Single Beidou system carrier phase difference positioning method

Abstract

The invention discloses a single Beidou system carrier phase differential positioning method, which belongs to the technical field of satellite navigation positioning, and comprises the following steps: receiving Beidou satellite signals, and obtaining carrier phase observation data of a base station and a moving station; carrying out multi-frequency combination processing on carrier phase observation data, fusing references of different time scales, carrying out phase prediction by adopting an autoregression model, adaptively adjusting a compensation gain according to a carrier motion state and prediction confidence, and carrying out real-time correction on an observation phase; and performing ambiguity fixation on the corrected observation phase, and executing quality control inspection to evaluate the reliability of the positioning result. The weight of the observation value is dynamically adjusted according to the signal quality, the elevation angle and the multi-path detection result, complex observation environments such as multi-path interference and signal quality change are effectively adapted, and the system stability is remarkably improved; according to the method, the calculation efficiency and the reliability test capability can be improved, the ambiguity fixing success rate is improved, and the real-time high-precision positioning requirement is met.

Description

Technical Field

[0001] This invention relates to the technical field of satellite navigation and positioning, and specifically to a single BeiDou system carrier phase differential positioning method. Background Technology

[0002] Carrier phase differential positioning (CPI) is a key technology for achieving centimeter-level high-precision positioning. Traditional CPI systems typically rely on a combination of GPS / BeiDou dual systems or multiple systems. In power grid precision positioning applications, the demand for high-precision and high-reliability positioning of a single system is increasingly urgent. However, existing single-BeiDou carrier phase differential positioning technologies suffer from a fundamental technical contradiction: while pursuing centimeter-level high-precision positioning, it is difficult to simultaneously guarantee real-time performance and system stability. When error processing strategies are used to improve positioning accuracy, the error propagation mechanism in traditional single-difference and double-difference processing becomes complex. Refined processing of error sources such as atmospheric delay and clock bias requires substantial computational resources, leading to a decrease in real-time performance. Conversely, simplifying error processing to ensure real-time performance results in incomplete elimination of satellite clock bias and receiver clock bias, unsatisfactory atmospheric delay error processing, and a significant decrease in positioning accuracy due to the lack of an effective mechanism for constructing carrier phase observation equations and optimization strategies for double-difference processing. When improving the accuracy of weighted least squares solutions, a complex adaptive weight allocation strategy is required, which comprehensively considers signal quality, elevation angle and multipath detection results. However, this refined weight calculation increases system complexity and reduces computational efficiency. On the other hand, the fixed-mode weight allocation strategy is simple to calculate and has good real-time performance, but it cannot effectively adapt to the dynamic changes of complex observation environments. In particular, under the complex electromagnetic environment of the power grid, the system stability is seriously insufficient.

[0003] Therefore, how to achieve centimeter-level high-precision positioning and ensure millisecond-level real-time response in single BeiDou system carrier phase differential positioning, while ensuring system stability under complex electromagnetic environment and geometrically constrained conditions, is the core technical contradiction and key technical problem that needs to be solved urgently in the current technology. Summary of the Invention

[0004] In view of the aforementioned problems, this invention is proposed. Therefore, this invention provides a single BeiDou system carrier phase differential positioning method to solve the problems of poor environmental adaptability due to fixed ambiguity, fixed frequency configuration, low search efficiency, and fixed verification threshold in existing technologies.

[0005] To solve the above-mentioned technical problems, the present invention provides the following technical solution: a single BeiDou system carrier phase differential positioning method, comprising: Receive BeiDou satellite signals and acquire carrier phase observation data from the base station and rover station; The carrier phase observation data is processed by multi-frequency combination, and the references of different time scales are fused. An autoregressive model is used for phase prediction. The compensation gain is adaptively adjusted according to the carrier motion state and prediction confidence to correct the observed phase in real time. The ambiguity of the corrected observation phase is fixed, and a quality control test is performed to evaluate the reliability of the positioning results.

[0006] As a preferred embodiment of the carrier phase differential positioning method for a single BeiDou system described in this invention, the acquisition of carrier phase observation data of the base station and the rover station includes: establishing carrier phase observation equations for the base station and the rover station based on the geometric and physical relationship between the satellite and the base station and the rover station, in order to obtain the carrier phase observation values ​​of the base station and the rover station; wherein, The carrier phase observation equation for the reference station is: in, For the reference station to monitor the satellite in the 19th Carrier phase observations for each epoch. For the reference station to the satellite at the Geometric distance of each epoch, As a benchmark station The clock difference of an epoch, For the satellite in the The clock difference of an epoch, As a benchmark station Tropospheric delay of one epoch, As a benchmark station Ionospheric delay per epoch, The integer ambiguity of the base station. As a benchmark station Epochal observation noise For carrier wavelength, At the speed of light, For observation epochs; The carrier phase observation equation for the rover station is: in, For the rover to monitor the satellite in the 1st Carrier phase observations for each epoch. For the rover to the satellite in the Geometric distance of each epoch, For the mobile station at the The clock difference of each calendar era, For the mobile station at the Tropospheric delay of one epoch, For the mobile station at the Ionospheric delay per epoch, For the integer ambiguity of the rover station, For the mobile station at the Observation noise for each epoch.

[0007] As a preferred embodiment of the single BeiDou system carrier phase differential positioning method described in this invention, it further includes: constructing a single-difference observation equation, and calculating the difference between the observation values ​​of the same satellite when observed simultaneously by the rover station and the base station, to obtain the single-difference carrier phase observation value between the rover station and the base station, expressed as: in, For the first Single-difference carrier phase observations between the rover station and the base station at each epoch. For the first Geometric distance difference between each epoch rover and the base station For the first Clock difference between each epoch rover and the base station For the first The tropospheric delay difference between each epoch rover and the base station For the first Ionospheric delay difference between each epoch rover and the base station The integer ambiguity difference between the rover and the base station. For the first The observation noise difference between the rover station and the base station at each epoch; Select reference satellite Based on the single-difference observation equation, a double-difference observation equation was constructed to obtain the relationship between the rover station and the base station regarding the satellite. The double-difference carrier phase observations are expressed as: in, The selected reference satellite number.

[0008] As a preferred embodiment of the single BeiDou system carrier phase differential positioning method described in this invention, the multi-frequency combination processing of the carrier phase observation data includes: performing multi-frequency carrier phase combination based on different frequency signals of the BeiDou system and the corresponding frequency carrier phase observation values; the multi-frequency carrier phase combination includes ionosphere-independent combination, wide-lane combination, and narrow-lane combination; The ionospherically independent combination uses the squares of the two frequencies of the BeiDou system as weights, performs a weighted subtraction of the carrier phase observations of the two frequencies, and then divides by the difference of the weights to obtain the ionospherically independent combination carrier phase. The wide-lane combination uses two frequencies of the BeiDou system as weights, performs a weighted subtraction of the carrier phase observations of the two frequencies, and then divides by the frequency difference to obtain the carrier phase observations of the wide-lane combination. The narrow-lane combination uses two frequencies of the BeiDou system as weights, adds the carrier phase observations of the two frequencies together, and then divides by the sum of the frequencies to obtain the carrier phase observations of the narrow-lane combination.

