A method for evaluating satellite navigation signal satellite-ground characteristic consistency and related products

Abstract

The application discloses a satellite navigation signal satellite-ground characteristic consistency evaluation method and related products, and belongs to the technical field of satellite navigation. The satellite navigation signal satellite-ground characteristic consistency evaluation method provided by the application specifically comprises the following steps: collecting first signal data samples of a satellite navigation load in a ground test stage and second signal data samples of the satellite navigation load in an on-orbit operation stage; extracting signal characteristic parameters under preset evaluation dimensions from the first signal data samples and the second signal data samples to form first and second characteristic parameter sequences; for each signal characteristic parameter, calculating a linear consistency index LCI, a monotonic consistency index MCI and a generalized consistency index GCI based on the corresponding characteristic parameter sequence; and outputting a satellite-ground characteristic consistency mode corresponding to the signal characteristic parameter based on a preset judgment rule according to the calculated index values. The application realizes quantitative comparison and consistency evaluation of signal characteristics of the navigation load in a whole cycle, and solves the problem of split satellite-ground evaluation data.

Description

Technical Field

[0001] This invention relates to the field of satellite navigation technology, specifically to a method for evaluating the consistency of satellite navigation signal characteristics between the satellite and the ground, and related products. Background Technology

[0002] The service quality of a satellite navigation system fundamentally depends on the accuracy and stability of its space signals. Currently, the evaluation of navigation signal performance mainly relies on two relatively independent phases: ground development and testing, and on-orbit performance monitoring. Ground testing is conducted in a controlled laboratory environment to verify whether the design performance of the navigation payload meets the expected specifications. However, the ground environment cannot fully replicate the complex on-orbit space environment, such as the actual impact of factors like vacuum, radiation, extreme temperature fluctuations, and microgravity effects on payload performance. In contrast, while on-orbit monitoring can directly obtain signal transmission performance under real space conditions, its data is a holistic black box result, making it difficult to effectively trace and quantify the specific impact of a particular environmental factor on signal characteristics, and even more difficult to accurately correlate and compare with the technical state benchmark established by ground testing.

[0003] This disconnect between satellite and ground assessment data has resulted in a core engineering science problem remaining unresolved for a long time: whether the key characteristics of the signals broadcast in orbit by the navigation payload, which has been fully validated on the ground, are consistent with ground test benchmarks, and what are the patterns of change and the range of deviations? This problem is becoming increasingly prominent not only in the development of traditional Global Navigation Satellite System (GNSS) payloads that require high reliability and long lifespan, but also in the development of emerging low-Earth orbit navigation constellations that emphasize low cost and mass production, becoming a technological bottleneck as well.

[0004] Therefore, how to establish a method for evaluating the consistency of navigation signals between the satellite and the ground, and to achieve quantitative comparison of the key characteristics of navigation payload signals throughout the entire lifecycle from ground testing to on-orbit operation, has become a technical problem that urgently needs to be solved by those skilled in the art. Summary of the Invention

[0005] The purpose of this invention is to provide a method for evaluating the consistency of satellite navigation signal characteristics between the satellite and the ground, so as to overcome the problem that the reliability of navigation system services is insufficient due to the inability to obtain closed-loop verification of the effectiveness of ground testing in the prior art.

[0006] The present invention solves the above-mentioned technical problems through the following technical solution: This invention provides a method for evaluating the consistency of satellite navigation signal satellite-to-ground characteristics, comprising the following steps: Collect the first signal data sample of the satellite navigation payload during the ground testing phase, and the second signal data sample during the on-orbit operation phase; From the first signal data sample and the second signal data sample, at least one signal feature parameter under at least one preset evaluation dimension is extracted respectively to form a first feature parameter sequence corresponding to the first signal data sample and a second feature parameter sequence corresponding to the second signal data sample. For each signal feature parameter, based on its corresponding first feature parameter sequence and second feature parameter sequence, calculate the linear consistency index LCI for measuring the degree of linear correlation, the monotonic consistency index MCI for measuring the degree of monotonic correlation, and the generalized consistency index GCI for measuring the degree of arbitrary statistical dependence. Based on the preset judgment rules, the satellite-to-ground characteristic consistency mode corresponding to the signal characteristic parameters is output according to the calculated values ​​of the linear consistency index (LCI), monotonic consistency index (MCI), and generalized consistency index (GCI).

[0007] A further improvement of the present invention is that the preset evaluation dimensions include time-domain waveform, frequency-domain spectrum, correlation function, and observation sequence; Signal characteristic parameters in the time-domain waveform dimension include digital distortion; Signal characteristic parameters in the frequency domain spectral shape dimension include at least one of the following: first-order fitting change of spectral residual, spectral residual jitter, and operating bandwidth. The signal characteristic parameters under the correlation function dimension include at least one of the following: main lobe bandwidth correlation loss, transmit bandwidth correlation loss, main lobe bandwidth S-curve slope deviation, transmit bandwidth S-curve slope deviation, main lobe bandwidth S-curve deviation, transmit bandwidth S-curve deviation, main lobe bandwidth differential receiver ranging deviation, and transmit bandwidth differential receiver ranging deviation. Signal characteristic parameters in the dimension of the observation sequence include code consistency and the stability of code consistency.

[0008] A further improvement of this invention is that the star-ground characteristic consistency mode includes at least a highly linear consistency mode, a monotonic nonlinear dominant mode, a moderately balanced statistical correlation mode, a complex non-monotonic dependency mode, and a weak correlation or independent mode.

[0009] A further improvement of this invention lies in the preset determination rule, which is specifically as follows: The criteria for determining a high linear consistency mode are: the linear consistency index (LCI) is greater than a preset first threshold, which is 80%; The criteria for determining the dominant monotonic nonlinear mode are as follows: the monotonic consistency index MCI is greater than a preset second threshold, and the difference between the monotonic consistency index MCI and the linear consistency index LCI is greater than a preset third threshold; the second threshold is 70%, and the third threshold is 20%. The criteria for determining the moderate equilibrium statistical association pattern are as follows: the linear consistency index (LCI), the monotonic consistency index (MCI), and the generalized consistency index (GCI) are all located within a preset first numerical range, which is 50% to 65%. The criteria for determining complex non-monotonic dependency patterns are as follows: the generalized consistency index (GCI) is greater than the preset fourth threshold, and both the linear consistency index (LCI) and the monotonic consistency index (MCI) are less than the preset fifth threshold; the fourth threshold is 55%, and the fifth threshold is 40%. The criteria for determining weak association or independent patterns are as follows: the linear consistency index (LCI), monotonic consistency index (MCI), and generalized consistency index (GCI) are all less than the preset sixth threshold, which is 35%.

