Unified estimation and integration method for GNSS code bias based on selected fiducials

Abstract

The application discloses a unified estimation and comprehensive method for GNSS code bias based on selected reference, and aims at solving the estimation and conversion of GNSS code bias in the existing multi-frequency and multi-type signal environment. The traditional DCB and OSB estimation method has problems of complicated steps, low efficiency, inconsistent estimation results and the like, and the application can efficiently and flexibly realize the unified estimation and comprehensive of multi-frequency and multi-type GNSS code bias based on the rank deficiency analysis on the basis of the non-difference non-combination model and in combination with the selected reference, and ensure the consistency and reliability of the estimation results, support the flexible conversion and comprehensive of the OSB and DCB in the same framework, and be suitable for the comprehensive estimation among multiple subnets and different types. The application has high universality, flexibility and adaptability, and can be widely applied to the fields of GNSS positioning, navigation, timing, atmospheric monitoring and the like, and meets the needs of the modern GNSS system for high-precision, high-efficiency estimation and comprehensive integration.

Description

TECHNICAL FIELD

[0001] The application relates to an improvement of a global navigation satellite system data processing technology and belongs to the field of GNSS data processing, in particular to a GNSS code bias unified estimation and comprehensive method based on selected reference. BACKGROUND

[0002] Code bias is an important error source in GNSS data processing. Code bias refers to the time interval from satellite signal generation to antenna transmission and the time interval from signal arrival at the receiver antenna to reception by the receiver, also known as hardware delay or instrument bias; the traditional differential code bias (DCB) method parameterizes the code bias error in a differential form.

[0003] However, the traditional method is only suitable for application in a single-frequency or single-type signal environment. When in a multi-frequency and multi-type signal environment, the traditional method has problems such as non-uniform definition of difference types and complicated estimation process. Subsequently, with the proposal of OSB, the code observation value is expressed by type to solve these problems.

[0004] The existing OSB estimation method based on non-difference and non-combination has not fully considered how to uniformly and compatibly estimate DCB in the framework of OSB estimation. Due to the selection of parameterization methods in the process of GNSS code bias estimation, there are systematic biases in the estimation of OSB or DCB of different GNSS observation subnets, and it is difficult to seamlessly convert and integrate products between different data sources and subnets, which cannot solve the unified estimation and comprehensive problem of different types of code bias (such as common OSB and DCB) in a multi-frequency and multi-system environment.

[0005] The Chinese patent application with the application number CN202310356451.8 and the application date of April 6, 2023 discloses a unified OSB estimation method for multi-frequency and multi-type GNSS signals. First, an extended multi-frequency geometry-free model for uniformly processing multi-system, multi-frequency and multi-type original observation data is proposed. This model can extract various linear combinations of the OSB of interest in one step using non-difference and non-combination observation values. Then, after ionospheric modeling based on a single station, the previously obtained OSB linear combinations of various types are regarded as virtual observation values. Under the guidance of the theory of rank deficiency, the unified rank deficiency type and number are identified, and the corresponding rank deficiency constraint application scheme is proposed. Finally, the full-rank equation containing all types of OSB parameters is obtained, thereby realizing a unified, flexible and accurate full constellation, full frequency point and full type OSB estimation method to meet the code bias correction application requirements of current multi-mode, multi-frequency and multi-signal type GNSS data processing. However, the above-mentioned scheme only solves the estimation of OSB and does not solve the unified estimation and comprehensive problem of different types of code bias in a multi-frequency and multi-system environment.

[0006] The information disclosed in this Background section is only for increasing the understanding of the general background of the present patent application and should not be taken as an acknowledgement or any form of suggestion that this information forms the prior art that is already publicly known. SUMMARY

[0007] The purpose of the present application is to overcome the problem in the prior art that different types of code bias cannot be uniformly estimated and integrated in a multi-frequency multi-system environment, and to provide a GNSS code bias uniform estimation and integration method based on selected reference, which can uniformly estimate and integrate different types of code bias in a multi-frequency multi-system environment.

[0008] To achieve the above purpose, the technical solution of the present application is: a GNSS code bias uniform estimation and integration method based on selected reference, which comprises the following steps:

[0009] Firstly, the global multi-station virtual observation equation of subnetwork A and subnetwork B is obtained by using the same method, and the estimation method comprises any one or any combination of them, as follows:

[0010] Firstly, when estimating by subnetwork A;

[0011] The global multi-station virtual observation equation of subnetwork A is subjected to the zero mean condition of satellite end and receiver end, the zero ionosphere condition of satellite end and the zero ionosphere condition of receiver end, so as to obtain the final full-rank OSB estimation method equation, and the OSB estimation result is solved;

[0012] The global multi-station virtual observation equation of subnetwork A is subjected to the zero mean condition of satellite end and receiver end, the zero ionosphere condition of satellite end and the zero ionosphere condition of receiver end, so as to obtain the final full-rank OSB estimation method equation, and the OSB estimation result is solved;

[0013] Secondly, when estimating by subnetwork B;

