GNSS elastic code deviation calculation method and system suitable for full scene

Abstract

The invention relates to the technical field of satellite navigation, and particularly discloses a GNSS elastic code deviation calculation method and system suitable for a full scene, and the method comprises the steps: analyzing the signal power variation amplitude of a monitoring station based on the satellite signal power variation of the GNSS monitoring station, so as to determine the state of the satellite signal power; constructing a high-time-resolution code deviation state equation and an observation equation, and realizing GNSS high-time-resolution code deviation estimation based on Kalman filtering epoch-by-epoch estimation code deviation, an improved phase smoothing pseudo-range method and an ionized layer spherical harmonic function; calculating an elastic code deviation elastic factor of the GNSS based on the GNSS signal power state, the code deviation variation amplitude of the GNSS, the product precision and the time sequence stability; and determining the elastic code deviation value of the GNSS according to the state of the satellite signal power, the code deviation estimation of the high time resolution of the GNSS and the elastic code deviation elastic factor of the GNSS.

Description

Technical Field

[0001] The present invention relates to the technical field of satellite navigation, and particularly relates to a GNSS elastic code bias calculation method and system applicable to all scenarios. Background Art

[0002] Satellite radio navigation systems have congenital technical weaknesses such as being vulnerable to occlusion, interference, and spoofing. The risks of service denial and degradation of space-based positioning, navigation, and timing (PNT) systems are increasing continuously, posing huge potential risks to the operation and development of social critical infrastructure. Satellite power enhancement technology is one of the main means of enhancing the signals of the Global Navigation Satellite System (GNSS), which affects the robustness and availability of PNT services and is also known as elastic power. GPS satellites have powerful radio reconstruction capabilities. At present, a total of 31 GPS second-generation and third-generation satellites provide services, among which 19 GPS satellites have the ability to enhance power. The BeiDou Satellite Navigation System (BDS) is currently jointly provided by BDS-2 and BDS-3. Both BDS-2 and BDS-3 have the ability to enhance the power of the B3 center frequency signal. While maintaining the long-term broadcast of civil signals, the B3 signal adds modern military signals. The B3 signal solves the problem of medium-power merging of civil and military frequency bands and can meet the needs of medium-power merging of on-board transmitters in multiple channels and the smooth transition of the B3 signal.

[0003] As the time delay of the navigation signal at the satellite and receiver ends, code bias is one of the main error sources of GNSS PNT services. During elastic power, the variation magnitude range of BDS and GPS code biases is from sub-nanoseconds to several hundred nanoseconds. The existing code bias products with low time resolution provided by institutions are difficult to ensure a rapid response to code bias changes. The broadcast ephemeris code bias parameters with hourly solutions and the post-processing code bias products with daily or even monthly solutions will inevitably affect the satellite availability and the overall performance of PNT services during satellite elastic power. Traditional code bias products are difficult to meet the needs of users for all-time PNT services. Combining with the existing elastic power judgment method, the present invention provides a GNSS elastic code bias calculation method and system applicable to all scenarios. Summary of the Invention

[0004] To achieve the object of the present invention, the present application provides a GNSS elastic code bias calculation method applicable to all scenarios, including: Step S1: Based on the change amount of the satellite signal power at the GNSS monitoring station, analyze the change amplitude of the monitoring station signal power to determine the state of the satellite signal power; Step S2: Construct a high-time-resolution code bias state equation and an observation equation, and estimate the code bias epoch by epoch based on Kalman filtering, and implement high-time-resolution code bias estimation of GNSS by using the improved phase-smoothed pseudorange method and ionospheric spherical harmonic functions. Step S3: Calculate the elastic code bias elasticity factor of GNSS based on the GNSS signal power state, the change amplitude of the GNSS code bias, the product accuracy, and the time series stability; Step S4: Determine the elastic code bias value of GNSS according to the state of the satellite signal power, the high time resolution code bias estimation of GNSS, and the elastic code bias elasticity factor of GNSS.

