Positioning method based on cloud interaction enhancement, storage medium and program product

Abstract

The invention discloses a positioning method based on cloud interaction enhancement, a storage medium and a program product, and the method comprises the steps: generating the ionized layer delay of each satellite based on observation data sent by each reference station; spatial interpolation is carried out based on the generated inclined ionosphere delay of each satellite, and ionosphere enhancement information is generated; sending the ionosphere enhancement information to a user side, so that the user side generates ionosphere delay of each satellite based on the received ionosphere enhancement information, and sends the ionosphere delay to a platform side; carrying out cloud interaction modeling based on the ionized layer delays of the satellites generated by the platform end and the ionized layer delays of the satellites generated by the user end, and obtaining interaction ionized layer enhancement information based on the cloud interaction modeling; and sending the interaction ionosphere enhancement information to a user side, so that the user side generates positioning data based on the received interaction ionosphere enhancement information. According to the method, the ionosphere information returned by massive users is fully utilized, cloud interaction integrated modeling is implemented, and the spatial resolution and precision of ionosphere modeling are improved while the station building cost is not increased.

Description

Technical Field

[0001] The present invention belongs to the field of positioning and navigation technology, and relates to a high-precision atmospheric modeling and enhanced positioning method, and specifically to a positioning method, storage medium and program product based on cloud-based interactive enhancement. Background Art

[0002] Precise Point Positioning-Real-Time Kinematic (PPP-RTK) technology usually refers to fast Precise Point Position (PPP) technology based on regional precise atmospheric enhancement. Through high-precision atmospheric enhancement information constraints, the correlation between the parameters to be estimated in PPP technology is quickly weakened to achieve rapid convergence. Within the coverage range of the reference station, the performance is equivalent to the Network Real-Time Kinematic Positioning (NRTK) technology, and it is an absolute positioning method that can achieve global wide-area high-precision positioning.

[0003] The service performance of PPP-RTK depends on the accuracy of regional atmospheric modeling. How to achieve high-precision regional atmospheric modeling is the key to this technology, especially since the temporal and spatial changes of the regional ionosphere are more complex, and it is more challenging to achieve high-precision modeling. The spatial correlation of the ionosphere weakens as the distance between stations increases. Therefore, in the long baseline (or sparse reference station) scenario, first, the physical spatial correlation of the ionosphere itself weakens, and second, the available information of sparse reference stations is limited and the redundancy is low. Conventional distance correlation model DIM, linear combination model LCM and other modeling methods are difficult to accurately describe the current spatial distribution of the ionosphere, and the modeling accuracy is difficult to guarantee, resulting in poor terminal service performance. Although the distance between reference stations can be shortened by building more reference stations, thereby improving the accuracy of ionosphere modeling. However, this also means high costs for station construction and subsequent operation and maintenance, and in harsh environments, the feasibility of high-density station construction is poor and the benefits are minimal. Summary of the invention

[0004] In view of the above problems, the present invention proposes a positioning method, storage medium and program product based on cloud interaction enhancement, which fully utilizes the ionospheric information sent back by massive users, implements cloud interaction integrated modeling, and improves the spatial resolution and accuracy of ionospheric modeling without increasing the cost of site construction, thereby improving the PPP-RTK service quality.

[0005] In order to achieve the above technical objectives and the above technical effects, the present invention is implemented through the following technical solutions:

[0006] In a first aspect, the present invention provides a satellite positioning method based on cloud interactive enhancement, which is applied to a platform end and includes:

[0007] Generate the ionospheric delay of each satellite based on the observation data sent by each reference station;

[0008] Perform spatial interpolation based on the generated slant ionospheric delays of each satellite to generate ionospheric enhancement information;

[0009] The ionospheric enhancement information is sent to the user end, so that the user end generates the ionospheric delay of each satellite based on the received ionospheric enhancement information, and sends it to the platform end;

[0010] Performing cloud-side interaction modeling based on the ionospheric delays of each satellite generated by the platform end and the ionospheric delays of each satellite generated by the user end, and obtaining interactive ionospheric enhancement information based on the cloud-side interaction modeling;

[0011] The interactive ionosphere enhancement information is sent to a user terminal, so that the user terminal generates positioning data based on the received interactive ionosphere enhancement information.

[0012] In combination with the first aspect, optionally, the ionospheric delay of each satellite generated by the platform end is generated by the following method:

[0013] A multi-frequency non-combined precise point positioning model is obtained, wherein the mathematical expression of the multi-frequency non-combined precise point positioning model is:

[0014] ;

[0015] In the formula, the superscript and subscript Represents satellites and receivers respectively; subscript They represent different frequency numbers, corresponding to Beidou frequencies B2I / B6I / B1c / B2a / B2b / B2ab; and Respectively represent the pseudorange and carrier observation values ​​of each frequency; Indicates the station-to-satellite distance between the satellite and the receiver; represents the tropospheric delay; is the speed of light in a vacuum; and Represent the receiver clock error and satellite clock error respectively; is the ionospheric delay corresponding to the B2I frequency; is the ionospheric amplification factor at different frequencies; is the floating point ambiguity of each frequency; and They represent the receiver-side inter-frequency bias and satellite-side inter-frequency bias introduced by multi-frequency pseudorange respectively; and are the observation noise of the pseudorange and carrier observation values ​​at each frequency respectively;

[0016] The reference station position is a known parameter, and the estimated parameters of the multi-frequency non-combined precise point positioning model are Including tropospheric delay, receiver clock error, receiver-end frequency deviation, satellite ionospheric delay, and frequency ambiguity of each satellite. The expression is:

[0017] ;

[0018] in , represents the unambiguous parameter; , represents the fuzziness parameter;

[0019] The fuzzy fixed algorithm is used to solve the ionospheric delay of each satellite.

