Satellite phase deviation resolving method considering signal distortion deviation

Abstract

The invention discloses a satellite phase deviation resolving method considering signal distortion deviation, and relates to the technical field of satellite navigation and positioning. The method comprises the following steps: selecting a GNSS base station network to obtain double-frequency observation data; classifying receiver types and unifying same-kind signal distortion delay; signal distortion modeling is introduced based on an MW combined observation equation, and wide-lane satellite phase deviation is obtained through adjustment and ambiguity fixed solution in combination with a precise orbit and clock correction; and based on the ionosphere-free combination observation equation, combining the solved wide-lane deviation, and obtaining the narrow-lane satellite phase deviation and the ionosphere-free combination signal distortion deviation through adjustment and ambiguity fixation. According to whether the receiver type is calibrated or not, the signal distortion deviation is directly applied or estimated in real time, more comprehensive error compensation is achieved in an observation equation, and the positioning precision and the convergence speed are improved. According to the method, synchronous separation and accurate estimation of the wide-lane and narrow-lane phase deviation and the signal distortion deviation of the satellite are realized, and the estimation accuracy and physical consistency of the phase deviation are effectively improved.

Description

Technical Field

[0001] This invention belongs to the field of satellite navigation and positioning technology, and specifically relates to a satellite phase deviation calculation method that takes into account signal distortion deviation. Background Technology

[0002] In the field of Global Navigation Satellite System (GNSS) precise positioning services, the performance improvement of real-time precise point positioning (PPP) technology is highly dependent on the accurate calculation of satellite phase deviation parameters. These deviation parameters, as a crucial component of augmentation information, directly affect the efficiency of rapid ambiguity fixation at the user end, as well as positioning accuracy and convergence speed. However, the theoretical framework of existing satellite phase deviation calculation methods has significant limitations, failing to fully consider the time-varying signal distortion deviations (also known as receiver channel delay deviations) generated by multi-mode, multi-frequency GNSS receivers in hardware components such as the RF front-end and signal processing links. This systematic deviation is not explicitly modeled in existing calculation models, leading to a systematic discrepancy between the estimated satellite phase deviation parameters and the actual physical quantities, ultimately limiting the real-time positioning accuracy of the user terminal. Summary of the Invention

[0003] To address the critical requirement of high-precision satellite phase deviation products in precision positioning technologies such as PPP-AR and PPP-RTK, this invention proposes a satellite phase deviation calculation method that takes into account signal distortion deviation. By integrating dual-frequency observation data from a GNSS reference station network and external precision satellite orbit and clock error products, it achieves simultaneous separation and accurate estimation of wide-lane and narrow-lane satellite phase deviation and signal distortion deviation. The method specifically includes the following steps:

[0004] A method for calculating satellite phase deviation that takes into account signal distortion bias includes the following steps:

[0005] (1) Construct a reference station network consisting of globally or regionally distributed GNSS reference stations, and each reference station is equipped with a receiving device capable of receiving pseudorange and phase dual-frequency observation data from at least one GNSS system (including but not limited to BeiDou, GPS, GLONASS, Galileo);

[0006] (2) Classify the receivers configured in the base station according to their type and number them as follows: ,in Assign receiver numbers and set the signal distortion delay corresponding to receivers of the same type to the same value;

[0007] (3) Based on the observation data of the reference station network, the precise satellite orbit and satellite clock error information, the MW combined observation equation is constructed, and the signal distortion deviation parameter is introduced into the model to ensure that the observation equation has full rank solvability;

[0008] (4) The wide lane floating-point ambiguity parameters obtained in step (3) are fixed to integers to obtain the wide lane integer ambiguity, and then substituted back into the MW combined observation equation to solve for the wide lane satellite phase deviation and wide lane signal distortion deviation.

