Positioning method based on correlation constraint, computer equipment and storage medium

Abstract

The invention provides a correlation constraint-based positioning method, computer equipment and a storage medium, and the method comprises the steps: constructing constraint information based on electron content information and positioning observation information, and the positioning observation information is obtained through the signal collection of a target satellite by a first single observation station; according to the constraint information, a Kalman filter is constrained to obtain a resolving parameter of the Kalman filter, and the Kalman filter is used for estimating the ionosphere delay parameter based on the correlation between the receiver differential code deviation and the ionosphere delay parameter; and determining a first target ionosphere delay parameter of the first single observation station based on the resolving parameter, the first target ionosphere delay parameter being used for positioning. And the Kalman filter is constrained by using the constructed constraint information, so that high-precision ionosphere delay parameters can be obtained when parameter estimation is carried out based on resolving parameters in the constrained Kalman filter, and the accuracy of a positioning result of positioning based on the ionosphere delay parameters is improved.

Description

Technical Field

[0001] The present application relates to the field of satellite navigation technology, and in particular to a positioning method, computer equipment, and storage medium based on correlation constraints. Background Art

[0002] Traditional Global Navigation Satellite System (GNSS) precision positioning technologies mainly include Precise Point Positioning (PPP) and Network Real-Time Kinematic (NRTK). Each technology has its own characteristics and is applied to its own corresponding scenarios. However, it is precisely because of the corresponding limitations between these technologies and scenarios that the promotion and application of the two technologies in different scenarios have brought challenges.

[0003] Against the backdrop of increasing demand for high-precision positioning in multiple scenarios, Precise Point Positioning-Real-Time Kinematic (PPP-RTK) technology has emerged. PPP-RTK technology includes combined PPP-RTK and non-combined PPP-RTK technology. For non-combined PPP-RTK technology, the receiver's differential code biases (DCBs) are not corrected when estimating ionospheric delay, resulting in the inclusion of receiver DCBs in the estimated ionospheric delay. Consequently, positioning based on the ionospheric delay containing receiver DCBs results in poor positioning accuracy. Therefore, a positioning method that improves the accuracy of positioning results is urgently needed. Summary of the Invention

[0004] In view of this, embodiments of the present application are directed to providing a positioning method, a computer device, and a storage medium based on correlation constraints.

[0005] In a first aspect, a positioning method based on correlation constraints is provided, the method comprising: constructing constraint information based on electron content information and positioning observation information, wherein the positioning observation information is obtained by a first single measuring station collecting signals for a target satellite; constraining a Kalman filter according to the constraint information to obtain solution parameters of the Kalman filter, wherein the Kalman filter is used to estimate the ionospheric delay parameters based on the correlation between the receiver differential code deviation and the ionospheric delay parameters; and determining a first target ionospheric delay parameter of the first single measuring station based on the solution parameters, wherein the first target ionospheric delay parameter is used for positioning.

[0006] According to a first aspect, constraint information is constructed based on electron content information and positioning observation information. The method includes: determining a covariance matrix based on the electron content information; determining constrained ionospheric delay parameters generated based on the electron content information and initial ionospheric delay parameters estimated by a Kalman filter based on the positioning observation information; determining an observation matrix and a residual vector based on the constrained ionospheric delay parameters and the initial ionospheric delay parameters; and constructing constraint information based on the covariance matrix, the observation matrix, and the residual vector.

[0007] According to the first aspect, or any implementation of the first aspect above, the distribution path of the electronic content information is a vertical path; based on the electronic content information, a covariance matrix is ​​determined, and the method includes: using an oblique path projection function and the altitude angle of the target satellite to project the electronic content information from the vertical path to the oblique path; based on the electronic content information of the oblique path, a covariance matrix is ​​generated.

[0008] According to the first aspect, or any implementation of the first aspect above, the Kalman filter is constrained according to the constraint information to obtain the solution parameters of the Kalman filter. The method includes: substituting the constraint information into the initial solution equation of the Kalman filter, and solving the initial solution equation to obtain the solution parameters of the Kalman filter.

[0009] According to the first aspect, or any implementation of the first aspect above, a first target ionospheric delay parameter of a first single measuring station is determined based on a solution parameter. The method includes: determining a floating-point solution vector based on the solution parameter; single-point fixing the floating-point solution vector to obtain a fixed solution vector, and extracting the first target ionospheric delay parameter of the first single measuring station from the fixed solution vector.

[0010] According to the first aspect, or any implementation manner of the first aspect above, the method is applied to a first server end, the first server end includes multiple first single measurement stations for signal acquisition of a target satellite, and the multiple first single measurement stations constitute a measurement station area for common view of the target satellite; after determining the first target ionospheric delay parameters of the first single measurement stations based on the solution parameters, the method further includes: constructing an ionospheric model to be solved, and obtaining the coordinates of a center of gravity point and the longitude and latitude information of the multiple first single measurement stations, wherein the center of gravity point is a reference point set in the measurement station area; substituting the coordinates of the center of gravity point, the longitude and latitude information of the multiple first single measurement stations, and the first target ionospheric delay parameters of the multiple first single measurement stations into the ionospheric model to be solved, and solving to obtain a first ionospheric model, wherein the first ionospheric model is used for positioning.

[0011] According to the first aspect, or any implementation of the first aspect above, the method is applied to a client, and the first single station is a station to be positioned.

[0012] According to the first aspect, or any implementation of the first aspect above, the electron content information is generated by a second ionospheric model obtained by the second server based on a second target ionospheric delay parameter of a second single measuring station.

[0013] According to the first aspect, or any implementation of the first aspect above, the electron content information is generated by GIMs.

[0014] In a second aspect, the present application provides a computer device comprising a memory and a processor, wherein the memory is used to store a computer program, and the processor is used to, when executing the computer program, execute the correlation constraint-based positioning method of any one of the first aspect and any possible implementation of the first aspect.