[0020] In combination with the first aspect, optionally, the adopting a fuzzy fixed algorithm to solve the ionospheric delay of each satellite includes:

[0021] The parameters to be estimated The variance-covariance matrix of Denoted as:

[0022] ;

[0023] In the formula, Denotes the unambiguous parameter The variance matrix of Represents the fuzziness parameter The variance matrix of and Represents the covariance matrix between two types of parameters;

[0024] For the parameter to be estimated Perform linear transformation to obtain the parameters to be estimated , constructing ultra-wide lane ambiguity , wide lane ambiguity and narrow lane ambiguity The parameter to be estimated The expression is:

[0025] ;

[0026] In the formula, is the unit matrix, They are the combination coefficient matrices of narrow lane, wide lane and super wide lane respectively;

[0027] According to the variance-covariance propagation law, the estimated parameters after linear transformation are obtained. The corresponding variance-covariance matrix , the variance-covariance matrix The expression is:

[0028] ;

[0029] In the formula, They represent the narrow lane ambiguity , wide lane ambiguity and ultra-wide lane ambiguity The variance matrix of and express and The covariance matrix between and express and The covariance matrix between and express and The covariance matrix between and express and The covariance matrix between and express and The covariance matrix between and express and The covariance matrix between ;

[0030] Fixing ultra-wide lane ambiguity step by step , wide lane ambiguity and narrow lane ambiguity , extract the ionospheric delay of each satellite with centimeter-level accuracy.

[0031] In combination with the first aspect, optionally, the interactive ionosphere enhancement information is obtained by the following method:

[0032] Generate a cloud-side interaction modeling equation, the expression of which is:

[0033] ;

[0034] In the formula, ~ It represents the ionospheric delay of each satellite generated by each platform. ~ represents the ionospheric delay of each satellite generated by each user, and denote the total number of reference stations and users, respectively; and represent the Gaussian plane coordinates of each reference station and user, respectively. ; is the model coefficient to be solved;

[0035] make ; ; ; ;

[0036] The expression of the cloud interaction modeling equation is obtained as follows: ;

[0037] Considering that the ionospheric delays corresponding to different reference stations and users may have different accuracy, the weight matrix is ​​introduced To characterize the accuracy of ionospheric delay, the weight matrix is a diagonal matrix, the weight matrix The expression is:

[0038]

[0039] In the formula, Represents the weight of ionospheric delay, weight matrix The initial value of is the unit matrix;

[0040] According to the least squares estimation theory, the following formula is used to solve the parameters :

[0041] ;

[0042] Given the approximate location of any user terminal, the interactive ionospheric enhancement information is calculated based on the cloud-side interactive modeling equation.

[0043] In combination with the first aspect, optionally, when performing parameter The solution process also includes the following steps:

[0044] The robust quality control strategy and residual consistency check strategy are adopted to eliminate the abnormal ionospheric delays of each satellite generated by the platform end and the ionospheric delays of each satellite generated by the user end.

[0045] In combination with the first aspect, optionally, the ionospheric delay of each satellite generated by the user terminal is generated by the following method:

[0046] An ionospheric weighted model is constructed, wherein the ionospheric weighted model includes a multi-frequency non-combined precise point positioning model and a constraint equation, wherein the mathematical expression of the multi-frequency non-combined precise point positioning model is:

[0047]

[0048] In the formula, the superscript and subscript Represents satellites and receivers respectively; subscript They represent different frequency numbers, corresponding to Beidou frequencies B2I / B6I / B1c / B2a / B2b / B2ab; and Respectively represent the pseudorange and carrier observation values ​​of each frequency; Indicates the station-to-satellite distance between the satellite and the receiver; represents the tropospheric delay; is the speed of light in a vacuum; and Represent the receiver clock error and satellite clock error respectively; is the ionospheric delay corresponding to the B2I frequency; is the ionospheric amplification factor at different frequencies; is the floating point ambiguity of each frequency; and They represent the receiver-side inter-frequency bias and satellite-side inter-frequency bias introduced by multi-frequency pseudorange respectively; and are the observation noise of the pseudorange and carrier observation values ​​at each frequency respectively;

[0049] The mathematical expression of the constraint equation is:

[0050] ;

[0051] in,

[0052]

[0053] In the formula, As a reference satellite, represents the remaining non-reference satellites; represents the intersatellite single difference operator, , Indicates the ionospheric enhancement information received by the user. , Represents the ionospheric delay of each satellite generated by the user end;

[0054] User Location is an unknown parameter, and the estimated parameter of the multi-frequency non-combined precise point positioning model is Including tropospheric delay, receiver clock error, receiver-end frequency deviation, satellite ionospheric delay, and frequency ambiguity of each satellite. The expression is:

[0055] ;

[0056] in , represents the unambiguous parameter; , represents the fuzziness parameter;

[0057] The fuzzy fixed algorithm is used to solve the ionospheric delay of each satellite.

[0058] In combination with the first aspect, optionally, the method for generating positioning data includes:

[0059] An ionospheric weighted model is constructed, wherein the ionospheric weighted model includes a multi-frequency non-combined precise point positioning model and a constraint equation, wherein the mathematical expression of the multi-frequency non-combined precise point positioning model is:

[0060]

[0061] In the formula, the superscript and subscript Represents satellites and receivers respectively; subscript They represent different frequency numbers, corresponding to Beidou frequencies B2I / B6I / B1c / B2a / B2b / B2ab; and Respectively represent the pseudorange and carrier observation values ​​of each frequency; Indicates the station-to-satellite distance between the satellite and the receiver; represents the tropospheric delay; is the speed of light in a vacuum; and Represent the receiver clock error and satellite clock error respectively; is the ionospheric delay corresponding to the B2I frequency; is the ionospheric amplification factor at different frequencies; is the floating point ambiguity of each frequency; and They represent the receiver-side inter-frequency bias and satellite-side inter-frequency bias introduced by multi-frequency pseudorange respectively; and are the observation noise of the pseudorange and carrier observation values ​​at each frequency respectively;

[0062] The mathematical expression of the constraint equation is:

[0063] ;

[0064] In the formula, As a reference satellite, represents the remaining non-reference satellites; represents the intersatellite single difference operator, Indicates the interactive ionospheric enhancement information received by the user. Represents the ionospheric delay of each satellite generated by the user end;

[0065] User Location are unknown parameters, and the estimated parameters of the multi-frequency non-combined precise point positioning model are Including tropospheric delay, receiver clock error, receiver-end frequency deviation, satellite ionospheric delay, and frequency ambiguity of each satellite. The expression is:

[0066] ;

[0067] in , represents the unambiguous parameter; , represents the fuzziness parameter;

[0068] The fuzzy fixed algorithm is used to solve the user location data.