[0009] (5) Using the observation data of the reference station network, the precise satellite orbit and clock difference information, and the wide lane integer ambiguity parameter obtained in step (4), construct the ionosphere-free combined observation equation, and synchronously introduce the signal distortion deviation parameter into the model to ensure that the equation is solvable at full rank;

[0010] (6) Fix the floating-point ambiguity parameters of the narrow lane obtained in step (5) to obtain the integer ambiguity of the narrow lane. Substitute it into the deionization combined observation equation to solve for the narrow lane satellite phase deviation and deionization combined signal distortion deviation.

[0011] (7) During the terminal user positioning stage, if the user receiver type already exists in the reference station network and has corresponding signal distortion deviation estimation data, then the satellite orbit, satellite clock error, satellite phase deviation and signal distortion deviation estimation are directly introduced into the observation equation, and the user terminal position parameters are solved after error correction.

[0012] (8) If the receiver type used by the terminal user is not covered in the reference station network and there is no corresponding signal distortion deviation estimate, then only the satellite orbit, satellite clock error and satellite phase deviation are corrected in the observation equation, and the user terminal position and signal distortion deviation parameters are solved simultaneously.

[0013] The specific method of step (3) is as follows:

[0014] (301) The original observation equations for a single-system dual-frequency system are constructed as follows: superscript Indicates satellite number, subscript Indicates the receiver number, subscript Indicates the frequency band number. These are pseudorange observations. For carrier phase observations, For receiver To satellite geometric distance, For tropospheric dry delay, This is the tropospheric wet delay projection function. For tropospheric zenith wet delay, For receiver The clock difference, For satellite The clock difference, For receiver Corresponding frequency pseudorange hardware delay, For satellite Corresponding frequency pseudorange hardware delay, For satellite Ionospheric delay at the first frequency, This is the ratio between the ionospheric delay at other frequencies and the ionospheric delay at the first frequency. For the corresponding frequency wavelength, Receiver type Corresponding satellite and frequency Signal distortion delay, For receiver The corresponding receiver type number, For receiver Corresponding frequency Phase hardware delay, For satellite Corresponding frequency Phase hardware delay, For receiver With satellite Frequency between The corresponding integer ambiguity; and Dimensionless , and The unit for is weeks, and the units for all other parameters are meters.

[0015] (302) Construct the MW combined observation equation ,in , , , , , , , These are the first and second frequencies, respectively. The superscripts and subscripts for each parameter are... This indicates the receiver or satellite selected as the reference.

[0016] (303) Process the observation data for a period of time using methods such as least squares adjustment or Kalman filter adjustment, solve the observation equation in (302), and obtain the results. , , , parameter.

[0017] The specific method of step (4) is as follows:

[0018] (401) Using an integer fuzziness fixing algorithm, such as the LAMBDA algorithm, the solution obtained in (303) is... Fixed as an integer, obtain the wide alley integer ambiguity. .

[0019] (402) The width lane integer ambiguity in (401) Substituting into the observation equation in (302), we can solve for the following: , , parameter.

[0020] The specific method of step (5) is as follows:

[0021] (501) Constructing the combined observation equations for ionospheric de-escalation ,in, This represents the ionosphere decomposition operator, where... , , , To introduce the satellite clock bias values ​​of known satellite clock bias products, This indicates the clock bias reference introduced by known satellite clock bias products. , , , , Represents the speed of light. , , , , , .

[0022] (502) combine the satellite orbit, satellite clock bias products, and the solution from (402). , , Substituting the observation equation into (501), selecting observation data for a period of time, and using methods such as least squares adjustment or Kalman filter adjustment, the solution is obtained. , , , , , parameter.

[0023] The specific method of step (6) is as follows:

[0024] (601) Using an integer fuzziness fixing algorithm, such as the LAMBDA algorithm, the solution obtained in (502) is... Fixed as an integer, obtain the integer ambiguity of the narrow alley. .

[0025] (602) The narrow alley integer ambiguity in (601) Substituting into the observation equation in (501), the ambiguity fixed solution for each parameter is obtained. , , , , .