[0009] As a preferred embodiment of the single BeiDou system carrier phase differential positioning method described in this invention, the fusion of references at different time scales includes: constructing a three-layer time window structure, including microsecond-level time windows. Millisecond time window and second-level time windows ; The time base fusion structure is established as follows: , in, As the time base after fusion, The weighting coefficients are for microsecond-level time windows. The weighting coefficients are for millisecond-level time windows. The weighting coefficients are for time windows on the order of seconds.

[0010] As a preferred embodiment of the single BeiDou system carrier phase differential positioning method described in this invention, the method employs an autoregressive model for phase prediction, adjusts the compensation gain based on the carrier motion state and prediction confidence, and performs real-time correction of the observed phase, including: establishing a third-order autoregressive phase prediction model, expressed as: in, For the future moment Phase prediction value, , , These are the phase observations at the current time and two past times. This is the random error term; The phase compensation structure is as follows: in, The phase value after compensation. The observed phase value, For the predicted phase value, To compensate for the gain, Based on the gain, The rate of change of the carrier velocity, is the attenuation constant.

[0011] As a preferred embodiment of the carrier phase differential positioning method for a single BeiDou system described in this invention, the method further includes: linearizing the double-difference observation equations to establish linearized observation equations, and using the weighted least squares method for differential positioning solution, expressed as: in, This is the weight matrix. To design the matrix, For position increment vector, The difference between the observed value and the calculated value; The weight matrix is ​​determined based on an adaptive weighting strategy, which includes signal strength weights, elevation angle weights, and multipath detection weights.

[0012] As a preferred embodiment of the carrier phase differential positioning method for a single BeiDou system described in this invention, the method further includes: extracting multipath feature vectors based on carrier phase observation data; and using the information in the feature vectors, confirming the multipath interference situation and calculating its confidence level through a multi-level decision mechanism. The multipath feature vector ,in For phase variance, For phase gradient, This is the inter-frequency correlation coefficient. For power spectral entropy, The width of the main peak. For signal-to-noise ratio, For Doppler frequency shift, For delay estimation; The first-level judgment condition of the multi-level judgment mechanism is: and The second-level decision is based on the spectral entropy threshold. The third-level judgment calculates the overall confidence level. ,in For each level of judgment indicator, the weights must meet the requirements. ; When the multipath type is specular reflection multipath, a phase compensation structure is used for correction; when the multipath type is scattering multipath, a weighted least squares suppression structure is used for correction.

[0013] As a preferred embodiment of the carrier phase differential positioning method for a single BeiDou system according to the present invention, it further includes: fixing the ambiguity of the carrier phase observation values; By calculating the correlation coefficient of the ambiguity covariance matrix, the ambiguity parameter to be estimated is divided into three correlation layers. If the correlation coefficient is greater than the first threshold, the ambiguity parameter to be estimated is divided into a strong correlation layer. If the correlation coefficient is not greater than the first threshold but greater than the second threshold, the ambiguity parameter to be estimated is divided into a medium correlation layer. If the correlation coefficient is not greater than the second threshold, the ambiguity parameter to be estimated is divided into a weak correlation layer. Group parameters with the same correlation to form the k-th layer parameter group, construct the sub-covariance matrix of the k-th layer parameter group, and calculate the condition number of the sub-covariance matrix; when the condition number of the sub-covariance matrix is ​​greater than the third threshold, perform the first transformation; when the condition number of the sub-covariance matrix is ​​not greater than the third threshold, perform the second transformation. If the stability index is greater than the fourth threshold during the transformation process, the transformation intensity is reduced; if the stability index is less than the fifth threshold during the transformation process, the transformation intensity is increased.

[0014] As a preferred embodiment of the single BeiDou system carrier phase differential positioning method described in this invention, the following steps are included: performing quality control checks on the corrected observed phase to evaluate the reliability of the positioning results: If the ratio test value of the ambiguity fixation result is greater than the test threshold, the ambiguity fixation result is accepted; if the ratio test value of the ambiguity fixation result is not greater than the ambiguity fixation reliability test threshold, floating-point ambiguity estimation continues. If the posterior unit weight variance is less than the variance threshold, the solution is considered reliable; if the posterior unit weight variance is not less than the variance threshold, the solution is considered unreliable. If the position accuracy attenuation factor is less than the geometric accuracy threshold, the current satellite geometric configuration is considered to have high positioning accuracy; if the position accuracy attenuation factor is not less than the geometric accuracy threshold, the current satellite geometric configuration is considered to have low positioning accuracy.

[0015] The beneficial effects of this invention are as follows: By constructing a complete carrier phase observation equation and a double-difference processing mechanism, this invention effectively eliminates the influence of satellite clock errors and receiver clock errors, significantly improves the atmospheric delay error processing effect, and achieves positioning accuracy down to the centimeter level; by dynamically adjusting the observation weights according to signal quality, elevation angle, and multipath detection results, it effectively adapts to complex observation environments such as multipath interference and signal quality changes, significantly improving system stability; this invention can improve computational efficiency and reliability verification capabilities, and the success rate of ambiguity fixation is improved, meeting the requirements for real-time high-precision positioning. Attached Figure Description

[0016] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0017] Figure 1 This is a schematic flowchart of a single BeiDou system carrier phase differential positioning method provided in one embodiment of the present invention.

[0018] Figure 2 The above is a flowchart of a single BeiDou system carrier phase differential positioning method provided in one embodiment of the present invention.

[0019] Figure 3 This invention provides a single BeiDou system carrier phase differential positioning method according to one embodiment. Flowchart of multi-frequency carrier phase combination and timing consistency compensation.

[0020] Figure 4 This is a diagram of the hierarchical downcorrelation transform (LAMBDA) algorithm architecture for a single BeiDou system carrier phase differential positioning method according to an embodiment of the present invention. Detailed Implementation

[0021] To make the above-mentioned objects, features, and advantages of the present invention more apparent and understandable, specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of them. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the protection scope of the present invention.

[0022] Example 1, referring to Figures 1-4 This is the first embodiment of the present invention, which provides a single BeiDou system carrier phase differential positioning method, including: S100: Receives BeiDou satellite signals and acquires carrier phase observation data from the base station and rover station; In this embodiment of the invention, step S100, obtaining carrier phase observation data of the base station and the rover, includes: establishing carrier phase observation equations for the base station and the rover based on the geometrical-physical relationship between the satellite and the base station and the rover, to obtain carrier phase observation values ​​for the base station and the rover; wherein, The carrier phase observation equation for the reference station is: in, For the reference station to monitor the satellite in the 19th Carrier phase observations for each epoch. For the reference station to the satellite at the Geometric distance of each epoch, As a benchmark station The clock difference of an epoch, For the satellite in the The clock difference of an epoch, As a benchmark station Tropospheric delay of one epoch, As a benchmark station Ionospheric delay per epoch, The integer ambiguity of the base station. As a benchmark station Epochal observation noise For carrier wavelength, At the speed of light, For the observation epoch; The carrier phase observation equation for the rover station is: in, For the rover to monitor the satellite in the 1st Carrier phase observations for each epoch. For the rover to the satellite in the Geometric distance of each epoch, For the mobile station at the The clock difference of an epoch, For the mobile station at the Tropospheric delay of one epoch, For the mobile station at the Ionospheric delay per epoch, For the integer ambiguity of the rover station, For the mobile station at the Observation noise for each epoch.