[0005] In some specific embodiments, step S1 includes: Step S11: Obtain the time series of the ratio of the GNSS signal carrier power to the noise in the 1Hz bandwidth for each frequency point; Step S12: Establish a functional relationship between the ratio of the GNSS signal carrier power to the noise in the 1Hz bandwidth and the satellite elevation angle; Step S13: Calculate the mathematical expectation of the value of the ratio of the GNSS signal carrier power to the noise in the 1Hz bandwidth, and determine whether the measured ratio of the GNSS signal carrier power to the noise in the 1Hz bandwidth exceeds the mathematical expectation by a certain range; Step S14: Determine whether the satellite signal power is in the normal power state or the elastic power state by setting a threshold.

[0006] In some specific embodiments, in step S12, the functional relationship between the ratio of the GNSS signal carrier power to the noise in the 1Hz bandwidth and the satellite elevation angle is determined according to the following formula: ; In the formula, SW r,j represents the value of the ratio of the GNSS signal carrier power to the noise in the 1Hz bandwidth at the j-th frequency point of the monitoring station, r represents the receiver, represents the satellite elevation angle, , , , and represent the model coefficients, represents the noise of the ratio of the GNSS signal carrier power to the noise in the 1Hz bandwidth.

[0007] In some specific embodiments, step S13 includes: When the satellite power is stable, the measured value of the ratio of the GNSS signal carrier power to the noise in the 1Hz bandwidth is close to the mathematical expectation, while when the satellite has elastic power, the measured value will exceed the mathematical expectation by a certain range, which is determined according to the following formula: ; Among them, is the expected range gap value, H0 is the normal power, H 1 is the elastic power.

[0008] In some specific embodiments, the elastic code deviation elastic factor is determined according to the following formula: ; wherein, H represents the GNSS signal power state, represents the epoch t time code deviation variance, represents the epoch t time historical continuous arc segment daily code deviation standard deviation, Δ t represents the epoch t time code deviation value and the absolute difference between the average value in the historical continuous arc segment.

[0009] To achieve the same invention purpose, the present application also provides a GNSS elastic code deviation calculation system applicable to all scenarios, including: GNSS elastic power judgment module: used to analyze the change amplitude of the monitoring station signal power based on the change amount of the satellite signal power of the GNSS monitoring station, so as to determine the state of the satellite signal power; High time resolution code deviation estimation module: used to construct a high time resolution code deviation state equation and an observation equation, and estimate the code deviation epoch by epoch based on the Kalman filter and implement the high time resolution code deviation estimation of GNSS by the improved phase-smoothed pseudorange method and the ionospheric spherical harmonic function; Elastic code deviation elastic factor generation module: used to calculate the elastic code deviation elastic factor of GNSS based on the GNSS signal power state, the code deviation change amplitude of GNSS, the product accuracy and the time series stability; Elastic code deviation value calculation module: used to determine the elastic code deviation value of GNSS according to the state of the satellite signal power, the high time resolution code deviation estimation of GNSS, and the elastic code deviation elastic factor of GNSS.

[0010] In some specific embodiments, the GNSS elastic power judgment module is used to perform the following steps: Step S11: Obtain the time series of the ratio of the GNSS signal carrier power to the noise in the 1Hz bandwidth for each frequency point; Step S12: Establish a functional relationship between the ratio of the GNSS signal carrier power to the noise in the 1Hz bandwidth and the satellite elevation angle; Step S13: Calculate the mathematical expectation of the value of the ratio of the GNSS signal carrier power to the noise in the 1Hz bandwidth, and judge whether the measured ratio of the GNSS signal carrier power to the noise in the 1Hz bandwidth exceeds the mathematical expectation by a certain range; Step S14: Determine whether the satellite signal power is in the normal power state or the elastic power state by setting a threshold.

[0011] In some specific embodiments, in step S12, the functional relationship between the ratio of the carrier power of the GNSS signal to the noise in a 1Hz bandwidth and the satellite elevation angle is determined according to the following formula: ; In the formula, SW r,j represents the value of the ratio of the carrier power of the GNSS signal at the j-th frequency point of the monitoring station to the noise in a 1Hz bandwidth, r represents the receiver, represents the satellite elevation angle, , , , and represent model coefficients, represents the noise of the ratio of the carrier power of the GNSS signal to the noise in a 1Hz bandwidth.