[0010] A further improvement of this invention is that the satellite navigation signal satellite-to-ground characteristic consistency evaluation method further includes the following steps: Engineering recommendations are given based on the determined satellite-to-ground characteristic consistency mode. When the determined satellite-to-ground characteristic consistency mode is a high linear consistency mode, the engineering recommendation is: the results of the ground test phase have high predictive value for the performance of the on-orbit operation phase, and the signal characteristic parameter performance of the on-orbit operation phase can be predicted by linear scaling or offset based on the signal characteristic parameter data of the ground test phase. When the determined satellite-to-ground characteristic consistency mode is a monotonic nonlinear dominant mode, the engineering recommendation is: there is a deterministic and modelable functional relationship between the results of the ground test phase and the data of the on-orbit operation phase. By identifying and establishing a nonlinear correction model, accurate prediction can be achieved. When the identified satellite-ground characteristic consistency mode is a moderate equilibrium statistical correlation mode, the engineering recommendation is: the satellite-ground relationship has basic predictability but there is uncertainty. In engineering applications, the trend should be used to conduct preliminary performance prediction and status monitoring, while design margins should be reserved and consistency limits should be included in the risk management threshold. When the determined satellite-to-ground characteristic consistency mode is a complex non-monotonic dependent mode, the engineering recommendation is: the results of the ground test phase have limitations, and it is necessary to carry out multi-factor coupling tests or on-orbit calibration. When the determined satellite-to-ground characteristic consistency mode is weakly correlated or independent, the engineering recommendation is that the results of the ground testing phase have weak predictive ability for the performance during the on-orbit operation phase. It is necessary to model the signal characteristic parameters and enhance the on-orbit calibration.

[0011] A further improvement of this invention lies in the linear consistency index (LCI), which measures the degree of linear correlation, specifically:

[0012] in, The first feature parameter sequence; The second feature parameter sequence; for The significance level; The Pearson correlation coefficient between the first feature parameter sequence and the second feature parameter sequence is as follows:

[0013] in, The standard deviation of the first characteristic sequence; The standard deviation of the second characteristic sequence; The mean of the first characteristic sequence; The mean of the second characteristic sequence; The covariance between the first feature parameter sequence and the second feature parameter sequence; The monotonic consistency index (MCI) is as follows:

[0014] in, for The significance level; The Spearman rank correlation coefficient is as follows:

[0015] in, It is the i-th subsequence in the first feature parameter sequence; for Rank in the first feature parameter sequence; It is the i-th subsequence in the second feature parameter sequence; The rank median of the first feature parameter sequence; for Rank in the second feature parameter sequence; The rank median of the second feature parameter sequence; The total number of subsequences in the first feature parameter sequence; The Generalized Consistency Index (GCI) is as follows:

[0016] in, This is the joint probability distribution of the first feature parameter sequence and the second feature parameter sequence; The marginal probability distribution of the first feature parameter sequence; The marginal probability distribution of the second feature parameter sequence.

[0017] The present invention also provides a satellite navigation signal satellite-to-ground characteristic consistency evaluation system, comprising: The first module is used to collect first signal data samples of the satellite navigation payload during the ground testing phase and second signal data samples during the on-orbit operation phase. The second module is used to extract at least one signal feature parameter under at least one preset evaluation dimension from the first signal data sample and the second signal data sample, respectively, to form a first feature parameter sequence corresponding to the first signal data sample and a second feature parameter sequence corresponding to the second signal data sample. The third module is used to calculate, for each signal feature parameter, the linear consistency index LCI (to measure the degree of linear correlation), the monotonic consistency index MCI (to measure the degree of monotonic correlation), and the generalized consistency index GCI (to measure the degree of arbitrary statistical dependence), based on the corresponding first feature parameter sequence and second feature parameter sequence. The fourth module is used to output the satellite-to-ground characteristic consistency mode corresponding to the signal characteristic parameters based on the preset judgment rules and the calculated values ​​of the linear consistency index (LCI), monotonic consistency index (MCI), and generalized consistency index (GCI).

[0018] The present invention also provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps of the satellite navigation signal satellite-to-ground characteristic consistency evaluation method described above.

[0019] The present invention also provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the steps of the satellite navigation signal satellite-to-ground characteristic consistency evaluation method described above.

[0020] The present invention also provides a computer program product, including a computer program that, when executed by a processor, implements the steps of the satellite navigation signal satellite-to-ground characteristic consistency evaluation method described above.

[0021] Compared with the prior art, the positive and progressive effects of the present invention are as follows: The satellite navigation signal-to-ground characteristic consistency evaluation method provided by this invention collects signal data samples during the ground testing and on-orbit operation phases, extracts multi-dimensional signal feature parameters to form a feature sequence, and then calculates the linear consistency index (LCI), monotonic consistency index (MCI), and generalized consistency index (GCI). This comprehensively quantifies the correlation between satellite and ground signal characteristics from three dimensions: linearity, monotonicity, and generalized statistical dependence. It overcomes the bottleneck of fragmented satellite-to-ground evaluation data and difficulty in quantitative comparison in existing technologies. It can accurately identify various consistency modes, such as high linear consistency, monotonic nonlinear dominance, moderate balanced statistical correlation, complex non-monotonic dependence, and weak correlation or independence, providing accurate quantitative basis for the development, testing, and on-orbit maintenance of navigation payloads. Simultaneously, it provides targeted engineering suggestions based on the determined consistency modes, effectively guiding the optimization of ground testing schemes and the prediction of on-orbit performance, thereby improving the service quality assurance capability of the navigation system. Attached Figure Description

[0022] The accompanying drawings are provided to further understand the invention and constitute a part of this invention. The illustrative embodiments of the invention and their descriptions are used to explain the invention and do not constitute an improper limitation of the invention.

