A Multipath Error Fusion Correction Method and System Based on BeiDou Multitrack Differences

Abstract

This invention discloses a multipath error fusion correction method and system based on BeiDou multi-orbit differences. The method includes: collecting BeiDou carrier phase observation data from a reference station and a monitoring station; obtaining double-difference residuals through preprocessing and double-difference calculation, and converting them into single-difference residual sequences; classifying BeiDou satellites into three categories based on their orbital types: geostationary orbit satellites, inclined geosynchronous orbit satellites, and medium Earth orbit satellites; extracting stable background information from the GEO residuals, slowly changing information from the IGSO residuals, and anomalous disturbance information from the MEO residuals by combining satellite azimuth and elevation angle information; constructing a basic hemispherical model based on the stable background information of each satellite, and using the stable background information, slowly changing information, and anomalous disturbance information to constrain and update the basic background correction value, generating the current epoch multipath correction value; and applying the correction value to the corresponding satellite observation value to complete the observation correction and deformation monitoring calculation.

Description

Technical Field

[0001] This invention relates to the field of satellite navigation and positioning technology, and in particular to a multipath error fusion correction method based on the differential characteristics of BeiDou multi-orbit satellites. Background Technology

[0002] Global Navigation Satellite Systems (GNSS) are widely used in high-precision deformation monitoring scenarios such as bridges, dams, slopes, high-speed railways, and geological disaster hazard points. In recent years, with the continuous construction and improvement of the BeiDou Navigation Satellite System, high-precision deformation monitoring technology based on BeiDou carrier phase observation has received increasing attention due to its advantages such as good observation continuity, strong regional service capability, and high equipment adaptability. During long-term continuous monitoring, the monitoring system usually obtains information on minute changes in the coordinates of monitoring points through high-precision calculations to achieve fine perception of structural deformation or surface displacement. However, in actual monitoring environments, the area around the monitoring station often contains complex environmental factors such as mountains, vegetation, bridge components, ancillary facilities, and surface reflectors, which can easily cause significant multipath effects during satellite signal propagation and reception. Multipath errors can cause periodic fluctuations in coordinate sequences, local abnormal shifts, and even pseudo-deformation phenomena, which have become important factors restricting the improvement of the accuracy and stability of BeiDou high-precision deformation monitoring.

[0003] However, current mainstream multipath error processing methods mainly include sidereal filtering in the time domain and hemispherical models in the spatial domain. Sidereal filtering utilizes the temporal repeatability of multipath errors within adjacent repeating orbit periods for correction. However, due to significant differences in trajectory repeating periods and observational geometry among BeiDou geostationary orbit (GEO), inclined geosynchronous orbit (IGSO), and medium Earth orbit (MEO) satellites, this method is highly dependent on period matching and time alignment accuracy, limiting its application in complex scenarios. Hemispherical models correct multipath errors by establishing a spatial mapping relationship between satellite elevation angle, azimuth angle, and multipath errors. Compared to time-domain methods, they generally have better adaptability and stability. However, most existing hemispherical models employ a uniform modeling strategy, only filtering, decomposing, or extracting residual sequences before overall modeling. This fails to fully utilize the differences in visibility, trajectory repeating characteristics, spatial geometric variations, and multipath error performance among BeiDou multi-orbit satellites, thus still exhibiting insufficient model adaptability and limited comprehensive correction capabilities in complex environments. Summary of the Invention

[0004] To address the technical problems in existing BeiDou high-precision deformation monitoring methods, such as insufficient utilization of features of satellites in different orbits, weak adaptability of unified hemispherical modeling, and unstable comprehensive correction effects in complex environments, this invention provides a multipath error fusion correction method and system based on BeiDou multi-orbit differences. The method leverages the differences in visibility, trajectory repetition characteristics, spatial geometric variations, and multipath error performance among BeiDou geostationary orbit (GEO), inclined geosynchronous orbit (IGSO), and medium Earth orbit (MEO) satellites. It differentiates the single-difference residual sequences of satellites in different orbits through modeling and achieves multipath error fusion correction within a hemispherical correction framework, thereby improving the observation quality, coordinate calculation accuracy, and result stability of BeiDou deformation monitoring in complex scenarios.

[0005] According to one aspect of the present invention, a multipath error fusion correction method based on BeiDou multi-track differences is provided, comprising: The BeiDou carrier phase observation data from the base station and monitoring station are collected, preprocessed and double-differenced to obtain double-differenced residuals, and then converted to obtain single-differenced residual sequences for each satellite. Based on their orbital type, BeiDou satellites are classified into geostationary orbit satellites, inclined geosynchronous orbit satellites, and medium Earth orbit satellites. Combining the azimuth and elevation angle information of the satellite, stable background information is extracted from the single-difference residual sequence of the geostationary orbit satellite, slowly changing information is extracted from the single-difference residual sequence of the inclined geosynchronous orbit satellite, and anomalous disturbance information is extracted from the single-difference residual sequence of the medium Earth orbit satellite. A basic hemispherical model is constructed based on the stable background information of each satellite. The basic background correction value of the basic hemispherical model is then updated by constraining the stable background information, slowly changing information and anomalous disturbance information to generate the multipath correction value for the current epoch. The multipath correction value is applied to the observation value of the corresponding satellite to complete the observation correction and deformation monitoring calculation.