[0015] In a third aspect, the present application provides a computer-readable storage medium storing a program code for computer execution, the program code including a correlation constraint-based positioning method for executing the first aspect and any possible implementation of the first aspect.

[0016] In a fourth aspect, an embodiment of the present application provides a computer program comprising instructions for executing the method for positioning based on correlation constraints in the first aspect and any possible implementation of the first aspect.

[0017] In this application, constraint information is constructed through electron content information and positioning observation information, and the constraint information is used to constrain the Kalman filter's estimation process of ionospheric delay parameters, so that when the solution parameters in the constrained Kalman filter are used for parameter estimation, high-precision ionospheric delay parameters can be obtained, thereby improving the accuracy of positioning results based on ionospheric delay parameters. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 A schematic flowchart of a positioning method based on correlation constraints provided in an embodiment of the present application.

[0019] Figure 2 A schematic flowchart of another positioning method based on correlation constraints provided in an embodiment of the present application.

[0020] Figure 3 A schematic structural diagram of a computer device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0021] The following will be combined with the drawings in the embodiments of this application to clearly and completely describe the technical solutions in the embodiments of this application. Obviously, the embodiments described are part of the embodiments of this application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field based on this application are within the scope of protection of this application.

[0022] The term "and / or" in this article is merely a description of the association relationship between associated objects, indicating that three relationships may exist. For example, A and / or B can mean: A exists alone, A and B exist at the same time, and B exists alone.

[0023] In the description and claims of the embodiments of this application, the terms "first" and "second" are used to distinguish different objects, rather than to describe a specific order of objects. For example, the terms "first target object" and "second target object" are used to distinguish different objects, rather than to describe a specific order of objects.

[0024] In the embodiments of this application, words such as "exemplary" or "for example" are used to indicate examples, illustrations, or descriptions. Any embodiment or design described as "exemplary" or "for example" in the embodiments of this application should not be interpreted as being preferred or advantageous over other embodiments or designs. Rather, the use of words such as "exemplary" or "for example" is intended to present the relevant concepts in a concrete manner.

[0025] It should be understood that the specific embodiments described herein are only used to explain the present application and are not intended to limit the present application.

[0026] Traditional GNSS precision positioning technologies mainly include PPP technology and NRTK technology. The two technologies have their own characteristics and are applied to their respective scenarios.

[0027] NRTK technology eliminates the error sources of the propagation path and stations and satellites through the station-satellite double difference method, where stations and satellites refer to base stations, mobile stations and satellites, leaving only the items related to the integer ambiguity parameters and the baseline vector, making it easy to separate the ambiguity parameters. The ambiguity parameters are combined with prior information (virtual station information, satellite orbit information, etc.) to determine the integer value of the ambiguity. This integer value is substituted into the double-difference observation equation to obtain the baseline vector. The advantage of NRTK technology is that it can quickly obtain high-precision positioning results, but it requires two-way communication between the server and the client, which increases the communication burden and limits the number of clients. Therefore, NRTK technology is not suitable for multi-user scenarios and is only suitable for scenarios with a small number of clients. In addition, due to the influence of atmospheric delay error, it is necessary to expand the scope of obtaining atmospheric data to establish an atmospheric model to eliminate the atmospheric delay error. However, there are differences in atmospheric data in different regions. As the scope expands, errors will exist in the atmospheric model, and the client will obtain atmospheric error correction information with large errors generated by the atmospheric model. Correcting the carrier phase observation value based on the atmospheric error correction information with large errors will cause the carrier phase observation value to still have large errors, affecting the rapid fixation of the ambiguity integer value, and thus leading to large errors in the positioning result.

[0028] PPP technology is flexible, supports parallel client operations, and receives corrections in the state domain, replacing the raw observation data (carrier phase differential data) that needs to be transmitted in NRTK technology, reducing reliance on high bandwidth. However, the various estimated parameters in PPP technology are mutually coupled, and without the constraints of external precise information, it is difficult to achieve error separation in a short period of time. Therefore, PPP technology has a long convergence time and is difficult to meet instantaneous application requirements. It is usually suitable for scenarios where positioning is performed after the fact.

[0029] It is precisely because of the corresponding limitations between the two technologies and scenarios, and the fact that the atmospheric delay error in NRTK technology has a greater impact on the accuracy of positioning results, that PPP-RTK technology came into being. PPP-RTK technology includes combined PPP-RTK and non-combined PPP-RTK technology. Combined PPP-RTK technology is based on the ionosphere-free combined estimation correction product, that is, the ionosphere delay is related to the signal frequency. By performing a specific linear combination of ionosphere observations of different frequencies, the ionosphere delay can be eliminated. Then, by comparing the ionosphere-free combined observations and the original carrier observations, or by using other related data models and algorithms, the ionosphere delay related information can be extracted from the carrier observations. Based on the extracted ionosphere delay related information, the positioning results can be further corrected to improve the accuracy of the positioning results.

[0030] In comparison, non-combined PPP-RTK technology estimates and optimizes all parameters to be estimated directly from the raw observation data, rather than first performing a specific linear combination and then delay estimation. This allows the final delay estimate to be closer to the true value, thereby improving the accuracy of the positioning results. Furthermore, in non-combined PPP-RTK technology, the estimated parameters are highly self-consistent, observation noise is low, and the model is highly scalable. However, the technical model corresponding to traditional non-combined PPP-RTK technology only corrects the DCBs on the satellite side when estimating the ionospheric delay parameters, without correcting the DCBs on the receiver side. This causes the estimated ionospheric delay parameters to include the DCBs on the receiver side, which can result in negative ionospheric delay parameters. Consequently, ambiguity fixation and positioning based on the ionospheric delay that includes the DCBs on the receiver side result in low performance in both ambiguity fixation and positioning results.