[0023] In this embodiment of the invention, step S100 further includes: constructing a single-difference observation equation, and calculating the difference between the observation values ​​of the same satellite when observed simultaneously by the rover station and the base station, to obtain the single-difference carrier phase observation value between the rover station and the base station, expressed as: in, For the first Single-difference carrier phase observations between the rover station and the base station at each epoch. For the first Geometric distance difference between each epoch rover and the base station For the first Clock difference between each epoch rover and the base station For the first The tropospheric delay difference between each epoch rover and the base station For the first Ionospheric delay difference between each epoch rover and the base station The integer ambiguity difference between the rover and the base station. For the first The observation noise difference between the rover station and the base station at each epoch; Select reference satellite Based on the single-difference observation equation, a double-difference observation equation was constructed to obtain the relationship between the rover station and the base station regarding the satellite. The double-difference carrier phase observations are expressed as: in, The selected reference satellite number.

[0024] Specifically, it also includes: The double-difference observation equation is linearized to establish the following linearized observation equation: in, For the residual vector, To design the matrix, For position increment vector, This represents the difference between the observed value and the calculated value.

[0025] S200: Performs multi-frequency combination processing on carrier phase observation data, fuses references at different time scales, and uses an autoregressive model for phase prediction. It adaptively adjusts the compensation gain based on the carrier motion state and prediction confidence to perform real-time correction of the observed phase. In this embodiment of the invention, step S200, which involves multi-frequency combination processing of carrier phase observation data, includes: performing multi-frequency carrier phase combination based on different frequency signals of the BeiDou system and the corresponding frequency carrier phase observation values; the multi-frequency carrier phase combination includes ionospherically independent combination, wide-lane combination, and narrow-lane combination; The ionospherically independent combination uses the squares of the two frequencies of the BeiDou system as weights, performs a weighted subtraction of the carrier phase observations of the two frequencies, and then divides by the difference of the weights to obtain the ionospherically independent combination carrier phase. The wide-lane combination uses two frequencies of the BeiDou system as weights, performs a weighted subtraction of the carrier phase observations of the two frequencies, and then divides by the frequency difference to obtain the carrier phase observations of the wide-lane combination. The narrow-lane combination uses two frequencies of the BeiDou system as weights, adds the carrier phase observations of the two frequencies together, and then divides by the sum of the frequencies to obtain the carrier phase observations of the narrow-lane combination.

[0026] Specifically, the ionosphere-independent combination is represented as: in, For ionospherically independent combined carrier phase, , There are two frequency values. , These are the carrier phase observations for the corresponding frequency.

[0027] Specifically, the wide alley combination is represented as: Specifically, the narrow alley combination is represented as: In this embodiment of the invention, the fusion of benchmarks at different time scales in step S200 includes: constructing a three-layer time window structure, including a microsecond-level time window. Millisecond time window and second-level time windows ; The time base fusion structure is established as follows: , in, As the time base after fusion, The weighting coefficients are for microsecond-level time windows. The weighting coefficients are for millisecond-level time windows. The weighting coefficients are for time windows on the order of seconds.

[0028] In an optional embodiment, the microsecond-level window can be set to... The millisecond window can be set to The second-level window can be set to .

[0029] Furthermore, in this embodiment of the invention, the weighting coefficient of the microsecond-level time window in step S200 is calculated as follows: in, The rate of change of the carrier velocity, The standard deviation of the velocity; The weighting coefficients for millisecond-level time windows are calculated as follows: in, For time intervals, The standard deviation is the time factor. The weighting coefficient for the second-level time window is calculated as follows: .

[0030] In this embodiment of the invention, step S200 uses an autoregressive model for phase prediction, adjusting the compensation gain based on the carrier motion state and prediction confidence level to perform real-time correction of the observed phase, including: establishing a third-order autoregressive phase prediction model, expressed as: in, For the future moment Phase prediction value, , , These are the phase observations at the current time and two past times. This is the random error term; The phase compensation structure is as follows: in, The phase value after compensation. The observed phase value, For the predicted phase value, To compensate for the gain, Based on the gain, The rate of change of the carrier velocity, is the attenuation constant.

[0031] It should be noted that, in this embodiment of the invention, the base gain can be set to 0.8, and the attenuation constant can be set to... .

[0032] In this embodiment of the invention, step S200 further includes: linearizing the double-difference observation equation, establishing a linearized observation equation, and performing differential positioning solution using the weighted least squares method, expressed as: in, This is the weight matrix. To design the matrix, For position increment vector, The difference between the observed value and the calculated value; The weight matrix is ​​determined based on an adaptive weighting strategy, which includes signal strength weight, elevation angle weight, and multipath detection weight.

[0033] Specifically, the signal strength weight is represented as follows: in, For the first Signal strength weights of each satellite For the first The carrier-to-noise ratio of each satellite It has the highest carrier-to-noise ratio among all satellites; The elevation angle weight is expressed as: in, For the first The elevation angle weight of each satellite For the first The elevation angle of the satellite The multipath detection weights are represented as follows: in, For the first Multipath detection weights for each satellite This is the multipath error estimate. The standard deviation of the multipath error; Furthermore, the overall weighting is expressed as follows: .

[0034] In this embodiment of the invention, step S200 further includes: extracting multipath feature vectors based on carrier phase observation data; using the information in the feature vectors, confirming the multipath interference situation and calculating its confidence level through a multi-level decision mechanism; Multipath feature vectors ,in For phase variance, For phase gradient, This is the inter-frequency correlation coefficient. For power spectral entropy, The width of the main peak. For signal-to-noise ratio, For Doppler frequency shift, For delay estimation; The first-level judgment condition of the multi-level judgment mechanism is: and The second-level decision is based on the spectral entropy threshold. The third-level judgment calculates the overall confidence level. ,in For each level of judgment indicator, the weights must meet the requirements. ; When the multipath type is specular reflection multipath, a phase compensation structure is used for correction; when the multipath type is scattering multipath, a weighted least squares suppression structure is used for correction.

[0035] Specifically, for specular reflection multipath, the phase compensation structure is represented as follows: in, As for the compensation range, For multipath delay, For carrier period, This is the initial phase; For scattering multipath, the weighted least squares suppression structure is represented as follows: Among them, the weight matrix , For the first The standard deviation of the multipath error for each observation.