[0026] The specific method of step (7) is as follows:

[0027] (701) For terminal users whose receiver type belongs to a certain type of receiver in the base station network, when performing positioning, first calculate the solution in step (4). and Substitute into the MW combined observation equation of the user receiver Solving for floating-point solutions and .

[0028] (702) Using an integer fuzziness fixing algorithm, such as the LAMBDA algorithm, to... Fixed as an integer Then, substituting back into the observation equation (701), we obtain the fixed solution. .

[0029] (703) The solution obtained in step (4) The solution in step (6) , and the solution in step (702) , Substituting into the de-ionization combined observation equation of the user receiver ,in , Indicates the end-user identifier. , Indicates the known satellite positions. This represents the unknown location of the user terminal. After incorporating known satellite orbit, satellite clock bias, tropospheric dry delay, and tropospheric wet delay projection model data, the equations can be solved using methods such as least squares adjustment or Kalman filtering to obtain the user terminal location. , , , and .

[0030] (704) Using an integer ambiguity fixing algorithm, such as the LAMBDA algorithm, to... Fixed as an integer Then, substituting back into the observation equation (703), the fixed ambiguity solutions for each parameter are obtained, including the terminal position, , , .

[0031] The specific method of step (8) is as follows:

[0032] (801) For terminal users whose receiver type does not belong to a certain type of receiver in the base station network, when performing positioning, first calculate the solution in step (4). Substitute into the MW combined observation equation of the user receiver Solving for floating-point solutions , and .

[0033] (802) Using an integer fuzziness fixing algorithm, such as the LAMBDA algorithm, to... Fixed as an integer Then, substituting back into the observation equation (801), we obtain the fixed solution. and .

[0034] (803) The solution obtained in step (4) The solution in step (6) and the solution in step (802) , Substituting into the de-ionization combined observation equation of the user receiver ,in , Indicates the end-user identifier. , Indicates the known satellite positions. This represents the unknown location of the user terminal. After incorporating known satellite orbit, satellite clock bias, tropospheric dry delay, and tropospheric wet delay projection model data, the equations can be solved using methods such as least squares adjustment or Kalman filtering to obtain the user terminal location. , , , , and .

[0035] (804) Using an integer fuzziness fixing algorithm, such as the LAMBDA algorithm, to... Fixed as an integer Then, substituting back into the observation equation (803), the fixed ambiguity solutions for each parameter are obtained, including the terminal position, , , , .

[0036] The present invention has the following advantages compared with existing methods:

[0037] (1) The MW observation equation composed of reference station network data includes signal distortion bias parameters and unknown parameters. , , and They are independent of each other, and the observation equation is solvable at full rank;

[0038] (2) The ionospheric de-polarization combined observation equation composed of reference station network data includes signal distortion bias parameters and unknown parameters. , , , , , They are independent of each other, and the observation equation is solvable at full rank;

[0039] (3) Signal distortion deviation In China, the benchmark You can choose one receiver type, or you can use an average of multiple receiver types, such as... This ensures that the benchmark can be operated flexibly.

[0040] (4) The signal distortion delay parameter is introduced into the observation equation. By estimating the signal distortion deviation, the unmodeled component in the system error can be effectively compensated, thereby reducing the error level and improving the estimation accuracy of parameters such as satellite phase deviation. Detailed Implementation

[0041] To better illustrate the purpose and advantages of the present invention, the technical solution of the present invention will be further described below, and the implementation steps are as follows:

[0042] A method for calculating satellite phase deviation that takes into account signal distortion bias includes the following steps:

[0043] 1. Construct a reference station network consisting of globally or regionally distributed GNSS reference stations, with each reference station equipped with receiving equipment capable of receiving pseudorange and phase dual-frequency observation data from at least one GNSS system (including but not limited to BeiDou, GPS, GLONASS, Galileo);

[0044] 2. Classify and number the receivers configured in the base station according to their type. ,in Assign receiver numbers and set the signal distortion delay corresponding to receivers of the same type to the same value;