[0031] In order to solve the above problems, in this application, constraint information is constructed through electronic content information and positioning observation information, and the constraint information is used to constrain the Kalman filter's estimation process of the ionospheric delay parameters, so that when the solution parameters in the constrained Kalman filter are used for parameter estimation, high-precision ionospheric delay parameters can be obtained, thereby improving the accuracy of the positioning results based on the ionospheric delay parameters.

[0032] The following combination Figure 1 , the embodiments of the present application are described in detail.

[0033] Figure 1 This is a schematic flowchart of a positioning method based on correlation constraints provided in an embodiment of the present application to solve the above problems.

[0034] Figure 1 The positioning method based on correlation constraints shown includes steps S110 to S130.

[0035] Step S110 : Constructing constraint information based on the electron content information and the positioning observation information, wherein the positioning observation information is obtained by collecting signals from the target satellite by the first single measuring station.

[0036] In some embodiments, the electron content information refers to the spatial distribution information of the electron content of the ionosphere, and the distribution path of the electron content information can be a vertical path or an oblique path. The electron content information can be generated by global ionospheric maps (GIMs), wherein GIMs are gridded products that describe the spatial distribution of the electron content of the ionosphere, generated by globally distributed GNSS observation station data, and provide a spatiotemporal distribution map of the vertical total electron content of the global ionosphere in the form of a latitude and longitude grid. The electron content information can also be generated by regional ionospheric maps. The accuracy of the ionospheric electron content contained in the electron content information generated by the regional ionospheric map is higher than that contained in the electron content information generated by GIMs. Accordingly, the accuracy of positioning based on the electron content information generated by the regional ionospheric map is higher. The electron content information can also be generated by a modeling product obtained by single-star modeling using the first target ionospheric delay parameter generated based on GIMs or regional ionospheric maps. There are many ways to select the method for generating electron content information, which can be selected according to the actual situation. This application does not impose any restrictions here.

[0037] In some embodiments, the first single station is a base station and / or reference station for collecting satellite signals. The coordinates of the first single station are typically fixed. Multiple fixed base stations form a regional reference network to form a common view area for the satellites. Corrections are calculated using observation data from the common view satellites to accurately position the mobile station. The corrections include an atmospheric delay correction, which includes at least an ionospheric delay correction. The ionospheric delay correction may also be referred to as an ionospheric delay parameter. The first single station is equipped with a receiver for collecting signals from the target satellite, and the receiver includes receiver DCBs.

[0038] In some embodiments, the target satellite is any satellite that assists in positioning. The first single station collects signals from the target satellite, obtains observation data, and generates positioning observation information based on the observation data. The observation data may include pseudorange observations, carrier phase observations, ionospheric and tropospheric delay information, and may also include initial orbit data and initial clock error data obtained from satellite broadcast ephemeris. Generating positioning observation information based on the observation data can be achieved by obtaining auxiliary information, using the coordinates, auxiliary information, and observation data of the first single station as the positioning observation information. The auxiliary information is used to assist in processing the observation data to obtain corrections.

[0039] Auxiliary information may include satellite orbit correction products, satellite clock correction products, satellite differential code deviation correction products, uncalibrated phase delay (UPDs, Uncalibrated Phase Delays) correction products, etc. Satellite orbit correction products provide precise orbit determination algorithms, which are used to calculate the precise orbit data of the satellite based on observation data and precise orbit determination algorithms, compare the precise orbit data with the initial orbit data, and generate orbit correction numbers. Satellite clock correction products provide clock correction algorithms, which are used to calculate precise clock error data based on observation data and precise orbit data, compare the precise clock error data with the initial clock error data, and generate clock error correction numbers. Satellite differential code deviation correction products are used to eliminate satellite differential code deviations in observation data. UPDs correction products are used to eliminate or weaken the uncalibrated phase delays present in the carrier phase observations in the observation data.

[0040] In some embodiments, after the observation data and auxiliary information are obtained, data quality inspection and screening are performed on the observation data and auxiliary information to remove abnormal observation data caused by signal shielding, multipath effects, receiver failure, etc.

[0041] The method of obtaining observation data is periodic. The periodic time interval can be between a few seconds and tens of seconds, or intervals of other time lengths. The specific selection is based on the corresponding application scenario, device performance, and data processing requirements.

[0042] In some embodiments, the constraint information is used to constrain a Kalman filter's estimation process for estimating a parameter to be estimated, such as an ionospheric delay parameter. The constraint information can be a constraint matrix composed of multiple matrices or a constraint value composed of multiple values.

[0043] There are many ways to construct constraint information based on electronic content information and positioning observation information. The specific method should be selected according to the actual situation. This application does not make any restrictions here.

[0044] In a possible implementation of the present application, a method for constructing constraint information based on electron content information and positioning observation information can be to determine a covariance matrix based on the electron content information; determine constrained ionospheric delay parameters generated based on the electron content information and initial ionospheric delay parameters estimated by the Kalman filter based on the positioning observation information; determine an observation matrix and a residual vector based on the constrained ionospheric delay parameters and the initial ionospheric delay parameters; and construct constraint information based on the covariance matrix, the observation matrix, and the residual vector.

[0045] The ionospheric electron content included in the electron content information may be in the form of a time series. The standard deviation of the ionospheric electron content in the time series form is solved, and the covariance matrix is ​​determined based on the square of the solved standard deviation. The time series may be determined based on a pre-set time interval or based on the time period for collecting observation data. The formula for calculating the covariance matrix is ​​shown in the following formula (1):

[0046] R=singma×singma (1)

[0047] Where R represents the covariance matrix and singa is the standard deviation.

