Extraction, analysis and evaluation method of inter-epoch variation characteristics of code multipath error

Abstract

The present application relates to the field of global satellite navigation systems and discloses a method for extracting, analyzing, and evaluating the inter-epoch variation characteristics of code multipath errors, including the following steps: S1. Acquiring GNSS observation data, including code observation values ​​and carrier phase observation values; S2. Using the code observation values ​​and carrier phase observation values, constructing a code multipath combination formula to eliminate the influence of geometric distance, clock error, tropospheric delay, and ionospheric delay; S3. Extracting the variation characteristics of the inter-epoch code multipath error through inter-epoch single difference; S4. Analyzing the dynamic variation trend of the code multipath error based on the extracted inter-epoch code multipath error variation characteristics; S5. Evaluating the impact of the code multipath error on the GNSS positioning accuracy based on the analysis results. The present invention significantly improves the accuracy of the GNSS positioning system by accurately extracting and analyzing the inter-epoch variation characteristics of the code multipath error.

Description

Technical Field

[0001] The present invention relates to the technical field of global satellite navigation systems, and in particular to a method for extracting, analyzing and evaluating inter-epoch variation characteristics of code multipath errors. Background Art

[0002] The Global Satellite Navigation System (GNSS) has become a key technology for providing navigation, positioning, and timing services, widely used in various civil and military fields. With the advancement of technology, systems such as GPS, Galileo, GLONASS, BDS, and QZSS have gradually matured, allowing users to enjoy more accurate and reliable positioning services. Precise satellite orbit determination and user-side precise positioning are two major research focuses driving the performance of GNSS services.

[0003] Despite significant advances in GNSS technology, multipath remains a major issue affecting positioning accuracy. Multipath occurs when satellite signals are reflected or scattered along multiple paths before reaching the receiver, resulting in delays and distortions in the received signal. This effect is particularly pronounced in environments such as urban canyons, oceans, and forested areas, severely impacting GNSS positioning accuracy.

[0004] Existing technologies attempt to extract and eliminate multipath errors through various methods, such as leveraging intersatellite links and spaceborne GNSS technology to improve orbit determination accuracy, or reducing the impact of multipath in positioning through methods such as multipath hemispherical maps and stellar filtering. However, these methods often rely on long-term data processing under static observation conditions and are difficult to adapt to rapidly changing dynamic environments. Furthermore, while CMC and other hardware-based multipath detection technologies offer some solutions, they often increase system complexity and cost, and processing large amounts of data can place a significant burden on the computing platform.

[0005] Furthermore, current methods still face numerous challenges in practical applications, including real-time data processing, balancing accuracy requirements with cost, and adaptability in complex environments. Therefore, developing a GNSS positioning method that can effectively cope with dynamic environmental changes and extract and process multipath errors in real time is crucial for improving the performance and application scope of positioning technology. Summary of the Invention

[0006] In response to the shortcomings of the existing technology, the present invention provides a method for extracting, analyzing and evaluating the inter-epoch variation characteristics of code multipath error, which solves the problem of inaccurate positioning caused by the multipath effect in the existing GNSS system in dynamic and complex environments.

[0007] To achieve the above objectives, the present invention is implemented through the following technical solutions: a method for extracting, analyzing and evaluating the inter-epoch variation characteristics of code multipath error, comprising the following steps:

[0008] Obtain GNSS observation data, including code observation values ​​and carrier phase observation values;

[0009] Using code observations and carrier phase observations, a code multipath combination formula is constructed to eliminate the effects of geometric distance, clock error, tropospheric delay, and ionospheric delay.

[0010] Through single difference between epochs, the variation characteristics of code multipath error between epochs are extracted;

[0011] Based on the extracted inter-epoch code multipath error variation characteristics, the dynamic variation trend of the code multipath error is analyzed;

[0012] Based on the analysis results of code multipath error, its impact on GNSS positioning accuracy is evaluated.