[0006] As a further technical solution, while performing orbit classification, the azimuth and elevation angles of each satellite at the same epoch are saved simultaneously to maintain the correspondence between the single-difference residual sequence and the spatial direction information.

[0007] As a further technical solution, the construction of the basic hemispherical model includes: Based on the satellite's spatial orientation position in the celestial hemisphere, stable background samples whose azimuth and elevation angles fall within the same directional region are assigned to that directional region. Statistical analysis is performed on samples within this directional region to obtain the basic multipath background value for the corresponding directional location; For a satellite whose current epoch needs correction, the basic multipath background value corresponding to the azimuth and elevation angles of the satellite is matched in the basic hemispherical model and used as the basic background correction value for the satellite.

[0008] As a further technical solution, the constraint update includes: Using the stable background information set of the geostationary orbit satellite, a stability reference quantity is constructed for the position of the satellite in the corresponding direction to be corrected. Using the slowly changing information set of the inclined geosynchronous orbit satellite, a slowly changing update amount of the position of the current satellite to be corrected in the corresponding direction is constructed; Using the anomalous disturbance information set of the medium Earth orbit satellite, an anomalous state index for the current epoch is constructed; The basic background correction value is corrected based on the stability reference value, the slowly changing update value, and the abnormal state index. When the stability reference value indicates high stability and the abnormal state index indicates low abnormality, the effect of the slowly changing update value on the background correction value is enhanced; conversely, the correction effect is weakened.

[0009] As a further technical solution, the transformation to obtain the single-difference residual sequence corresponding to each satellite includes: Based on the linear combination relationship between double-difference residuals and single-difference residuals, and by introducing a zero-mean constraint for single-difference residuals based on satellite elevation angles, a double-difference to single-difference transformation matrix is ​​constructed. The single-difference residuals of each satellite are recovered from the double-difference residuals of each epoch using the transformation matrix.

[0010] According to one aspect of the present invention, a multipath error fusion correction system based on BeiDou multi-track differences is provided, comprising: The data acquisition and preprocessing module is used to acquire BeiDou carrier phase observation data from the base station and monitoring station, and to perform preprocessing and double-difference calculation to obtain double-difference residuals and convert them into single-difference residual sequences for each satellite. The multi-orbit information classification and extraction module is used to classify BeiDou satellites into geostationary orbit satellites, inclined geosynchronous orbit satellites, and medium Earth orbit satellites according to their orbit types. It also extracts stable background information from the single-difference residual sequence of geostationary orbit satellites, slowly changing information from the single-difference residual sequence of inclined geosynchronous orbit satellites, and abnormal disturbance information from the single-difference residual sequence of medium Earth orbit satellites, based on the satellite's azimuth and elevation angle information. The half-sky fusion model construction module is used to build a basic half-sky model based on the stable background information of each satellite, and to use the extracted stable background information, slowly changing information and anomalous disturbance information to constrain and update the basic background correction value, and generate the multipath correction value for the current epoch. The observation correction and calculation module is used to apply the multipath correction value to the observation value of the corresponding satellite to complete the observation correction and deformation monitoring calculation.

[0011] As a further technical solution, the hemispherical fusion model construction module is further configured as follows: Using the stable background information set of the geostationary orbit satellite, a stability reference quantity is constructed for the current position of the satellite to be corrected in the corresponding direction. The slowly changing information set of the inclined geosynchronous orbit satellite is used to construct the slowly changing update amount of the position of the satellite in the corresponding direction to be corrected. An anomalous state index for the current epoch is constructed using the anomalous perturbation information set of the medium Earth orbit satellite. The basic background correction value is adjusted based on the stability reference value, the slowly changing update value, and the abnormal state index.

[0012] As a further technical solution, the multi-orbit information classification and extraction module simultaneously saves the azimuth and elevation angles of each satellite at the same epoch while performing orbit classification, so as to maintain the correspondence between the single-difference residual sequence and the spatial direction information.

[0013] According to one aspect of the present invention, an electronic device is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the multipath error fusion correction method based on BeiDou multitrack differences.

[0014] According to one aspect of the present invention, a computer-readable storage medium is provided having a computer program stored thereon, which, when executed by a processor, implements the multipath error fusion correction method based on BeiDou multitrack differences.