[0036] In this embodiment of the invention, step S200 further includes: fixing the ambiguity of the carrier phase observation value; By calculating the correlation coefficient of the ambiguity covariance matrix, the ambiguity parameter to be estimated is divided into three correlation layers. If the correlation coefficient is greater than the first threshold, the ambiguity parameter to be estimated is divided into a strong correlation layer. If the correlation coefficient is not greater than the first threshold but greater than the second threshold, the ambiguity parameter to be estimated is divided into a medium correlation layer. If the correlation coefficient is not greater than the second threshold, the ambiguity parameter to be estimated is divided into a weak correlation layer. Group parameters with the same correlation to form the k-th layer parameter group, construct the sub-covariance matrix of the k-th layer parameter group, and calculate the condition number of the sub-covariance matrix; when the condition number of the sub-covariance matrix is ​​greater than the third threshold, perform the first transformation; when the condition number of the sub-covariance matrix is ​​not greater than the third threshold, perform the second transformation. If the stability index is greater than the fourth threshold during the transformation process, the transformation intensity is reduced; if the stability index is less than the fifth threshold during the transformation process, the transformation intensity is increased.

[0037] It should be noted that, in this embodiment of the invention, the first threshold can be set to 0.8, the second threshold can be set to 0.3, and the third threshold can be set to... The fourth threshold can be set to 15, and the fifth threshold can be set to 8.

[0038] For example, the least-squares AMbiguity decorrelation adjustment (LAMBDA) algorithm with a hierarchical decorrelation transformation structure is used to fix integer ambiguities, specifically including: The structure for calculating the correlation coefficient of the covariance matrix is ​​represented as follows: in, The covariance matrix is ​​the first Line number Column elements, , These are diagonal elements.

[0039] Establish a three-layer classification structure based on correlation coefficient thresholds: strong correlation layer Medium-level related layers Weakly correlated layer ; For the k-th layer parameter group Construct the sub-covariance matrix Calculate the condition number: in, For matrix The condition number of For matrix The largest eigenvalue, For matrix The smallest eigenvalue.

[0040] Establish a transformation intensity control structure: when When, apply the LLL transform; when At that time, apply the standard reduction transformation; It should be noted that the LLL lattice reduction algorithm (Lenstra-Lenstra-Lovász lattice reduction, LLL) is a process of transforming a set of irregular basis vectors into regular basis vectors.

[0041] Construct a numerical stability monitoring structure: in, Weighting factors and , The transformation matrix is... As a stability index, Transformation matrix The minimum value of the diagonal elements. Transformation matrix The maximum absolute value of all elements in the set.

[0042] when When the transformation intensity is reduced, Increase the intensity of the transformation.

[0043] S300: Fix the ambiguity of the corrected observation phase and perform quality control checks to evaluate the reliability of the positioning results; In this embodiment of the invention, step S300, which involves performing a quality control check on the corrected observation phase to assess the reliability of the positioning results, includes: If the ratio test value of the ambiguity fixation result is greater than the test threshold, the ambiguity fixation result is accepted; if the ratio test value of the ambiguity fixation result is not greater than the ambiguity fixation reliability test threshold, floating-point ambiguity estimation continues. If the posterior unit weight variance is less than the variance threshold, the solution is considered reliable; if the posterior unit weight variance is not less than the variance threshold, the solution is considered unreliable. If the position accuracy attenuation factor is less than the geometric accuracy threshold, the current satellite geometric configuration is considered to have high positioning accuracy; if the position accuracy attenuation factor is not less than the geometric accuracy threshold, the current satellite geometric configuration is considered to have low positioning accuracy.

[0044] Specifically, the ambiguity fixed reliability ratio test is expressed as follows: in, The optimal integer candidate solution. The second-best integer candidate solution. This is the ratio test value.

[0045] The posterior variance estimate is expressed as: in, The number of observations. The number of unknown parameters.

[0046] The geometric accuracy factor evaluation is expressed as: in, , , represents the diagonal elements of the position covariance matrix.

[0047] It should be noted that, in the embodiments of the present invention, the inspection threshold, variance threshold, and geometric accuracy threshold can be adjusted according to the operating conditions.

[0048] Furthermore, embodiments of the present invention also include geometric configuration optimization and reinforcement, specifically including: Virtual satellites are constructed using data from adjacent observation stations: in, For the first Data from one observation station, The distance between observation stations For feature distance, For the first Reliability factor of each observation station For the first The weight of each observation station.

[0049] Real-time monitoring of geometric accuracy factor: in, For the observation geometry matrix, This is the weight matrix. This is for matrix trace operations.

[0050] when At that time, construct the enhanced geometry matrix: in, For the actual satellite geometric matrix, For virtual satellite geometric matrix, This is the constraint matrix.

[0051] Furthermore, embodiments of the present invention also include a real-time processing strategy, specifically including: A fixed-length sliding window is used to ensure that a certain number of historical observations are included in the solution. Predict the approximate position of the current epoch by using the solution results of the previous epoch, thus accelerating convergence; Real-time monitoring of observation residuals and elimination of abnormal observations; Construct a spatiotemporal correlation balancer and calculate temporal correlation: in, For mathematical expectation, For observation purposes, This is a time delay.

[0052] Calculate spatial correlation: in, For spatial observation, Spatial distance Comprehensive correlation indicators: in, .

[0053] Furthermore, the spatiotemporal correlation balancer also includes: constructing a motion state detection structure: in, for Time of the first Each coordinate component For time intervals; Establish the balance factor calculation structure: in, For the speed threshold and , The scale parameter and ; Constructing a correlation imbalance detection and compensation structure: When When the digital compensation filter structure is enabled: The filter coefficients are adaptively adjusted as follows: It should be noted that this invention addresses the problems of complex error propagation mechanisms and inadequate handling of error sources such as atmospheric delay clock bias in traditional single-difference and double-difference processing techniques mentioned in existing technologies. This invention effectively eliminates the influence of satellite clock bias and receiver clock bias by constructing a complete carrier phase observation equation and a double-difference processing mechanism, significantly improving the atmospheric delay error processing effect and achieving centimeter-level positioning accuracy. Regarding the issue that existing weighted least squares solutions often employ fixed weight allocation strategies that cannot effectively adapt to dynamic changes in complex observation environments, this invention adopts an adaptive weight strategy. It dynamically adjusts the observation weights based on signal quality, elevation angle, and multipath detection results, effectively adapting to complex observation environments such as multipath interference and signal quality variations, significantly improving system stability. Finally, addressing the shortcomings of traditional ambiguity fixing algorithms in terms of computational efficiency and reliability verification, making it difficult to meet the requirements of real-time high-precision positioning, this invention employs the LAMBDA algorithm with a hierarchical downcorrelation transform structure. This improves computational efficiency and reliability verification capabilities, increasing the success rate of ambiguity fixing and meeting the requirements of real-time high-precision positioning.

[0054] Example 2: This example is for power grid transmission line inspection and fault location applications with a baseline length of less than 10 kilometers. It focuses on using double-difference processing to eliminate the effects of atmospheric delay and achieve rapid and high-precision positioning.