[0023] Figure 1 This is a flowchart illustrating the satellite navigation signal-to-ground characteristic consistency evaluation method of the present invention.

[0024] Figure 2 A comparison diagram of digital distortion satellite-to-ground characteristic parameter sequences; Figure 3 A comparison chart of the satellite-ground characteristic parameter sequences for the slope deviation at the zero-crossing point of the S-curve; Figure 4 A comparison chart of the satellite-to-ground characteristic parameter sequences for the zero-crossing deviation of the S-curve; Figure 5 A comparison chart of satellite-to-ground characteristic parameter sequences for differential receiver ranging bias; Figure 6 A comparison chart of satellite-to-ground characteristic parameter sequences showing the standard deviation of code consistency; Figure 7 This is a schematic diagram of the comprehensive evaluation framework for the consistency of satellite and ground features. Detailed Implementation

[0025] 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.

[0026] In the description of this invention, it should be understood that the terms "comprising" and "including" indicate the presence of the described features, integrals, steps, operations, elements and / or components, but do not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components and / or collections thereof.

[0027] It should also be understood that the terminology used in this specification is for the purpose of describing particular embodiments only and is not intended to limit the invention. As used in this specification and the appended claims, the singular forms “a,” “an,” and “the” are intended to include the plural forms unless the context clearly indicates otherwise.

[0028] It should be understood that although terms such as first, second, third, etc., may be used in the embodiments of the present invention to describe the preset range, these preset ranges should not be limited to these terms. These terms are only used to distinguish the preset ranges from one another. For example, without departing from the scope of the embodiments of the present invention, the first preset range may also be referred to as the second preset range, and similarly, the second preset range may also be referred to as the first preset range.

[0029] Depending on the context, the word "if" as used here can be interpreted as "when," "when," "in response to determination," or "in response to detection." Similarly, depending on the context, the phrase "if determination" or "if detection (of the stated condition or event)" can be interpreted as "when determination," "in response to determination," "when detection (of the stated condition or event)," or "in response to detection (of the stated condition or event)."

[0030] The present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments. This is an explanation of the present invention and not a limitation thereof.

[0031] Currently, due to the lack of systematic evaluation methods, the effectiveness of ground tests cannot be verified in a closed loop, and the prediction of on-orbit performance and the diagnosis of anomalies lack reliable basis, which may ultimately affect the reliability of navigation system services.

[0032] Therefore, this application provides a method for evaluating the consistency of satellite navigation signal characteristics between the satellite and the ground, comprising the following steps: See Figure 1 The system collects first signal data samples of the satellite navigation payload during the ground testing phase and second signal data samples during the on-orbit operation phase. From the first signal data sample and the second signal data sample, at least one signal feature parameter under at least one preset evaluation dimension is extracted respectively to form a first feature parameter sequence corresponding to the first signal data sample and a second feature parameter sequence corresponding to the second signal data sample. For each signal feature parameter, based on its corresponding first feature parameter sequence and second feature parameter sequence, calculate the linear consistency index LCI for measuring the degree of linear correlation, the monotonic consistency index MCI for measuring the degree of monotonic correlation, and the generalized consistency index GCI for measuring the degree of arbitrary statistical dependence. Based on the preset judgment rules, the satellite-to-ground characteristic consistency mode corresponding to the signal characteristic parameters is output according to the calculated values ​​of the linear consistency index (LCI), monotonic consistency index (MCI), and generalized consistency index (GCI).

[0033] The satellite navigation signal-to-ground characteristic consistency evaluation method provided by this invention collects signal data samples during the ground testing and on-orbit operation phases, extracts multi-dimensional signal feature parameters to form a feature sequence, and then calculates the Linear Consistency Index (LCI), Monotonic Consistency Index (MCI), and Generalized Consistency Index (GCI), comprehensively quantifying the correlation between satellite and ground signal characteristics from three dimensions: linearity, monotonicity, and generalized statistical dependence. This technical solution overcomes the bottleneck of fragmented satellite-to-ground evaluation data and difficulty in quantitative comparison in existing technologies. It can accurately identify various consistency modes, such as high linear consistency, monotonic nonlinear dominance, moderate balanced statistical correlation, complex non-monotonic dependence, and weak correlation or independence, providing accurate quantitative basis for the development, testing, and on-orbit maintenance of navigation payloads. Simultaneously, it provides targeted engineering suggestions based on the determined consistency modes, effectively guiding the optimization of ground testing schemes and the prediction of on-orbit performance, significantly improving the service quality assurance capability of navigation systems.

[0034] The first signal data sample refers to the raw signal data or observation data obtained before satellite launch, in a controlled laboratory environment on the ground, using specialized testing equipment to test the navigation payload. The environmental conditions (such as temperature and vacuum) at this stage are artificially set to verify the payload's design specifications. The second signal data sample refers to the actual broadcast signal data collected after the satellite enters orbit, under real space conditions (such as radiation, microgravity, and extreme temperature fluctuations), through ground monitoring stations or onboard monitoring equipment. It should be understood that, to ensure the validity of subsequent comparisons, the first and second signal data samples should have temporal correspondence or cover the same test conditions, such as both covering the payload's operating frequency range or specific modulation scheme. The sample size should meet statistical requirements to ensure data representativeness.

[0035] The raw signal data samples are massive and contain noise, making direct comparison difficult to capture key characteristics. Therefore, feature extraction is required from the raw data based on pre-defined evaluation dimensions (such as time domain, frequency domain, etc.). Each signal feature parameter represents the signal's performance in a specific physical dimension, such as power, phase noise, or chip waveform distortion. The extraction process can be for a single value from a single test or for a time series formed by continuous monitoring. A series of feature parameters extracted during the ground testing phase are arranged in chronological or operational order to form the first feature parameter sequence; similarly, the feature parameters extracted during the on-orbit operation phase form the second feature parameter sequence. These two sequences are logically one-to-one corresponding, forming the mathematical basis for subsequent consistency calculations.