[0014] The global multi-station virtual observation equation of subnetwork B is subjected to the zero mean condition of satellite end and receiver end, the zero ionosphere condition of satellite end and the zero ionosphere condition of receiver end, so as to obtain the final full-rank OSB estimation method equation, and the OSB estimation result is solved;

[0015] The global multi-station virtual observation equation of subnetwork B is subjected to the zero mean condition of satellite end and receiver end, the zero ionosphere condition of satellite end and the zero ionosphere condition of receiver end, so as to obtain the final full-rank OSB estimation method equation, and the OSB estimation result is solved;

[0016] Thirdly, when estimating DCB or OSB by subnetwork A and subnetwork B;

[0017] Firstly, the parameter list of subnets A and B is reorganized, and then the normal equations of subnets A and B are integrated based on the parameter list, and the constraint normal equations estimated by OSB are superimposed to obtain the full-rank normal equations of subnets A and B for comprehensive estimation of OSB, and then the estimation results of OSB of the comprehensive subnets A and B are solved by the least square method.

[0018] Firstly, the parameter list of subnets A and B is reorganized, and then the normal equations of subnets A and B are integrated based on the parameter list, and the constraint normal equations estimated by DCB are superimposed to obtain the full-rank normal equations of subnets A and B for comprehensive estimation of DCB, and then the estimation results of DCB of the comprehensive subnets A and B are solved by the least square method.

[0019] The global multi-station virtual observation equation of subnet A and subnet B is obtained by the same method, which is specifically:

[0020] a. First, the pseudorange and phase observation data of the GNSS single station in subnet A in the station network are obtained to construct the original observation equation of the GNSS pseudorange and phase observation data, and then the rank defect in the original observation equation is eliminated by parameter reorganization, and the original code bias of different frequencies is absorbed to obtain a full-rank no-geometry model;

[0021] b. First, the local ionospheric vertical total electron content of the GNSS single station is modeled according to the phase observation data of the GNSS single station and combined with the GTS model to obtain an ionospheric model, and then the code bias information is separated from the ionospheric model by solving the sequential least square method combined with the ionospheric model and the full-rank no-geometry model;

[0022] c. First, the separated code bias information is taken as a virtual observation value, and then the single-station virtual observation equation is constructed by combining the virtual observation value and the code bias information, and the single-station virtual observation equation is solved by the least square method to obtain a single-station normal equation;

[0023] d. First, the single-station normal equations of all stations in subnet A are combined, and then the global multi-station virtual observation equation of subnet A is obtained by superposition;

[0024] The global multi-station virtual observation equation of subnet B in the station network is obtained according to the above steps.

[0025] The code bias information is separated from the ionospheric model by solving the sequential least square method combined with the ionospheric model and the full-rank no-geometry model, which is specifically:

[0026] The ionospheric model is as follows:

[0027] ;

[0028] wherein, ; is a coefficient to convert ionospheric delay into total electron content, is a single layer mapping function to convert slant STEC into VTEC, wherein denotes the zenith distance of the satellite with respect to the receiver; is a local ionospheric VTEC model based on GTS, which is a function of geographic latitude and local time of the piercing point , the code bias information and the coefficients of the single station VTEC model are estimated based on sequential least squares method by solving total normal equations from all stacked observations at the same station.

[0029] The conversion function from STEC to VTEC is:

[0030] ;

[0031] wherein R is the radius of the earth, taken as 6371 km, H denotes the height of the ionospheric shell model, denotes the zenith distance of the satellite with respect to the receiver.

[0032] The separated code bias information is first taken as a virtual observation value, then combined with the code bias to construct a single station virtual observation equation, and the single station virtual observation equation is solved by least squares method to obtain the single station normal equation, which is specifically:

[0033] The three terms , , of the code bias information of each station are taken as virtual observation values, and the mathematical relationship between them and the original undifferenced code bias is:

[0034] ;

[0035] The code bias estimation needs to first traverse the observation data of all stations in the station network to obtain a list of global parameters to be estimated, and then establish a single station virtual observation equation based on the above mathematical relationship:

[0036] , wherein, is the station number, is the residual vector; is the coefficient matrix, is the observation vector, including all the above three types of virtual observation values; is composed of the variance and covariance information of the estimated three types of virtual observation values; is the corresponding weight matrix;

[0037] According to the single station observation equation, a least square equation is obtained:

[0038] According to the results of all stations in the joint station network, a superposition equation is obtained ; that is ;

[0039] where n is the number of stations.

[0040] The rank-deficient equation is The constraint equation of the OSB / DCB estimation is superimposed , and the final full-rank solvable total equation is obtained , that is .

[0041] There are three types of rank deficiency in the estimation system, specifically:

[0042] Caused by the linear correlation between satellite and receiver code biases, the rank deficiency number is the number of code bias types;

[0043] Caused by the linear correlation between different types of code biases of each satellite, the rank deficiency number is the number of satellites;

[0044] Caused by the linear correlation between different types of code biases of each receiver, the rank deficiency number is the number of stations.