[0069] In combination with the first aspect, optionally, a fuzzy fixed algorithm is used to solve the ionospheric delay of each satellite and the user position data, including:

[0070] The parameters to be estimated The variance-covariance matrix of Denoted as:

[0071] ;

[0072] In the formula, Denotes the unambiguous parameter The variance matrix of Represents the fuzziness parameter The variance matrix of and Represents the covariance matrix between two types of parameters;

[0073] For the parameter to be estimated Perform linear transformation to obtain the parameters to be estimated , constructing ultra-wide lane ambiguity , wide lane ambiguity and narrow lane ambiguity The parameter to be estimated The expression is:

[0074] ;

[0075] In the formula, is the unit matrix, They are the combination coefficient matrices of narrow lane, wide lane and super wide lane respectively;

[0076] According to the variance-covariance propagation law, the estimated parameters after linear transformation are obtained. The corresponding variance-covariance matrix , the variance-covariance matrix The expression is:

[0077] ;

[0078] In the formula, They represent the narrow lane ambiguity , wide lane ambiguity and ultra-wide lane ambiguity The variance matrix of and express and The covariance matrix between and express and The covariance matrix between and express and The covariance matrix between and express and The covariance matrix between and express and The covariance matrix between and express and The covariance matrix between ;

[0079] Fixing ultra-wide lane ambiguity step by step , wide lane ambiguity and narrow lane ambiguity , extracting the ionospheric delay of each satellite and user position data with centimeter-level accuracy.

[0080] In a second aspect, the present invention provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the satellite positioning method based on cloud interaction enhancement as described in any one of the first aspects.

[0081] In a third aspect, the present invention provides a computer program product, including a computer program / instruction, which, when executed by a processor, implements the satellite positioning method based on cloud interaction enhancement as described in any one of the first aspects.

[0082] Compared with the prior art, the present invention has the following beneficial effects:

[0083] The present invention improves modeling redundancy and spatial resolution by comprehensively utilizing the ionospheric delay generated by the reference station and the ionospheric delay generated by the user end. At the same time, the segmented anti-error and residual consistency test strategies are adopted to weaken or eliminate the influence of abnormal ionospheric influence, thereby ensuring the stability and reliability of modeling. Through interactive modeling and enhancement, the ionospheric modeling accuracy and user end positioning performance are improved, thereby improving the service quality of PPP-RTK. BRIEF DESCRIPTION OF THE DRAWINGS

[0084] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative labor, among which:

[0085] Figure 1 is a flow chart of the satellite positioning method based on cloud interaction enhancement according to the present invention;

[0086] Figure 2 It is a schematic diagram of spatial encryption under different numbers of backhaul terminals by applying the interactive modeling method of the present invention;

[0087] Figure 3 The ionospheric modeling error of Beidou C27 satellite is compared using the method of the present invention and the conventional method;

[0088] Figure 4 The ionospheric modeling errors of all Beidou satellites are compared using the method of the present invention and the conventional method;

[0089] Figure 5 The effect of enhancing positioning by using the method of the present invention and conventional methods is compared. DETAILED DESCRIPTION

[0090] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0091] In addition, if there are descriptions involving "first", "second", etc. in the embodiments of the present invention, the descriptions of "first", "second", etc. are only used for descriptive purposes and cannot be understood as indicating or suggesting their relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined as "first" and "second" may explicitly or implicitly include at least one of the features. In addition, the technical solutions between the various embodiments can be combined with each other, but they must be based on the ability of ordinary technicians in the field to implement them. When the combination of technical solutions is contradictory or cannot be implemented, it should be deemed that such a combination of technical solutions does not exist and is not within the scope of protection required by the present invention.

[0092] Example 1

[0093] An embodiment of the present invention provides a satellite positioning method based on cloud interaction enhancement, which is applied to a platform end and includes the following steps:

[0094] (1) Generate the ionospheric delay of each satellite based on the observation data sent by each reference station;

[0095] (2) Perform spatial interpolation based on the generated slant ionospheric delays of each satellite to generate ionospheric enhancement information;

[0096] (3) sending the ionospheric enhancement information to the user end, so that the user end generates the ionospheric delay of each satellite based on the received ionospheric enhancement information and sends it to the platform end;

[0097] (4) performing cloud-side interaction modeling based on the ionospheric delays of each satellite generated by the platform end and the ionospheric delays of each satellite generated by the user end, and obtaining interactive ionospheric enhancement information based on the cloud-side interaction modeling;

[0098] (5) sending the interactive ionospheric enhancement information to a user terminal, so that the user terminal generates positioning data based on the received interactive ionospheric enhancement information.

[0099] In a specific implementation manner of the embodiment of the present invention, the ionospheric delay of each satellite generated by the platform end is generated by the following method:

[0100] A multi-frequency non-combined precise point positioning model is obtained, wherein the mathematical expression of the multi-frequency non-combined precise point positioning model is:

[0101] ;

[0102] In the formula, the superscript and subscript Represents satellites and receivers respectively; subscript They represent different frequency numbers, corresponding to Beidou frequencies B2I / B6I / B1c / B2a / B2b / B2ab; and Respectively represent the pseudorange and carrier observation values ​​of each frequency; Indicates the station-to-satellite distance between the satellite and the receiver; represents the tropospheric delay; is the speed of light in a vacuum; and Represent the receiver clock error and satellite clock error respectively; is the ionospheric delay corresponding to the B2I frequency; is the ionospheric amplification factor at different frequencies; is the floating point ambiguity of each frequency; and They represent the receiver-side inter-frequency bias and satellite-side inter-frequency bias introduced by multi-frequency pseudorange respectively; and are the observation noise of the pseudorange and carrier observation values ​​at each frequency respectively;

[0103] The reference station position is a known parameter, and the estimated parameters of the multi-frequency non-combined precise point positioning model are Including tropospheric delay, receiver clock error, receiver-end frequency deviation, satellite ionospheric delay, and frequency ambiguity of each satellite. The expression is:

[0104] ;

[0105] in , represents the unambiguous parameter; , represents the fuzziness parameter;

[0106] The fuzzy fixed algorithm is used to solve the ionospheric delay of each satellite.