[0012] In some specific embodiments, step S13 includes: when the satellite power is stable, the measured value of the ratio of the carrier power of the GNSS signal to the noise in a 1Hz bandwidth is close to the mathematical expectation, while when the satellite has elastic power, the measured value will exceed the mathematical expectation by a certain range, and is determined according to the following formula: ; Wherein, is the expected range difference value, H 0 is the normal power, H 1 is the elastic power.

[0013] In some specific embodiments, the elastic code deviation elastic factor is determined according to the following formula: ; In the formula, H represents the GNSS signal power state, represents the epoch t time code deviation variance, represents the epoch t time historical continuous arc segment daily code deviation standard deviation, Δ t represents the epoch t time code deviation value and the absolute difference between the average value in the historical continuous arc segment.

[0014] Advantages of the above technical solutions: A GNSS elastic code bias calculation method and system applicable to all scenarios provided by the present invention, by means of filtering epoch-by-epoch estimation, analyzes the change of code bias state, takes the characteristics of traditional code bias products as a reference, improves the adaptability of code bias, and makes the code bias product develop from a single low-time-resolution mode to a self-adjusting and self-improving mode of dynamic adaptive time-resolution adjustment, with characteristics such as high efficiency, high precision, high integrity, high continuity and high availability, and has the ability to cope with different scenarios. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the drawings in the following description are only some embodiments of the present application. For those skilled in the art, without creative efforts, other drawings can be obtained based on these drawings.

[0016] Figure 1 A flowchart of a GNSS elastic code bias calculation method applicable to all scenarios provided by an embodiment of the present invention; Figure 2 A schematic diagram of the basic framework of a GNSS elastic code bias calculation method applicable to all scenarios provided by an embodiment of the present invention; Figure 3 A calculation flowchart of a GNSS elastic code bias calculation method applicable to all scenarios provided by an embodiment of the present invention; Figure 4 An elastic power judgment flowchart of a GNSS elastic code bias calculation method applicable to all scenarios provided by an embodiment of the present invention; Figure 5 A schematic diagram of the structure of a GNSS elastic code bias calculation system applicable to all scenarios provided by an embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0017] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, rather than all embodiments.

[0018] Examples of the embodiments are shown in the drawings, where the same or similar signs represent the same or similar elements or elements with the same or similar functions from beginning to end. The embodiments described below by referring to the drawings are exemplary and are intended to explain the present invention and should not be construed as limiting the present invention.

[0019] Embodiment 1 An embodiment of the present invention provides a GNSS elastic code deviation calculation method applicable to all scenarios. Referring to Figure 1 、 2 as shown, it includes: Step S1: Analyze the change amplitude of the signal power of the monitoring station based on the change amount of the satellite signal power of the GNSS monitoring station to determine the state of the satellite signal power.

[0020] In a specific embodiment of the present invention, step S1 includes: Step S11: Obtain the time series of the ratio of the GNSS signal carrier power to the noise in a 1Hz bandwidth for each frequency point; Step S12: Establish a functional relationship between the ratio of the GNSS signal carrier power to the noise in a 1Hz bandwidth and the satellite elevation angle; Step S13: Calculate the mathematical expectation of the value of the ratio of the GNSS signal carrier power to the noise in a 1Hz bandwidth, and determine whether the measured ratio of the GNSS signal carrier power to the noise in a 1Hz bandwidth exceeds the mathematical expectation by a certain range; Step S14: Determine whether the satellite signal power is in a normal power state or an elastic power state by setting a threshold.

[0021] In a specific embodiment of the present invention, in step S12, using the daily repetition feature of the GNSS satellite observations, based on the real-time stream observation data provided by the IGS monitoring station, obtain the time series of the ratio of the GNSS signal carrier power to the noise in a 1Hz bandwidth for each frequency point, and then combine the satellite elevation angle of the corresponding monitoring station calculated by the broadcast ephemeris. The functional relationship between the ratio of the GNSS signal carrier power to the noise in a 1Hz bandwidth and the satellite elevation angle is determined according to the following formula: ; In the formula, SW r,j represents the value of the ratio of the GNSS signal carrier power to the noise in a 1Hz bandwidth at the j-th frequency point of the monitoring station, r represents the receiver, represents the satellite elevation angle, 、 、 、 and represent the model coefficients, represents the noise of the ratio of the GNSS signal carrier power to the noise in a 1Hz bandwidth.