[0045] In the second step, the zero mean conditions applied to the satellite end and the receiver end, the zero ionosphere conditions at the satellite end, and the zero ionosphere conditions at the receiver end of the OSB estimation are specifically:

[0046] A, the zero mean conditions at the satellite end and the receiver end;

[0047] ;

[0048] Under this constraint, the code biases at the satellite end and the receiver end are separated;

[0049] B, the zero ionosphere condition at the satellite end;

[0050] ; wherein , ;

[0051] C, the zero ionosphere condition at the receiver end;

[0052] .

[0053] The specific type zero conditions applied to the satellite end and the receiver end of the DCB are specifically:

[0054] A, the specific type zero condition at the satellite end;

[0055] ;

[0056] B, receiver end specific type zero condition;

[0057] .

[0058] The each type of reference constraint, column constraint equation: Wherein, Indicates the constraint type, Is the design matrix corresponding to the specific constraint, Is a column vector composed of constraint values, Is the weight matrix;

[0059] According to the constraint equation, the constraint equation corresponding to each type of rank deficiency can be obtained:

[0060] And then superimpose three types of constraint equations to obtain the total constraint equation Wherein , is the total constraint type number.

[0061] Compared with the prior art, the beneficial effects of the present application are:

[0062] 1、The GNSS code bias unified estimation and comprehensive method based on selected reference in the application adopts the superposition equation mode in the code bias estimation, superimposes the basic rank deficiency equation driven by the virtual observation value and the constraint equation driven by the reference selection, can freely select the reference according to the actual demand, superimposes the corresponding constraint equation, and then flexibly realizes the estimation of various types of code bias, and OSB and DCB can be compatible in a multi-frequency multi-system environment, breaks through the limitations of the prior art, meets the current application requirements of unified, efficient and accurate expression and estimation of multi-frequency and multi-type GNSS code bias, and can better serve the GNSS positioning, navigation, timing and atmospheric monitoring and modeling fields. Therefore, the application can unify and comprehensively estimate different types of code bias in a multi-frequency multi-system environment.

[0063] 2、The GNSS code bias unified estimation and comprehensive method based on selected reference in the application ensures the consistency between the estimation results of different types of code bias through the rank deficiency elimination method of selected reference, and in the traditional method, the estimation of OSB and DCB usually depends on different reference frequencies or code types, so that the references are inconsistent between different products. The scheme eliminates such inconsistency through unified constraint conditions, can adapt to a multi-frequency and multi-type signal environment, and uniformly estimates the code bias of any frequency and signal type, and has strong universality. Therefore, the application has high consistency and high reliability.

[0064] 3、The GNSS code bias unified estimation and comprehensive method based on selected reference, the method also supports flexible conversion and comprehensive of code bias between any subnet and different types, can integrate subnet A and subnet B, first reorganize the parameter list of A and B subnets, including the code bias of all stations and visible satellites of two subnets, then integrate the original saved basic method equation of two subnets based on the adaptive parameter list, and then superimpose the constraint method equation of OSB estimation to obtain the full rank method equation of final subnet A and B comprehensive estimation OSB, and then the OSB estimation result of integrated subnet A and B can be solved, which completely unifies the estimation and unified problem of code bias product, breaks through the limitation of the prior art, and meets the application requirements of unified, efficient and accurate expression and estimation of current multi-frequency and multi-type GNSS code bias. Therefore, seamless conversion and comprehensive estimation between different subnets can be realized. BRIEF DESCRIPTION OF DRAWINGS

[0065] Figure 1 is a flowchart of the present application. DETAILED DESCRIPTION

[0066] The application will be further described in detail in combination with the description of the drawings and specific embodiments.

[0067] Referring to Figure 1 A GNSS code bias unified estimation and comprehensive method based on selected reference, the GNSS code bias unified estimation and comprehensive method based on selected reference comprises the following steps:

[0068] Firstly, the global multi-station virtual observation equation of subnet A and subnet B is obtained by using the same method, and the estimation method comprises any one or any combination of them, and the specific embodiments are as follows:

[0069] Firstly, when estimating by subnet A;

[0070] The global multi-station virtual observation equation of subnet A is subjected to the zero mean condition of satellite end and receiver end, the zero ionosphere condition of satellite end and the zero ionosphere condition of receiver end, so as to obtain the final full rank OSB estimation method equation, and the OSB estimation result is solved;

[0071] The global multi-station virtual observation equation of subnet A is subjected to the satellite end specific type zero condition and the receiver end specific type zero condition, so as to obtain the full rank DCB estimation method equation, and the DCB estimation result is solved;

[0072] Secondly, when estimating by subnet B;

[0073] The global multi-station virtual observation equation of the subnet B is subjected to the satellite end and receiver end zero mean conditions, the satellite end zero ionosphere condition and the receiver end zero ionosphere condition to obtain a final full-rank OSB estimation method equation, and the OSB estimation result is solved;

[0074] The global multi-station virtual observation equation of the subnet B is subjected to the satellite end and receiver end zero mean conditions, the satellite end zero ionosphere condition and the receiver end zero ionosphere condition to obtain a final full-rank OSB estimation method equation, and the OSB estimation result is solved;