[0015] Compared with the prior art, the beneficial effects of the present invention are as follows: 1. This invention addresses the differences in trajectory repetition characteristics, spatial geometric changes, and multipath error performance of BeiDou GEO, IGSO, and MEO satellites, extracting and utilizing effective information from satellites in different orbits. Compared to existing unified hemispherical modeling methods, this invention can make fuller use of observation information from multiple orbit satellites. 2. Under the hemispherical correction framework, this invention fuses multipath information extracted from satellites in different orbits to construct a comprehensive multipath correction value, which helps to improve the precision of multipath error modeling and the comprehensive correction capability in complex environments. 3. This invention can better adapt to monitoring scenarios where long-term stable reflection, slow environmental changes and local abnormal disturbances coexist. It helps to reduce the periodic fluctuations and local abnormal offsets of coordinate sequences caused by multipath errors, and improves the stability and reliability of BeiDou high-precision deformation monitoring results. Attached Figure Description

[0016] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the accompanying drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the accompanying drawings described below are 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 flowchart illustrating the multipath error fusion correction method based on BeiDou multi-track differences provided in an embodiment of the present invention.

[0018] Figure 2 This is a schematic diagram illustrating the extraction results of single-difference multipath sequences of BeiDou satellites in different orbits, provided in an embodiment of the present invention.

[0019] Figure 3 This is a comparison diagram of the coordinate sequences before and after correction using the method of this invention. Detailed Implementation

[0020] This invention is mainly used to solve the technical problems in existing BeiDou high-precision deformation monitoring, such as insufficient utilization of the characteristics of satellites in different orbits, weak adaptability of unified hemispherical modeling, and unstable comprehensive correction effect in complex environments. The method provided by this invention first reconstructs the single-difference residual sequence corresponding to each satellite based on the carrier phase double-difference solution results, and classifies the residual sequence according to the satellite orbit type to form a multipath error feature information set for three types of satellites: GEO, IGSO, and MEO. Then, based on the relatively stable spatial position of GEO satellites, its long-term stable multipath information is extracted to construct a half-sky stable background correction term. Based on the strong daily repeatability and rich spatial scanning capabilities of IGSO satellites, its slowly varying multipath error correction information is extracted to construct a half-sky evolution correction term. Based on the rich geometric changes and sensitivity to non-repeating disturbances of MEO satellites, its real-time anomalous disturbance information is extracted to construct a half-sky anomaly compensation term. Finally, based on the modeling results of satellites in different orbits, the orientation matching relationship, and the residual characteristics of the current epoch, the stable background correction term, the evolution correction term, and the anomaly compensation term are fused to obtain the comprehensive multipath correction value corresponding to the current epoch and the current satellite, and the observation value is corrected accordingly.

[0021] The terms “comprising” and “having”, and any variations thereof, in the specification, claims, and accompanying drawings of this invention are intended to cover a non-exclusive inclusion, such as a process, method, system, product, or apparatus that includes a series of steps or units, not necessarily limited to those explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0022] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention. In addition, the technical features of the various embodiments or individual embodiments provided by the present invention can be arbitrarily combined to form new technical solutions. Such combinations are not bound by the order of steps and / or structural composition patterns, but must be based on the ability of those skilled in the art to implement them. When the combination of technical solutions is contradictory or cannot be implemented, it should be considered that such a combination of technical solutions does not exist and is not within the scope of protection claimed by the present invention.

[0023] like Figure 1As shown in the figure, this invention provides a multipath error fusion correction method based on BeiDou multi-orbit differences. By classifying and extracting the single-difference residual sequences of BeiDou GEO, IGSO, and MEO satellites, stable background information, slowly changing information, and anomalous disturbance information are obtained respectively. Combined with a hemispherical model, the basic background correction value of the current satellite is updated under constraints. This effectively enhances the adaptability of the multipath error modeling process to complex reflection environments and time-varying disturbances, achieving collaborative correction of multipath information from multiple orbit satellites. The specific process is as follows:

[0024] S1: Construction of single-difference residual sequences.

[0025] The system acquires BeiDou carrier phase observation data from the base station and monitoring station within the same time period, and preprocesses the raw observations. This preprocessing includes data synchronization, missing value removal, gross error detection and removal, cycle slip detection and repair, and low-quality observation screening. Observations that do not meet continuity or quality requirements are marked as invalid and excluded from subsequent double-difference calculations; observations that meet the requirements are retained as valid observation data. After the above processing, a valid BeiDou double-difference observation dataset with epoch alignment and controllable quality is formed.

[0026] The carrier phase double-difference observation equation for the base station and the monitoring station can be expressed as: , In the formula, j and k represent the BeiDou reference station and monitoring station, respectively, and r and s represent the reference satellite and the mobile satellite, respectively; Indicates the carrier wavelength; This represents the carrier phase double-difference observations constructed by the base station and monitoring station for the reference satellite and the mobile satellite; Represents the double-difference geometric distance term; This represents the integer ambiguity term of the double difference; This represents the double-difference multipath error term; This represents the double-difference noise term and other unmodeled error terms. Other systematic errors, including satellite clock error, receiver clock error, ionospheric delay, tropospheric delay, and satellite and receiver hardware delay, have been eliminated or significantly reduced after the double-difference construction. Other influencing terms, such as phase winding error, antenna phase center correction, relativistic correction, and tidal correction, can be corrected using existing models.