[0036] This method constructs a three-level index system: The Linear Consistency Index (LCI), based on the Pearson correlation coefficient, is used to quantify the degree of linear fit between two sets of sequences. A high LCI value indicates a simple linear proportional relationship between ground test data and on-orbit data, meaning that ground data can be directly used to predict on-orbit performance through linear scaling.

[0037] The Monotonic Consistency Index (MCI) is based on the Spearman rank correlation coefficient principle and is used to measure the monotonicity of two sets of sequences. Even if the relationship is not linear, a high MCI value indicates that an increase (or decrease) in one variable is accompanied by a definite change in the other. This can capture non-linear deterministic relationships, such as exponential or logarithmic growth.

[0038] The Generalized Consistency Index (GCI), based on statistical principles such as mutual information, measures the degree of statistical dependence between two sets of sequences in any form, including non-monotonic nonlinear relationships. When both the LCI and MCI values ​​are low, but the GCI value is high, it indicates that there is a complex nonlinear coupling relationship between the star-ground data, which, although difficult to describe with a simple function, is by no means independent.

[0039] By combining these three indices, we can comprehensively characterize the correlation between satellite-to-ground signal characteristics, from simple to complex and from linear to nonlinear, thus avoiding the one-sidedness of evaluation by a single index.

[0040] Specifically, the preset evaluation dimensions include time-domain waveform, frequency-domain spectrum, correlation function, and observation sequence; Signal characteristic parameters in the time-domain waveform dimension include digital distortion; Signal characteristic parameters in the frequency domain spectral shape dimension include at least one of the following: first-order fitting change of spectral residual, spectral residual jitter, and operating bandwidth. The signal characteristic parameters under the correlation function dimension include at least one of the following: main lobe bandwidth correlation loss, transmit bandwidth correlation loss, main lobe bandwidth S-curve slope deviation, transmit bandwidth S-curve slope deviation, main lobe bandwidth S-curve deviation, transmit bandwidth S-curve deviation, main lobe bandwidth differential receiver ranging deviation, and transmit bandwidth differential receiver ranging deviation. Signal characteristic parameters in the dimension of the observation sequence include code consistency and the stability of code consistency.

[0041] The time-domain waveform dimension primarily focuses on the physical changes of the signal along the time axis. In this dimension, signal characteristic parameters include digital distortion (DDistortion). DDistortion quantifies the degree of waveform distortion caused by digital circuit processing, filter non-ideal characteristics, or amplifier nonlinearity during the generation, amplification, and transmission of navigation signals. For example, by calculating the deviation time between the actual waveform and the ideal waveform at zero-crossing points or specific levels, the potential loss of signal ranging accuracy can be assessed. During ground testing, DDistortion is typically measured under controlled temperature and humidity conditions; however, during on-orbit operation, it may drift due to space radiation or device aging. By comparing the satellite-to-ground sequence of this parameter, the consistency of the payload hardware status can be effectively evaluated.

[0042] The frequency domain spectral shape dimension primarily focuses on the energy distribution and occupancy of the signal in the frequency domain. In this dimension, signal characteristic parameters include at least one of the following: first-order fit variation of the spectral residual, spectral residual jitter, and operating bandwidth. The first-order fit variation of the spectral residual characterizes the deviation trend between the actual signal spectrum and the theoretical spectral template, reflecting whether the frequency response characteristics of the transmission channel have changed. Spectral residual jitter measures the severity of subtle spectral fluctuations and is closely related to phase noise or spurious interference. The operating bandwidth directly reflects the effective spectral resource occupancy of the signal.

[0043] The correlation function dimension is a core dimension for evaluating the ranging performance of navigation signals, directly affecting the user's positioning accuracy. Within this dimension, signal characteristic parameters include at least one of the following: main lobe bandwidth correlation loss, transmit bandwidth correlation loss, main lobe bandwidth S-curve slope deviation, transmit bandwidth S-curve slope deviation, main lobe bandwidth S-curve deviation, transmit bandwidth S-curve deviation, main lobe bandwidth differential receiver ranging deviation, and transmit bandwidth differential receiver ranging deviation. These parameters characterize the shape of the correlation function from different perspectives. For example, the correlation loss parameter directly reflects the signal power utilization rate; the S-curve slope deviation is closely related to the gain of the code tracking loop, directly affecting the ranging error; and the differential receiver ranging deviation simulates the ranging results of an actual receiver, possessing direct engineering reference value. By extracting these parameters, it is possible to deeply analyze the consistency of satellite-to-ground signals at the ranging mechanism level.

[0044] The observation sequence dimension focuses on the quality of the raw observation data output after the signal has been processed by the receiver. In this dimension, signal characteristic parameters include code consistency and code consistency stability. Code consistency refers to the degree of synchronization between the ranging code phase and the carrier phase, which is a key indicator for measuring signal quality. Its abnormality usually indicates delay variations or group delay fluctuations in the transmission channel. The stability of code consistency further examines how this indicator fluctuates with time or environmental changes. Since the observation sequence comes directly from the receiver output, the characteristic parameters in this dimension better reflect the overall performance of the signal in the actual propagation and reception process.

[0045] By extracting feature parameters across these four dimensions, the abstract signal quality assessment is transformed into a concrete, quantifiable sequence of multidimensional parameters. This multidimensional feature extraction method not only captures subtle differences that are difficult to detect in a single dimension, but also improves the robustness of the consistency assessment results through cross-validation between parameters of different dimensions. For example, when the consistency of time-domain waveform parameters is high, but the consistency of correlation function parameters is low, it may indicate differences in the receiver processing algorithm rather than performance drift of the payload itself, thus providing a more accurate basis for subsequent engineering recommendations.

[0046] Specifically, the star-ground characteristic consistency models include at least the highly linear consistency model, the monotonic nonlinear dominant model, the moderately balanced statistical correlation model, the complex non-monotonic dependency model, and the weak correlation or independence model.