[0075] In the third step, the DCB or OSB is estimated uniformly in the subnet A and the subnet B;

[0076] Firstly, the parameter list of the subnet A and the subnet B is reorganized, then the method equations of the subnet A and the subnet B are integrated based on the parameter list, and the constraint method equation of the OSB estimation is superimposed to obtain a full-rank method equation for the comprehensive OSB estimation of the subnet A and the subnet B, and then the OSB estimation result of the comprehensive subnet A and the subnet B is solved by the least square method;

[0077] Firstly, the parameter list of the subnet A and the subnet B is reorganized, then the method equations of the subnet A and the subnet B are integrated based on the parameter list, and the constraint method equation of the DCB estimation is superimposed to obtain a full-rank method equation for the comprehensive DCB estimation of the subnet A and the subnet B, and then the DCB estimation result of the comprehensive subnet A and the subnet B is solved by the least square method.

[0078] The global multi-station virtual observation equation of the subnet A and the subnet B is obtained by the same method, which is specifically:

[0079] a. The pseudorange and phase observation data of the GNSS single station in the subnet A in the station network are acquired first to construct the original observation equation of the GNSS pseudorange and phase observation data, then the rank defect in the original observation equation is eliminated by parameter reorganization, and the original code bias of different frequencies is absorbed to obtain a full-rank no-geometry model;

[0080] b. The local ionosphere vertical total electron content of the GNSS single station is modeled first according to the phase observation data of the GNSS single station and combined with the GTS model to obtain an ionosphere model, then the code bias information is separated from the ionosphere model by the sequential least square method combined with the ionosphere model and the full-rank no-geometry model;

[0081] c. The separated code bias information is taken as a virtual observation value first, then the single-station virtual observation equation is constructed combined with the virtual observation value and the code bias information, and the single-station method equation is obtained by solving the single-station virtual observation equation by the least square method;

[0082] d. The single-station method equations of all stations in the subnet A are combined first, and then the global multi-station virtual observation equation of the subnet A is obtained by superposition;

[0083] According to the above steps, the global multi-station virtual observation equation of the sub-network B in the station network is obtained.

[0084] The local ionospheric vertical total electron content of the GNSS single station is modeled according to the phase observation data of the GNSS single station and in combination with the GTS model, so as to obtain an ionospheric model, and then the ionospheric model and the full-rank non-geometry model are combined, and the code bias information is separated from the ionospheric model through the sequential least squares method, specifically as follows:

[0085] The ionospheric model is as follows:

[0086] ;

[0087] Wherein, ; is a coefficient for converting ionospheric delay into total electron content, is a single-layer mapping function for converting oblique STEC into VTEC, wherein represents the zenith distance of the satellite relative to the receiver; is a local ionospheric VTEC model based on GTS, which is a function of geographic latitude and local time of the piercing point , the code bias information and the coefficient of the single-station VTEC model are estimated based on the sequential least squares method from the total normal equation superimposed from all of the same station.

[0088] The conversion function of STEC to VTEC is as follows:

[0089] ;

[0090] Wherein, R is the radius of the earth, taking 6371km, H represents the height of the ionospheric thin layer model, represents the zenith distance of the satellite relative to the receiver.

[0091] The separated code bias information is taken as a virtual observation value, and then the virtual observation value and the code bias are combined to construct a single-station virtual observation equation, and the single-station virtual observation equation is solved through the least squares method to obtain a single-station normal equation, specifically as follows:

[0092] The three terms , , of each station containing code bias information are taken as virtual observation values, and the mathematical relationship between the virtual observation values and the original undifferenced code bias is as follows:

[0093] ;

[0094] Code bias estimation needs to traverse all the observation data of the stations in the network to obtain the list of global parameters to be estimated, and then to establish the single station virtual observation equation based on the above mathematical relationship:

[0095] , wherein, is the station number, is the residual vector; is the coefficient matrix, is the observation vector, including all the above three types of virtual observation values; is composed of the variance and covariance information of the estimated three types of virtual observation values; is the corresponding weight matrix;

[0096] According to the single station observation equation, the least squares equation can be obtained:

[0097] , the results of all stations in the network are combined, and the superposition equation can be obtained ; that is ;

[0098] Wherein, n is the number of stations.

[0099] The rank-deficient equation is The constraint equation of the OSB / DCB estimation is superimposed , that is, the final full-rank solvable total equation is , that is .

[0100] The estimation system has three types of rank deficiency, specifically:

[0101] Caused by the linear correlation between satellite and receiver code bias, the rank deficiency number is the number of code bias types;

[0102] Caused by the linear correlation between different types of code bias of each satellite, the rank deficiency number is the number of satellites;

[0103] Caused by the linear correlation between different types of code bias of each receiver, the rank deficiency number is the number of stations.