[0027] After linearizing the above equation, the double-difference observation error equation can be obtained as shown in the following equation. , In the formula, A represents the coefficient matrix, X represents the vector of parameters to be estimated, L represents the constant vector composed of observed values ​​minus calculated terms, and V represents the residual vector. The vector of parameters to be estimated typically includes baseline parameters and double-difference ambiguity parameters, etc.

[0028] The least squares method is used to solve for the parameters to be estimated, and the solution can be expressed as: , In the formula, P represents the weight matrix. This represents the least squares estimate of the parameter to be estimated. This will yield the double-difference residual vector for the corresponding epoch. This represents the double-difference residual vector. After eliminating the main geometric and ambiguity terms, the double-difference residual still contains residual error components such as multipath error and random noise.

[0029] The residual sequence obtained by double-difference calculation still uses the reference satellite as a benchmark, which is not convenient for subsequent multipath information extraction and hemispherical modeling based on individual satellites. Therefore, it is necessary to further convert the double-difference residuals into single-difference residuals corresponding to each satellite. Suppose there are n visible satellites in a certain epoch, and the first satellite is selected as the reference satellite, then the relationship between the double-difference residuals and the single-difference residuals can be expressed as: , In the formula, This represents the i-th set of double-difference residuals constructed with the first satellite as the reference star; Let represent the single-difference residual corresponding to the i-th satellite; the coefficient matrix in the above formula represents the construction relationship obtained by linear combination of the single-difference residuals for the double-difference residuals.

[0030] Since the number of unknown single-difference residuals in the above equation is n, while the number of double-difference residuals is only n-1, the system of equations is not full rank and cannot be solved directly and uniquely. Therefore, a zero-mean constraint on the single-difference residuals is introduced, as shown below: , In the formula, Indicates the first The weighting factors corresponding to each satellite can generally be determined based on the satellite's elevation angle to reflect the differences in observation quality between different satellites. By solving these factors together, the transformation relationship between double-difference residuals and single-difference residuals can be obtained. , In the formula, T represents the double-difference to single-difference transformation matrix determined by the double-difference construction matrix and the zero-mean constraint. Using this transformation matrix, the single-difference residuals of each satellite can be recovered from the double-difference residuals of each epoch.

[0031] Figure 2 This is a schematic diagram illustrating the extraction results of single-difference multipath sequences from BeiDou satellites in different orbits. Figure 2It is evident that the multipath sequences extracted from GEO, IGSO, and MEO satellites all exhibit good continuity and inter-day consistency. Among them, the GEO satellite sequences are generally more stable, the IGSO satellite sequences show some slow variation characteristics, and the MEO satellite sequences have relatively more obvious local perturbations. This indicates that there are differences in the multipath characteristics of satellites in different orbits, and all of them can be extracted relatively effectively.

[0032] S2: Multi-track information classification and extraction.

[0033] After constructing the single-difference residual sequence, the orbit types of the BeiDou satellites involved in the calculation are identified and classified based on the broadcast ephemeris or precise ephemeris information corresponding to each satellite. BeiDou satellites can be classified into three categories according to their orbital characteristics: geostationary orbit (GEO), inclined geosynchronous orbit (IGSO), and medium Earth orbit (MEO). Let the single-difference residual of the i-th satellite at epoch t be... Based on orbit type, all single-difference residual sequences can be divided into GEO residual sets, IGSO residual sets, and MEO residual sets, denoted as follows: .

[0034] To maintain the correspondence between the residual sequence and spatial orientation information, the azimuth angle of each satellite at epoch t is determined during orbit classification. and elevation angle Synchronously saved. Therefore, the residual information of the i-th satellite at epoch t can be uniformly expressed as: , In the formula, This represents the residual information vector of the i-th satellite at epoch t; This represents the corresponding single-difference residual; and These represent the azimuth and elevation angles corresponding to the satellite, respectively.

[0035] Information was extracted from the single-difference residuals of GEO, IGSO and MEO satellites respectively.

[0036] For GEO satellites, due to their relatively stable spatial position and small variations in azimuth and elevation angles, their single-difference residual sequences typically consist mainly of low-frequency, stationary, and highly repeatable background components. Therefore, extracting long-term stable background information is preferred. This stable background information can be obtained using methods such as moving average, low-pass filtering, robust smoothing, singular spectrum analysis, or wavelet low-frequency reconstruction. Let the stable background information extracted from the i-th GEO satellite be... Then there is , In the formula, This describes a method for extracting stable background information from GEO systems. This refers to a collection of GEO satellites.