[0013] Preferably, the step of using code observation values ​​and carrier phase observation values ​​to construct a code multipath combination formula to eliminate the influence of geometric distance, clock error, tropospheric delay and ionospheric delay includes:

[0014] Using code observations and carrier phase observations, the following basic observation equations are established:

[0015] P i =ρ+c(δt r -δt s )+I i +T+M i +d r,i +d s,i +ε(P i )

[0016] L i =ρ+c(δt r -δt s )-I i +T+m i +b r,i +b s,i +λ i N i +ε(L i )

[0017] Where P and L represent the code observation value and carrier phase observation value respectively, the subscript i represents different frequencies, ρ represents the geometric distance from the satellite to the station antenna, c is the speed of light in vacuum, and δt r and δt s represent the clock difference between the receiver clock and the satellite clock, I i represents the ionospheric delay, T is the tropospheric delay, Mi and m i denote code multipath and carrier phase multipath, respectively, d r,i and d s,i are the frequency-related code hardware delays at the receiver and satellite ends, respectively, and b r,i and b s,i are the frequency-related phase hardware delays at the receiver and satellite ends, λ i Indicates frequency f i The corresponding wavelength, N i is the carrier phase integer ambiguity, ε(P i ) and ε(L i ) are the observation noise of code and carrier phase respectively;

[0018] Based on the observation equation, a code multipath combination formula is constructed to eliminate the effects of geometric distance, receiver clock error, satellite clock error, tropospheric delay, and ionospheric delay. The formula is:

[0019]

[0020] Among them, MP i is the code multipath combination value, f i and f j Two different frequencies, L i and L j is the carrier phase observation value of the corresponding frequency;

[0021] By using the code multipath combination formula, the geometric distance ρ and the receiver clock error δt are eliminated. r , satellite clock error δt s , ionospheric delay I i and the influence of tropospheric delay T, the code multipath error M is obtained i , Hardware Delay B i and observation noise ε(P i ) combined observations;

[0022] Process the combined observations, remove the error terms, and obtain the code multipath error MP for analysis i .

[0023] Preferably, the step of extracting the variation characteristics of the inter-epoch code multipath error by inter-epoch single difference comprises:

[0024] According to the constructed code multipath combination formula, the code multipath combination observation value of each epoch is obtained

[0025] Calculate the difference in code multipath combination observations between adjacent epochs, specifically:

[0026]

[0027] in, Indicates the change in code multipath error between adjacent epochs, t represents the current epoch, and t-1 represents the previous epoch;

[0028] The hardware delay between the receiver and the satellite and the integer ambiguity N are eliminated by differential operation. i Long-term stable deviation caused by

[0029] The obtained inter-epoch change Used for subsequent error analysis and evaluation as a direct representation of the dynamic variation characteristics of code multipath errors.

[0030] Preferably, the step of analyzing the dynamic change trend of the code multipath error based on the extracted inter-epoch code multipath error change characteristics includes:

[0031] The time series analysis method is used to analyze the variation of code multipath error between epochs. Perform analysis to identify the dynamic characteristics of multipath errors;

[0032] Evaluate the changing trends of multipath errors over different time periods and the potential impact of these changes on GNSS positioning accuracy.

[0033] Preferably, the time series analysis includes:

[0034] right Perform spectrum analysis on the changes of multipath error to determine the frequency components of the multipath error changes;

[0035] By analyzing the obtained frequency components, the key multipath error components that affect positioning accuracy are identified.

[0036] Preferably, the step of evaluating the impact of the code multipath error on the GNSS positioning accuracy based on the analysis result of the code multipath error includes:

[0037] Compare and analyze the stability and influence of multipath error changes under static and dynamic observation conditions;

[0038] The positioning accuracy changes in different GNSS usage environments are evaluated based on the changing characteristics of multipath errors.

[0039] Preferably, the method further comprises: optimizing the GNSS positioning system to reduce the impact of multipath errors on positioning results.

[0040] The present invention also provides a device for extracting, analyzing, and evaluating characteristics of code multipath error inter-epoch variations, comprising:

[0041] Data acquisition module, used to obtain GNSS observation data;

[0042] A data processing module is used to construct a code multipath combination formula and extract the inter-epoch code multipath error variation characteristics;

[0043] Analysis module, used to analyze the dynamic trend of code multipath error and evaluate its impact on GNSS positioning accuracy;

[0044] The optimization module is used to optimize the GNSS positioning system based on the analysis results.

[0045] The present invention also provides a computer device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the above-mentioned method when executing the computer program.

[0046] The present invention also provides a storage medium on which a computer program is stored. When the computer program is executed by a processor, the method described above is implemented.