[0055] In the precise positioning project of power transmission lines, the base station is set at a known control point of the substation, and the rover is mounted on a drone to conduct real-time inspection and positioning of the power transmission lines. In this embodiment, the parameters are set as follows: Timing consistency compensation: The base gain K0 is set to 0.8, and the attenuation constant is... Set to 10 m / s.

[0056] Quality control inspection: Ambiguity fixed reliability ratio inspection threshold Set to 3.0; posterior unit weighted variance threshold Set to 0.05m.

[0057] Geometric configuration optimization: When constructing the virtual satellite, the characteristic distance dc between adjacent observation stations is set to 20km.

[0058] Specific implementation effects Positioning accuracy: In a typical power transmission line inspection environment, the rover carried by the UAV achieved real-time dynamic positioning accuracy better than 2cm in plane and better than 3cm in elevation.

[0059] Blur fixation: The success rate of blur fixation reaches over 99.5%, the average time for the first fixation is less than 20 seconds under cold start, and the recapture time is less than 5 seconds.

[0060] System reliability: Through adaptive weighting and quality control strategies, the system can maintain centimeter-level positioning output in environments with partial signal obstruction or weak multipath interference, meeting the requirements of high precision and high reliability for power grid inspection and fault location.

[0061] The system configuration in this embodiment is as follows: (1) Observation data acquisition: Simultaneously receive carrier phase signals of three frequencies of Beidou B1I, B2a and B3I, with a sampling rate of 1Hz and a data update cycle of 1 second.

[0062] (2) Establishment of carrier phase observation equations: Establishment of carrier phase observation equations for the reference station: in, The carrier phase observations of the satellite from the base station. The geometric distance from the base station to the satellite. For the base station clock error, For satellite clock bias, For tropospheric delay, For ionospheric delay, For integer ambiguity, To observe the noise, For carrier wavelength, At the speed of light, For observation epochs.

[0063] Rover carrier phase observation equation: in, The carrier phase observations of the satellite from the rover station. The geometric distance from the rover to the satellite. For the clock difference of the mobile station, , , , These represent the tropospheric delay, ionospheric delay, integer ambiguity, and observation noise corresponding to the rover station.

[0064] (3) Single-difference and double-difference processing: Constructing the single-difference observation equation: in, The single-difference carrier phase between the base station and the rover station. For single-difference geometric distance, It is a single-difference clock error. For single-difference tropospheric delay, For single-difference ionospheric delay, For single-difference integer ambiguity, This is single-difference observation noise.

[0065] Select reference satellite (in (For the selected reference satellite number), construct the double-difference observation equation: in, It is a double-difference carrier phase. It is a double-difference geometric distance. For double-difference tropospheric delay, For double-difference ionospheric delay, For double-difference integer ambiguity, This is double-difference observation noise.

[0066] (4) Multi-frequency carrier phase combination: A multi-frequency combination strategy is adopted to construct an ionosphere-independent combination: in, For ionospherically independent combined carrier phase, , There are two frequency values. , These are the carrier phase observations for the corresponding frequency.

[0067] Wide Alley Combination: in, This refers to the wide-lane combined carrier phase.

[0068] Narrow Alley Combination: in, This refers to the narrow-lane combined carrier phase.

[0069] (5) Adaptive compensation for temporal consistency: Construct a three-layer time window structure, including microsecond-level windows. Millisecond window and second-level window Establish a time-based fusion structure: in, To establish a fusion time base, the weighting coefficients satisfy the constraints. and The weighting coefficients are calculated as follows: in, The rate of change of the carrier velocity, For the speed standard deviation, For time intervals, This represents the time standard deviation.

[0070] Establish a third-order autoregressive phase prediction structure: in, for Phase value at time, , , These are the autoregressive coefficients. This represents the prediction error.

[0071] Phase compensation structure: in, To compensate for the phase, For observing the phase, To predict the phase, To compensate for the gain.

[0072] (6) Adaptive weighting strategy: Implement an adaptive weighting strategy, including signal strength weighting: in, Let be the carrier-to-noise ratio of the i-th satellite. This represents the maximum carrier-to-noise ratio.

[0073] Elevation angle weight: in, Let be the elevation angle of the i-th satellite.

[0074] Multipath detection weights: in, This is the multipath error estimate. This represents the standard deviation of the multipath error.

[0075] Overall weighting: in, Let be the overall weight of the i-th satellite.

[0076] (7) Layered downcorrelation transformation LAMBDA algorithm: After fixing the integer ambiguity, construct the covariance matrix correlation coefficient calculation structure: in, The covariance matrix is ​​the first Line number Column elements, , diagonal elements The correlation coefficient.

[0077] Establish a three-layer classification structure based on correlation coefficient thresholds: strong correlation layer Medium-level related layers Weakly correlated layer .

[0078] (8) Multipath interference identification and suppression: Construct an 8-dimensional multipath feature vector ,in For phase variance, For phase gradient, This is the inter-frequency correlation coefficient. For power spectral entropy, The width of the main peak. For signal-to-noise ratio, For Doppler frequency shift, For delayed estimation, a three-level threshold decision structure is established: the first-level decision condition is... and The second-level decision is based on the spectral entropy threshold. The third-level judgment calculates the overall confidence level. ,in For each level of judgment indicator, the weights must meet the requirements. .

[0079] For multipath reflections from mirrors, a phase compensation structure is used: in, As for the compensation range, For multipath delay, For carrier period, This is the initial phase.

[0080] For scattering multipath, a weighted least squares suppression structure is used: in, These are the corrected observations. For the observation matrix, This is the weight matrix. Let be the standard deviation of the multipath error for the i-th observation. This is the vector of observations.

[0081] (9) Quality control inspection: Conduct a complete quality control process, including: Ambiguity fixation reliability ratio test: in, The optimal integer candidate solution. The second-best integer candidate solution. The threshold for the Ratio test is when... The result is accepted with a fixed degree of ambiguity.

[0082] Posterior variance estimation: in, For the residual vector, This is the weight matrix. The number of observations. The number of unknown parameters. As the variance threshold, when The solution was considered reliable at that time.

[0083] Geometric accuracy factor evaluation: in, , , represents the diagonal elements of the position covariance matrix.

[0084] (10) Geometric configuration optimization and reinforcement: Constructing a virtual satellite using data from adjacent observation stations: in, For the first The data from each observation station are weighted as follows: in, The distance between observation stations For feature distance, For the first Reliability factor of each observation station.

[0085] Real-time monitoring of geometric accuracy factor: in, For the observation geometry matrix, This is the weight matrix. This is for matrix trace operations.

[0086] when At that time, construct the enhanced geometry matrix: in, For the actual satellite geometric matrix, For virtual satellite geometric matrix, This is the constraint matrix.

[0087] (11) Real-time processing strategy: Construct a spatiotemporal correlation balance regulator and calculate temporal correlation: in, For mathematical expectation, For observation purposes, This is a time delay.

[0088] Calculate spatial correlation: in, For spatial observation, This refers to spatial distance.