[0037] For IGSO satellites, due to their strong daily repeatability and richer directional variations compared to GEO satellites, their single-difference residual sequences, in addition to containing a stable background, typically exhibit directionally correlated slow drift characteristics; that is, the residual background within the same directional unit continuously shifts relative to the historical mean. Therefore, extracting slowly varying information is preferred. This slowly varying information can be obtained using sliding time window statistics, adjacent repeating period differencing, wavelet multi-scale decomposition, low-frequency trend separation, or other methods suitable for characterizing slowly varying features. Let the slowly varying information extracted from the i-th IGSO satellite be... Then there is , In the formula, This describes a method for extracting information about the slow changes in IGSO. This refers to the IGSO satellite set.

[0038] For MEO satellites, due to their rapid geometric changes and long repetition periods, local perturbations deviating from the stable background in their single-difference residual sequences are more easily observed, typically manifesting as short-term abrupt changes, local deviations, or non-repetitive anomalies. Therefore, extracting anomalous perturbation information is preferred. This anomalous perturbation information can be obtained using real-time residual threshold discrimination, local statistical tests, median deviation tests, or other methods suitable for anomaly identification. Let the anomalous perturbation information extracted from the i-th MEO satellite be... Then there is , In the formula, This describes the method for extracting MEO anomaly perturbation information. This refers to the MEO satellite collection.

[0039] Thus, we can obtain the GEO stable background information set, the IGSO slowly changing information set, and the MEO anomalous perturbation information set, respectively. , In the formula, and These represent sets of feature information extracted from satellites in different orbits.

[0040] S3: Construction of the hemispherical fusion model.

[0041] The preferred approach is to extract the stable multipath background components of each satellite from the single-difference residual sequence through methods such as smoothing, low-pass filtering, low-frequency reconstruction, or historical residual accumulation, and then construct a basic hemispherical model by combining the azimuth and elevation angles of the corresponding epochs.

[0042] Based on the satellite's spatial orientation position in the celestial hemisphere, stable background samples with similar azimuth and elevation angles are grouped into the same directional region. The samples within this region are then statistically analyzed to obtain the basic multipath background value for the corresponding directional position. This establishes a basic celestial hemisphere model. For any satellite to be corrected in the current epoch, its azimuth and elevation angles are matched with the corresponding background value in the basic celestial hemisphere model, which is then used as the satellite's current basic background correction value, denoted as […]. , In the formula, This represents the background correction value for the i-th satellite at epoch t. This represents the basic hemispherical model, which reflects the long-term stable multipath background distribution at different directional locations on the hemispherical surface. This represents the background estimation result of the basic hemispherical model at the current position of the satellite.

[0043] Obtaining the basic background correction value Subsequently, the GEO stable background information set obtained in S2 was further utilized. IGSO slowly changing information set and MEO anomaly perturbation information set Constraints are updated on the current satellite's baseline background correction values.

[0044] For satellite i at the current epoch to be corrected, the GEO stable background information set is first used. Construct a stability reference quantity for its corresponding directional position, denoted as . , In the formula, This represents the stability reference value of the i-th satellite at epoch t; This represents the directional domain mapping relationship based on the GEO stable background information set; and These represent the azimuth and elevation angles of the satellite at epoch t, ​​respectively.

[0045] Utilizing the slowly changing information set of IGSO Construct a slowly changing update of the current position in the direction, denoted as . , In the formula, This represents the slow change in the position of the i-th satellite in the direction corresponding to epoch t; This represents the directional domain mapping relationship based on the slowly changing information set of IGSO. The slowly changing update amount is used to characterize the persistent shift of the current directional position relative to the historical stable background.

[0046] Using MEO anomaly perturbation information set Construct an anomaly index for the current epoch, denoted as . , In the formula, An index representing the abnormal state of epoch t; This represents the anomalous aggregation relationship based on the MEO anomalous perturbation information set. The anomalous state index is used to reflect the degree of anomalousness in the current epoch observation environment or residual state.

[0047] In subsequent constraints, stability reference quantity Used to reflect the reliability of the baseline background estimate for the current orientation position; slowly changing update amount. Used to characterize the persistent shift of the current directional position relative to the historical stable background; anomaly state index This is used to reflect the degree of anomaly in the current epoch observation environment or residual state. Based on the above three quantities, the baseline background correction value for the i-th satellite at epoch t is corrected to obtain the updated background correction value: , In the formula, This represents the background correction value of the i-th satellite after being updated with multi-orbit information constraints at epoch t. When the current position stability is high and the anomaly level is low, the effect of slowly changing update amounts on the background correction value is enhanced; when the current position stability is low or the anomaly level is high, the effect of slowly changing update amounts on the background correction value is weakened, and the updated result is closer to the original basic background correction value. Therefore, while maintaining the continuity of the basic hemispherical model, the stability of background estimation can be improved by using GEO stable background information, the slowly changing IGSO information can be used to reflect background drift characteristics, and the MEO anomaly perturbation information can be used to suppress unreasonable updates under anomalous conditions.