[0107] In a specific implementation of the embodiment of the present invention, the fuzzy fixed algorithm is used to solve the ionospheric delay of each satellite, including:

[0108] The parameters to be estimated The variance-covariance matrix of Denoted as:

[0109] ;

[0110] In the formula, Denotes the unambiguous parameter The variance matrix of Represents the fuzziness parameter The variance matrix of and Represents the covariance matrix between two types of parameters;

[0111] For the parameter to be estimated Perform linear transformation to obtain the parameters to be estimated , constructing ultra-wide lane ambiguity , wide lane ambiguity and narrow lane ambiguity The parameter to be estimated The expression is:

[0112] ;

[0113] In the formula, is the unit matrix, They are the combination coefficient matrices of narrow lane, wide lane and super wide lane respectively;

[0114] According to the variance-covariance propagation law, the estimated parameters after linear transformation are obtained. The corresponding variance-covariance matrix , the variance-covariance matrix The expression is:

[0115] ;

[0116] In the formula, They represent the narrow lane ambiguity , wide lane ambiguity and ultra-wide lane ambiguity The variance matrix of and express and The covariance matrix between and express and The covariance matrix between and express and The covariance matrix between and express and The covariance matrix between and express and The covariance matrix between and express and The covariance matrix between ;

[0117] Fixing ultra-wide lane ambiguity step by step , wide lane ambiguity and narrow lane ambiguity , extract the ionospheric delay of each satellite with centimeter-level accuracy.

[0118] In a specific implementation of the embodiment of the present invention, the interactive ionospheric enhancement information is obtained by the following method:

[0119] Generate a cloud-side interaction modeling equation, the expression of which is:

[0120] ;

[0121] In the formula, ~ It represents the ionospheric delay of each satellite generated by each platform. ~ represents the ionospheric delay of each satellite generated by each user, and denote the total number of reference stations and users, respectively; and represent the Gaussian plane coordinates of each reference station and user, respectively. ; is the model coefficient to be solved;

[0122] make ; ; ; ;

[0123] The expression of the cloud interaction modeling equation is obtained as follows: ;

[0124] Considering that the ionospheric delays corresponding to different reference stations and users may have different accuracy, the weight matrix is ​​introduced To characterize the accuracy of ionospheric delay, the weight matrix is a diagonal matrix, the weight matrix The expression is:

[0125]

[0126] In the formula, Represents the weight of ionospheric delay, weight matrix The initial value of is the unit matrix;

[0127] According to the least squares estimation theory, the following formula is used to solve the parameters :

[0128] ;

[0129] Given the approximate location of any user terminal, the interactive ionospheric enhancement information is calculated based on the cloud-side interactive modeling equation.

[0130] In a specific implementation of the embodiment of the present invention, when performing parameter The solution process also includes the following steps:

[0131] Adopt the robust quality control strategy and residual consistency check strategy to eliminate the abnormal ionospheric delays of each satellite ionospheric delay generated by the platform end and the ionospheric delays of each satellite generated by the user end;

[0132] The formula used in the robust quality control strategy is:

[0133] ;

[0134] in, is the standardized residual; It represents the equivalent weight of the ionospheric information generated by each reference station or user terminal; and is the threshold value.

[0135] In a specific implementation of the embodiment of the present invention, the ionospheric delay of each satellite generated by the user end is generated by the following method:

[0136] An ionospheric weighted model is constructed, wherein the ionospheric weighted model includes a multi-frequency non-combined precise point positioning model and a constraint equation, wherein the mathematical expression of the multi-frequency non-combined precise point positioning model is:

[0137]

[0138] In the formula, the superscript and subscript Represents satellites and receivers respectively; subscript They represent different frequency numbers, corresponding to Beidou frequencies B2I / B6I / B1c / B2a / B2b / B2ab; and Respectively represent the pseudorange and carrier observation values ​​of each frequency; Indicates the station-to-satellite distance between the satellite and the receiver; represents the tropospheric delay; is the speed of light in a vacuum; and Represent the receiver clock error and satellite clock error respectively; is the ionospheric delay corresponding to the B2I frequency; is the ionospheric amplification factor at different frequencies; is the floating point ambiguity of each frequency; and They represent the receiver-side inter-frequency bias and satellite-side inter-frequency bias introduced by multi-frequency pseudorange respectively; and are the observation noise of the pseudorange and carrier observation values ​​at each frequency respectively;

[0139] The mathematical expression of the constraint equation is:

[0140] ;

[0141] In the formula, As a reference satellite, represents the remaining non-reference satellites; represents the intersatellite single difference operator, Indicates the ionospheric enhancement information received by the user. Represents the ionospheric delay of each satellite generated by the user end;

[0142] User Location are unknown parameters, and the estimated parameters of the multi-frequency non-combined precise point positioning model are Including tropospheric delay, receiver clock error, receiver-end frequency deviation, satellite ionospheric delay, and frequency ambiguity of each satellite. The expression is:

[0143] ;

[0144] in , represents the unambiguous parameter; , represents the fuzziness parameter;

[0145] The fuzzy fixed algorithm is used to solve the ionospheric delay of each satellite.

[0146] In a specific implementation of the embodiment of the present invention, the method for generating positioning data includes:

[0147] An ionospheric weighted model is constructed, wherein the ionospheric weighted model includes a multi-frequency non-combined precise point positioning model and a constraint equation, wherein the mathematical expression of the multi-frequency non-combined precise point positioning model is:

[0148]

[0149] In the formula, the superscript and subscript Represents satellites and receivers respectively; subscript They represent different frequency numbers, corresponding to Beidou frequencies B2I / B6I / B1c / B2a / B2b / B2ab; and Respectively represent the pseudorange and carrier observation values ​​of each frequency; Indicates the station-to-satellite distance between the satellite and the receiver; represents the tropospheric delay; is the speed of light in a vacuum; and Represent the receiver clock error and satellite clock error respectively; is the ionospheric delay corresponding to the B2I frequency; is the ionospheric amplification factor at different frequencies; is the floating point ambiguity of each frequency; and They represent the receiver-side inter-frequency bias and satellite-side inter-frequency bias introduced by multi-frequency pseudorange respectively; and are the observation noise of the pseudorange and carrier observation values ​​at each frequency respectively;

[0150] The mathematical expression of the constraint equation is:

[0151] ;

[0152] In the formula, As a reference satellite, represents the remaining non-reference satellites; represents the intersatellite single difference operator, Indicates the interactive ionospheric enhancement information received by the user. Represents the ionospheric delay of each satellite generated by the user end;

[0153] User Location are unknown parameters, and the estimated parameters of the multi-frequency non-combined precise point positioning model are Including tropospheric delay, receiver clock error, receiver-end frequency deviation, satellite ionospheric delay, and frequency ambiguity of each satellite. The expression is:

[0154] ;

[0155] in , represents the unambiguous parameter; , represents the fuzziness parameter;

[0156] The fuzzy fixed algorithm is used to solve the user location data.