[0048] Generating the constrained ionospheric delay parameter based on the electron content information may include obtaining the coordinates of a first single measuring station, the current time, and the acquisition time of the observation data; performing spatial interpolation and temporal interpolation from the electron content information based on the coordinates of the first single measuring station, the current time, and the acquisition time of the observation data; and generating the constrained ionospheric delay parameter based on the electron content data and the signal frequency.

[0049] Generating the constrained ionospheric delay parameter based on the electron content data and the signal frequency may include obtaining a conversion coefficient, dividing the product of the conversion coefficient and the electron content data by the square of the signal frequency to obtain the constrained ionospheric delay parameter. For example, the conversion coefficient is 40.3, and the signal frequency may be the L1 frequency of 1575.42 MHz.

[0050] The Kalman filter estimates the parameters to be estimated based on the positioning observation information to obtain the estimated initial ionospheric delay parameters. The Kalman filter can estimate the parameters to be estimated based on the defined and initialized state vector, covariance matrix, and observation data to obtain the initial ionospheric delay parameters. Alternatively, the Kalman filter can predict the state vector and covariance matrix based on the state vector and covariance matrix at the previous cycle, and update the predicted state vector and covariance matrix based on the observation data to obtain the initial ionospheric delay parameters from the updated results.

[0051] In some embodiments, the distribution path of the electron content information is a vertical path. Accordingly, before generating the constrained ionospheric delay parameters based on the electron content information, the electron content information must first be projected onto an oblique path so as to generate the constrained ionospheric constraint information based on the electron content information on the oblique path.

[0052] Among them, the electron content is projected onto the oblique path by using the oblique path projection function and the altitude angle of the target satellite to project the electron content information from the vertical path to the oblique path; based on the electron content information of the oblique path, a covariance matrix is ​​generated.

[0053] The slant path projection function can be a specific projection formula or a projection product based on the projection formula. The target satellite's altitude angle is substituted into the slant path projection function and solved to obtain the electron content information of the slant path. The slant path projection function can also include parameters such as the Earth's radius and the average ionospheric height. By substituting the target satellite's altitude angle, Earth's radius, and the average ionospheric height into the slant path projection function and solving for it, the electron content information of the slant path is obtained.

[0054] The standard deviation of the ionospheric electron content contained in the electron content information of the slant path is solved, and the square of the standard deviation is determined as the covariance matrix.

[0055] Determining the observation matrix and residual vector based on the generated constrained ionospheric delay parameter and the initial ionospheric delay parameter can be accomplished by subtracting the constrained ionospheric delay parameter from the initial ionospheric delay parameter to obtain a residual vector, and determining the observation matrix based on the parameter coefficient of the initial ionospheric delay parameter in the residual vector. For example, if the parameter coefficient of the initial ionospheric delay parameter is -1, then H = -1, where H represents the observation matrix.

[0056] The formula for obtaining the residual vector by subtracting the constrained ionospheric delay parameter from the initial ionospheric delay parameter is shown in the following formula (2):

[0057] v= i on CON―i on EKF (2)

[0058] Where v represents the residual vector, ion CON represents the constrained ionospheric delay parameter, and ion EKF represents the initial delay parameter. For example, if the electron content information for generating the constrained ionospheric delay parameter is generated by GIMs, then the corresponding formula (2) can be transformed into the following formula (3):

[0059] v= ion GIMs―i on EKF (3)

[0060] where ion GIMs represents the constrained ionospheric delay parameters.

[0061] The relationship between the covariance matrix, observation matrix and residual vector and the solution parameters in the Kalman filter is established to construct the constraint information.

[0062] In another possible implementation of the present application, the implementation method of constructing constraint information based on electronic content information and positioning observation information can be to pre-train the neural network model based on the sample electronic content information, sample positioning observation information and label constraint information to obtain a constraint information generation model that meets the requirements, input the electronic content information and positioning observation information into the constraint information generation model, and obtain the constraint information output by the constraint information generation model.

[0063] Through the electron content information and positioning observation information, a method for determining the covariance matrix, observation matrix and residual vector is provided, which ensures the accuracy of the generated constraint information and correspondingly ensures the accuracy of the target ionospheric delay parameters generated based on the accurate constraint information.

[0064] Step S120: constraining a Kalman filter according to the constraint information to obtain solution parameters of the Kalman filter, wherein the Kalman filter is used to estimate the ionospheric delay parameter based on the correlation between the receiver differential code deviation and the ionospheric delay parameter.

[0065] In some embodiments, the solution parameters include at least the Kalman gain, the state vector, and the error covariance matrix, wherein the Kalman gain is used to assist in determining the state vector and the error covariance matrix. Receiver differential code bias refers to the systematic biases in the reception of observation data at different frequencies by the hardware receiving unit included in the first single station. The delay in the satellite signal propagation path caused by the influence of free electrons in the ionosphere when the satellite signal passes through the Earth's ionosphere is called ionospheric delay.

[0066] The Kalman filter is constrained according to the constraint information, and the solution parameters of the Kalman filter can be obtained by substituting the constraint information into the initial solution equation of the Kalman filter, and solving the initial solution equation to obtain the solution parameters of the Kalman filter.

[0067] Before the constraint information is brought into the Kalman filter, the correlation between the constraint information and the initial solution equation in the Kalman filter is first obtained, so that based on the correlation, the constraint information is brought into the initial solution equation of the Kalman filter, the initial solution equation is solved, and the solution parameters are obtained.