[0104] In the second step, the zero mean condition of the satellite end and the receiver end, the zero ionosphere condition of the satellite end and the zero ionosphere condition of the receiver end imposed by the estimation of OSB are specifically:

[0105] A, the zero mean condition of the satellite end and the receiver end;

[0106] ;

[0107] Under this constraint, the code bias of the satellite end and the receiver end can be separated;

[0108] B, the zero ionosphere condition of the satellite end;

[0109] ; wherein, , ;

[0110] C. Receiver end zero ionosphere condition;

[0111] .

[0112] The satellite end specific type zero condition and the receiver end specific type zero condition applied by the DCB are specifically:

[0113] A. Satellite end specific type zero condition;

[0114] ;

[0115] B. Receiver end specific type zero condition;

[0116] .

[0117] The column constraint equation of each type of reference constraint is: wherein, indicates the constraint type, is a design matrix corresponding to the specific constraint, is a column vector composed of constraint values, is a weight matrix;

[0118] According to the constraint equation, the constraint method equation corresponding to each type of rank deficiency can be obtained:

[0119] , and then the total constraint method equation is obtained by superimposing the three types of constraint method equations wherein is the total number of constraint types.

[0120] Embodiment 1:

[0121] A unified estimation and comprehensive method for GNSS code bias based on selected reference, comprising the following steps:

[0122] First, the station network includes subnetwork A and subnetwork B, and the global multi-station virtual observation equation of subnetwork A and subnetwork B is obtained by using the same method, and the estimation method includes any one or any combination of them, as follows:

[0123] First, when estimating by subnetwork A;

[0124] The satellite end and receiver end zero mean conditions, the satellite end zero ionosphere condition and the receiver end zero ionosphere condition are applied to the global multi-station virtual observation equation of the subnet A, so that a final full-rank OSB estimation method equation is obtained, and an OSB estimation result is obtained by solving;

[0125] The satellite end specific type zero condition and the receiver end specific type zero condition are applied to the global multi-station virtual observation equation of the subnet A, so that a full-rank DCB estimation method equation is obtained, and a DCB estimation result is obtained by solving;

[0126] In the second step, the estimation is performed through the subnet B;

[0127] The satellite end and receiver end zero mean conditions, the satellite end zero ionosphere condition and the receiver end zero ionosphere condition are applied to the global multi-station virtual observation equation of the subnet B, so that a final full-rank OSB estimation method equation is obtained, and an OSB estimation result is obtained by solving;

[0128] The satellite end specific type zero condition and the receiver end specific type zero condition are applied to the global multi-station virtual observation equation of the subnet B, so that a full-rank DCB estimation method equation is obtained, and a DCB estimation result is obtained by solving;

[0129] In the third step, the DCB or OSB is estimated by the subnet A and the subnet B;

[0130] Firstly, the parameter list of the subnet A and the subnet B is reorganized, then the method equations of the subnet A and the subnet B are integrated based on the parameter list, and the constraint method equation of the OSB estimation is superimposed, so that a full-rank method equation for the comprehensive OSB estimation of the subnet A and the subnet B is obtained, and then the OSB estimation result of the comprehensive subnet A and the subnet B is obtained by solving by the least square method;

[0131] Firstly, the parameter list of the subnet A and the subnet B is reorganized, then the method equations of the subnet A and the subnet B are integrated based on the parameter list, and the constraint method equation of the DCB estimation is superimposed, so that a full-rank method equation for the comprehensive DCB estimation of the subnet A and the subnet B is obtained, and then the DCB estimation result of the comprehensive subnet A and the subnet B is obtained by solving by the least square method.

[0132] Embodiment 2:

[0133] Embodiment 2 is basically the same as embodiment 1, and the difference is that:

[0134] The global multi-station virtual observation equation of the subnet A and the subnet B is obtained by the same method, which is:

[0135] a. The pseudorange and phase observation data of the GNSS single station in the subnet A in the station network are obtained first to construct the original observation equation of the GNSS pseudorange and phase observation data, then the rank defect in the original observation equation is eliminated by parameter reorganization, and then the original code bias of different frequencies is absorbed, so that a full-rank geometry-free model is obtained;

[0136] b. First, based on the phase observation data of a single GNSS station and combined with the GTS model, model the total vertical electron content of the local ionosphere of a single GNSS station to obtain the ionospheric model. Then, combine the ionospheric model with the full-rank geometric model and solve it using the sequential least squares method to separate the code deviation information from the ionospheric model.

[0137] c. First, use the separated code deviation information as virtual observations. Then, combine the virtual observations with the code deviation information to construct a single-station virtual observation equation. Finally, solve the single-station virtual observation equation using the least squares method to obtain the single-station method equation.

[0138] d. First, combine the single-station method equations of all stations in the joint subnet A, and then superimpose them to obtain the global multi-station virtual observation equations of subnet A;

[0139] Based on the above steps, obtain the global multi-station virtual observation equations for subnet B in the station network;

[0140] If the station network also includes subnets C, D, etc., the global multi-station virtual observation equations can be obtained by following the steps above. Subnets C, D, etc. can also be estimated by applying different constraints according to different requirements of DCB or OSB, and can also be estimated by combining them.