[0045] 3. Based on the observation data of the reference station network, precise satellite orbit and satellite clock error information, the MW combined observation equation is constructed, and the signal distortion deviation parameter is introduced into the model to ensure that the observation equation has full-rank solvability;

[0046] (301) The original observation equations for a single-system dual-frequency system are constructed as follows: superscript Indicates satellite number, subscript Indicates the receiver number, subscript Indicates the frequency band number. These are pseudorange observations. For carrier phase observations, For receiver To satellite geometric distance, For tropospheric dry delay, This is the tropospheric wet delay projection function. For tropospheric zenith wet delay, For receiver The clock difference, For satellite The clock difference, For receiver Corresponding frequency pseudorange hardware delay, For satellite Corresponding frequency pseudorange hardware delay, For satellite Ionospheric delay at the first frequency, This is the ratio between the ionospheric delay at other frequencies and the ionospheric delay at the first frequency. For the corresponding frequency wavelength, Receiver type Corresponding satellite and frequency Signal distortion delay, For receiver The corresponding receiver type number, For receiver Corresponding frequency Phase hardware delay, For satellite Corresponding frequency Phase hardware delay, For receiver With satellite Frequency between The corresponding integer ambiguity; and Dimensionless , and The unit for is weeks, and the units for all other parameters are meters.

[0047] (302) Construct the MW combined observation equation ,in , , , , , In the superscripts and subscripts of each parameter This indicates the receiver or satellite selected as the reference.

[0048] (303) Process the observation data for a period of time using methods such as least squares adjustment or Kalman filter adjustment, solve the observation equation in (302), and obtain the results. , , , parameter.

[0049] 4. Implement integer fixation on the wide lane floating-point ambiguity parameters obtained in step (3), obtain the wide lane integer ambiguity, and substitute it back into the MW combined observation equation to solve for the wide lane satellite phase deviation and wide lane signal distortion deviation;

[0050] (401) Using an integer fuzziness fixing algorithm, such as the LAMBDA algorithm, the solution obtained in (303) is... Fixed as an integer, obtain the wide alley integer ambiguity. .

[0051] (402) The width lane integer ambiguity in (401) Substituting into the observation equation in (302), we can solve for the following: , , parameter.

[0052] 5. Using the observation data of the reference station network, the precise satellite orbit and clock error information, and the wide lane integer ambiguity parameter calculated in (4), the ionosphere-free combined observation equation is constructed. At the same time, the signal distortion deviation is modeled to ensure that the equation is solvable at full rank.

[0053] (501) Constructing the combined observation equations for ionospheric de-escalation ,in, This represents the ionosphere decomposition operator, where... , , , To introduce the satellite clock bias values ​​of known satellite clock bias products, This indicates the clock bias reference introduced by known satellite clock bias products. , , , , Represents the speed of light. , , , , , .

[0054] (502) combine the satellite orbit, satellite clock bias products, and the solution from (402). , , Substituting the observation equation into (501), selecting observation data for a period of time, and using methods such as least squares adjustment or Kalman filter adjustment, the solution is obtained. , , , , , parameter.

[0055] 6. Fix the floating-point ambiguity parameters of the narrow lane obtained in step 5 to obtain the integer ambiguity of the narrow lane, and substitute it into the deionization combined observation equation to obtain the narrow lane satellite phase deviation and the deionization combined signal distortion deviation.

[0056] (601) Using an integer fuzziness fixing algorithm, such as the LAMBDA algorithm, the solution obtained in (502) is... Fixed as an integer, obtain the integer ambiguity of the narrow alley. .

[0057] (602) The narrow alley integer ambiguity in (601) Substituting into the observation equation in (501), the ambiguity fixed solution for each parameter is obtained. , , , , .

[0058] 7. When locating end users, for user receivers with corresponding signal distortion deviation estimation data, the observation equations are corrected directly using satellite orbit, satellite clock error products, and satellite phase deviation and signal distortion deviation estimation data, and then the parameters such as the user terminal position are solved.