[0047] The present invention provides a method for extracting, analyzing, and evaluating the inter-epoch variation characteristics of code multipath error. It has the following beneficial effects:

[0048] 1. This invention significantly improves the accuracy of GNSS positioning systems by precisely extracting and analyzing the inter-epoch variation characteristics of code multipath error. Traditional GNSS systems often fail to distinguish between short-term and long-term variations when dealing with multipath effects. However, this invention, by meticulously analyzing inter-epoch variations, can more effectively identify and suppress short-term multipath errors, reducing their negative impact on positioning results. This refined multipath error analysis and processing technology can significantly improve the accuracy and reliability of positioning results, particularly in environments with complex multipath effects, such as urban canyons and forest cover.

[0049] 2. The method of this invention enables real-time extraction and analysis of code multipath errors. This not only improves the GNSS system's adaptability to dynamic environmental changes but also allows the system to respond quickly to sudden environmental changes. For example, as a GNSS device passes through different urban building layouts, the receiver can calculate and adjust multipath errors in real time, ensuring consistently accurate positioning data. This real-time responsiveness is particularly important for applications requiring high precision and reliability.

[0050] 3. By deeply analyzing the characteristics and impacts of multipath errors, this invention can guide GNSS system design and optimization, making system development and maintenance more efficient. Furthermore, because this invention primarily relies on software-level improvements, it is less expensive and easier to implement than strategies that require physical equipment upgrades. This approach can leverage existing hardware and improve the overall system's cost-effectiveness by optimizing algorithms and data processing flows, making it particularly suitable for implementation in resource-constrained environments.

[0051] 4. The method of the present invention has excellent universality and scalability, applicable to various types of GNSS systems and different application environments. This method is not specific to any one satellite navigation system; the technology proposed in this invention can be applied to GPS, GLONASS, Galileo, and BeiDou. Furthermore, due to its clear principles and simple operation, it can be easily extended to more areas of geographic information systems and positioning-related technologies, promoting technological advancement and widespread application in these fields. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] Figure 1 Schematic diagram of the method flow of the present invention;

[0053] Figure 2 Schematic diagram of the multipath relative change extraction strategy for the cycle-slip-free arc segment inner code according to the present invention;

[0054] Figure 3 Schematic diagram of the device structure of the present invention;

[0055] Figure 4 Schematic diagram of the computer device structure of the present invention.

[0056] Among them, 100, data acquisition module; 200, data processing module; 300, analysis module; 400, optimization module; 40, computer equipment; 41, processor; 42, memory; 43, storage medium. DETAILED DESCRIPTION

[0057] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the drawings in the present specification. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0058] Please see the attached Figure 1 -Attached Figure 2 The embodiment of the present invention provides a method for extracting, analyzing, and evaluating the inter-epoch variation characteristics of code multipath error, including the following steps:

[0059] S1. Obtain GNSS observation data

[0060] In the embodiments of the present invention, acquiring GNSS observation data is a crucial first step in the entire method, as this data will be used in subsequent analysis and evaluation steps to ensure accurate extraction and evaluation of multipath errors. The accuracy of this step directly affects the effectiveness and precision of the entire method.

[0061] In this embodiment, two types of basic data are first acquired using receivers compatible with various GNSS systems (e.g., GPS, GLONASS, and Galileo): code observations and carrier phase observations. The receivers must support multi-frequency signal reception to capture observation data at different frequencies, which is crucial for subsequently eliminating ionospheric delay and other frequency-dependent errors.

[0062] Code observation value P i : These are raw measurement data provided directly by the GNSS receiver, reflecting the signal propagation time from the satellite to the receiver, which is affected by multiple factors such as multipath effects and atmospheric delay.

[0063] Carrier phase observation value L i : Compared with code observations, carrier phase provides higher measurement accuracy, but also requires appropriate processing to extract useful information, especially in environments with significant multipath effects.

[0064] When acquiring this data, it is also necessary to record the data for each epoch, including timestamp information, to ensure the timing accuracy and integrity of data processing. Ensuring the high quality and integrity of the observation data provides a solid foundation for subsequent steps, enabling accurate extraction and analysis of multipath errors. Furthermore, considering varying observation environments and equipment, the receiver must be appropriately configured during data acquisition to optimize data quality and minimize environmental interference.

[0065] S2. Construction code multi-path combination formula

[0066] In this embodiment of the present invention, step S2 eliminates the main error sources that affect measurement accuracy, such as geometric distance, clock error, and tropospheric and ionospheric delays, by constructing a code multipath combination formula. This step is fundamental to accurately extracting multipath error characteristics, as only by effectively eliminating these major errors can errors caused by multipath effects be accurately identified and evaluated.