[0022] In a specific embodiment of the present invention, step S13 includes: when the satellite power is stable, the measured value of the ratio of the GNSS signal carrier power to the noise in a 1 Hz bandwidth is close to the mathematical expectation, while when the satellite has elastic power, the measured value will exceed the mathematical expectation by a certain range, which is determined according to the following formula: ; wherein, is the expected range gap value, H 0 is the normal power, H 1 is the elastic power.

[0023] Specifically, when the satellite power is stable, the C / N0 observation value changes relatively stably, and the measured value is close to the mathematical expectation. While when the satellite has elastic power, the measured value will exceed the mathematical expectation by a certain range, which is expressed as: ; wherein, is the expected range gap value, the C / N0 observation value is the ratio of the GNSS signal carrier power to the noise in a 1 Hz bandwidth, which can be used as an important parameter to measure the signal quality. The change of the C / N0 value of a single receiver can be used as an index to monitor the power change.

[0024] Using the actual results of n groups of receiver monitoring stations in the IGS monitoring stations for grouping and counting can form the elastic power probability statistic , that is: ; Furthermore, the elastic power can be judged by setting a threshold (significance level), that is: ; After solving the corresponding parameters based on the long-term characteristics of the GNSS monitoring stations and combining with the satellite elastic power examples, a satellite elastic power judgment model based on each monitoring station can be established. On this basis, by selecting a certain number of globally distributed monitoring stations with uniform distribution, a real-time monitoring system for satellite elastic power based on the threshold detection mechanism can be constructed. When a certain number of selected monitoring stations simultaneously observe a jump change in the C / N0 observation value of the same type of satellite signal, it can be considered that the system has an elastic power phenomenon.

[0025] Step S2: Construct a code bias state equation and an observation equation with high time resolution, and realize the high time resolution code bias estimation of GNSS based on the Kalman filter to estimate the code bias epoch by epoch and the improved phase-smoothed pseudorange method and the ionospheric spherical harmonic function.

[0026] Specifically, the Kalman filter estimates the code bias epoch by epoch, including the code bias value and the code bias variance.

[0027] See Figure 3 As shown, the code bias calculation engine generates code bias products with high time resolution epoch by epoch through building a mathematical model and using Kalman filtering, including code bias values and code bias variances. The generation of elastic code bias products includes the elastic optimization integration of the calculation engine, the elastic adjustment of the function model, and the elastic optimization process of the stochastic model, which provides code bias RI and code bias values. The code bias calculation engine analyzes the change of code bias state through filtering epoch by epoch estimation, and takes the characteristics of traditional code bias products as a reference to improve the adaptability of code bias, so that the code bias products develop from a single low time resolution mode to a self-adjusting and self-improving mode with dynamic adaptive time resolution adjustment. The setting of the code bias RI parameter is to reflect characteristics such as availability, continuity, integrity, robustness, and accuracy. The self-adjusting code bias reflects efficiency and includes two different time resolution states: normal code bias and elastic code bias. Therefore, elastic code bias products can still meet the minimum requirements of users in terms of accuracy, integrity, and continuity of PNT services even in complex scenarios such as elastic power.

[0028] Figure 4 The generation process of elastic code bias products is based on the framework of traditional code bias products, uses the high time resolution filtered code bias solution as the calculation engine, and takes the elastic adjustment of the function model and the elastic optimization of the stochastic model as means, integrating background field information such as external complex environments or uncertain factors such as elastic power, so that the code bias products have characteristics such as high efficiency, high accuracy, high integrity, high continuity, and high availability. Elastic code bias products have the ability to cope with different scenarios.