[0068] The constraint information includes the covariance matrix R, the observation matrix H, and the residual vector v. The constraint information is substituted into the initial solution equation to obtain the solution parameters. See the following formulas (4)-(6):

[0069]

[0070] Among them, H k and are all deformations of the measurement matrix H. For example, the measurement matrix H is determined as the measurement matrix at time k, then the measurement matrix H is H k , find the transpose of the observation matrix, and determine Correspondingly, the covariance matrix R is determined as the covariance matrix at time k, then the covariance matrix R is R k .P k,k―1 represents the variance-covariance matrix of the floating-point solution, X k,k―1 Represents the floating-point solution vector, P k,k―1 and X k,k―1 K is the prediction matrix generated based on the state prediction and covariance prediction at k-1 moment. k 、X k and P k is the solution parameter, K k represents the Kalman gain, X k represents the state vector, P k represents the error covariance matrix, K k The transpose of . I represents the identity matrix.

[0071] By substituting the constraint information into the initial solution equations of the Kalman filter based on the correlation relationship between the constraint information, the solution parameters are obtained, so that the solution parameters constrained based on the electron content information are obtained, so that the corresponding first target ionospheric delay parameters are subsequently determined based on the solution parameters, thereby ensuring the accuracy of the first target ionospheric delay parameters.

[0072] Optionally, in an embodiment of the present application, a Kalman filter is used to estimate the ionospheric delay parameter based on the correlation between the receiver differential code deviation and the ionospheric delay parameter, and the constraint information is used to constrain the Kalman filter, that is, the constraint information is used to reduce the correlation between the receiver differential code deviation and the ionospheric delay parameter, so that when the Kalman filter performs parameter estimation, the receiver differential code deviation and the ionospheric delay parameter can be separated more quickly, and the first target ionospheric delay parameter from which the receiver differential code deviation is separated can be obtained.

[0073] Step S130: determining a first target ionospheric delay parameter of the first single measuring station based on the calculated parameters, wherein the first target ionospheric delay parameter is used for positioning.

[0074] After steps S110 and S120, if the changes in the state vector and error covariance matrix in the solution parameters based on the iterations of the previous moments are small, then step S130 is determined to be executed, wherein the previous moments are determined based on the current moment; if the changes are large, then based on the positioning observation information collected at the next moment, the Kalman filter is iterated until the iterative solution parameters change little, then step S130 is determined to be executed. That is, the above steps S110 and S120 are an iteration between a certain moment and the next moment of the Kalman filter to obtain the solution parameters corresponding to the correlation constraint. That is, in a possible implementation of the present application, an initial constraint is generated for the Kalman filter based on the electronic content information, so that the Kalman filter continues to iterate based on the solution parameters of the constrained Kalman filter and the positioning observation information at the next moment until the change is small, the final solution parameters are obtained, and then the following step S130 is executed. Whether the change is small can be judged by setting a preset threshold. If it is less than the preset threshold, it is determined that the change is small. If it is greater than or equal to the preset threshold, it is determined that the change is large. The above-mentioned implementation method of judging whether the Kalman filter should proceed to the next iteration based on whether the change is small is an example implementation method. Specifically, it can also be judged by setting external conditions, solving the state vector and error covariance in the parameters to see whether you are stable, etc. The embodiments of this application do not impose any restrictions on this.

[0075] The solution parameters include parameters for generating a first target ionospheric delay parameter of the first single measuring station. The parameter is a state vector, and the first target ionospheric delay parameter of the first single measuring station is extracted from the state vector.

[0076] The first target ionospheric delay parameter may be of a floating point type or a fixed type.

[0077] In some embodiments, the first target ionospheric delay parameter is of a fixed type, and an intermediate delay vector is extracted from the state vector. The intermediate delay vector is called a floating-point solution vector, and a single-point fixed intermediate delay vector is used to obtain a fixed solution vector. The first target ionospheric delay parameter of the first single measuring station is extracted from the fixed solution vector.

[0078] In some embodiments, if the first target ionospheric delay parameter is of a floating-point type, the floating-point first target ionospheric delay parameter is fixed at a single point to obtain a fixed-type first target ionospheric delay parameter.

[0079] For example, single-point fixation of floating-point type parameters may be implemented using Precise Point Positioning-Ambiguity Resolution (PPP-AR) technology.

[0080] Optionally, the ionospheric delay parameters determined in the related art include receiver DCBs, so the receiver DCBs cannot be determined. However, in the embodiment of the present application, by introducing electron content information for constructing constraint information, the Kalman filter constrained by the constraint information estimates the ionospheric delay parameters based on the accuracy benchmark of the introduced electron content information. The benchmark of the first target ionospheric delay parameter estimated by the Kalman filter is consistent with the benchmark of the electron content information, that is, the receiver DCBs are separated from the determined first target ionospheric delay parameter based on the benchmark based on the electron content information. The higher the accuracy benchmark of the electron content information, the higher the corresponding accuracy of the determined first target ionospheric delay parameter, that is, the more accurate the value of the separated receiver DCBs, and accordingly, the receiver DCBs can also be determined from the Kalman filter.

[0081] By determining the first target ionospheric delay parameter as a fixed solution, the accuracy of the determined first target ionospheric delay parameter is guaranteed, and at the same time, it is convenient to establish an ionospheric model when applied to the server, thereby improving the adaptability of the first target ionospheric delay parameter to the establishment of the ionospheric model and correspondingly improving the efficiency of establishing the ionospheric model.

[0082] The ionospheric modeling method for applied positioning originated from NRTK technology. However, NRTK technology uses the residual after atmospheric differential to establish the ionospheric model, which means that the modeling data has eliminated the interference between the satellite end and the receiver DCBs. For the related technology PPP-RTK technology, the temporal and spatial variation characteristics of the ionosphere are altitude and area, while the temporal and spatial variation characteristics of the receiver DCBs are frequency. The temporal and spatial variation characteristics of the two are inconsistent, especially when the receiver types are different. That is, the ionospheric delay parameters determined in the related art include receiver DCBs, and the ionospheric model is established based on the ionospheric delay parameters including the receiver DCBs, so that the accuracy of the established ionospheric model is affected by different receiver DCBs, resulting in low accuracy of the ionospheric model. To solve this technical problem, in an optional embodiment of the present application, a positioning method based on correlation constraints can be applied to a first server end, and the first server end includes multiple first single measurement stations for signal collection of target satellite signals. The first single measurement stations constitute a measurement station area for common view of the target satellite, and the measurement station area can become a regional reference network.