[0048] S4: Correction value generation and observation correction.

[0049] To obtain the updated background correction value for the i-th satellite at epoch t. Then, this is applied to the corresponding satellite observations to obtain the corrected observations. Let the original observations be... The corrected observation can then be expressed as , In the formula, This represents the corrected observation value. This represents the original observation value before correction. This represents the background correction value for the i-th satellite in epoch t.

[0050] Based on the corrected observations, the positioning or deformation monitoring calculations are re-performed, and the corrected coordinate or displacement sequences are output. This process reduces the impact of multipath errors on the observations and monitoring results, improving the stability and reliability of BeiDou high-precision deformation monitoring results in complex monitoring scenarios.

[0051] Figure 3 This is a comparison chart of the coordinate sequences before and after correction using the method of this invention. Before correction, the standard deviations of the north, east, and elevation direction coordinate sequences were 1.04 cm, 0.76 cm, and 2.05 cm, respectively, with root mean square errors of 1.05 cm, 0.96 cm, and 7.64 cm, respectively. After correction, the standard deviations of the north, east, and elevation direction coordinate sequences decreased to 0.12 cm, 0.09 cm, and 0.33 cm, respectively, with root mean square errors decreasing to 0.12 cm, 0.09 cm, and 0.34 cm, respectively. Figure 3 As can be seen, after adopting the method of the present invention, the fluctuations of the coordinate sequences in all three directions are significantly reduced, especially the improvement effect in the elevation direction is the most significant, indicating that the method of the present invention can effectively reduce multipath error and improve the stability and reliability of coordinate solution results.

[0052] This invention also provides a multipath error fusion correction system based on BeiDou multi-track differences, used to implement the steps in the above method embodiments. The system includes:

[0053] The data acquisition and preprocessing module is used to acquire BeiDou carrier phase observation data from the reference station and monitoring station, and to perform preprocessing and double-difference calculation to obtain double-difference residuals and convert them into single-difference residual sequences for each satellite. Specifically, this module performs data synchronization, missing value removal, gross error detection and removal, cycle slip detection and repair, and low-quality observation screening on the original observation data in the manner described in step S1, forming an effective observation dataset with epoch alignment and controllable quality; obtains double-difference residuals through double-difference observation equations and least squares calculation; and then converts the double-difference residuals into single-difference residual sequences for each satellite based on the double-difference-single-difference transformation matrix.

[0054] The multi-orbit information classification and extraction module is used to classify BeiDou satellites into geostationary orbit satellites, inclined geosynchronous orbit satellites, and medium Earth orbit satellites based on their orbit types. Combining the azimuth and elevation angle information of the satellites, it extracts stable background information from the single-difference residual sequence of geostationary orbit satellites, slowly changing information from the single-difference residual sequence of inclined geosynchronous orbit satellites, and anomalous disturbance information from the single-difference residual sequence of medium Earth orbit satellites. Specifically, following the method described in step S2, this module identifies the orbit type of each satellite using broadcast ephemeris or precise ephemeris, synchronously saves the azimuth and elevation angles of each satellite at the same epoch, and extracts stable background information using methods such as moving average, low-pass filtering, and wavelet low-frequency reconstruction. It extracts slowly changing information using methods such as sliding time window statistics, adjacent repeating period difference, and low-frequency trend separation, and extracts anomalous disturbance information using methods such as real-time residual threshold discrimination, local statistical testing, or median deviation testing.

[0055] The hemispherical fusion model construction module is used to construct a basic hemispherical model based on the stable background information of each satellite, and to update the basic background correction value using the extracted stable background information, slowly changing information, and anomalous disturbance information to generate the multipath correction value for the current epoch. Specifically, this module divides the hemispherical into multiple directional regions according to azimuth and elevation angles, as described in step S3, assigns each stable background sample to the corresponding directional region, statistically analyzes the samples within the region to obtain the basic multipath background value, and stores it in the basic hemispherical model; for the satellite to be corrected, the corresponding basic background correction value is matched from the basic hemispherical model based on its azimuth and elevation angles; then, a stability reference quantity is constructed using the stable background information set of geostationary orbit satellites, a slowly changing update quantity is constructed using the slowly changing information set of inclined geosynchronous orbit satellites, and an anomalous state index is constructed using the anomalous disturbance information set of medium Earth orbit satellites, and the basic background correction value is corrected based on the above three quantities to generate the multipath correction value for the current epoch.

[0056] The observation correction and calculation module is used to apply the multipath correction value to the observation value of the corresponding satellite to complete the observation correction and deformation monitoring calculation. Specifically, this module subtracts the multipath correction value of the corresponding satellite from the original observation value in the manner described in step S4 to obtain the corrected observation value, and re-performs the positioning calculation or deformation monitoring calculation based on the corrected observation value, and outputs the corrected coordinate sequence or displacement sequence.