[0047] Specifically, the preset judgment rules are as follows: The criteria for determining a high linear consistency mode are: the linear consistency index (LCI) is greater than a preset first threshold, which is 80%; The criteria for determining the dominant monotonic nonlinear mode are as follows: the monotonic consistency index MCI is greater than a preset second threshold, and the difference between the monotonic consistency index MCI and the linear consistency index LCI is greater than a preset third threshold; the second threshold is 70%, and the third threshold is 20%. The criteria for determining the moderate equilibrium statistical association pattern are as follows: the linear consistency index (LCI), the monotonic consistency index (MCI), and the generalized consistency index (GCI) are all located within a preset first numerical range, which is 50% to 65%. The criteria for determining complex non-monotonic dependency patterns are as follows: the generalized consistency index (GCI) is greater than the preset fourth threshold, and both the linear consistency index (LCI) and the monotonic consistency index (MCI) are less than the preset fifth threshold; the fourth threshold is 55%, and the fifth threshold is 40%. The criteria for determining weak association or independent patterns are as follows: the linear consistency index (LCI), monotonic consistency index (MCI), and generalized consistency index (GCI) are all less than the preset sixth threshold, which is 35%.

[0048] By dividing numerical intervals and making logical judgments, abstract statistical indices are transformed into intuitive engineering models, achieving a precise mapping from quantitative calculations to qualitative decisions. This provides standardized criteria for generating targeted engineering recommendations. It should be understood that the aforementioned thresholds (such as 80%, 70%, 20%, etc.) are statistical empirical values ​​based on a large amount of measured data from navigation payloads, covering the vast majority of normal and abnormal operating conditions. However, in specific applications, the thresholds can be fine-tuned according to the characteristics of specific payload models to further improve the accuracy of the judgments.

[0049] Specifically, the satellite navigation signal-to-ground characteristic consistency assessment method also includes the following steps: Engineering recommendations are given based on the determined satellite-to-ground characteristic consistency mode. When the determined satellite-to-ground characteristic consistency mode is a high linear consistency mode, the engineering recommendation is: the results of the ground test phase have high predictive value for the performance of the on-orbit operation phase, and the signal characteristic parameter performance of the on-orbit operation phase can be predicted by linear scaling or offset based on the signal characteristic parameter data of the ground test phase. When the determined satellite-to-ground characteristic consistency mode is a monotonic nonlinear dominant mode, the engineering recommendation is: there is a deterministic and modelable functional relationship between the results of the ground test phase and the data of the on-orbit operation phase. By identifying and establishing a nonlinear correction model, accurate prediction can be achieved. When the identified satellite-ground characteristic consistency mode is a moderate equilibrium statistical correlation mode, the engineering recommendation is: the satellite-ground relationship has basic predictability but there is uncertainty. In engineering applications, the trend should be used to conduct preliminary performance prediction and status monitoring, while design margins should be reserved and consistency limits should be included in the risk management threshold. When the determined satellite-to-ground characteristic consistency mode is a complex non-monotonic dependent mode, the engineering recommendation is: the results of the ground test phase have limitations, and it is necessary to carry out multi-factor coupling tests or on-orbit calibration. When the determined satellite-to-ground characteristic consistency mode is weakly correlated or independent, the engineering recommendation is that the results of the ground testing phase have weak predictive ability for the performance during the on-orbit operation phase. It is necessary to model the signal characteristic parameters and enhance the on-orbit calibration.

[0050] By providing differentiated engineering recommendations for different consistency modes, a complete evaluation-judgment-decision closed-loop system was constructed. This system not only solves the problem of how consistent the system is, but also addresses the engineering challenge of what to do if the consistency is poor, thereby enhancing the practical value of the technical solution in the development and maintenance of actual satellite navigation payloads.

[0051] Specifically, the Linear Consistency Index (LCI), which measures the degree of linear correlation, is as follows:

[0052] in, The first feature parameter sequence; The second feature parameter sequence; for The significance level; The Pearson correlation coefficient between the first feature parameter sequence and the second feature parameter sequence is as follows:

[0053] in, The standard deviation of the first characteristic sequence; The standard deviation of the second characteristic sequence; The mean of the first characteristic sequence; The mean of the second characteristic sequence; The covariance between the first feature parameter sequence and the second feature parameter sequence; The monotonic consistency index (MCI) is as follows:

[0054] in, for The significance level; The Spearman rank correlation coefficient is as follows:

[0055] in, It is the i-th subsequence in the first feature parameter sequence; for Rank in the first feature parameter sequence; It is the i-th subsequence in the second feature parameter sequence; The rank median of the first feature parameter sequence; for Rank in the second feature parameter sequence; The rank median of the second feature parameter sequence; The total number of subsequences in the first feature parameter sequence; The Generalized Consistency Index (GCI) is as follows:

[0056] in, This is the joint probability distribution of the first feature parameter sequence and the second feature parameter sequence; The marginal probability distribution of the first feature parameter sequence; The marginal probability distribution of the second feature parameter sequence.

[0057] The present invention also provides a satellite navigation signal satellite-to-ground characteristic consistency evaluation system, comprising: The first module is used to collect first signal data samples of the satellite navigation payload during the ground testing phase and second signal data samples during the on-orbit operation phase. The second module is used to extract at least one signal feature parameter under at least one preset evaluation dimension from the first signal data sample and the second signal data sample, respectively, to form a first feature parameter sequence corresponding to the first signal data sample and a second feature parameter sequence corresponding to the second signal data sample. The third module is used to calculate, for each signal feature parameter, the linear consistency index LCI (to measure the degree of linear correlation), the monotonic consistency index MCI (to measure the degree of monotonic correlation), and the generalized consistency index GCI (to measure the degree of arbitrary statistical dependence), based on the corresponding first feature parameter sequence and second feature parameter sequence. The fourth module is used to output the satellite-to-ground characteristic consistency mode corresponding to the signal characteristic parameters based on the preset judgment rules and the calculated values ​​of the linear consistency index (LCI), monotonic consistency index (MCI), and generalized consistency index (GCI).

[0058] In a specific embodiment of the present invention, in order to conduct a satellite-to-ground characteristic consistency assessment, data samples from both ground testing and on-orbit testing are collected for the same navigation payload or navigation payloads of the same model and batch. Ground testing data is collected before satellite launch and tested via a wired connection at the antenna front end, denoted as Data A satellite sample. On-orbit testing data is acquired after satellite launch using space signal data obtained through a high-gain antenna monitoring station, denoted as Data B satellite sample.