[0141] Example 3:

[0142] Example 3 is basically the same as Example 1, except that:

[0143] Among the parameters to be estimated in the extended full-rank geometry-free model, The project requires ionospheric modeling to separate the code bias information contained therein. The generalized triangular series (GTS) model based on a single station is used to model the local ionospheric vertical total electron content of the station as follows:

[0144] ;

[0145] in, ; It is the coefficient that converts ionospheric delay into total electron content. It is a single-layer mapping function used to convert skewed STEC to VTEC, where This indicates the zenith distance between the satellite and the receiver; It is a local ionospheric VTEC model based on GTS, which is based on geographic latitude. and the local time of the puncture point The function, based on the sequential least squares method, solves all data from the same station. The superimposed total normal equation is used to estimate the code deviation information. And the coefficients of the single-station VTEC model.

[0146] The conversion function from STEC to VTEC is:

[0147] ;

[0148] Where R is the Earth's radius, taken as 6371 km, and H represents the height of the thin ionospheric model. The zenith distance between the satellite and the receiver is represented by a carefully selected reference method to eliminate rank deficiency, ensuring that the estimation system always maintains full rank. A unified method of superimposing normal equations is adopted to ensure the consistency and reliability of the estimation results for different types of code biases.

[0149] Example 4:

[0150] Example 4 is basically the same as Example 1, except that:

[0151] First, the separated code deviation information is used as virtual observations. Then, the virtual observations and code deviations are combined to construct a single-station virtual observation equation. Finally, the single-station virtual observation equation is solved using the least squares method, resulting in the following single-station method equation:

[0152] Each station includes three items containing code deviation information. , , As a virtual observation, its mathematical relationship with the original difference code deviation is as follows:

[0153] The meaning of all identifiers in the equation being consistent with the identifiers in the preceding equation;

[0154] Code bias estimation first requires traversing the observation data of all stations in the station network to obtain a global list of parameters to be estimated. (All type code deviations of all visible satellites and stations in the station network), and then establish a single-station virtual observation equation based on the above mathematical relationship:

[0155] In the formula, Station number, The residual vector; The coefficient matrix is ​​specifically determined by the model and parameters. The location determines, The observation vector includes all three types of virtual observations mentioned above; It consists of variance and covariance information estimated from three types of virtual observations; This is the corresponding weight matrix;

[0156] The least squares equation can be obtained from the single-station observation equation:

[0157] , the results of all stations in the joint station network are combined,

[0158] The superposition equation can be obtained ;

[0159] That is ;

[0160] Where n is the number of stations, and it needs to be clear that at this time the superposition is still a free rank defect equation and cannot be directly solved.

[0161] The rank defect equation The constraint equation of the OSB / DCB estimate is superimposed again That is, the final full-rank solvable total equation can be obtained That is .

[0162] In the traditional code bias estimation method, it is usually necessary to estimate step by step or by type, which means that different estimation steps need to be used to process different types of code bias. Although this method can complete the estimation task to a certain extent, in a multi-frequency multi-type signal environment, it is often inefficient and difficult to ensure the consistency and conversion between different types of bias. Compared with the prior art, the advantage of the present application is that the "selected reference" constraint method is used, and through a unified mathematical model framework, the unified estimation of the global network and all types of code bias can be realized. The global equation based on the above three types of virtual observations has been obtained, and by identifying and selecting the reference to impose constraints to eliminate the rank defect, different types of code bias estimation under the unified algorithm framework can be realized.

[0163] Caused by the linear correlation between satellite and receiver code bias, the rank defect number is the number of code bias types;

[0164] Caused by the linear correlation between different types of code bias of each satellite, the rank defect number is the number of satellites;

[0165] Caused by the linear correlation between different types of code bias of each receiver, the rank defect number is the number of stations.

[0166] For OSB estimation or DCB estimation, different bias references need to be selected and constraints need to be imposed, as shown in the following table:

[0167] Type of rank deficiency OSB estimation DCB estimation First category Apply a zero-mean condition over all relevant satellites (SZMC) to each code type Apply a zero-mean condition over all relevant satellites (SZMC) to each code type Second category Add a satellite-end zero-ionosphere-free condition (SZIFC) to each satellite Add a satellite-end specific-type-to-zero condition (SZSTC) to each satellite Third category Apply a receiver-end zero-ionosphere-free condition (RZIFC) to each receiver Add a receiver-end specific-type-to-zero condition (RZSTC) to each receiver

[0168] More broadly, OSB and DCB are two common code bias products in actual GNSS application scenarios, which correspond to different reference selection. In other words, the type of code bias is determined by the reference selection, and the unified algorithm framework proposed in the present application can flexibly, rigorously and efficiently estimate any type of code bias, and has high adaptability and universality.

[0169] Example 5:

[0170] Example 5 is substantially the same as Example 1, except that:

[0171] The specific conditions of zero mean at the satellite end and the receiver end, zero ionosphere-free at the satellite end, and zero ionosphere-free at the receiver end imposed by the OSB are:

[0172] A, zero mean at the satellite end and the receiver end (SZMC);

[0173] ;

[0174] Under this constraint, the code bias at the satellite end and the receiver end is separated;

[0175] B, zero ionosphere-free at the satellite end (SZIFC);

[0176] ; wherein, , ;

[0177] C, zero ionosphere-free at the receiver end (RZIFC);

[0178] .