[0057] The modules mentioned above interact with each other through data interfaces or communication buses, and work together to complete the entire process from inputting raw observation data to outputting corrected solution results.

[0058] Based on the same inventive concept as the foregoing embodiments, this embodiment of the invention also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor.

[0059] The memory can be a read-only memory (ROM), random access memory (RAM), flash memory, hard disk, or other media with storage functions. The processor can be a central processing unit (CPU), digital signal processor (DSP), application-specific integrated circuit (ASIC), field-programmable gate array (FPGA), or other programmable logic devices.

[0060] When the processor executes the program, it implements the steps of the above-mentioned multipath error fusion correction method based on BeiDou multi-track differences, specifically including: The BeiDou carrier phase observation data from the base station and monitoring station are collected, preprocessed and double-differenced to obtain double-differenced residuals, and then converted to obtain single-differenced residual sequences for each satellite. Based on their orbital type, BeiDou satellites are classified into geostationary orbit satellites, inclined geosynchronous orbit satellites, and medium Earth orbit satellites. Combining the azimuth and elevation angle information of the satellite, stable background information is extracted from the single-difference residual sequence of the geostationary orbit satellite, slowly changing information is extracted from the single-difference residual sequence of the inclined geosynchronous orbit satellite, and anomalous disturbance information is extracted from the single-difference residual sequence of the medium Earth orbit satellite. A basic hemispherical model is constructed based on the stable background information of each satellite. The basic background correction value of the basic hemispherical model is then updated by constraining the stable background information, slowly changing information and anomalous disturbance information to generate the multipath correction value for the current epoch. The multipath correction value is applied to the observation value of the corresponding satellite to complete the observation correction and deformation monitoring calculation.

[0061] In practical applications, the electronic device can be a GNSS receiver, a monitoring station host, a server, an embedded industrial control computer, a laptop computer, or a cloud computing device. This electronic device can receive raw observation data from the base station and monitoring station via wired or wireless means, process it internally, and then output corrected deformation monitoring results.

[0062] Based on the same inventive concept as the foregoing embodiments, this embodiment of the invention also provides a computer-readable storage medium storing a computer program thereon. The computer-readable storage medium can be any tangible medium that contains or stores a program, such as: a disk, optical disk, magneto-optical disk, flash drive, solid-state drive, read-only memory (ROM), random access memory (RAM), erasable programmable read-only memory (EPROM), registers, etc.

[0063] When the computer program is executed by the processor, it implements the steps of the multipath error fusion correction method based on BeiDou multi-track differences. The specific implementation steps are the same as those described in the electronic device embodiment, and will not be repeated here.

[0064] By burning or installing the computer program into GNSS monitoring equipment, data processing servers, or portable terminals, the equipment can be equipped with multipath error fusion and correction capabilities, thereby improving the stability and reliability of BeiDou high-precision deformation monitoring.

[0065] In summary, the method provided by this invention includes: collecting BeiDou carrier phase observation data from reference stations and monitoring stations; obtaining double-difference residuals through preprocessing and double-difference calculation; and converting these residuals into a single-difference sequence; classifying BeiDou satellites into three categories based on their orbital types: geostationary orbit satellites, inclined geosynchronous orbit satellites, and medium Earth orbit satellites; extracting stable background information from the GEO residuals, slowly changing information from the IGSO residuals, and anomalous disturbance information from the MEO residuals by combining satellite azimuth and elevation angle information; constructing a basic hemispherical model based on the stable background information of each satellite; and using the stable background information, slowly changing information, and anomalous disturbance information to constrain and update the basic background correction value, generating a multipath correction value for the current epoch; and applying the correction value to the corresponding satellite observation value to complete the observation correction and deformation monitoring calculation. This invention realizes the classification, extraction, and fusion application of multipath information from BeiDou multi-orbit satellites, improving the stability and adaptability of multipath error correction in complex environments.

[0066] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features therein; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the technical solutions of the embodiments of the present invention.

Claims

1. A multi-path error fusion correction method based on BeiDou multi-track differences, characterized in that, include: The BeiDou carrier phase observation data from the base station and monitoring station are collected, preprocessed and double-differenced to obtain double-differenced residuals, and then converted to obtain single-differenced residual sequences for each satellite. Based on their orbital type, BeiDou satellites are classified into geostationary orbit satellites, inclined geosynchronous orbit satellites, and medium Earth orbit satellites. Combining the azimuth and elevation angle information of the satellite, stable background information is extracted from the single-difference residual sequence of the geostationary orbit satellite, slowly changing information is extracted from the single-difference residual sequence of the inclined geosynchronous orbit satellite, and anomalous disturbance information is extracted from the single-difference residual sequence of the medium Earth orbit satellite. A basic hemispherical model is constructed based on the stable background information of each satellite. The basic background correction value of the basic hemispherical model is then updated by constraining the stable background information, slowly changing information and anomalous disturbance information to generate the multipath correction value for the current epoch. The multipath correction value is applied to the observation value of the corresponding satellite to complete the observation correction and deformation monitoring calculation.