[0059] After acquiring the A and B phase data, features were extracted from the data. A multi-dimensional quantitative evaluation and analysis system was constructed based on the key characteristics of satellite navigation signals. This system aims to systematically diagnose the quality of navigation signals, extracting physically meaningful and engineering-valued feature parameters from dimensions such as time-domain waveform, spectral shape, correlation function, and observation sequence. It provides an observational benchmark for satellite-to-ground consistency comparison. The feature parameters of the multi-dimensional index evaluation system are shown in Table 1.

[0060] Table 1. Multidimensional Indicator Evaluation System for Satellite Navigation Signals

[0061] Since the signal characteristic parameter estimation methods of the above evaluation system are mature methods, they will not be elaborated further.

[0062] After feature extraction is completed for data samples A and B, a satellite-to-ground dual-stage signal feature consistency assessment is performed for each quantized feature parameter.

[0063] The sequence of characteristic parameters involved in the consistency assessment is as follows:

[0064]

[0065] in, This represents a characteristic parameter of satellite k in stage A. This represents a characteristic parameter of satellite k in stage B. The number of satellites participating in the conformity assessment.

[0066] The sequence of various characteristic parameters of satellite samples from data A and 25 satellite samples from data B were compared. Figures 2-6 These are comparative diagrams of characteristic sequences during the ground testing and on-orbit operation phases, respectively, for digital distortion, S-curve zero-crossing slope deviation, S-curve zero-crossing deviation, differential receiver ranging deviation, and code consistency standard deviation.

[0067] It can be seen that it is difficult to quantitatively assess the consistency level of signal characteristics between two stages simply by comparing the sequence of feature parameters. Since there are differences in satellite testing environment and transmission conditions between data satellite samples A and B, this invention measures the correlation of satellite-to-ground signal characteristics to gain a deeper understanding of the physical root causes of these differences. This invention defines three progressively deeper consistency indices: Linear Consistency Index (LCI), Monotonic Consistency Index (MCI), and Generalized Consistency Index (GCI).

[0068] The Pearson correlation coefficient is suitable for parameter sequences that are continuous and stable, but it is greatly affected by outliers. It is used to measure the strength and direction of the linear correlation between two variables, and is defined as the ratio of the product of the covariance of the two variables to their respective standard deviations.

[0069] The Spearman rank correlation coefficient is a nonparametric statistical method used to measure the strength of a monotonic relationship between two variables. It does not assume that the data follows a specific distribution and is insensitive to outliers. The correlation is calculated using the rank of the variables rather than their original values.

[0070] The mutual information method is used for generalized consistency assessment to detect any form of statistical dependence between satellite and ground features. Mutual information is based on Shannon information theory and measures the amount of information shared between two variables. It can capture any form of statistical dependence (linear, nonlinear, non-monotonic) and give a metric value in bits. In the specific calculation, continuous data needs to be discretized to estimate the probability distribution.

[0071] A hierarchical satellite-to-ground integrated consistency assessment framework is proposed based on three consistency indices: LCI, MCI, and GCI. For example... Figure 7As shown, this hierarchical framework comprehensively uses Pearson correlation coefficient, Spearman rank correlation coefficient and mutual information method to make a comprehensive judgment on consistency patterns. It can systematically analyze the consistency structure of satellite and ground data and enhance the robustness and interpretability of consistency assessment results.

[0072] First, the ideal linear consistency is tested using LCI; when LCI>80%, it is defined as Mode A: high linear consistency, indicating that ground tests can well predict on-orbit performance.

[0073] If the Mode A test fails, then the LCI and MCI tests are used to determine if a modelable monotonic trend exists; when MCI > 70% and MCI - LCI > 20%, it is defined as Mode B: Monotonic Nonlinear Dominance (MCI) The LCI (Limited Interchange Function) indicates that there is a modelable nonlinear transformation relationship in the two-stage "space-to-ground" signal characteristics. Once this nonlinear function f is identified and modeled, accurate predictions from the ground to on-orbit transmission can still be achieved.

[0074] If the tests for Pattern A and Pattern B are not passed, then LCI, MCI, and GCI are used to determine whether any meaningful deep correlations still exist between the data.

[0075] When 50% < three indicators < 65%, it is defined as Mode C: Moderate equilibrium statistical correlation, indicating that the relationship between the planet and the Earth has basic predictability, but the prediction has significant uncertainty.

[0076] When GCI > 55% & LCI < 40% & MCI < 40%, it is defined as Mode D: Complex Non-Monotonic Dependence, indicating that this is the mode that requires the most vigilance and in-depth analysis, and further research into its physical mechanism is needed.

[0077] When all three indicators are weak, all below 35%, it is defined as Mode E: weak correlation or independence, indicating that the ground test has a very weak ability to predict the on-orbit performance of this parameter, and the effectiveness of the ground test must be re-examined.

[0078] Based on the constructed multi-dimensional index evaluation system, the characteristic parameters of data sample A from the ground test phase of 25 satellites and the characteristic parameters of data sample B from the on-orbit test phase were selected to carry out a satellite-ground integrated consistency analysis. The analysis results of key signal characteristic parameters such as digital distortion, spectral residual, operating bandwidth, code carrier consistency, correlation loss, S-curve zero-crossing slope deviation, S-curve zero-crossing deviation, and differential receiver ranging deviation are shown in Table 2.

[0079] Table 2. Quantitative Analysis of Overall Consistency

[0080] Based on the comprehensive consistency feature analysis results, the following physical mechanism can be derived by analyzing the satellite-ground feature consistency mode.

[0081] The parameters such as digital distortion, first-order fitting change of spectral residual, working bandwidth, correlation loss, and deviation of the S-curve slope of the transmit bandwidth are strongly linearly correlated between satellite and ground, belonging to a highly linearly consistent mode.

[0082] Engineering recommendation: Ground test results have extremely high predictive value for on-orbit performance. On-orbit performance can be predicted with high accuracy based on ground data through simple linear scaling or offsetting.

[0083] The slope deviation of the S-curve under the main lobe bandwidth belongs to the moderate equilibrium correlation mode.