[0179] The specific conditions of zero at the satellite end and the receiver end imposed by the DCB are:

[0180] A, specific zero condition at the satellite end (SZSTC)

[0181] ;

[0182] B, specific zero condition at the receiver end (RZSTC)

[0183] .

[0184] The specific code bias type imposed as a reference constraint can be flexibly determined according to actual scene needs.

[0185] For each type of reference constraint, the column constraint equation is: wherein, indicates the constraint type, is a design matrix corresponding to the specific constraint, is a column vector composed of constraint values, is a weight matrix;

[0186] According to the constraint equation, the constraint equation corresponding to each type of rank deficiency can be obtained:

[0187] Then, by superimposing the three types of constraint equations, the total constraint equation is obtained. ,in , where is the total number of constraint types.

[0188] Example 6:

[0189] Example 6 is basically the same as Example 1, except that:

[0190] The original observation equations for constructing GNSS pseudorange and phase observation data are as follows:

[0191] ;

[0192] superscript and subscript These respectively represent specific satellites, receivers, and GNSS frequency numbers; and Represents the original pseudorange and phase observations, with all necessary corrections added (phase center shift / variation, entanglement effect, tidal effect, etc.). This represents the corresponding mathematical expectation value; This refers to the geometric distance between the satellite's antenna reference point and the receiver; This represents the speed of light in a vacuum. and These are the clock biases of the receiver and the satellite, respectively; and These are the hardware delays of the receiver and the satellite, respectively. This represents the oblique tropospheric delay along the entire signal propagation path; similarly, It is the first frequency of oblique ionospheric delay ( (Considering only first-order ionospheric effects, higher-order ionospheric effects are ignored) and Represents the frequency correlation coefficient, where It is the wavelength of the corresponding carrier wave; This represents floating-point phase ambiguity, which absorbs phase hardware delays from the receiver and satellite; observation noise and other unmodeled effects are considered as zero-mean white noise.

[0193] Based on the above observation equations, the rank deficiency is eliminated through parameter renormalization, and a setting is made in the pseudorange observation equations. Parameters are used to absorb the first Frequency original code deviation, introduced Parameters are used to ensure consistency between non-reference type code observations and reference type observations. An extended full-rank, geometry-free model based on non-difference, non-combination observations is established, and the observation equation is:

[0194] ,in ,otherwise , the parameters to be estimated:

[0195] .

[0196] The above description is merely that of the preferred embodiments of the present application, and is not intended to limit the protection scope of the present application. Any equivalent modifications or changes made according to the spirit of the present application by those skilled in the art shall be included in the protection scope of the claims.

Claims

1. A method for unified estimation and integration of GNSS code bias based on selected fiducials, the method comprising: The unified estimation and synthesis method for GNSS code offset based on selected benchmarks includes the following steps: ​ The station network includes subnet A and subnet B. The global multi-station virtual observation equations for subnet A and subnet B are obtained using the same method. The estimation method includes any one of the following or any combination thereof, as detailed below: Step 1: Estimating separately using subnet A and subnet B; Apply zero-mean conditions, zero-ionospheric conditions at the satellite end and zero-ionospheric conditions at the receiver end to the global multi-station virtual observation equations of subnets A and B to obtain the final full-rank OSB estimation equations, and solve them to obtain the OSB estimation results. Apply satellite-specific zero conditions and receiver-specific zero conditions to the global multi-station virtual observation equations of subnets A and B to obtain full-rank DCB estimation equations, and solve them to obtain DCB estimation results. The second step involves estimating the DCB or OSB for subnets A and B in a unified manner. First, the parameter lists of subnets A and B are reorganized. Then, the normal equations of subnets A and B are integrated based on the parameter lists. Finally, the constraint normal equations of OSB estimation are superimposed to obtain the full-rank normal equations of OSB estimation for subnets A and B. Then, the OSB estimation results of integrated subnets A and B are obtained by solving the least squares method. First, the parameter lists of subnets A and B are reorganized. Then, the normal equations of subnets A and B are integrated based on the parameter lists. Finally, the constraint normal equations of DCB estimation are superimposed to obtain the full-rank normal equations of DCB for the comprehensive estimation of subnets A and B. Then, the DCB estimation results of the comprehensive subnets A and B are obtained by solving the least squares method.