[0157] In a specific implementation of the embodiment of the present invention, a fuzzy fixed algorithm is used to solve the ionospheric delay of each satellite and the user position data, including:

[0158] The parameters to be estimated The variance-covariance matrix of Denoted as:

[0159] ;

[0160] In the formula, Denotes the unambiguous parameter The variance matrix of Represents the fuzziness parameter The variance matrix of and Represents the covariance matrix between two types of parameters;

[0161] For the parameter to be estimated Perform linear transformation to obtain the parameters to be estimated , constructing ultra-wide lane ambiguity , wide lane ambiguity and narrow lane ambiguity The parameter to be estimated The expression is:

[0162] ;

[0163] In the formula, is the unit matrix, They are the combination coefficient matrices of narrow lane, wide lane and super wide lane respectively;

[0164] According to the variance-covariance propagation law, the estimated parameters after linear transformation are obtained. The corresponding variance-covariance matrix , the variance-covariance matrix The expression is:

[0165] ;

[0166] In the formula, They represent the narrow lane ambiguity , wide lane ambiguity and ultra-wide lane ambiguity The variance matrix of and express and The covariance matrix between and express and The covariance matrix between and express and The covariance matrix between and express and The covariance matrix between and express and The covariance matrix between and express and The covariance matrix between ;

[0167] Fixing ultra-wide lane ambiguity step by step , wide lane ambiguity and narrow lane ambiguity , extracting the ionospheric delay of each satellite and user position data with centimeter-level accuracy.

[0168] The satellite positioning method based on cloud interaction enhancement in an embodiment of the present invention is described in detail below in conjunction with a specific implementation manner.

[0169] Step 1: Based on the multi-frequency non-combined precise point positioning model (PPP), the platform implements a step-by-step ambiguity fixation strategy for each long-spaced sparse reference station, extracts the ionospheric delay of each satellite, and performs ionospheric space modeling to generate ionospheric enhancement information.

[0170] For Beidou multi-frequency observation data, we first construct a multi-frequency non-combined precise point positioning model as shown in formula (1):

[0171] (1);

[0172] In the formula, the superscript and subscript Represents satellites and receivers respectively; subscript They represent different frequency numbers, corresponding to Beidou frequencies B2I / B6I / B1c / B2a / B2b / B2ab; and Respectively represent the pseudorange and carrier observation values ​​of each frequency; Indicates the station-to-satellite distance between the satellite and the receiver; represents the tropospheric delay; is the speed of light in a vacuum; and Represent the receiver clock error and satellite clock error respectively; is the ionospheric delay corresponding to the B2I frequency; is the ionospheric amplification factor at different frequencies; is the floating point ambiguity of each frequency; and They represent the receiver-side inter-frequency bias and satellite-side inter-frequency bias introduced by the multi-frequency pseudorange, respectively. The satellite-side inter-frequency bias can be corrected by using the differential code bias product. and are the observation noise of the pseudorange and carrier observation values ​​at each frequency respectively.

[0173] The sparse reference station positions are known parameters, and the estimated parameters of the multi-frequency non-combined precise point positioning model are Including tropospheric delay, receiver clock error, receiver-end frequency deviation, satellite ionospheric delay, and frequency ambiguity of each satellite. The expression is:

[0174] (2)

[0175] in , represents the unambiguous parameter; , represents the fuzziness parameter.

[0176] in , represents the unambiguous parameter; , represents the fuzziness parameter. The variance-covariance matrix of Denoted as:

[0177] (3)

[0178] In the formula, Denotes the unambiguous parameter The variance matrix of Represents the fuzziness parameter The variance matrix of and Represents the covariance matrix between two types of parameters.

[0179] Based on the theory of multi-frequency observation combination, one narrow lane, one wide lane and one ionospheric amplification factor are selected by comprehensively considering factors such as wavelength, noise level and ionospheric amplification factor. The ultra-wide lane combination is Perform linear transformation to construct narrow lane ambiguity , wide lane ambiguity and ultra-wide lane ambiguity , as follows:

[0180] (4);

[0181] In the formula, is the unit matrix, are the combination coefficient matrices of narrow lane, wide lane and super wide lane respectively. At the same time, according to the variance-covariance propagation law, the transformed parameters are obtained The corresponding variance-covariance matrix .

[0182] (5)

[0183] In the formula, They represent the narrow lane ambiguity , wide lane ambiguity and ultra-wide lane ambiguity The variance matrix of and express and The covariance matrix between and express and The covariance matrix between and express and The covariance matrix between and express and The covariance matrix between and express and The covariance matrix between and express and The covariance matrix between .

[0184] Afterwards, the LAMBDA method is used to fix the ultra-wide lane, wide lane and narrow lane ambiguities step by step. After each level of ambiguity is successfully fixed, the remaining parameters to be estimated can be updated; in particular, once the narrow lane ambiguity is successfully fixed, the ionospheric delay with centimeter-level accuracy can be extracted, and the narrow lane fixed solution The update method is as follows.

[0185] (6);

[0186] In the formula, represents the narrow lane ambiguity after fixation; represents the narrow lane fixed solution.

[0187] Based on the high-precision slant ionospheric delay of each satellite extracted from the narrow lane fixed solution, a linear combination model is used to perform spatial interpolation modeling on the platform side to generate ionospheric enhancement information, which is then broadcast to the user side in the reference station network for use;

[0188] Step 2: Each user terminal in the sparse reference station network receives the ionospheric enhancement information of the platform, adopts the ionospheric weighted model, supplemented by loose threshold constraints, and attempts to fix the narrow lane ambiguity. If it is successfully fixed, the ionospheric information of the terminal is extracted and transmitted back to the platform.

[0189] For users in a sparse reference network, based on the multi-frequency non-combined precise point positioning model described in formula (1), As a reference satellite, Representing the remaining non-reference satellites, the receiver-correlated errors common to different satellites are eliminated through inter-satellite single difference. The ionospheric pseudo-observation equation constraint is used to jointly construct the ionospheric weighted model with formula (1). The constraint strength depends on the variance of the ionospheric enhancement information. , the constraint equation is as follows:

[0190] (7)

[0191] In the formula, represents the inter-satellite single difference operator, which is mainly used to eliminate the receiver-related errors common to different satellites. Specifically,

[0192] (8)

[0193] In the formula, Indicates the ionospheric enhancement information received by the user. Represents the ionospheric delay of each satellite generated by the user end.