[0029] The high time resolution code bias mathematical model is the basis of the elastic code bias product engine. Based on this, different state products of elastic code bias products are generated. The high time resolution code bias of any observation channel at epoch t can be expressed as: ; The elastic code bias RI is calculated based on indicators such as GNSS signal power state, the change amplitude of code bias, product accuracy, and time series stability, and is expressed as follows: ; In the formula, H 0 and H 1 represent the elastic and normal states of code bias, , , and represent coefficient proportionality factors, which are generated through linear regression calculation using one-year observation data in the normal state. represents the code bias variance at epoch t , represents the code bias variance at epoch tStandard Deviation (STD) of the Daily Code Deviation within the Historical Continuous Arc End at a Moment, represents an epoch t The absolute difference between the code deviation value at a moment and the average value within the historical continuous arc end. When the code deviation RI is between 0 and 0.5, the code deviation is marked as the normal state; when it is between 0.5 and 1.0, it is marked as the elastic state; when it is equal to 1.0, it is marked as the warning state; and when it is N / A, it is marked as the abnormal state.

[0030] It should be noted that the GNSS elastic code deviation products applicable to all scenarios in the present invention are usually in the normal and elastic states, with different time resolutions. When the code deviation RI value is in the normal state, its time resolution is consistent with that of the code deviation products of each analysis institution, and a daily-resolution code deviation product is provided. When the state of the code deviation product is elastic or in the warning state, it is in the elastic code deviation state, and a high-time-resolution code deviation product is provided epoch by epoch. When in the abnormal state, the code deviation is set to be unavailable for downstream users, indicating that an abnormality has occurred in the code deviation engine.

[0031] Step S3: Calculate the elastic factor of the GNSS elastic code deviation based on the GNSS signal power state, the change amplitude of the GNSS code deviation, the product accuracy, and the time series stability.

[0032] In a specific embodiment of the present invention, the elastic factor of the elastic code deviation is determined according to the following formula: ; In the formula, H represents the GNSS signal power state, represents an epoch t the code deviation variance at a moment, represents an epoch t the standard deviation of the daily code deviation within the historical continuous arc segment at a moment, Δ t represents an epoch t the absolute difference between the code deviation value at a moment and the average value within the historical continuous arc segment.

[0033] In order to reflect the characteristics such as the availability, continuity, integrity, robustness, and accuracy of the code deviation. The elastic code deviation RI incorporates background field information such as external complex environments or uncertainty factors like elastic power, and is calculated based on indicators such as the GNSS signal power state, the change amplitude of the code deviation, the product accuracy, and the time series stability.

[0034] Step S4: Determine the elastic code deviation value of the GNSS according to the state of the satellite signal power, the high-time-resolution code deviation estimation of the GNSS, and the elastic factor of the GNSS elastic code deviation.

[0035] The elastic code bias value uses function model elastic adjustment and random model elastic optimization as means, is based on the traditional code bias product framework, and uses the high-time-resolution filtered code bias solution as the calculation engine. It has the ability to handle different scenarios and includes four states: elastic, normal, warning, and abnormal.

[0036] The described elastic code bias product can address issues of fault prevention and system recovery in the face of external complex environments or uncertain factors. Even in complex scenarios such as elastic power, it can still meet the minimum requirements in aspects such as the accuracy, integrity, and continuity of the user's PNT service.

[0037] Embodiment 2 An embodiment of the present invention provides a GNSS elastic code bias calculation system applicable to all scenarios. Referring to Figure 5 as shown, it includes: GNSS elastic power judgment module 10: used to analyze the change amplitude of the signal power of the monitoring station based on the change amount of the satellite signal power of the GNSS monitoring station to determine the state of the satellite signal power; High-time-resolution code bias estimation module 20: used to construct a high-time-resolution code bias state equation and observation equation, and perform high-time-resolution code bias estimation of GNSS based on Kalman filtering for epoch-by-epoch estimation of code bias and the improved phase-smoothed pseudorange method and ionospheric spherical harmonic function; Elastic code bias elastic factor generation module 30: used to calculate the elastic code bias elastic factor of GNSS based on the GNSS signal power state, the change amplitude of the GNSS code bias, product accuracy, and time series stability; Elastic code bias value calculation module 40: used to determine the elastic code bias value of GNSS according to the state of the satellite signal power, the high-time-resolution code bias estimation of GNSS, and the elastic code bias elastic factor of GNSS.