[0067] In this embodiment, the code observation value P obtained in step S1 is used i and carrier phase observation value L i , first establish the following observation equation:

[0068] P i =ρ+c(δt r -δt s )+I i +T+M i +d r,i +d s,i +ε(P i ) (1)

[0069] L i =ρ+c(δtr -δt s )-I i +T+m i +b r,i +b s,i +λ i N i +ε(L i ) (2)

[0070] Where P and L represent the code observation value and carrier phase observation value respectively, both in meters, the subscript i represents different frequencies, ρ represents the geometric distance from the satellite to the station antenna, c is the speed of light in vacuum, δt r and δt s Represent the clock difference between the receiver clock and the satellite clock, in seconds, I i Represents ionospheric delay, T is tropospheric delay, the unit is meter, M i and m i Represents code multipath and carrier phase multipath respectively, in meters, d r,i and d s,i are the frequency-related uncalibrated code delay (UCD) at the receiver and satellite ends, respectively. r,i and b s,i are the frequency-related phase hardware delays (UPD, uncalibrated phase delay) at the receiver and satellite ends respectively; λ i Indicates frequency f i The corresponding wavelength is in meters per week; N i is the carrier phase integer ambiguity, in weeks; ε(P i ) and ε(L i ) are the observation noise of code and carrier phase respectively.

[0071] Next, we construct a code multipath combination formula, which aims to use frequency differences to eliminate non-multipath errors and improve the accuracy of multipath error identification. The combination formula is as follows:

[0072]

[0073] In formula (3), it can be clearly seen that the geometric distance ρ from the satellite to the station antenna and the receiver clock error δt r 、Satellite clock error δt s , tropospheric delay T, and ionospheric delay I will all be eliminated. Phase multipath error m i Relative to the code multipath error M i Very small, the phase observation noise ε(L i ) relative to the code observation noise ε(P i ) is very small, so mi and ε(L i ) can be ignored. The code observation noise ε(P) exhibits a zero-mean characteristic and can reach the centimeter or decimeter level. Equation (3) can be further expressed as follows:

[0074] MP i =M i +B i +ε(P i ) (4)

[0075] Among them B i Expressed as:

[0076]

[0077] Among them, f i and f j Two different frequencies, L i and L j is the carrier phase observation value of the corresponding frequency. Obviously, MP i There are also other deviation terms in , whose influence needs to be eliminated to extract the code multipath.

[0078] In equation (5), the code hardware delay d at the receiver and satellite is r,i and d s,i , the phase hardware delay b between the receiver and satellite r,i and b s,i It has short-term stability; at the same time, when no cycle slip occurs, the whole cycle ambiguity N i It will also be a constant. So in general, on the arc segment without cycle slip, B i It is relatively stable and can be regarded as a constant.

[0079] Previous studies were based on Figure 2 The strategy shown here removes B by subtracting the average value of the code multipath combination observations over the entire cycle slip segment. i The calculation formula is shown in formula (6).

[0080] exist Figure 2 In the figure, n represents the number of epochs in the cycle slip-free arc segment, and the orange dotted line represents the length of the time window for averaging. and They represent the code multipath observation value of the tth epoch on the cycle slip-free arc segment and its corresponding average value. It can be seen that each observation epoch in the cycle slip-free arc segment corresponds to All equal.

[0081]

[0082] Among them, <·> is the symbol for finding the average value.i It has good stability and can be regarded as a constant, so ε(P i ) has zero mean, so At the same time, the constant term of code multipath error is also removed The final relative change of the code multipath The changing characteristics of multipath can be studied based on its time series.

[0083] By implementing this step, the present invention can more effectively analyze and evaluate the impact of multipath errors on GNSS positioning accuracy in subsequent steps.