[0059] (701) For terminal users whose receiver type belongs to a certain type of receiver in the base station network, when performing positioning, first calculate the solution in step (4). and Substitute into the MW combined observation equation of the user receiver Solving for floating-point solutions and .

[0060] (702) Using an integer fuzziness fixing algorithm, such as the LAMBDA algorithm, to... Fixed as an integer Then, substituting back into the observation equation (701), we obtain the fixed solution. .

[0061] (703) The solution obtained in step (4) The solution in step (6) , and the solution in step (702) , Substituting into the de-ionization combined observation equation of the user receiver ,in , Indicates the end-user identifier. , Indicates the known satellite positions. This represents the unknown location of the user terminal. After incorporating known satellite orbit, satellite clock bias, tropospheric dry delay, and tropospheric wet delay projection model data, the equations can be solved using methods such as least squares adjustment or Kalman filtering to obtain the user terminal location. , , , and .

[0062] (704) Using an integer ambiguity fixing algorithm, such as the LAMBDA algorithm, to... Fixed as an integer Then, substituting back into the observation equation (703), the fixed ambiguity solutions for each parameter are obtained, including the terminal position, , , .

[0063] 8. When locating end users, for user receivers without corresponding signal distortion deviation estimation data, after correcting for satellite orbit, satellite clock error and satellite phase deviation in the observation equation, solve for parameters such as user terminal position and signal distortion deviation.

[0064] (801) For terminal users whose receiver type does not belong to a certain type of receiver in the base station network, when performing positioning, first calculate the solution in step (4). Substitute into the MW combined observation equation of the user receiver Solving for floating-point solutions , and .

[0065] (802) Using an integer fuzziness fixing algorithm, such as the LAMBDA algorithm, to... Fixed as an integer Then, substituting back into the observation equation (801), we obtain the fixed solution. and .

[0066] (803) The solution obtained in step (4) The solution in step (6) and the solution in step (802) , Substituting into the de-ionization combined observation equation of the user receiver ,in , Indicates the end-user identifier. , Indicates the known satellite positions. This represents the unknown location of the user terminal. After incorporating known satellite orbit, satellite clock bias, tropospheric dry delay, and tropospheric wet delay projection model data, the equations can be solved using methods such as least squares adjustment or Kalman filtering to obtain the user terminal location. , , , , and .

[0067] (804) Using an integer fuzziness fixing algorithm, such as the LAMBDA algorithm, to... Fixed as an integer Then, substituting back into the observation equation (803), the fixed ambiguity solutions for each parameter are obtained, including the terminal position, , , , .

[0068] This invention innovatively proposes an improved satellite phase deviation calculation method. By constructing an enhanced parameterized model that fuses signal distortion deviation, it overcomes the theoretical limitations of traditional algorithms. This method synchronously introduces receiver-type-related signal distortion deviation parameters into the observation equations of the MW combination and the ionosphere-free combination, constructing a joint estimation model for wide-lane and narrow-lane satellite phase deviation and signal distortion deviation. Based on spatiotemporal correlation processing of multi-reference station network observation data, it achieves synchronous and precise calculation of the two types of deviation parameters, thereby significantly improving the physical consistency and service accuracy of satellite phase deviation products. This technological breakthrough not only provides core algorithmic support for new augmentation services such as PPP-AR and PPP-RTK, but also has significant engineering value for promoting the large-scale application of high-precision positioning in fields such as autonomous driving and intelligent transportation.

[0069] Those skilled in the art will recognize that the described embodiments are intended to help readers understand the principles of the invention and should be understood as not limiting the scope of protection of the invention to the described embodiments. Various modifications and variations can be made to the invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the invention should be included within the scope of the claims of the invention.