[0089] Comprehensive correlation indicators: in, .

[0090] Construct a motion state detection structure: in, for Time of the first Each coordinate component For time intervals.

[0091] Establish the balance factor calculation structure: in, For speed threshold, This is the scale parameter.

[0092] It should be noted that the double-difference processing to eliminate the effects of atmospheric delay is the core of achieving high-precision positioning. First, by subtracting the observations of the same satellite from both the base station and the rover (single difference), satellite clock errors can be eliminated. Next, a reference satellite is selected, and the single-difference observations of other satellites from the base station and the rover are subtracted again from the single-difference observations of the reference satellite (double difference), thus eliminating receiver clock errors. In short-baseline (less than 10 km) applications such as power transmission line inspection, because the base station and rover are very close, ionospheric and tropospheric delays have a high spatial correlation. Therefore, during the double-difference processing, atmospheric delay errors (…) are significantly reduced. and The ambiguity is largely eliminated, leaving only parameters related to geometric distance and integer ambiguity, which forms the basis for achieving centimeter-level accuracy.

[0093] Example 3 focuses on the application of high-precision equipment positioning and health monitoring of power equipment in power grid substations, highlighting the application effect of the time-phase consistency adaptive compensation system.

[0094] (1) System initialization: Start a three-layer time window architecture, microsecond-level window Set to 5μs, millisecond window Set to 20ms, second-level window Set to 2s, establish a time-base fusion structure: in, To establish a fusion time base, the weighting coefficients satisfy the constraints. and .

[0095] (2) Inspection robot motion detection: Real-time monitoring of the inspection robot's speed and acceleration changes. When the detected speed change rate exceeds 1 m / s², a time-phase consistency compensation mechanism is triggered. Motion state detection adopts: in, for Time of the first Each coordinate component For time intervals.

[0096] (3) Phase prediction model: Establish a third-order autoregressive prediction model: in, for Phase value at time, , , These are the autoregressive coefficients. To mitigate prediction errors, the model parameters were trained using phase data from 50 historical epochs.

[0097] (4) Adaptive weight adjustment: The time base fusion weights are dynamically adjusted based on the motion state of the inspection robot. in, The rate of change of the carrier velocity, For the speed standard deviation, For time intervals, This represents the time standard deviation.

[0098] (5) Compensation gain control: Calculate the compensation gain : in, Based on the gain, This is the attenuation constant, ensuring that the compensation intensity is appropriately adjusted when moving within the substation.

[0099] (6) Phase compensation implementation: The observed phase is compensated according to the phase compensation structure: in, To compensate for the phase, For observing the phase, To predict the phase.

[0100] (7) Quality monitoring: Continuously monitor the phase synchronization accuracy of the multi-frequency combination. When the synchronization error exceeds 0.1 cycles, adjust the compensation parameters.

[0101] Example 4: This example demonstrates the technical effectiveness of a multipath interference intelligent identification and suppression system for application scenarios involving complex electromagnetic environments and numerous metal reflective surfaces within power grid substations.

[0102] When performing precise positioning measurements of equipment in substations, the presence of numerous metal devices and reflective surfaces within the station leads to severe multipath interference, often resulting in positioning errors at the decimeter level using traditional methods.

[0103] (1) Multidimensional feature extraction: Constructing an 8-dimensional multipath feature vector ,in For phase variance, For phase gradient, This is the inter-frequency correlation coefficient. For power spectral entropy, The width of the main peak. For signal-to-noise ratio, For Doppler frequency shift, For delayed estimation, a sliding window of 50 epochs is set with an overlap rate of 75%, and features are updated every 12.5 epochs.

[0104] (2) Three-level decision tree classification: Level 1: When And the correlation coefficient At that time, the second level of suspicion of multipath propagation is determined: when the spectral entropy... When the peak width is [not specified], it is classified as "complex scattering"; when the peak width is [not specified], it is classified as "complex scattering". When the value is twice the normal value, it is classified as "specular reflection" level 3: Calculate the overall confidence level. ,in For each level of judgment indicator, the weights must meet the requirements. ,when When confirming the multipath detection results, in this embodiment, when calculating the comprehensive confidence level of the third-level decision, the weights of the decision indicators at each level are set as follows: w1 (based on signal-to-noise ratio and inter-frequency correlation coefficient) = 0.4, w2 (based on spectral entropy and peak width) = 0.4, w3 (based on Doppler frequency shift and other indicators) = 0.2.

[0105] (3) Adaptive suppression strategy: For specular reflection multipath: Phase compensation method is used. in, As for the compensation range, For multipath delay, For carrier period, Initial phase, amplitude coefficient Set to adaptive adjustment between 0.5 and 0.9. For scattering multipath: weighted least squares method is used: in, These are the corrected observations. For the observation matrix, This is the weight matrix. Let be the standard deviation of the multipath error for the i-th observation. The observation vector is used, and the weight matrix is ​​constructed based on the inverse of the multipath power. (4) Evaluation of suppression effect: Real-time calculation of signal-to-noise ratio improvement ratio ,when When the suppression effect is deemed inadequate, the values ​​are reverted to the original observations; when At the same time, maintain the current suppression strategy.

[0106] (5) Dynamic parameter adjustment: Based on the feedback of the suppression effect, the suppression parameters are dynamically adjusted, and the learning rate is set to 0.1 to ensure the stability of parameter adjustment.

[0107] (6) System integration: The observations after multipath suppression are input into the differential positioning solution, and the overall positioning performance is further improved by combining the adaptive weight strategy.

[0108] It should be noted that after applying the method of this invention, the positioning accuracy within the substation is significantly improved from the decimeter level (10-20cm) of traditional methods to within 3cm, meeting the requirements for precise equipment positioning. The system can effectively identify more than 85% of multipath signals and process them separately according to two types: "specular reflection" and "scattering," reducing the root mean square (RMS) of the residuals of the suppressed observations by 70%. In strong multipath environments, the system's positioning interruption rate is reduced by more than 90%, ensuring the continuity and stability of the inspection robot's operation.

[0109] Example 5: This example demonstrates the application effect of the adaptive hierarchical downcorrelation transformation (LAMBDA) algorithm in the resource-constrained power grid distribution terminal RTK module.

[0110] (1) Layer threshold setting: Based on the computing power of the embedded system, the threshold for the strongly correlated layer is set to 0.8, the threshold for the moderately correlated layer is set to 0.3-0.8, and the threshold for the weakly correlated layer is less than 0.3.

[0111] (2) Covariance matrix preprocessing: Real-time monitoring of the condition number of the covariance matrix; when the condition number exceeds... The hierarchical processing mode is activated at the specified time to calculate the correlation coefficient of the covariance matrix: in, The covariance matrix is ​​the first Line number Column elements, , diagonal elements The correlation coefficient.

[0112] (3) Implementation of hierarchical transformation: For strongly correlated layers: a refined LLL transformation is used to ensure the transformation effect. For moderately correlated layers: an improved lattice basis reduction algorithm is used to balance effect and efficiency. For weakly correlated layers: a simplified processing method is used to save computational resources. (4) Numerical stability control: Calculate the stability index: in, As a weighting factor, Let be the transformation matrix, when When the transformation intensity is reduced, Increase the intensity of the transformation.