[0084] Engineering Recommendations: While satellite-to-ground relationships offer basic predictability, significant uncertainties exist in these predictions. In engineering applications, trends should be utilized for preliminary performance forecasting and condition monitoring, but sufficient design margins must be maintained, and consistency limits should be incorporated into risk management thresholds to avoid over-reliance on precise point-to-point predictions.

[0085] The parameters such as the change in spectral residual, the zero-crossing deviation of the S-curve, and the mean of code carrier consistency exhibit weak linearity but moderate generalized dependence, belonging to a complex dependence mode.

[0086] Engineering Recommendation: This is the pattern that requires the most vigilance and in-depth analysis. It demonstrates the fundamental limitations of ground-based single-factor, static testing methods. Multi-factor coupled experiments or on-orbit calibration must be conducted to reveal the true physical mechanisms.

[0087] The correlation indicators for code consistency and stability are all low, indicating a weak correlation / independence pattern.

[0088] Engineering recommendation: Ground tests have a weak predictive ability for the on-orbit performance of this parameter. The effectiveness of ground tests needs to be re-evaluated, which may require adding new test items, modeling the data for this indicator, and strengthening on-orbit calibration.

[0089] The satellite navigation signal-to-ground characteristic consistency evaluation method proposed in this invention does not depend on a specific orbit (GEO / MEO / IGSO / LEO) or a specific signal system (BPSK / BOC / MCSK, etc.), and can be widely applied to signal quality evaluation of various satellite navigation systems. Through joint analysis of multi-dimensional indicators at the physical layer and measurement layer, combined with three-level statistical correlation tests, it can go from phenomenon to essence, not only judging whether there is consistency, but also preliminarily diagnosing where the inconsistency is and why (such as the existence of uncontrolled environmental variables, environmentally sensitive coupling, etc.). The evaluation results can be directly transformed into engineering improvement inputs for: optimizing ground test outlines and stress screening conditions, calibrating on-orbit performance prediction models, guiding on-orbit anomaly root cause analysis, supporting backup payload switching decisions, and providing feedback for next-generation payload design, comprehensively improving the reliability, predictability, and life management level of navigation systems.

[0090] Based on the same inventive concept, this application provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the steps of a satellite navigation signal-to-ground characteristic consistency evaluation method. The memory may include main memory, such as high-speed random access memory, or it may also include non-volatile memory, such as at least one disk storage device. The processor, network interface, and memory are interconnected via an internal bus, which may be an industry-standard architecture bus, a peripheral component interconnection standard bus, an extended industry-standard architecture bus, etc. The bus can be divided into an address bus, a data bus, a control bus, etc. The memory is used to store the program; specifically, the program may include program code, which includes computer operation instructions. The memory may include main memory and non-volatile memory, and provides instructions and data to the processor.

[0091] Based on the same inventive concept, embodiments of this application provide a computer-readable storage medium storing a computer program. When executed by a processor, the computer program implements the steps of the satellite navigation signal satellite-to-ground characteristic consistency evaluation method. Specifically, the computer-readable storage medium includes, but is not limited to, volatile memory and / or non-volatile memory. The volatile memory may include RAM (Random Access Memory) and / or cache memory, etc. The non-volatile memory may include ROM (Read-Only Memory), hard disk, flash memory, optical disk, magnetic disk, etc.

[0092] Based on the same inventive concept, this application provides a computer program product, which includes a computer program stored on a computer-readable storage medium. The computer program includes program instructions, which, when executed by a computer device, cause the computer device to perform the steps of the above-described satellite navigation signal satellite-to-ground characteristic consistency evaluation method.

[0093] Those skilled in the art will understand that embodiments of the present invention can be provided as methods or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM (Compact Disc Read-Only Memory), optical storage, etc.) containing computer-usable program code.

[0094] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, as well as combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer apparatus or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0095] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer device or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0096] These computer program instructions may also be loaded onto a computer device or other programmable data processing equipment to cause a series of operational steps to be performed on the computer device or other programmable equipment to produce a process implemented by the computer device, thereby providing instructions that execute on the computer device or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0097] Although preferred embodiments of the invention have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including both the preferred embodiments and all changes and modifications falling within the scope of the invention.

[0098] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. Therefore, if these modifications and variations fall within the scope of the claims of this invention and their equivalents, this invention also intends to include these modifications and variations.

Claims

1. A method for evaluating the consistency of satellite navigation signal satellite-to-ground characteristics, characterized in that, Includes the following steps: Collect the first signal data sample of the satellite navigation payload during the ground testing phase, and the second signal data sample during the on-orbit operation phase; From the first signal data sample and the second signal data sample, at least one signal feature parameter under at least one preset evaluation dimension is extracted respectively to form a first feature parameter sequence corresponding to the first signal data sample and a second feature parameter sequence corresponding to the second signal data sample. For each signal feature parameter, based on its corresponding first feature parameter sequence and second feature parameter sequence, calculate the linear consistency index LCI for measuring the degree of linear correlation, the monotonic consistency index MCI for measuring the degree of monotonic correlation, and the generalized consistency index GCI for measuring the degree of arbitrary statistical dependence. Based on the preset judgment rules, the satellite-to-ground characteristic consistency mode corresponding to the signal characteristic parameters is output according to the calculated values ​​of the linear consistency index (LCI), monotonic consistency index (MCI), and generalized consistency index (GCI).

2. The method for evaluating the consistency of satellite navigation signal satellite-to-ground characteristics according to claim 1, characterized in that, The preset evaluation dimensions include time-domain waveform, frequency-domain spectrum, correlation function, and observation sequence; Signal characteristic parameters in the time-domain waveform dimension include digital distortion; Signal characteristic parameters in the frequency domain spectral shape dimension include at least one of the following: first-order fitting change of spectral residual, spectral residual jitter, and operating bandwidth. The signal characteristic parameters under the correlation function dimension include at least one of the following: main lobe bandwidth correlation loss, transmit bandwidth correlation loss, main lobe bandwidth S-curve slope deviation, transmit bandwidth S-curve slope deviation, main lobe bandwidth S-curve deviation, transmit bandwidth S-curve deviation, main lobe bandwidth differential receiver ranging deviation, and transmit bandwidth differential receiver ranging deviation. Signal characteristic parameters in the dimension of the observation sequence include code consistency and the stability of code consistency.