Claims

1. A method for calculating satellite phase deviation considering signal distortion bias, characterized in that, Includes the following steps: (1) Construct a reference station network consisting of globally or regionally distributed GNSS reference stations, with each reference station equipped with a receiving device capable of receiving at least one GNSS system pseudorange and phase dual-frequency observation data; (2) Classify the receivers configured in the base station according to their type and number them as follows: ,in Assign receiver numbers, and treat the signal distortion delay corresponding to receivers of the same type as the same value; (3) Based on the observation data of the reference station network, the precise satellite orbit and satellite clock error information, the MW combined observation equation is constructed, and the signal distortion deviation parameter is introduced into the model to ensure that the observation equation has full rank solvability; (4) The wide lane floating-point ambiguity parameters obtained in step (3) are fixed to integers to obtain the wide lane integer ambiguity, and then substituted back into the MW combined observation equation to solve for the wide lane satellite phase deviation and wide lane signal distortion deviation. (5) Using the observation data of the reference station network, the precise satellite orbit and clock difference information, and the wide lane integer ambiguity parameter obtained in step (4), construct the ionosphere-free combined observation equation, and synchronously introduce the signal distortion deviation parameter into the model to ensure that the equation is solvable at full rank; (6) Fix the floating-point ambiguity parameters of the narrow lane obtained in step (5) to obtain the integer ambiguity of the narrow lane. Substitute it into the deionization combined observation equation to solve for the narrow lane satellite phase deviation and deionization combined signal distortion deviation. (7) During the terminal user positioning stage, if the user receiver type already exists in the reference station network and has corresponding signal distortion deviation estimation data, then the satellite orbit, satellite clock error, satellite phase deviation and signal distortion deviation estimation are directly introduced into the observation equation, and the user terminal position parameters are solved after error correction. If the receiver type used by the end user is not covered in the reference station network and there is no corresponding signal distortion deviation estimate, then only the satellite orbit, satellite clock error and satellite phase deviation are corrected in the observation equation, and the user terminal position and signal distortion deviation parameters are solved simultaneously.

2. The satellite phase deviation calculation method considering signal distortion deviation according to claim 1, characterized in that, The specific method for step (3) is as follows: (301) The original observation equations for a single-system dual-frequency system are constructed as follows: superscript Indicates satellite number, subscript Indicates the receiver number, subscript Indicates the frequency band number. These are pseudorange observations. For carrier phase observations, For receiver To satellite geometric distance, For tropospheric dry delay, This is the tropospheric wet delay projection function. For tropospheric zenith wet delay, For receiver The clock difference, For satellite The clock difference, For receiver Corresponding frequency pseudorange hardware delay, For satellite Corresponding frequency pseudorange hardware delay, For satellite Ionospheric delay at the first frequency, This is the ratio between the ionospheric delay at other frequencies and the ionospheric delay at the first frequency. For the corresponding frequency wavelength, Receiver type Corresponding satellite and frequency Signal distortion delay, For receiver The corresponding receiver type number, For receiver Corresponding frequency Phase hardware delay, For satellite Corresponding frequency Phase hardware delay, For receiver With satellite Frequency between The corresponding integer ambiguity; and Dimensionless , and The unit for is weeks, and the units for all other parameters are meters; (302) Construct the MW combined observation equation ,in , , , , , , , These are the first and second frequencies, respectively. The superscripts and subscripts for each parameter are... Indicates the satellite or receiver selected as the reference; (303) Process the observation data for a period of time using the least squares adjustment or Kalman filter adjustment method, solve the observation equation in (302), and obtain the results. , , , parameter.

3. The satellite phase deviation calculation method considering signal distortion deviation according to claim 2, characterized in that, The specific method of step (4) is as follows: (401) Using the integer fuzziness fixing algorithm, the solution obtained in (303) is... Fixed as an integer, obtain the wide alley integer ambiguity. ; (402) The width lane integer ambiguity in (401) Substituting into the observation equation in (302), we can solve for the following: , , parameter.