[0038] In some specific embodiments, the GNSS elastic power judgment module 10 is used to perform the following steps: Step S11: Obtain the time series of the ratio of the carrier power of the GNSS signal of each frequency point to the noise in the 1Hz bandwidth; Step S12: Establish a functional relationship between the ratio of the carrier power of the GNSS signal to the noise in the 1Hz bandwidth and the satellite elevation angle; Step S13: Calculate the mathematical expectation of the value of the ratio of the carrier power of the GNSS signal to the noise in the 1Hz bandwidth, and determine whether the measured ratio of the carrier power of the GNSS signal to the noise in the 1Hz bandwidth exceeds the mathematical expectation by a certain range; Step S14: Determine whether the satellite signal power is in the normal power state or the elastic power state by setting a threshold.

[0039] In some of these specific embodiments, in step S12, the functional relationship between the ratio of the fitted GNSS signal carrier power to the noise in a 1 Hz bandwidth and the satellite elevation angle is determined according to the following formula: ; In the formula, SW r,j represents the value of the ratio of the GNSS signal carrier power at the jth frequency point of the monitoring station to the noise in a 1 Hz bandwidth, r represents the receiver, represents the satellite elevation angle, , , , and represent the model coefficients, represents the noise of the ratio of the GNSS signal carrier power to the noise in a 1 Hz bandwidth.

[0040] In some of these specific embodiments, step S13 includes: when the satellite power is stable, the measured value of the ratio of the GNSS signal carrier power to the noise in a 1 Hz bandwidth is close to the mathematical expectation, while when the satellite has elastic power, the measured value will exceed the mathematical expectation by a certain range, and is determined according to the following formula: ; wherein, is the expected range gap value, H 0 is the normal power, H 1 is the elastic power.

[0041] In some of these specific embodiments, the elastic code deviation elastic factor is determined according to the following formula: ; In the formula, H represents the GNSS signal power state, represents the epoch t the variance of the code deviation at the moment, represents the epoch t the standard deviation of the daily code deviation within the historical continuous arc segment at the moment, Δ t represents the epoch t the absolute difference between the code deviation value at the moment and the average value within the historical continuous arc segment.

[0042] A GNSS elastic code bias calculation system applicable to all scenarios provided by the present invention analyzes the change of code bias state by means of filtering epoch-by-epoch estimation. With the characteristics of traditional code bias products as a reference, it improves the adaptability of the code bias, enabling the code bias product to develop from a single low-time-resolution mode to a self-adjusting and self-improving mode of dynamic adaptive time-resolution adjustment, and having characteristics such as high efficiency, high precision, high integrity, high continuity, and high availability, thus having the ability to cope with different scenarios.

[0043] The above is only the specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art within the technical scope disclosed by the present invention can easily think of changes or substitutions, which should all be covered within the protection scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.

[0044] Each embodiment in this specification is described in a progressive manner. The key point of each embodiment is to illustrate the differences from other embodiments. The same or similar parts among the embodiments can be referred to each other. The embodiments of the present invention are described with reference to the flowcharts and / or block diagrams of methods, terminal devices (systems), and computer program products according to the embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram can be implemented by computer program instructions, and the combination of processes and / or blocks in the flowchart and / or block diagram can also be implemented by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing terminal devices to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing terminal devices generate a device for realizing the functions specified in Figure 1 one process or multiple processes and / or blocks Figure 1 one block or multiple blocks. These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing terminal device to work in a specific manner, so that the instructions stored in the computer-readable memory generate a manufactured product including an instruction device, and the instruction device realizes the functions specified in Figure 1 one process or multiple processes and / or blocks Figure 1 one block or multiple blocks. These computer program instructions can also be loaded onto a computer or other programmable data processing terminal device, so that a series of operation steps are executed on the computer or other programmable terminal device to generate a computer-implemented process, and thus the instructions executed on the computer or other programmable terminal device provide for realizing the functions specified in Figure 1 one process or multiple processes and / or blocks Figure 1Steps of the functions specified in one or more boxes. Although the preferred embodiments of the embodiments of the present invention have been described, those skilled in the art can make additional changes and modifications to these embodiments once they know the basic creative concept. Therefore, the appended claims are intended to be construed as including the preferred embodiments and all changes and modifications falling within the scope of the embodiments of the present invention. Finally, it should also be noted that in this article, relational terms such as first and second are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the term "comprising", "including" or any other variant thereof is intended to cover non-exclusive inclusion, so that a process, method, article or terminal device comprising a series of elements not only includes those elements, but also includes other elements not expressly listed, or also includes elements inherent to such process, method, article or terminal device. Without further limitation, an element defined by the statement "comprising one..." does not exclude the existence of additional identical elements in the process, method, article or terminal device comprising the said element.