[0083] After determining the first target ionospheric delay parameter of the first single measuring station based on the solution parameter, it also includes constructing a first ionospheric model based on the first target ionospheric delay parameters of multiple first single measuring stations, so as to perform positioning using the constructed first ionospheric model.

[0084] In some embodiments, positioning using the first ionosphere model may include sending the constructed first ionosphere model to the client, so that the client performs positioning based on the first ionosphere model.

[0085] In some embodiments, the ionospheric model may be constructed by constructing an ionospheric model to be solved, substituting the first target ionospheric delay parameters of multiple first single measurement stations into the ionospheric model to be solved, and solving the ionospheric model to obtain a first ionospheric model.

[0086] Among them, the first target ionospheric delay parameters of multiple first single measuring stations are substituted into the ionospheric model to be solved and solved. In the process of obtaining the first ionospheric model, the coordinates of the center of gravity and the longitude and latitude information of the multiple first single measuring stations can be obtained. The coordinates of the center of gravity, the longitude and latitude information of the multiple first single measuring stations and the first target ionospheric delay parameters of the multiple first single measuring stations are substituted into the ionospheric model to be solved, and the first ionospheric model is obtained by solving. The center of gravity is a reference point set in the measuring station area.

[0087] The formula for obtaining the first ionospheric model is given by the following formula (7):

[0088]

[0089] in, represents the first target ionospheric delay parameter between the first single station i and the target satellite s (i = 1, 2, ..., n); n is the total number of first single stations in common view with respect to the target satellite; α0, α1, α2 and α3 are the model coefficients of the ionospheric model to be solved; (φ i ,λ i ) represents the latitude and longitude of the first single measuring station i, and (φ0, λ0) represents the longitude and latitude coordinates of the centroid.

[0090] The model coefficients of the ionospheric model to be solved are solved based on the first target ionospheric delay parameters of multiple first single measuring stations to obtain a first ionospheric model so that positioning can be performed using the first ionospheric model. The first ionospheric model is constructed using the high-precision first target ionospheric delay parameters, so that the model processing of the constructed first ionospheric model is also high-precision, thereby improving the accuracy of positioning using the first ionospheric model.

[0091] The server improves the accuracy of the first target ionospheric delay parameter determined based on the Kalman filter based on the electron content information, and performs ionospheric delay modeling based on the first target ionospheric delay parameters of multiple first single stations to obtain a first ionospheric model, and broadcasts the first ionospheric model to the client. The client needs to perform further data processing and positioning based on the data played by the server, so the client and the server must be consistent in data accuracy. Therefore, after the client receives the first ionospheric model obtained by modeling based on the high-precision first target ionospheric delay parameter broadcast by the server, it is necessary to improve the accuracy of the data processing performed on the client itself, that is, to improve the accuracy of the estimated ionospheric delay parameter. Therefore, in another optional embodiment of the present application, a positioning method based on correlation constraints can be applied to the client, and the first single station is the station to be positioned, wherein the station to be positioned can be fixed or mobile.

[0092] Exemplarily, the client may be a car computer, a mobile terminal, a smart watch or other device, and correspondingly these devices may be equipped with a processing unit for executing steps such as generating constraint information, constraining the Kalman filter based on the constraint information, determining the first target ionospheric delay parameter, and determining the client positioning.

[0093] The electron content information may be generated through GIMs or through a second ionospheric model obtained by the second server based on the second target ionospheric delay parameter of the second single measuring station. The manner in which the second server obtains the second ionospheric model based on the second target ionospheric delay parameter of the second single measuring station is the same as or similar to the manner in which the first server obtains the first ionospheric model based on the first target ionospheric delay parameter of the first single measuring station, and is not further described herein.

[0094] If the electron content information is generated via GIMs, the Kalman filter in the client performs corresponding processing based on the electron content information generated via GIMs and the third ionospheric model broadcast by the third server to obtain an estimated third target ionospheric delay parameter, so that the client can use the third target ionospheric delay parameter for positioning. The third ionospheric model can be a third ionospheric model constructed using a fourth target ionospheric delay parameter obtained using a correlation-constrained positioning method, or an ionospheric model constructed using ionospheric delay parameters obtained in related techniques. The third ionospheric model serves to provide initial model parameters for the Kalman filter to estimate the first target ionospheric model, without the need for providing correlation constraints.

[0095] If the electron content information is generated by a second ionospheric model obtained by the second server based on the second target ionospheric delay parameter of the second single measuring station, the second ionospheric model correspondingly provides correlation constraints for the first target ionospheric delay parameter performed by the Kalman filter in the client, so as to improve the accuracy of the determined first target ionospheric delay parameter.

[0096] The following combined Figure 2 , for the above Figure 1 The positioning method based on correlation constraint is further explained. Figure 2 This is a schematic flowchart of another positioning method based on correlation constraints provided in an embodiment of the present application.

[0097] Figure 2 In the method shown, the server target ionospheric delay parameter is determined for any single station from single station 1 to single station n; based on the server target ionospheric delay parameters corresponding to single stations 1 to single station n, single satellite modeling is performed for the target satellite s to obtain an ionospheric model; the client target ionospheric delay parameter is determined for the station to be located by the client, and the station to be located is located based on the determined client target ionospheric delay parameter.

[0098] The target ionospheric delay parameter of the server is determined for any single measuring station from single measuring station 1 to single measuring station n. The single measuring station x is taken as an example for illustration. The single measuring station x is any one of the single measuring station 1 to single measuring station n.