[0194] It should be noted that the difference between the user-side reference station and the platform-side reference station is that the user-side 3D coordinates Often unknown, they need to be used as parameters to be estimated. Including tropospheric delay, receiver clock error, receiver-end frequency deviation, satellite ionospheric delay, and frequency ambiguity of each satellite. The expression is:

[0195] (9);

[0196] The accuracy of ionospheric enhancement information modeling is closely related to the spatial distribution of reference stations. Considering the insufficient spatial coverage of the sparse reference station network, the accuracy of ionospheric enhancement information modeling is limited. When the ionospheric enhancement information generated by only sparse reference stations in step 1 is directly used, a looser threshold constraint is adopted. For example, the variance of the pseudo-observation constraint is set to = The Kalman filter algorithm is used to perform smoothing on an epoch-by-epoch basis, and a step-by-step fixation strategy similar to formula (4) to formula (6) is used to fix the ambiguity. When the narrow lane ambiguity is fixed, the user can obtain high-precision position and ionospheric delay at the same time.

[0197] In conventional schemes, users are only users of enhanced information, focusing mainly on their high-precision position information, while insufficiently utilizing atmospheric information such as ionospheric delay obtained synchronously. To this end, the present invention further transmits the high-precision ionospheric delay of the user end back to the platform end, and uses it for subsequent ionospheric space modeling on the platform end. Through the two-way interactive mode of ionospheric information between the platform end and the user end, the user's role transformation as both a user of enhanced information and a generator of enhanced information is realized.

[0198] Step 3: Comprehensively utilize the ionospheric delays of each satellite generated by the platform and the ionospheric delays of each satellite generated by the user, eliminate abnormal ionospheric delays through quality control and consistency verification, implement cloud-based interactive modeling, and improve the spatial resolution of ionospheric modeling.

[0199] The user end is regarded as a virtual reference station, and the spatial encryption of ionospheric delay is realized through the interaction between the platform and the user end. With the increase in the number of encrypted stations, based on the conventional triangulated linear modeling, second-order or even higher-order surfaces can be used in theory to more finely characterize the irregular spatial distribution characteristics of the ionosphere and make up for the problem of insufficient spatial coverage of sparse reference stations. Taking the second-order surface fitting model as an example, the cloud-side interactive modeling equation is constructed as follows:

[0200] (10);

[0201] In the formula, and denote the number of sparse reference stations and interactive user stations, respectively; and represent the Gaussian plane coordinates of each site; are the model coefficients to be solved.

[0202] Conventional modeling methods only consider The ionospheric delay of the reference station is also used in formula (10). Based on the idea of ​​platform-terminal interaction, in theory, the more terminals that transmit ionospheric delay, the higher the spatial resolution of ionospheric modeling, which can effectively improve the accuracy of ionospheric modeling in sparse reference station scenarios.

[0203] For formula (10), considering that the ionospheric delays corresponding to different reference stations and user stations may have different accuracy, the weight matrix is ​​introduced To characterize its accuracy, Is a diagonal matrix, and the diagonal elements are the weights of different ionospheric delays:

[0204] (11);

[0205] Represents the initial weight, weight matrix The initial value can be a unit matrix, and the residual can be solved based on the least squares method and iterative adjustment can be implemented. For the convenience of expression, the following abbreviation is made:

[0206] (12)

[0207] In the formula, represents the observation vector, represents the coefficient matrix, represents the parameters to be estimated, Represents the residual. Then formula (9) can be expressed as follows:

[0208] (13)

[0209] According to the least squares estimation theory, the following formula is used to solve the estimated parameters of the model:

[0210] (14)

[0211] Compared with the conventional modeling method that only uses sparse reference stations, interactive modeling uses the ionospheric delays transmitted by a large number of users, which greatly improves the redundancy of the model. However, at the same time, it is also necessary to consider that the observation conditions of users vary greatly. The transmitted ionospheric delays may contain ionospheric anomalies caused by factors such as ambiguity fixation errors, which will reduce the overall modeling accuracy. Taking this factor into account, on the basis of the model of formula (10), the IGG III anti-error scheme shown in formula (15) is introduced to weaken or eliminate the influence of abnormal ionospheric delays.

[0212] (15)

[0213] In the formula, is the standardized residual; Indicates the equivalent weight of ionospheric information at each station; and As the threshold, the present invention takes and ;

[0214] Based on the above solution, adjust the weight matrix in formula (11) The value of is used to implement cyclic iterative robust estimation until there is no new abnormal ionospheric information that needs to be downgraded or eliminated, then the iteration is terminated. Based on the robust estimation, the chi-square Test, perform consistency test on the residual sum of squares after the test,

[0215] (16);

[0216] In the formula, is the necessary number of observations.

[0217] The above-mentioned robust quality control and residual consistency check strategies are comprehensively adopted to weaken or eliminate the influence of abnormal ionospheric delay on the solution of model coefficients. Furthermore, given the rough position of any user, the interactive ionospheric enhancement information of cloud-based interactive modeling can be generated based on the above-mentioned model coefficients.

[0218] Step 4: The user end uses the interactive ionospheric enhancement information in step 3, adopts the ionospheric weighted model and stricter threshold constraints to achieve rapid convergence of the estimated parameters and centimeter-level high-precision positioning at the user end. The more interactive terminals there are, the higher the spatial resolution of ionospheric modeling, the higher the corresponding modeling accuracy, and the faster the positioning convergence speed.

[0219] Figure 2 Schematic diagram of spatial encryption of ionospheric modeling corresponding to different numbers of backhaul terminals, i.e., 0 (initial spatial distribution), 1, 3, and 5. It can be seen that the more backhaul terminals there are in interactive modeling, the better the spatial encryption effect.

[0220] Figure 3 The ionosphere modeling errors of the Beidou C27 satellite are compared using the method of the present invention and the conventional method. It can be seen that the modeling error of the conventional method is obviously larger and the jitter is more severe, and the modeling accuracy is 9.3 cm. In comparison, the modeling error of the method of the present invention is smaller, and the accuracy is 3.8 cm, which is 59.1% higher than that of the conventional method.