2. The method for unified estimation and synthesis of GNSS code deviation based on a selected benchmark, as described in claim 1, is characterized in that: The specific steps for obtaining the global multi-station virtual observation equations for subnet A and subnet B using the same method are as follows: a. First, obtain the pseudorange and phase observation data of the GNSS single station in subnet A of the station network to construct the original observation equation of the GNSS pseudorange and phase observation data. Then, eliminate the rank deficiency in the original observation equation through parameter renormalization and absorb the original code deviation of different frequencies to obtain a full-rank geometric model. b. First, based on the phase observation data of a single GNSS station and combined with the GTS model, model the total vertical electron content of the local ionosphere of a single GNSS station to obtain the ionospheric model. Then, combine the ionospheric model with the full-rank geometric model and solve it using the sequential least squares method to separate the code deviation information from the ionospheric model. c. First, use the separated code deviation information as virtual observations. Then, combine the virtual observations with the code deviation information to construct a single-station virtual observation equation. Finally, solve the single-station virtual observation equation using the least squares method to obtain the single-station method equation. d. First, combine the single-station method equations of all stations in the joint subnet A, and then superimpose them to obtain the global multi-station virtual observation equations of subnet A; Based on the above steps, obtain the global multi-station virtual observation equations for subnet B in the station network.

3. The method for unified estimation and synthesis of GNSS code deviation based on a selected benchmark, as described in claim 2, is characterized in that: First, based on the phase observation data of a single GNSS station and combined with the GTS model, the vertical total electron content of the local ionosphere of a single GNSS station is modeled to obtain an ionospheric model. Then, combining the ionospheric model with a full-rank geometricless model, the code bias information is separated from the ionospheric model using the sequential least squares method. Specifically: The ionosphere model is as follows: ； in, ; It is the coefficient that converts ionospheric delay into total electron content. It is a single-layer mapping function used to convert skewed STEC to VTEC, where This indicates the zenith distance between the satellite and the receiver; It is a local ionospheric VTEC model based on GTS, which is based on geographic latitude. and the local time of the puncture point The function, based on the sequential least squares method, solves all data from the same station. The superimposed total normal equation is used to estimate the code deviation information. And the coefficients of the single-station VTEC model.

4. The method for unified estimation and synthesis of GNSS code deviation based on a selected benchmark, as described in claim 3, is characterized in that: The conversion function from STEC to VTEC is: ； Where R is the Earth's radius, taken as 6371 km, and H represents the height of the thin ionospheric model. This indicates the zenith distance between the satellite and the receiver.

5. The method for unified estimation and synthesis of GNSS code deviation based on a selected benchmark, as described in claim 2, is characterized in that: The process begins by using the separated code deviation information as virtual observations. Then, the virtual observations and code deviations are combined to construct a single-station virtual observation equation. Finally, the single-station virtual observation equation is solved using the least squares method, resulting in the following single-station equation: Each station includes three items containing code deviation information. , , As a virtual observation, its mathematical relationship with the original difference code deviation is as follows: ； Code bias estimation first requires traversing the observation data of all stations in the station network to obtain a global list of parameters to be estimated, and then establishing a single-station virtual observation equation based on the above mathematical relationships: In the formula, Station number, It is the residual vector; The coefficient matrix, The observation vector includes all three types of virtual observations mentioned above; It consists of variance and covariance information estimated from three types of virtual observations; This is the corresponding weight matrix; The least squares equation can be obtained from the single-station observation equation: The results from all stations in the joint station network can be obtained using the superposition method equation. ;Right now ; Where n is the number of stations.

6. The method for unified estimation and synthesis of GNSS code deviation based on a selected benchmark, as described in claim 5, is characterized in that: The rank deficiency equation Then superimposed on the constraint method equations estimated by OSB / DCB This yields the final solvable total normal equation. ,Right now .

7. The method for unified estimation and synthesis of GNSS code deviation based on a selected benchmark, as described in claim 6, is characterized in that: The estimation system exhibits three types of rank deficiency, specifically: Caused by the linear correlation between satellite and receiver code bias, the rank deficiency is the number of code bias types; This is caused by the linear correlation between the different types of code deviations of each satellite; the rank deficiency is the number of satellites. This is caused by the linear correlation between different types of code deviations of each receiver, and the rank deficiency is the number of stations.

8. The method for unified estimation and synthesis of GNSS code deviation based on a selected benchmark, as described in claim 1, is characterized in that: In the second step, the estimation of the zero-mean condition, the zero-ionospheric condition at the satellite end, and the zero-ionospheric condition at the receiver end applied by the OSB is specifically as follows: A. Zero-mean condition at both the satellite and receiver ends; ； Under this constraint, the code deviations at the satellite end and the receiver end can be separated; B. Zero ionospheric conditions at the satellite end; ;in, , ; C. Zero ionosphere conditions at the receiver end; 。 9. The method for unified estimation and synthesis of GNSS code deviation based on a selected benchmark, as described in claim 1, is characterized in that: The specific zero conditions applied by the DCB at the satellite end and the receiver end are as follows: A. Zero conditions specific to satellite terminals; ； B. Specific type of zero condition at the receiver end; 。 10. The method for unified estimation and synthesis of GNSS code deviation based on a selected benchmark, as described in claim 9, is characterized in that: For each type of datum constraint, the constraint equations are listed as follows: ,in, Refers to the constraint type. The design matrix corresponding to the specific constraints, A column vector consisting of constraint values. For the power formation; The constraint normal equations for each type of rank deficiency can be obtained from the constraint equations: Then, by superimposing the three types of constraint equations, the total constraint equation is obtained. ,in , where is the total number of constraint types.

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