[0045] The above has introduced in detail the methods and devices provided by the present invention. Specific examples are used in this article to elaborate on the principles and implementation manners of the present invention. The description of the above embodiments is only used to help understand the methods and their core ideas of the present invention; at the same time, for those of ordinary skill in the art, according to the ideas of the present invention, there will be changes in the specific implementation manners and application scopes. In summary, the content of this specification should not be construed as a limitation to the present invention.

[0046] In the description of this specification, the description with reference to terms such as "one embodiment", "some embodiments", "example", "specific example", "one specific embodiment" or "some examples" means that the specific features, structures, materials or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic description of the terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any one or more embodiments or examples in a suitable manner.

[0047] Finally, it should be noted that: the above embodiments are only used to illustrate the technical solutions of the present application, and are not intended to limit them; although the present application has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements on some of the technical features; and these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A GNSS elastic code deviation calculation method applicable to all scenarios, characterized in that: include: Step S1: Based on the satellite signal power change of the GNSS monitoring station, analyzing the signal power change amplitude of the monitoring station to determine the state of the satellite signal power; Step S2: constructing high time resolution code bias state equation and observation equation, estimating code bias by epoch based on Kalman filter and improved phase smoothing pseudorange method and ionospheric spherical harmonic function to realize high time resolution code bias estimation of GNSS; Step S3: Calculate the elastic code deviation elasticity factor of the GNSS based on the GNSS signal power state and the GNSS code deviation change amplitude, product accuracy and time series stability; Step S4: Determine the elastic code deviation value of the GNSS according to the state of the satellite signal power, the high time resolution code deviation estimation of the GNSS and the elastic code deviation elasticity factor of the GNSS.

2. The GNSS elastic code deviation calculation method applicable to all scenarios according to claim 1 is characterized in that: Step S1 includes: Step S11: obtaining a time series of the ratio of the GNSS signal carrier power at each frequency point to the noise in a 1 Hz bandwidth; Step S12: establishing a functional relationship between the ratio of the carrier power of the fitting GNSS signal to the noise in the 1 Hz bandwidth and the satellite elevation angle; Step S13: Calculate the mathematical expectation of the ratio of the GNSS signal carrier power to the noise in the 1 Hz bandwidth, and determine whether the ratio of the measured GNSS signal carrier power to the noise in the 1 Hz bandwidth exceeds a certain range of the mathematical expectation; Step S14: determining whether the satellite signal power is in a normal power state or an elastic power state by setting a threshold.

3. The GNSS elastic code deviation calculation method applicable to all scenarios according to claim 2 is characterized in that: In step S12, the functional relationship between the ratio of the carrier power of the fitted GNSS signal to the noise in the 1 Hz bandwidth and the satellite elevation angle is determined according to the following formula: ； In the formula, SW r,j It represents the ratio of the GNSS signal carrier power at the jth frequency point of the monitoring station to the noise in the 1 Hz bandwidth. r Represents the receiver, represents the satellite altitude angle, , , , and represents the model coefficient, Noise is the ratio of the GNSS signal carrier power to the noise in a 1 Hz bandwidth.