[0084] S3. Extract the variation characteristics of the code multipath error between epochs through single difference between epochs

[0085] In an embodiment of the present invention, step S3 inherits the result after eliminating the main error source through the code multipath combination formula in step S2, and focuses on extracting the variation characteristics of the code multipath error through the single difference method between epochs. The above calculation method can only obtain the long-term variation trend of the code multipath error relative to its average value on the cycle slip arc segment, and can only be performed after the fact. This embodiment further extracts the variation characteristics of the code multipath error based on the single difference between epochs of GNSS observation values, which can meet the real-time extraction requirements of multipath errors under dynamic and static observation conditions. The calculation formula is shown in formula (7), is the epoch-to-epoch variation of the code multipath error. i It is relatively stable on the arc segment without cycle slips, and the subtraction between epochs can eliminate its influence. Compared with formula (6), the variation value of the code multipath extracted in this embodiment is affected by the variation between epochs of the observation noise rather than the observation noise, but The main changes are still in the multi-path.

[0086]

[0087] in, represents the change in code multipath error between time t and t-1, is the code multipath combination value at the t-th epoch, is the code multipath combination value at the t-1th epoch.

[0088] This differencing method effectively removes the effects of receiver and satellite clock errors, integer ambiguities, etc., which are assumed to be constant between consecutive epochs. By subtracting the value of the previous epoch, the dynamic changes of multipath effects caused by environmental changes (such as changes in reflecting surfaces) can be enhanced.

[0089] The obtained inter-epoch differential data will be used for further analysis to identify the short-term and long-term variation trends of the multipath effect and provide accurate input data for subsequent steps.

[0090] according to The changing characteristics of the code multipath error can be grasped in real time, and the The long-term variation characteristics of the code multipath error are used to study the stability of the code multipath error, which can more comprehensively explore the impact of the multipath effect on GNSS observations.

[0091] S4. Analyze the dynamic trend of code multipath error

[0092] In the embodiment of the present invention, step S4 is to perform an in-depth analysis on the variation characteristics of the inter-epoch code multipath error after extracting the variation characteristics thereof, with the purpose of identifying and understanding the dynamic variation trend of the multipath error.

[0093] In this embodiment, analyzing the dynamic change trend of the inter-epoch code multipath error includes the following steps:

[0094] 1. Constructing a time series: Based on the inter-epoch code multipath error variation extracted in step S3 Construct a time series dataset to track and record the changes of multipath error over time.

[0095] 2. Dynamic change trend analysis: By analyzing the time series, the dynamic change trend of multipath error is identified.

[0096] 3. Spectral Analysis: Spectral analysis is further performed on the time series data to identify possible periodic components or changes in specific frequencies. This analysis helps understand the relationship between multipath errors and changes in the observation environment, especially those error patterns associated with specific reflecting surfaces or motion states.

[0097] 4. Anomaly Detection: During time series analysis, identify abnormal multipath error change events, such as sudden error jumps or short-term sharp fluctuations. These anomalies may be related to environmental factors such as satellite signal obstruction and changes in reflective surfaces.

[0098] Through the above analysis steps, the present invention can deeply understand the changing characteristics of multipath errors, providing an important basis for subsequent error compensation and positioning system optimization.

[0099] S5. Based on the analysis results of code multipath error, evaluate its impact on GNSS positioning accuracy.

[0100] In an embodiment of the present invention, step S5 is to evaluate the specific impact of the dynamic change trend of the code multipath error analyzed in the above steps on the positioning accuracy of the global satellite navigation system (GNSS).

[0101] In this embodiment, this step includes the following specific operations:

[0102] Positioning Error Model Construction: First, a positioning error model is constructed that takes into account the multipath error variation characteristics identified in step S4. The purpose of the model is to quantify how multipath error affects overall positioning accuracy. Based on theoretical analysis and historical data, this model evaluates the specific impact of multipath error on positioning results in different environments and conditions.

[0103] 1. Error Impact Analysis: By comparing the multipath error trend with the actual observed positioning error, we assess the impact of multipath error on positioning accuracy. This analysis helps identify the contribution of multipath error to positioning accuracy and indicates the conditions under which multipath error is most significant.

[0104] 2. Accuracy Assessment Methods: Statistical analysis methods, such as error variance analysis and signal-to-noise ratio comparison, are used to quantify the specific impact of multipath error on positioning accuracy. Furthermore, the accuracy and practicality of the multipath error model can be further verified by simulating positioning operations in different multipath environments.

[0105] 3. Optimization Suggestions: Based on the analysis of the impact of multipath error on positioning accuracy, possible system optimization suggestions are proposed. These suggestions may include adjusting the receiver's multipath processing algorithm, improving the system's signal processing strategy, or proposing the use of hardware more suitable for complex environments.

