coordinates to be solved, and γ... f The ionospheric amplification factor at the f-th frequency is For the sth m The ionospheric delay of each satellite at the first frequency For the sth m spurious ionospheric observations from a single satellite at the first frequency; This corresponds to the floating-point fuzziness. The stochastic model is used to determine the precision impact of each observation based on prior precision, and it is expressed as follows: Where E is the satellite elevation angle, and a and b are the prior precisions of elevation angle-independent and elevation angle-dependent observations, respectively; After determining the function model, the stochastic model, and the corresponding constraint equations, S33 uses sequential least squares estimation or Kalman filtering to solve the floating-point solution of the parameter to be estimated X, and substitutes the corresponding floating-point solution back into the function model to calculate the residuals of each observation. S34 uses the IGG III scheme to adjust the previous stochastic model based on the obtained residual vector, and then uses the adjusted stochastic model to recalculate the estimated parameter X. The S35 iteration steps S32-S34 are repeated several times to obtain a high-precision floating-point solution corresponding to the parameter X to be estimated.

5. The BeiDou router enhanced positioning local differential preservation method according to claim 4, characterized in that, In step S3, the precise coordinates of the current router are calculated using an ionospheric weighted model, and the precise coordinates and observation data are encoded into local differential data. This step also includes: S36 constructs the ambiguity corresponding to different frequencies based on the obtained floating-point solution of the parameter to be estimated X, including ultra-wide alley, wide alley, and narrow alley combined floating-point ambiguity, expressed as: in, For the sth m The floating-point ambiguity corresponding to the f-th frequency of a satellite. For the sth m The floating-point ambiguity corresponding to the second frequency of each satellite For the sth m The floating-point ambiguity corresponding to the first frequency of each satellite Indicates the sth m Floating-point ambiguity of a satellite at the ultra-wide lane frequency (EWL) For the sth m Floating-point ambiguity of a satellite at the wide-lane frequency WL For the sth m Floating-point ambiguity of a satellite at narrow alleyway frequency NL; Therefore, the floating-point solution X1 of the parameter to be estimated after transformation is expressed as: S37 employs an ambiguity fixing strategy, sequentially fixing the floating-point ambiguities of the ultra-wide alley, wide alley, and narrow alley to integers, thereby obtaining a fixed solution X2 with higher accuracy and reliability, expressed as: in, This refers to high-precision fixed-solution router coordinates.

6. The BeiDou router enhanced positioning local differential preservation method according to claim 5, characterized in that, In step S3, the precise coordinates of the current router are calculated using an ionospheric weighted model, and the precise coordinates and observation data are encoded into local differential data. This step also includes: S38 encodes the high-precision fixed solution router coordinates and local observation data according to the RTCM protocol to generate local differential data, which is then broadcast to the positioning terminals in the area via LoRa to achieve the effect of local differential preservation when the server differential data is unavailable.

7. A local differential hold system for enhanced positioning of a Beidou router, characterized in that, The system includes; The differential data availability determination module is used to establish a local Beidou router as an edge between the server and the user. The local Beidou router receives differential data from the server and determines the availability of the differential data through CRC check and the output result of the local RTK positioning module. If the determination result is available, it enters the first broadcast module; otherwise, it enters the second broadcast module. The first broadcast module is used to broadcast server differential data to users in the area via LoRa according to the local Beidou router; The second broadcast module is used to calculate the precise coordinates of the current router using an ionospheric weighted model, encode the precise coordinates and observation data into local differential data, and broadcast them to users in the area via the LoRa local area network; The positioning module is used to broadcast differential data from the server or local data via LoRa. Users receive the differential data to achieve high-precision enhanced positioning.

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