4. The GNSS elastic code deviation calculation method applicable to all scenarios according to claim 3 is characterized in that: Step S13 includes: when the satellite power is stable, the measured value of the ratio of the GNSS signal carrier power to the noise in the 1 Hz bandwidth is close to the mathematical expectation, while when the satellite power is elastic, the measured value will exceed the mathematical expectation by a certain range, which is determined according to the following formula: ； in, is the expected range gap value, H 0 is normal power, H 1 is the elastic power.

5. The GNSS elastic code deviation calculation method applicable to all scenarios according to claim 1 is characterized in that: The elastic code deviation elasticity factor is determined according to the following formula: ； In the formula, H Indicates the GNSS signal power status, Represents epoch t The time code deviation variance, Represents epoch t The standard deviation of daily code deviation in the continuous arc segment of time history, Δ t Represents epoch t The absolute difference between the time code deviation value and the average value in the historical continuous arc segment.

6. A GNSS elastic code deviation calculation system suitable for all scenarios, characterized in that: include: GNSS elastic power judgment module: used to analyze the amplitude of the signal power change of the monitoring station based on the satellite signal power change of the GNSS monitoring station to determine the state of the satellite signal power; High time resolution code bias estimation module: used to construct high time resolution code bias state equation and observation equation, and to achieve high time resolution code bias estimation of GNSS based on epoch-by-epoch estimation of code bias by Kalman filtering, improved phase smoothing pseudorange method and ionospheric spherical harmonic function; Elastic code deviation elasticity factor generation module: used to calculate the elastic code deviation elasticity factor of GNSS based on the GNSS signal power state and the GNSS code deviation change amplitude, product accuracy and time series stability; The elastic code deviation value calculation module is used to determine the elastic code deviation value of the GNSS according to the state of the satellite signal power, the high time resolution code deviation estimation of the GNSS and the elastic code deviation elasticity factor of the GNSS.

7. The GNSS elastic code deviation calculation system applicable to all scenarios according to claim 6 is characterized in that: The GNSS elastic power judgment module is used to perform the following steps: Step S11: obtaining a time series of the ratio of the GNSS signal carrier power at each frequency point to the noise in a 1 Hz bandwidth; Step S12: establishing a functional relationship between the ratio of the carrier power of the fitted GNSS signal to the noise in a 1 Hz bandwidth and the satellite elevation angle; Step S13: Calculate the mathematical expectation of the ratio of the GNSS signal carrier power to the noise in the 1 Hz bandwidth, and determine whether the ratio of the measured GNSS signal carrier power to the noise in the 1 Hz bandwidth exceeds a certain range of the mathematical expectation; Step S14: determining whether the satellite signal power is in a normal power state or an elastic power state by setting a threshold.

8. The GNSS elastic code deviation calculation system applicable to all scenarios according to claim 7, characterized in that: In step S12, the functional relationship between the ratio of the carrier power of the fitted GNSS signal to the noise in the 1 Hz bandwidth and the satellite elevation angle is determined according to the following formula: ； In the formula, SW r,j It represents the ratio of the GNSS signal carrier power at the jth frequency point of the monitoring station to the noise in the 1 Hz bandwidth. r Represents the receiver, represents the satellite altitude angle, , , , and represents the model coefficient, Noise represents the ratio of the GNSS signal carrier power to the noise in a 1 Hz bandwidth.

9. The GNSS elastic code deviation calculation system applicable to all scenarios according to claim 8, characterized in that: Step S13 includes: when the satellite power is stable, the measured value of the ratio of the GNSS signal carrier power to the noise in the 1 Hz bandwidth is close to the mathematical expectation, while when the satellite power is elastic, the measured value will exceed the mathematical expectation by a certain range, which is determined according to the following formula: ； in, is the expected range gap value, H 0 is normal power, H 1 is the elastic power.

10. The GNSS elastic code deviation calculation system applicable to all scenarios according to claim 6, characterized in that: The elastic code deviation elasticity factor is determined according to the following formula: ； In the formula, H Indicates the GNSS signal power status, Represents epoch t The time code deviation variance, Represents epoch t The standard deviation of daily code deviation in the continuous arc segment of time history, Δ t Represents epoch t The absolute difference between the time code deviation value and the average value in the historical continuous arc segment.

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