4. The satellite phase deviation calculation method considering signal distortion deviation according to claim 3, characterized in that, The specific method for step (5) is as follows: (501) Constructing the combined observation equations for ionospheric de-escalation ,in, This represents the ionosphere decomposition operator, where... , , , To introduce the satellite clock bias values ​​of known satellite clock bias products, This indicates the clock bias reference introduced by known satellite clock bias products. , , , , Represents the speed of light. , , , , , ; (502) Combine the satellite orbit, the satellite clock bias value of the known satellite clock bias product, and the solution from (402). , , Substituting the observation equation into (501), selecting observation data for a period of time, and using the least squares adjustment or Kalman filter adjustment method, we can obtain the solution. , , , , , parameter.

5. A satellite phase deviation calculation method considering signal distortion deviation according to claim 4, characterized in that, The specific method for step (6) is as follows: (601) Using the integer fuzziness fixing algorithm, the solution obtained in (502) is... Fixed as an integer, obtain the integer ambiguity of the narrow alley. ; (602) The narrow alley integer ambiguity in (601) Substituting into the observation equation in (501), the ambiguity fixed solution for each parameter is obtained. , , , , .

6. The satellite phase deviation calculation method considering signal distortion deviation according to claim 1, characterized in that, In step (7), if the user receiver type already exists in the reference station network and has corresponding signal distortion deviation estimation data, then the satellite orbit, satellite clock error, satellite phase deviation, and signal distortion deviation estimation are directly introduced into the observation equation. After error correction, the specific method for solving the user terminal position parameters is as follows: (701a) For terminal users whose receiver type belongs to a certain type of receiver in the base station network, when performing positioning, the solution obtained in step (4) is first... and Substitute into the MW combined observation equation of the user receiver Solving for floating-point solutions and ; (702a) Using an integer ambiguity fixing algorithm, the ambiguity is fixed. Fixed as an integer Then, substituting back into the observation equation (701a), we obtain the fixed solution. ; (703a) The solution obtained in step (4) The solution in step (6) , and the solution in step (702a) , Substituting into the de-ionization combined observation equation of the user receiver ,in , Indicates the end-user identifier. , Indicates the known satellite positions. The unknown user terminal location is represented by the equations. After incorporating known satellite orbit, satellite clock error, tropospheric dry delay, and tropospheric wet delay projection model data, the equations can be solved using least squares adjustment or Kalman filtering methods to obtain the user terminal location. , , , and ; (704a) Using an integer ambiguity fixing algorithm, the ambiguity is fixed. Fixed as an integer Then, substituting back into the observation equation (703a), the fixed ambiguity solutions for each parameter are obtained, including the terminal position, , , .

7. A satellite phase deviation calculation method considering signal distortion deviation according to claim 1, characterized in that, In step (7), if the receiver type used by the terminal user is not covered in the reference station network and there is no corresponding signal distortion deviation estimate, then the specific method for correcting only the satellite orbit, satellite clock error, and satellite phase deviation in the observation equation, and simultaneously solving for the user terminal position and signal distortion deviation parameters, is as follows: (701b) For terminal users whose receiver type does not belong to a certain type of receiver in the base station network, when performing positioning, the solution obtained in step (4) is first... Substitute into the MW combined observation equation of the user receiver Solving for floating-point solutions , and ; (702b) Using an integer ambiguity fixing algorithm, the ambiguity is fixed. Fixed as an integer Then, substituting back into the observation equation (701b), we obtain the fixed solution. and ; (703b) The solution obtained in step (4) The solution in step (6) And the solution in step (702b) , Substituting into the de-ionization combined observation equation of the user receiver ,in , Indicates the end-user identifier. , Indicates the known satellite positions. The unknown user terminal location is represented by the equations. After incorporating known satellite orbit, satellite clock error, tropospheric dry delay, and tropospheric wet delay projection model data, the equations can be solved using least squares adjustment or Kalman filtering methods to obtain the user terminal location. , , , , and ; (704b) Using an integer ambiguity fixing algorithm, the ambiguity is fixed. Fixed as an integer Then, substituting back into the observation equation (703b), the fixed ambiguity solutions for each parameter are obtained, including the terminal position, , , , .

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