[0106] By implementing step S5, the present invention can not only provide an in-depth understanding of the impact of multipath errors, but also propose specific improvement measures based on empirical data, thereby significantly improving the positioning accuracy and reliability of the GNSS system in a changing environment.

[0107] The method described in this paper significantly improves GNSS positioning accuracy through refined multipath error analysis, particularly in urban canyons and forest environments where multipath effects are significant. By monitoring and analyzing multipath errors in real time, system settings and algorithms can be adjusted as necessary to adapt to complex external environments.

[0108] In a preferred embodiment of the present invention, in addition to the aforementioned steps of extracting, analyzing, and evaluating multipath error features, a key step is also included: optimizing the GNSS positioning system to reduce the impact of multipath error on positioning results. This step is a crucial supplement to the overall invention, directly optimizing the impact of multipath error determined through analysis at the system level, thereby improving positioning accuracy and system reliability.

[0109] 1. Multipath Error Identification and Modeling

[0110] In this preferred embodiment, based on the analysis results of step S5, the multipath errors that affect positioning accuracy are first identified and modeled in detail. This includes defining the most influential multipath error types (such as reflection, diffraction, etc.) and establishing a specific mathematical model for each error type.

[0111] 2. Adjustment and optimization of positioning algorithms

[0112] Adjust and optimize existing positioning algorithms based on the multipath error model. This may include introducing new multipath error correction techniques, such as improved filtering techniques or multipath mitigation algorithms, to more effectively reduce the impact of multipath error. For example, advanced Kalman filters or particle filters can be used to improve adaptability and dynamic response to multipath effects.

[0113] 3. Optimization of receiver hardware

[0114] In some cases, the receiver hardware may also need to be optimized to better handle multipath effects. This may include updating the receiver antenna design, using a higher-performance signal processor, or introducing more advanced signal processing techniques such as array signal processing to separate direct and reflected signals.

[0115] 4. System-level testing and verification

[0116] After adjustments and optimizations, positioning systems need to be thoroughly tested and validated to ensure that the newly introduced technologies or improvements have indeed reduced the impact of multipath errors and improved overall positioning accuracy. These tests can be conducted in a laboratory environment or in a real-world application environment to fully evaluate system performance.

[0117] 5. Continuous monitoring and iterative optimization

[0118] The optimization process is a continuous cycle. After the system is deployed, its performance is continuously monitored, and iterative optimization is performed based on issues discovered during actual operation. This is achieved through remote software updates, ensuring that the system is always in optimal condition.

[0119] By implementing the system optimization steps in this preferred embodiment, the present invention can not only effectively reduce the impact of multipath errors on GNSS positioning results, but also improve the robustness and reliability of the positioning system, especially in complex environments.

[0120] The device for extracting, analyzing, and evaluating the characteristics of the code multipath error between epochs described below and the method for extracting, analyzing, and evaluating the characteristics of the code multipath error between epochs described above can be used in correspondence with each other.

[0121] Please see the attached Figure 3 The present invention also provides a method and apparatus for extracting, analyzing, and evaluating characteristics of code multipath error epoch-to-epoch variations, including:

[0122] The data acquisition module 100 is used to obtain GNSS observation data;

[0123] The data processing module 200 is used to construct a code multipath combination formula and extract the inter-epoch code multipath error variation characteristics;

[0124] An analysis module 300 is used to analyze the dynamic change trend of code multipath error and evaluate its impact on GNSS positioning accuracy;

[0125] The optimization module 400 is used to optimize the GNSS positioning system according to the analysis results.

[0126] The device of this embodiment can be used to execute the above method embodiment, and its principles and technical effects are similar, so they will not be repeated here.

[0127] Please see the attached Figure 4 The present invention further provides a computer device 40, comprising: a processor 41 and a memory 42, wherein the memory 42 stores a computer program executable by the processor, and when the computer program is executed by the processor, the above method is performed.

[0128] The present invention further provides a storage medium 43 on which a computer program is stored. When the computer program is run by the processor 41 , the above method is executed.

[0129] Among them, the storage medium 43 can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic disk or optical disk.

[0130] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.