[0099] Single station x collects signals from the target satellite, obtaining initial observation data. It also acquires initial orbit data and initial clock data from the satellite broadcast ephemeris. The observation data is then determined based on these initial observation data, initial orbit data, and initial clock data. The coordinates of single station x are fixed. Single station x acquires satellite orbit (ORB) correction products, satellite clock (CLK) correction products, satellite differential code bias (DCBs) correction products, and uncalibrated phase delay (UPDs) correction products. Data preprocessing is performed based on the acquired observation data, fixed coordinates, and satellite orbit correction products, satellite clock correction products, satellite differential code bias correction products, and uncalibrated phase delay correction products. Data preprocessing removes poor quality and incomplete data from the observations. The carrier phase observations in the observations are checked for whole-cycle jumps (i.e., phase discontinuities). If so, these anomalies are repaired or marked to obtain phase-continuous carrier phase observations.

[0100] The pre-processed data and the electron content information generated by GIMs are input into the Kalman filter, and constraint information is generated based on the pre-processed data and the electron content information, so that the Kalman filter constrains the estimation process of the ionospheric delay parameters based on the correlation between the receiver differential code bias and the ionospheric delay parameters, and the solution parameters of the Kalman filter are obtained.

[0101] Based on the solution parameters, the Kalman filter is detected to see whether it meets the conditions for stopping iteration. If not, the data after data preprocessing at the next moment is input into the Kalman filter to make the Kalman filter iterate, and the cycle repeats until the conditions for stopping iteration are met. The solution parameters are then determined to be the final solution parameters.

[0102] The server target ionospheric delay parameter for a single station x is extracted from the state vector included in the solution parameters, along with the receiver DCBs. The extracted server target ionospheric delay parameter is of floating-point type, which is fixed using PPP-AR to obtain a fixed server target ionospheric delay parameter.

[0103] Among them, single-star modeling is performed for the target satellite s to obtain the ionospheric model, which includes: constructing the ionospheric model to be solved, solving the ionospheric model to be solved based on the server-side target ionospheric delay parameters of single-station 1-single-station n, the coordinates of the center of gravity point, and the longitude and latitude information of single-station 1-single-station n, obtaining the ionospheric model for single-star modeling of the target satellite s, and broadcasting the ionospheric model obtained by the single-star modeling to the client.

[0104] The determination of the client's target ionospheric delay parameters for the station to be located by the client includes: the station to be located obtains observation data and satellite orbit (ORB) correction products / satellite clock error (CLK) correction products, satellite differential code bias (DCBs) correction products / uncalibrated phase delay (UPDs) correction products for data preprocessing, removes poor quality and incomplete data in the observation data, and detects whether the carrier phase observation value in the observation data has a whole cycle jump (i.e., phase discontinuity). If so, these abnormal points are repaired or marked to obtain phase-continuous carrier phase observation values. The server receives the ionospheric model broadcast by the server, obtains the electron content information generated by the ionospheric model, and inputs the preprocessed observation data and electron content information into the Kalman filter. Constraint information is then generated based on the preprocessed observation data and electron content information, and the Kalman filter is constrained based on the constraint information to obtain solution parameters. The client ionospheric delay parameter is determined based on the solution parameters in the same manner as the server target ionospheric delay parameter for a single station x. Determining the client ionospheric delay parameter also determines the receiver DCBs. The client target ionospheric delay parameter is of floating-point type. PPP-AR is used to fix the floating-point client target ionospheric delay parameter to obtain a fixed client target ionospheric delay parameter. By fixing the client target ionospheric delay parameter to a fixed type, it is possible to subsequently locate the station to be located based on the fixed client ionospheric delay parameter, thereby obtaining a PPP-RTK positioning result.

[0105] As can be seen from the above, the present application constructs constraint information through electron content information and positioning observation information, and uses the constraint information to constrain the Kalman filter's estimation process of the ionospheric delay parameters, so that when the solution parameters in the constrained Kalman filter are used for parameter estimation, high-precision ionospheric delay parameters can be obtained, thereby improving the accuracy of the positioning results based on the ionospheric delay parameters.

[0106] Figure 3 This is a schematic structural diagram of a computer device provided in an embodiment of the present application. Figure 3 The dotted line in the figure indicates that the unit or module is optional. The computer device 300 can be used to implement the method described in the above method embodiment.

[0107] The computer device 300 may include one or more processors 310. The processor 310 may support the computer device 300 to implement the method described in the above method embodiment. The processor 310 may be a general-purpose processor or a special-purpose processor. For example, the processor may be a central processing unit (CPU). Alternatively, the processor may be another general-purpose processor, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field programmable gate array (FPGA) or other programmable logic device, a discrete gate or transistor logic device, a discrete hardware component, etc. The general-purpose processor may be a microprocessor or the processor may be any conventional processor, etc.

[0108] The computer device 300 may further include one or more memories 320. The memories 320 store computer programs that can be executed by the processor 310, causing the processor 310 to perform the methods described in the above method embodiments. The memories 320 may be independent of the processor 310 or integrated into the processor 310.

[0109] The computer device 300 may further include a transceiver 330, and the processor 310 may communicate with other devices via the transceiver 330. For example, the processor 310 may transmit and receive data with other devices via the transceiver 330.

[0110] In one embodiment of the present application, the above components of the computer device 300 and Figure 3 Other components not shown in the figure may also be connected to each other. It should be understood that Figure 3 The computer device structure block diagram shown is for illustrative purposes only and is not intended to limit the scope of the present application. Those skilled in the art may add or replace other components as needed.

[0111] The above is a schematic diagram of a computer device according to this embodiment. It should be noted that the technical solution of this computer device and the technical solution of the aforementioned correlation-constrained positioning method are based on the same concept. For details not described in detail in the technical solution of the computer device, please refer to the description of the technical solution of the aforementioned correlation-constrained positioning method.