[0113] (5) Fixed-point number algorithm: 32-bit fixed-point number operation is used, with the fractional part occupying 16 bits to achieve saturation operation and prevent overflow. All matrix operations are performed directly in the covariance matrix storage space.

[0114] (6) Block processing strategy: Decompose the large covariance matrix into 8×8 sub-blocks, significantly reducing peak memory usage, and pre-compute commonly used small-scale transformation matrix templates.

[0115] (7) Early stopping strategy: Set the maximum number of iterations to twice the number of parameters. When the improvement of the fuzzy resolution result of three consecutive iterations is less than 1%, the iteration is terminated in advance to achieve computation time control.

[0116] (8) Intelligent search optimization: The search order is optimized based on the characteristics of the transformed covariance matrix, the search radius is set to 2-3 times the standard deviation, and intelligent pruning is used to reduce the search space.

[0117] It should be noted that, compared to the standard LAMBDA algorithm, the hierarchical processing, numerical stability control, and early stopping strategy employed in this invention reduce the average ambiguity resolution time on the embedded RTK module by 45%. Through block processing and fixed-point algorithms, peak memory usage is reduced by 35%, meeting the stringent requirements of resource-constrained terminals. While reducing computational complexity, the success rate of ambiguity fixing remains above 99% through hierarchical decorrelation and intelligent search optimization, matching the performance of standard algorithms and ensuring the reliability of the positioning results. The reduced computational load leads to an approximately 15% decrease in the overall power consumption of the RTK module, extending the battery life of the terminal device.

[0118] Example 6: In the inspection project of 500kV transmission line across mountainous areas of the power grid, due to geographical location and terrain obstruction, the geometric distribution of Beidou satellites is uneven, and the PDOP value often exceeds 10, which seriously affects the positioning accuracy of transmission line equipment.

[0119] (1) Real-time monitoring of GDOP: Real-time calculation of geometric precision factor: in, For the observation geometry matrix, This is the weight matrix. For matrix trace operations, a threshold of 6.0 is set. When GDOP exceeds the threshold, geometry enhancement mode is automatically activated.

[0120] (2) Virtual satellite construction: A virtual satellite is constructed using data from three adjacent base stations. in, For the first The data from each observation station are weighted as follows: in, The distance between observation stations For feature distance, For the first Reliability factor of each observation station, characteristic distance Set to 30km.

[0121] (3) Constraint Design: Height constraint: ±5m height variation constraint based on geographic information; Speed ​​constraint: ±2m / s speed constraint based on the motion characteristics of the inspection vehicle; Heading constraint: ±10° heading constraint based on the direction of the transmission line. (4) Construction of Enhanced Geometry Matrix: Constructing the enhanced geometry matrix: in, For the actual satellite geometric matrix, For virtual satellite geometric matrix, The constraint matrix integrates real satellite observations, virtual satellite observations, and constraint-condition observations.

[0122] (5) Dynamic weight allocation: Set the weight of the data source and the weight of the actual satellite observation. Virtual satellite observation weights Constraint weights Adjustments will be made dynamically based on the quality of observations.

[0123] (6) Enhancement effect evaluation: Real-time monitoring of geometric enhancement effect, calculation of GDOP improvement ratio ,when Maintain enhanced mode when Adjust virtual satellite parameters as needed.

[0124] (7) Enhanced quality control: Under the geometric enhancement mode, the Ratio test threshold is appropriately relaxed. Up to version 2.5, the weight of the posterior variance test is increased to ensure the reliability of the positioning results.

[0125] Example 7: In this example, during the intelligent inspection of high-voltage transmission lines in the power grid, the inspection drone flies along the line at a speed of 50 km / h and needs to achieve millisecond-level real-time positioning response to ensure the accuracy and timeliness of the inspection data.

[0126] (1) Sliding window processing: A sliding window with a fixed length of 20 epochs is used to keep historical observations in the calculation and balance real-time performance and positioning accuracy.

[0127] (2) Prediction assistance: The approximate position of the current epoch is predicted by using the solution results of the previous epoch. The convergence is accelerated based on the uniform motion model, and the initialization time is shortened to 3-5 epochs.

[0128] (3) Anomaly detection: Real-time monitoring of observation residuals. When the residual exceeds 3 times the mean error, abnormal observations are automatically removed to ensure data quality.

[0129] (4) Spatiotemporal correlation balance: Construct a spatiotemporal correlation balance regulator and calculate temporal correlation: in, For mathematical expectation, For observation purposes, This is a time delay.

[0130] Calculate spatial correlation: in, For spatial observation, This refers to spatial distance.

[0131] Comprehensive correlation indicators: in, .

[0132] (5) Motion state detection: Calculate motion speed: in, for Time of the first Each coordinate component For time intervals.

[0133] (6) Calculation of balance factor: in, For speed threshold, This is the scale parameter.

[0134] (7) Imbalance Compensation: When When this occurs, activate the digital compensation filter: Adaptive adjustment of filter coefficients: in, For delay operators, , These are the adaptively adjusted filter coefficients.

[0135] This invention effectively eliminates the influence of satellite clock errors and receiver clock errors by constructing a complete carrier phase observation equation and a double-difference processing mechanism, significantly improving the atmospheric delay error processing effect and achieving positioning accuracy down to the centimeter level. It dynamically adjusts the observation weights based on signal quality, elevation angle, and multipath detection results, effectively adapting to complex observation environments such as multipath interference and signal quality variations, significantly improving system stability. Furthermore, this invention enhances computational efficiency and reliability verification capabilities, improves the success rate of ambiguity fixation, and meets the requirements for real-time high-precision positioning.

[0136] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A carrier phase differential positioning method for a single BeiDou system, characterized in that, include: Receive BeiDou satellite signals and acquire carrier phase observation data from the base station and rover station; Multi-frequency combination processing is performed on carrier phase observation data. By fusing references at different time scales and using an autoregressive model for phase prediction, the compensation gain is adaptively adjusted according to the carrier motion state and prediction confidence to correct the observed phase in real time. The ambiguity of the corrected observation phase is fixed, and a quality control test is performed to evaluate the reliability of the positioning results.

2. The single BeiDou system carrier phase differential positioning method as described in claim 1, characterized in that, The acquisition of carrier phase observation data from the base station and rover includes: establishing carrier phase observation equations for the base station and rover based on the geometric and physical relationship between the satellite and the base station and rover, in order to obtain the carrier phase observation values ​​of the base station and rover; wherein, The carrier phase observation equation for the reference station is: in, For the reference station to monitor the satellite in the 19th Carrier phase observations for each epoch. For the reference station to the satellite at the Geometric distance of each epoch, As a benchmark station The clock difference of an epoch, For the satellite in the The clock difference of an epoch, As a benchmark station Tropospheric delay of one epoch, As a benchmark station Ionospheric delay per epoch, The integer ambiguity of the base station. As a benchmark station Epochal observation noise For carrier wavelength, At the speed of light, For the observation epoch; The carrier phase observation equation for the rover is: in, For the rover to monitor the satellite in the 1st Carrier phase observations for each epoch. For the rover to the satellite in the Geometric distance of each epoch, For the mobile station at the The clock difference of an epoch, For the mobile station at the Tropospheric delay of one epoch, For the mobile station at the Ionospheric delay per epoch, For the integer ambiguity of the rover station, For the mobile station at the Observation noise for each epoch.