[0221] Figure 4 The ionosphere modeling errors of all Beidou satellites are compared using the method of the present invention and the conventional method. It can be clearly seen that the conventional method has a large error distribution and a more scattered distribution, which can reach 40 cm, while the modeling errors of the method of the present invention are mostly within 10 cm, generally not exceeding 20 cm. According to statistics, the accuracy of the conventional method is 7.0 cm, and the accuracy of the method of the present invention is 2.9 cm, which is 58.6% higher than that of the conventional method.

[0222] Figure 5 The comparison of the effects of enhanced positioning using the method of the present invention and the conventional method shows that the results of enhanced positioning using the conventional method converge more slowly, while after enhanced positioning using the method of the present invention, positioning better than 10 cm is achieved in the first epoch, with almost no convergence time required, thus achieving instantaneous centimeter-level positioning.

[0223] Example 2

[0224] In an embodiment of the present invention, a computer-readable storage medium is provided, on which a computer program is stored. When the program is executed by a processor, the satellite positioning method based on cloud interaction enhancement as described in any one of Embodiment 1 is implemented.

[0225] Example 3

[0226] In an embodiment of the present invention, a computer program product is provided, including a computer program / instruction, which, when executed by a processor, implements the satellite positioning method based on cloud interaction enhancement as described in any one of Embodiment 1.

[0227] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present application may adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program codes.

[0228] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0229] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0230] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0231] The embodiments of the present invention are described above in conjunction with the accompanying drawings, but the present invention is not limited to the above-mentioned specific implementation methods. The above-mentioned specific implementation methods are merely illustrative and not restrictive. Under the enlightenment of the present invention, ordinary technicians in this field can also make many forms without departing from the scope of protection of the purpose of the present invention and the claims, which all fall within the protection of the present invention.

[0232] The above shows and describes the basic principles and main features of the present invention and the advantages of the present invention. It should be understood by those skilled in the art that the present invention is not limited to the above embodiments. The above embodiments and descriptions are only for explaining the principles of the present invention. Without departing from the spirit and scope of the present invention, the present invention may have various changes and improvements, which fall within the scope of the present invention to be protected. The scope of protection of the present invention is defined by the attached claims and their equivalents.

Claims

1. A satellite positioning method based on cloud interactive enhancement, characterized in that: Applied to the platform side, including: Generate the ionospheric delay of each satellite based on the observation data sent by each reference station; Perform spatial interpolation based on the generated slant ionospheric delays of each satellite to generate ionospheric enhancement information; The ionospheric enhancement information is sent to the user end, so that the user end generates the ionospheric delay of each satellite based on the received ionospheric enhancement information, and sends it to the platform end; Performing cloud-side interaction modeling based on the ionospheric delays of each satellite generated by the platform end and the ionospheric delays of each satellite generated by the user end, and obtaining interactive ionospheric enhancement information based on the cloud-side interaction modeling; The interactive ionosphere enhancement information is sent to a user terminal, so that the user terminal generates positioning data based on the received interactive ionosphere enhancement information.

2. The satellite positioning method based on cloud interactive enhancement according to claim 1, characterized in that: The ionospheric delay of each satellite generated by the platform is generated by the following method: A multi-frequency non-combined precise point positioning model is obtained, wherein the mathematical expression of the multi-frequency non-combined precise point positioning model is: ； In the formula, the superscript and subscript Represents satellites and receivers respectively; subscript They represent different frequency numbers, corresponding to Beidou frequencies B2I / B6I / B1c / B2a / B2b / B2ab; and Respectively represent the pseudorange and carrier observation values ​​of each frequency; Indicates the station-to-satellite distance between the satellite and the receiver; represents the tropospheric delay; is the speed of light in a vacuum; and Represent the receiver clock error and satellite clock error respectively; is the ionospheric delay corresponding to the B2I frequency; is the ionospheric amplification factor at different frequencies; is the floating point ambiguity of each frequency; and They represent the receiver-side inter-frequency bias and satellite-side inter-frequency bias introduced by multi-frequency pseudorange respectively; and are the observation noise of the pseudorange and carrier observation values ​​at each frequency respectively; The reference station position is a known parameter, and the estimated parameters of the multi-frequency non-combined precise point positioning model are Including tropospheric delay, receiver clock error, receiver-end frequency deviation, satellite ionospheric delay, and frequency ambiguity of each satellite. The expression is: ； in , represents the unambiguous parameter; , represents the fuzziness parameter; The fuzzy fixed algorithm is used to solve the ionospheric delay of each satellite.

3. The satellite positioning method based on cloud interactive enhancement according to claim 2, characterized in that: The fuzzy fixed algorithm is used to solve the ionospheric delay of each satellite, including: The parameters to be estimated The variance-covariance matrix of Denoted as: ； In the formula, Denotes the unambiguous parameter The variance matrix of Represents the fuzziness parameter The variance matrix of and Represents the covariance matrix between two types of parameters; For the parameter to be estimated Perform linear transformation to obtain the parameters to be estimated , constructing ultra-wide lane ambiguity , wide lane ambiguity and narrow lane ambiguity The parameter to be estimated The expression is: ； In the formula, is the unit matrix, They are the combination coefficient matrices of narrow lane, wide lane and super wide lane respectively; According to the variance-covariance propagation law, the estimated parameters after linear transformation are obtained. The corresponding variance-covariance matrix , the variance-covariance matrix The expression is: ； In the formula, They represent the narrow lane ambiguity , wide lane ambiguity and ultra-wide lane ambiguity The variance matrix of and express and The covariance matrix between and express and The covariance matrix between and express and The covariance matrix between and express and The covariance matrix between and express and The covariance matrix between and express and The covariance matrix between ; Fixing ultra-wide lane ambiguity step by step , wide lane ambiguity and narrow lane ambiguity , extract the ionospheric delay of each satellite with centimeter-level accuracy.