[0112] In addition, an embodiment of the present application also proposes a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a computer, the operations in the correlation constraint-based positioning method provided in the above embodiment are implemented. The specific steps are not repeated here.

[0113] The present application also provides a computer program product. The computer program product includes a program / instruction. When the computer program / instruction is executed by a processor, the steps of the above-mentioned correlation constraint-based positioning method are implemented.

[0114] It should be noted that, in this document, relational terms such as first and second, etc., are used only to distinguish one entity / operation / object from another entity / operation / object, and do not necessarily require or imply any actual relationship or order between these entities / operations / objects; the terms "include", "comprise", or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, article, or system that includes a series of elements includes not only those elements, but also other elements not explicitly listed, or elements inherent to such process, method, article, or system. In the absence of further limitations, an element defined by the phrase "includes a ..." does not exclude the presence of other identical elements in the process, method, article, or system that includes the element.

[0115] As for the device embodiment, since it is basically similar to the method embodiment, the description is relatively simple. For relevant details, please refer to the partial description of the method embodiment. The device embodiment described above is merely illustrative, and the units described as separate components may or may not be physically separated. Some or all of the modules can be selected according to actual needs to achieve the purpose of the present application scheme. Those of ordinary skill in the art can understand and implement it without paying any creative work.

[0116] The serial numbers of the above embodiments of the present application are for description only and do not represent the advantages or disadvantages of the embodiments.

[0117] Through the description of the above implementation methods, those skilled in the art can clearly understand that the above-mentioned embodiment methods can be implemented by means of software plus the necessary general hardware platform, and of course can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of the present application is essentially or the part that contributes to the prior art can be embodied in the form of a software product, which is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) as described above, and includes a number of instructions for enabling a terminal device (which can be a mobile phone, computer, server, TV, or network device, etc.) to execute the methods described in each embodiment of the present application.

[0118] The above are merely embodiments of the present application and are not intended to limit the patent scope of the present application. Any equivalent structure or equivalent process transformation made using the contents of the present application specification and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present application.

Claims

1. A positioning method based on correlation constraints, characterized in that: The method comprises: Constructing constraint information based on the electron content information and the positioning observation information, wherein the positioning observation information is obtained by collecting signals from the target satellite by the first single measuring station; constraining a Kalman filter according to the constraint information to obtain solution parameters of the Kalman filter, wherein the Kalman filter is used to estimate the ionospheric delay parameter based on a correlation between a receiver differential code deviation and the ionospheric delay parameter; Based on the solution parameters, a first target ionospheric delay parameter of the first single measuring station is determined, wherein the first target ionospheric delay parameter is used for positioning.

2. The method according to claim 1, characterized in that The constraining information is constructed based on the electron content information and the positioning observation information, including: determining a covariance matrix based on the electron content information; Determining a constrained ionospheric delay parameter generated based on the electron content information and an initial ionospheric delay parameter estimated by a Kalman filter based on positioning observation information; Determining an observation matrix and a residual vector according to the constrained ionospheric delay parameter and the initial ionospheric delay parameter; Constraint information is constructed based on the covariance matrix, the observation matrix and the residual vector.

3. The method according to claim 2, characterized in that The distribution path of the electron content information is a vertical path; and determining a covariance matrix based on the electron content information includes: Projecting the electron content information from a vertical path to a slant path using a slant path projection function and the altitude angle of the target satellite; Based on the electron content information of the oblique paths, a covariance matrix is ​​generated.

4. The method according to claim 1, wherein The constraining the Kalman filter according to the constraint information to obtain the solution parameters of the Kalman filter includes: Substituting the constraint information into an initial solution equation of the Kalman filter, and solving the initial solution equation to obtain solution parameters of the Kalman filter.

5. The method according to claim 1, wherein The determining, based on the calculated parameters, a first target ionospheric delay parameter of the first single measuring station includes: Determining a floating-point solution vector based on the solution parameters; The floating-point solution vector is fixed at a single point to obtain a fixed solution vector, and a first target ionospheric delay parameter of the first single measuring station is extracted from the fixed solution vector.

6. The method according to claim 1, characterized in that The method is applied to a first server, the first server comprising a plurality of first single measurement stations for collecting signals from the target satellite, the plurality of first single measurement stations constituting a measurement station area for common viewing of the target satellite; After determining the first target ionospheric delay parameter of the first single measuring station based on the solution parameter, the method further includes: Constructing an ionospheric model to be solved, and obtaining coordinates of a center of gravity point and latitude and longitude information of the plurality of first single measuring stations, wherein the center of gravity point is a reference point set in the measuring station area; Substituting the coordinates of the center of gravity, the longitude and latitude information of the multiple first single measurement stations, and the first target ionospheric delay parameters of the multiple first single measurement stations into the ionospheric model to be solved, and obtaining a first ionospheric model, wherein the first ionospheric model is used for positioning.

7. The method according to claim 1, characterized in that The method is applied to a client, and the first single station is a station to be positioned.

8. The method according to claim 7, characterized in that The electron content information is generated by a second ionospheric model obtained by the second server based on a second target ionospheric delay parameter of a second single measuring station.

9. The method according to any one of claims 1 to 7, characterized in that The electron content information is generated by GIMs.

10. A computer device, characterized in that: The method comprises a memory and a processor, wherein the memory is used to store a computer program, and the processor is used to implement the method according to any one of claims 1 to 9 when executing the computer program.

11. A computer-readable storage medium, characterized in that The computer-readable storage medium stores a program of a positioning method based on correlation constraints, and when the program of the positioning method based on correlation constraints is executed by a processor, the method according to any one of claims 1 to 9 is implemented.

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