3. The single BeiDou system carrier phase differential positioning method as described in claim 2, characterized in that, Also includes: By constructing a single-difference observation equation and calculating the difference between the observations of the same satellite when observed simultaneously by the rover station and the base station, the single-difference carrier phase observation values ​​between the rover station and the base station are obtained as follows: in, For the first Single-difference carrier phase observations between the rover station and the base station at each epoch. For the first Geometric distance difference between each epoch rover and the base station For the first Clock difference between each epoch rover and the base station For the first The tropospheric delay difference between each epoch rover and the base station For the first Ionospheric delay difference between each epoch rover and the base station The integer ambiguity difference between the rover and the base station. For the first The difference in observation noise between the rover station and the base station at each epoch; Select reference satellite Based on the single-difference observation equation, a double-difference observation equation was constructed to obtain the relationship between the rover station and the base station regarding the satellite. The double-difference carrier phase observations are expressed as: in, The selected reference satellite number.

4. The single BeiDou system carrier phase differential positioning method as described in claim 3, characterized in that, Multi-frequency combination processing of the carrier phase observation data includes: combining multiple frequency carrier phases based on different frequency signals of the BeiDou system and the corresponding frequency carrier phase observation values; the multi-frequency carrier phase combination includes ionospherically independent combination, wide-lane combination, and narrow-lane combination; The ionospherically independent combination uses the squares of the two frequencies of the BeiDou system as weights, performs a weighted subtraction of the carrier phase observations of the two frequencies, and then divides by the difference of the weights to obtain the ionospherically independent combination carrier phase. The wide-lane combination uses two frequencies of the BeiDou system as weights, performs a weighted subtraction of the carrier phase observations of the two frequencies, and then divides by the frequency difference to obtain the carrier phase observations of the wide-lane combination. The narrow-lane combination uses two frequencies of the BeiDou system as weights, adds the carrier phase observations of the two frequencies together, and then divides by the sum of the frequencies to obtain the carrier phase observations of the narrow-lane combination.

5. The single BeiDou system carrier phase differential positioning method as described in claim 4, characterized in that, The fusion of benchmarks at different time scales includes: constructing a three-layer time window structure, including microsecond-level time windows. Millisecond time window and second-level time windows ; The time base fusion structure is established as follows: ， in, As the time base after merging, The weighting coefficients are for microsecond-level time windows. The weighting coefficients are for millisecond-level time windows. The weighting coefficients are for time windows on the order of seconds.

6. The single BeiDou system carrier phase differential positioning method as described in claim 5, characterized in that, Phase prediction using an autoregressive model, adjusting the compensation gain based on the carrier motion state and prediction confidence, and performing real-time correction of the observed phase include: establishing a third-order autoregressive phase prediction model, expressed as: in, For the future moment Phase prediction value, , , These are the phase observations at the current time and two past times. This is the random error term; The phase compensation structure is as follows: in, The phase value after compensation. The observed phase value, For the predicted phase value, To compensate for the gain, Based on the gain, The rate of change of the carrier velocity. is the attenuation constant.

7. A single BeiDou system carrier phase differential positioning method as described in claim 2 or 6, characterized in that, Also includes: The double-difference observation equation is linearized to establish a linearized observation equation, and the difference positioning solution is obtained using the weighted least squares method, expressed as follows: in, This is the weight matrix. To design the matrix, For position increment vector, The difference between the observed value and the calculated value; The weight matrix is ​​determined based on an adaptive weighting strategy, which includes signal strength weight, elevation angle weight, and multipath detection weight.

8. The single BeiDou system carrier phase differential positioning method as described in claim 7, characterized in that, Also includes: Multipath feature vectors are extracted based on carrier phase observation data; Using the information in the feature vectors, a multi-level decision mechanism is used to confirm the multipath interference and calculate its confidence level. The multipath feature vector ,in For phase variance, For phase gradient, This is the inter-frequency correlation coefficient. For power spectral entropy, The width of the main peak. For signal-to-noise ratio, For Doppler frequency shift, For delay estimation; The first-level judgment condition of the multi-level judgment mechanism is: and The second-level decision is based on the spectral entropy threshold. ; The third-level judgment calculates the overall confidence level. ,in For each level of judgment indicator, the weights must meet the requirements. ; When the multipath type is specular reflection multipath, a phase compensation structure is used for correction; when the multipath type is scattering multipath, a weighted least squares suppression structure is used for correction.

9. A single BeiDou system carrier phase differential positioning method as described in claim 8, characterized in that, Also includes: Ambiguity is fixed for carrier phase observations; By calculating the correlation coefficient of the ambiguity covariance matrix, the ambiguity parameters to be estimated are divided into three correlation layers; If the correlation coefficient is greater than the first threshold, the ambiguity parameter to be estimated is divided into a strongly correlated layer; if the correlation coefficient is not greater than the first threshold but greater than the second threshold, the ambiguity parameter to be estimated is divided into a moderately correlated layer; if the correlation coefficient is not greater than the second threshold, the ambiguity parameter to be estimated is divided into a weakly correlated layer. Group parameters with the same correlation to form the k-th layer parameter group, construct the sub-covariance matrix of the k-th layer parameter group, and calculate the condition number of the sub-covariance matrix; when the condition number of the sub-covariance matrix is ​​greater than the third threshold, perform the first transformation; when the condition number of the sub-covariance matrix is ​​not greater than the third threshold, perform the second transformation. If the stability index is greater than the fourth threshold during the transformation process, the transformation intensity is reduced; if the stability index is less than the fifth threshold during the transformation process, the transformation intensity is increased.

10. A single BeiDou system carrier phase differential positioning method as described in claim 9, characterized in that, A quality control check is performed on the corrected observation phase to assess the reliability of the positioning results, including: If the ratio test value of the ambiguity fixation result is greater than the test threshold, the ambiguity fixation result is accepted; if the ratio test value of the ambiguity fixation result is not greater than the ambiguity fixation reliability test threshold, floating-point ambiguity estimation continues. If the posterior unit weight variance is less than the variance threshold, the solution is considered reliable; if the posterior unit weight variance is not less than the variance threshold, the solution is considered unreliable. If the position accuracy attenuation factor is less than the geometric accuracy threshold, the current satellite geometric configuration is considered to have high positioning accuracy; if the position accuracy attenuation factor is not less than the geometric accuracy threshold, the current satellite geometric configuration is considered to have low positioning accuracy.

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