2. The multipath error fusion correction method based on BeiDou multi-track differences according to claim 1, characterized in that, While performing orbit classification, the azimuth and elevation angles of each satellite at the same epoch are saved simultaneously to maintain the correspondence between the single-difference residual sequence and the spatial orientation information.

3. The multipath error fusion correction method based on BeiDou multi-track differences according to claim 1, characterized in that, The construction of the basic hemispherical model includes: Based on the satellite's spatial orientation position in the celestial hemisphere, stable background samples whose azimuth and elevation angles fall within the same directional region are assigned to that directional region. Statistical analysis is performed on samples within this directional region to obtain the basic multipath background value for the corresponding directional location; For a satellite whose current epoch needs correction, the basic multipath background value corresponding to the azimuth and elevation angles of the satellite is matched in the basic hemispherical model and used as the basic background correction value for the satellite.

4. The multipath error fusion correction method based on BeiDou multi-track differences according to claim 1, characterized in that, The constraint update includes: Using the stable background information set of the geostationary orbit satellite, a stability reference quantity is constructed for the position of the satellite in the corresponding direction to be corrected. Using the slowly changing information set of the inclined geosynchronous orbit satellite, a slowly changing update amount of the position of the current satellite to be corrected in the corresponding direction is constructed; Using the anomalous disturbance information set of the medium Earth orbit satellite, an anomalous state index for the current epoch is constructed; The basic background correction value is corrected based on the stability reference value, the slowly changing update value, and the abnormal state index. When the stability reference value indicates high stability and the abnormal state index indicates low abnormality, the effect of the slowly changing update value on the background correction value is enhanced; conversely, the correction effect is weakened.

5. The multipath error fusion correction method based on BeiDou multi-track differences according to claim 1, characterized in that, The transformation yields the single-difference residual sequences corresponding to each satellite, including: Based on the linear combination relationship between double-difference residuals and single-difference residuals, and by introducing a zero-mean constraint for single-difference residuals based on satellite elevation angles, a double-difference to single-difference transformation matrix is ​​constructed. The single-difference residuals of each satellite are recovered from the double-difference residuals of each epoch using the transformation matrix.

6. A multipath error fusion correction system based on BeiDou multi-track differences, characterized in that, include: The data acquisition and preprocessing module is used to acquire BeiDou carrier phase observation data from the base station and monitoring station, and to perform preprocessing and double-difference calculation to obtain double-difference residuals and convert them into single-difference residual sequences for each satellite. The multi-orbit information classification and extraction module is used to classify BeiDou satellites into geostationary orbit satellites, inclined geosynchronous orbit satellites, and medium Earth orbit satellites according to their orbit types. It also extracts stable background information from the single-difference residual sequence of geostationary orbit satellites, slowly changing information from the single-difference residual sequence of inclined geosynchronous orbit satellites, and abnormal disturbance information from the single-difference residual sequence of medium Earth orbit satellites, based on the satellite's azimuth and elevation angle information. The half-sky fusion model construction module is used to build a basic half-sky model based on the stable background information of each satellite, and to use the extracted stable background information, slowly changing information and anomalous disturbance information to constrain and update the basic background correction value, and generate the multipath correction value for the current epoch. The observation correction and solution module is used to apply the multipath correction value to the observation value of the corresponding satellite to complete the observation correction and deformation monitoring solution.

7. The multipath error fusion correction system based on BeiDou multi-track differences according to claim 6, characterized in that, The hemispherical fusion model construction module is configured as follows: Using the stable background information set of the geostationary orbit satellite, a stability reference quantity is constructed for the current position of the satellite to be corrected in the corresponding direction. The slowly changing information set of the inclined geosynchronous orbit satellite is used to construct the slowly changing update amount of the position of the satellite in the corresponding direction to be corrected. An anomalous state index for the current epoch is constructed using the anomalous perturbation information set of the medium Earth orbit satellite. The basic background correction value is adjusted based on the stability reference value, the slowly changing update value, and the abnormal state index.

8. The multipath error fusion correction system based on BeiDou multi-track differences according to claim 6, characterized in that, The multi-orbit information classification and extraction module, while classifying orbits, simultaneously saves the azimuth and elevation angles of each satellite at the same epoch, so as to maintain the correspondence between the single-difference residual sequence and the spatial direction information.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the multipath error fusion correction method based on the differences of Beidou multitrack as described in any one of claims 1-5.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the multipath error fusion correction method based on the differences of Beidou multitrack as described in any one of claims 1-5.

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