Claims

1. A method for extracting, analyzing, and evaluating the inter-epoch variation characteristics of code multipath error, characterized in that: The following steps are involved: Obtain GNSS observation data, including code observation values ​​and carrier phase observation values; Using code observations and carrier phase observations, a code multipath combination formula is constructed to eliminate the effects of geometric distance, clock error, tropospheric delay, and ionospheric delay. Through single difference between epochs, the variation characteristics of code multipath error between epochs are extracted; Based on the extracted inter-epoch code multipath error variation characteristics, the dynamic variation trend of the code multipath error is analyzed; Based on the analysis results of code multipath error, evaluate its impact on GNSS positioning accuracy; The step of constructing a code multipath combination formula by using code observation values ​​and carrier phase observation values ​​to eliminate the influence of geometric distance, clock error, tropospheric delay and ionospheric delay includes: Using code observations and carrier phase observations, the following basic observation equations are established: in, and Represent the code observation value and carrier phase observation value respectively, the subscript Indicates different frequencies, represents the geometric distance from the satellite to the station antenna, is the speed of light in vacuum, and represent the clock differences between the receiver clock and the satellite clock, represents the ionospheric delay, is the tropospheric delay, and represent code multipath and carrier phase multipath, respectively. and are the frequency-related code hardware delays at the receiver and satellite ends, and are the frequency-related phase hardware delays at the receiver and satellite ends, Indicates frequency The corresponding wavelength, is the carrier phase integer ambiguity, and are the observation noise of code and carrier phase respectively; Based on the observation equation, a code multipath combination formula is constructed to eliminate the effects of geometric distance, receiver clock error, satellite clock error, tropospheric delay, and ionospheric delay. The formula is: in, is the code multipath combination value, and Two different frequencies, and is the carrier phase observation value of the corresponding frequency; Eliminate the geometric distance by the code multipath combination formula , receiver clock error , satellite clock error , ionospheric delay and tropospheric delay The influence of code multipath error is obtained. , hardware delay and observation noise The combined observation value of Process the combined observations, remove the error terms, and obtain the code multipath error for analysis ; The step of extracting the variation characteristics of the inter-epoch code multipath error by inter-epoch single difference comprises: According to the constructed code multipath combination formula, the code multipath combination observation value of each epoch is obtained ; Calculate the difference in code multipath combination observations between adjacent epochs, specifically: in, represents the change in code multipath error between adjacent epochs, represents the current epoch, represents the previous epoch; Eliminate hardware delays and integer ambiguities caused by the receiver and satellite through differential operation Long-term stable deviation caused by The obtained inter-epoch change Used for subsequent error analysis and evaluation as a direct representation of the dynamic characteristics of code multipath errors; The step of analyzing the dynamic change trend of the code multipath error based on the extracted inter-epoch code multipath error change characteristics includes: The time series analysis method is used to analyze the variation of code multipath error between epochs. Perform analysis to identify the dynamic characteristics of multipath errors; Evaluate the changing trends of multipath errors over different time periods and the potential impact of these changes on GNSS positioning accuracy; The time series analysis includes: right Perform spectrum analysis on the changes of multipath error to determine the frequency components of the multipath error changes; By analyzing the obtained frequency components, the key multipath error components that affect positioning accuracy are identified.

2. The method for extracting, analyzing and evaluating the inter-epoch variation characteristics of code multipath error according to claim 1, characterized in that: The step of evaluating the impact of code multipath error on GNSS positioning accuracy based on the analysis results of the code multipath error includes: Compare and analyze the stability and influence of multipath error changes under static and dynamic observation conditions; The changes in positioning accuracy under different GNSS usage environments are evaluated based on the changing characteristics of multipath errors.

3. The method for extracting, analyzing and evaluating the inter-epoch variation characteristics of code multipath error according to claim 1, characterized in that: The method further includes optimizing the GNSS positioning system to reduce the impact of multipath errors on positioning results.

4. A device for extracting, analyzing, and evaluating the characteristics of code multipath error inter-epoch variation, configured to implement the method for extracting, analyzing, and evaluating the characteristics of code multipath error inter-epoch variation according to any one of claims 1 to 3, characterized in that: include: Data acquisition module, used to obtain GNSS observation data; A data processing module is used to construct a code multipath combination formula and extract the inter-epoch code multipath error variation characteristics; Analysis module, used to analyze the dynamic trend of code multipath error and evaluate its impact on GNSS positioning accuracy; The optimization module is used to optimize the GNSS positioning system based on the analysis results.

5. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the computer program, the method according to any one of claims 1 to 3 is implemented.

6. A storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method according to any one of claims 1 to 3 is implemented.

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