3. The method for evaluating the consistency of satellite navigation signal satellite-to-ground characteristics according to claim 1, characterized in that, The star-ground characteristic consistency models include at least the highly linear consistency model, the monotonic nonlinear dominant model, the moderately balanced statistical correlation model, the complex non-monotonic dependency model, and the weak correlation or independence model.

4. The method for evaluating the consistency of satellite navigation signal satellite-to-ground characteristics according to claim 3, characterized in that, The preset judgment rules are as follows: The criteria for determining a high linear consistency mode are: the linear consistency index (LCI) is greater than a preset first threshold, which is 80%; The criteria for determining the dominant monotonic nonlinear mode are as follows: the monotonic consistency index MCI is greater than a preset second threshold, and the difference between the monotonic consistency index MCI and the linear consistency index LCI is greater than a preset third threshold; the second threshold is 70%, and the third threshold is 20%. The criteria for determining the moderate equilibrium statistical association pattern are as follows: the linear consistency index (LCI), the monotonic consistency index (MCI), and the generalized consistency index (GCI) are all located within a preset first numerical range, which is 50% to 65%. The criteria for determining complex non-monotonic dependency patterns are as follows: the generalized consistency index (GCI) is greater than the preset fourth threshold, and both the linear consistency index (LCI) and the monotonic consistency index (MCI) are less than the preset fifth threshold; the fourth threshold is 55%, and the fifth threshold is 40%. The criteria for determining weak association or independent patterns are as follows: the linear consistency index (LCI), monotonic consistency index (MCI), and generalized consistency index (GCI) are all less than the preset sixth threshold, which is 35%.

5. The method for evaluating the consistency of satellite navigation signal satellite-to-ground characteristics according to claim 4, characterized in that, The satellite navigation signal satellite-to-ground characteristic consistency assessment method also includes the following steps: Engineering recommendations are given based on the determined satellite-to-ground characteristic consistency mode. When the determined satellite-to-ground characteristic consistency mode is a high linear consistency mode, the engineering recommendation is: the results of the ground test phase have high predictive value for the performance of the on-orbit operation phase, and the signal characteristic parameter performance of the on-orbit operation phase can be predicted by linear scaling or offset based on the signal characteristic parameter data of the ground test phase. When the determined satellite-to-ground characteristic consistency mode is a monotonic nonlinear dominant mode, the engineering recommendation is: there is a deterministic and modelable functional relationship between the results of the ground test phase and the data of the on-orbit operation phase. By identifying and establishing a nonlinear correction model, accurate prediction can be achieved. When the identified satellite-ground characteristic consistency mode is a moderate equilibrium statistical correlation mode, the engineering recommendation is: the satellite-ground relationship has basic predictability but there is uncertainty. In engineering applications, the trend should be used to conduct preliminary performance prediction and status monitoring, while design margins should be reserved and consistency limits should be included in the risk management threshold. When the determined satellite-to-ground characteristic consistency mode is a complex non-monotonic dependent mode, the engineering recommendation is: the results of the ground test phase have limitations, and it is necessary to carry out multi-factor coupling tests or on-orbit calibration. When the determined satellite-to-ground characteristic consistency mode is weakly correlated or independent, the engineering recommendation is that the results of the ground testing phase have weak predictive ability for the performance during the on-orbit operation phase. It is necessary to model the signal characteristic parameters and enhance the on-orbit calibration.

6. The method for evaluating the consistency of satellite navigation signal satellite-to-ground characteristics according to claim 1, characterized in that, The Linear Consistency Index (LCI), which measures the degree of linear correlation, is as follows: in, The first feature parameter sequence; The second feature parameter sequence; for The significance level; The Pearson correlation coefficient between the first feature parameter sequence and the second feature parameter sequence is as follows: in, The standard deviation of the first characteristic sequence; The standard deviation of the second characteristic sequence; The mean of the first characteristic sequence; The mean of the second characteristic sequence; The covariance between the first feature parameter sequence and the second feature parameter sequence; The monotonic consistency index (MCI) is as follows: in, for The significance level; The Spearman rank correlation coefficient is as follows: in, It is the i-th subsequence in the first feature parameter sequence; for Rank in the first feature parameter sequence; It is the i-th subsequence in the second feature parameter sequence; The rank median of the first feature parameter sequence; for Rank in the second feature parameter sequence; The rank median of the second feature parameter sequence; The total number of subsequences in the first feature parameter sequence; The Generalized Consistency Index (GCI) is as follows: in, This is the joint probability distribution of the first feature parameter sequence and the second feature parameter sequence; The marginal probability distribution of the first feature parameter sequence; The marginal probability distribution of the second feature parameter sequence.

7. A satellite navigation signal satellite-to-ground characteristic consistency evaluation system, characterized in that, include: The first module is used to collect first signal data samples of the satellite navigation payload during the ground testing phase and second signal data samples during the on-orbit operation phase. The second module is used to extract at least one signal feature parameter under at least one preset evaluation dimension from the first signal data sample and the second signal data sample, respectively, to form a first feature parameter sequence corresponding to the first signal data sample and a second feature parameter sequence corresponding to the second signal data sample. The third module is used to calculate, for each signal feature parameter, the linear consistency index LCI (to measure the degree of linear correlation), the monotonic consistency index MCI (to measure the degree of monotonic correlation), and the generalized consistency index GCI (to measure the degree of arbitrary statistical dependence), based on the corresponding first feature parameter sequence and second feature parameter sequence. The fourth module is used to output the satellite-to-ground characteristic consistency mode corresponding to the signal characteristic parameters based on the preset judgment rules and the calculated values ​​of the linear consistency index (LCI), monotonic consistency index (MCI), and generalized consistency index (GCI).

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the satellite navigation signal star-to-ground characteristic consistency evaluation method according to any one of claims 1 to 6.

9. 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 steps of the satellite navigation signal star-to-ground characteristic consistency evaluation method according to any one of claims 1 to 6.

10. A computer program product, comprising a computer program, characterized in that, When executed by a processor, the computer program implements the steps of the satellite navigation signal star-to-ground characteristic consistency evaluation method according to any one of claims 1 to 6.

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