4. The satellite positioning method based on cloud interactive enhancement according to claim 1, characterized in that: The interactive ionosphere enhancement information is obtained by the following method: Generate a cloud-side interaction modeling equation, the expression of which is: ； In the formula, ~ It represents the ionospheric delay of each satellite generated by each platform. ~ represents the ionospheric delay of each satellite generated by each user, and denote the total number of reference stations and users, respectively; and represent the Gaussian plane coordinates of each reference station and user, respectively. ; is the model coefficient to be solved; make ; ; ; ; The expression of the cloud interaction modeling equation is obtained as follows: ; Considering that the ionospheric delays corresponding to different reference stations and users may have different accuracy, the weight matrix is ​​introduced To characterize the accuracy of ionospheric delay, the weight matrix is a diagonal matrix, the weight matrix The expression is: ； In the formula, Represents the weight of ionospheric delay, weight matrix The initial value of is the unit matrix; According to the least squares estimation theory, the following formula is used to solve the parameters : ； Given the approximate location of any user terminal, the interactive ionospheric enhancement information is calculated based on the cloud-side interactive modeling equation.

5. The satellite positioning method based on cloud interactive enhancement according to claim 4, characterized in that: In the parameters The solution process also includes the following steps: The robust quality control strategy and residual consistency check strategy are adopted to eliminate the abnormal ionospheric delays of each satellite generated by the platform end and the ionospheric delays of each satellite generated by the user end.

6. The satellite positioning method based on cloud interactive enhancement according to claim 1, characterized in that: The ionospheric delay of each satellite generated by the user end is generated by the following method: An ionospheric weighted model is constructed, wherein the ionospheric weighted model includes a multi-frequency non-combined precise point positioning model and a constraint equation, wherein the mathematical expression of the multi-frequency non-combined precise point positioning model is: ； In the formula, the superscript and subscript Represents satellites and receivers respectively; subscript They represent different frequency numbers, corresponding to Beidou frequencies B2I / B6I / B1c / B2a / B2b / B2ab; and Respectively represent the pseudorange and carrier observation values ​​of each frequency; Indicates the station-to-satellite distance between the satellite and the receiver; represents the tropospheric delay; is the speed of light in a vacuum; and Represent the receiver clock error and satellite clock error respectively; is the ionospheric delay corresponding to the B2I frequency; is the ionospheric amplification factor at different frequencies; is the floating point ambiguity of each frequency; and They represent the receiver-side inter-frequency bias and satellite-side inter-frequency bias introduced by multi-frequency pseudorange respectively; and are the observation noise of the pseudorange and carrier observation values ​​at each frequency respectively; The mathematical expression of the constraint equation is: ； in, ； In the formula, As a reference satellite, represents the remaining non-reference satellites; represents the intersatellite single difference operator, , Indicates the ionospheric enhancement information received by the user. , Represents the ionospheric delay of each satellite generated by the user end; User Location is an unknown parameter, and the estimated parameter of the multi-frequency non-combined precise point positioning model is Including tropospheric delay, receiver clock error, receiver-end frequency deviation, satellite ionospheric delay, and frequency ambiguity of each satellite. The expression is: ； in , represents the unambiguous parameter; , represents the fuzziness parameter; The fuzzy fixed algorithm is used to solve the ionospheric delay of each satellite.

7. The satellite positioning method based on cloud interactive enhancement according to claim 1, characterized in that: The method for generating positioning data comprises: An ionospheric weighted model is constructed, wherein the ionospheric weighted model includes a multi-frequency non-combined precise point positioning model and a constraint equation, wherein the mathematical expression of the multi-frequency non-combined precise point positioning model is: ； In the formula, the superscript and subscript Represents satellites and receivers respectively; subscript They represent different frequency numbers, corresponding to Beidou frequencies B2I / B6I / B1c / B2a / B2b / B2ab; and Respectively represent the pseudorange and carrier observation values ​​of each frequency; Indicates the station-to-satellite distance between the satellite and the receiver; represents the tropospheric delay; is the speed of light in a vacuum; and Represent the receiver clock error and satellite clock error respectively; is the ionospheric delay corresponding to the B2I frequency; is the ionospheric amplification factor at different frequencies; is the floating point ambiguity of each frequency; and They represent the receiver-side inter-frequency bias and satellite-side inter-frequency bias introduced by multi-frequency pseudorange respectively; and are the observation noise of the pseudorange and carrier observation values ​​at each frequency respectively; The mathematical expression of the constraint equation is: ； In the formula, As a reference satellite, represents the remaining non-reference satellites; represents the intersatellite single difference operator, Indicates the interactive ionospheric enhancement information received by the user. Represents the ionospheric delay of each satellite generated by the user end; User Location is an unknown parameter, and the estimated parameter of the multi-frequency non-combined precise point positioning model is Including tropospheric delay, receiver clock error, receiver-end frequency deviation, satellite ionospheric delay, and frequency ambiguity of each satellite. The expression is: ； in , represents the unambiguous parameter; , represents the fuzziness parameter; The fuzzy fixed algorithm is used to solve the user location data.

8. A satellite positioning method based on cloud interactive enhancement according to claim 6 or 7, characterized in that: The fuzzy fixed algorithm is used to solve the ionospheric delay of each satellite and the user position data, including: The parameters to be estimated The variance-covariance matrix of Denoted as: ； In the formula, Denotes the unambiguous parameter The variance matrix of Represents the fuzziness parameter The variance matrix of and Represents the covariance matrix between two types of parameters; For the parameter to be estimated Perform linear transformation to obtain the parameters to be estimated , constructing ultra-wide lane ambiguity , wide lane ambiguity and narrow lane ambiguity The parameter to be estimated The expression is: ； In the formula, is the unit matrix, They are the combination coefficient matrices of narrow lane, wide lane and super wide lane respectively; According to the variance-covariance propagation law, the estimated parameters after linear transformation are obtained. The corresponding variance-covariance matrix , the variance-covariance matrix The expression is: ； In the formula, They represent the narrow lane ambiguity , wide lane ambiguity and ultra-wide lane ambiguity The variance matrix of and express and The covariance matrix between and express and The covariance matrix between and express and The covariance matrix between and express and The covariance matrix between and express and The covariance matrix between and express and The covariance matrix between ; Fixing ultra-wide lane ambiguity step by step , wide lane ambiguity and narrow lane ambiguity , extracting the ionospheric delay of each satellite and user position data with centimeter-level accuracy.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the satellite positioning method based on cloud interaction enhancement as described in any one of claims 1 to 8 is implemented.

10. A computer program product comprising a computer program / instructions, characterized in that When the computer program / instruction is executed by a processor, the satellite positioning method based on cloud interaction enhancement described in any one of claims 1 to 8 is implemented.

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