Satellite networking routing method and system

Abstract

The application discloses a satellite networking routing method and system, and relates to the technical field of satellite navigation and positioning. The method comprises the following steps: in response to obtaining the elevation angle data of each candidate satellite corresponding to a target device and satellite working condition parameters, first preferred factors of each candidate satellite are determined based on the elevation angle data of each candidate satellite and the satellite working condition parameters; the satellite working condition parameters are used for representing the actual working condition performance of the candidate satellite; and the target satellite of the target device is determined based on the first preferred factors of each candidate satellite; and the target satellite is used for positioning the target device. The application can improve the positioning accuracy of satellite networking routing in an outdoor scene.

Description

Technical Field

[0001] This invention relates to the field of satellite navigation and positioning technology, specifically to a satellite networking routing method and system. Background Technology

[0002] Multiple satellite types, including Low Earth Orbit (LEO), Medium Earth Orbit (MEO), and Geostationary Earth Orbit (GEO), form a satellite network through inter-satellite links and satellite-to-ground links, enabling positioning support for terminal devices in outdoor areas without public terrestrial network coverage. At least four satellites are required for device routing and positioning calculations; accurately selecting suitable satellites can significantly improve the positioning accuracy of outdoor devices.

[0003] Traditional satellite selection schemes rely solely on the satellite's real-time elevation angle data relative to the terminal device to select satellites, directly choosing the top four satellites based on the real-time elevation angle data for route positioning, and relying on only the single dimension of static elevation angle to complete the satellite selection judgment.

[0004] However, the satellites selected by this method have poor adaptability, which ultimately significantly reduces the positioning accuracy of satellite networking routing in outdoor scenarios. Summary of the Invention

[0005] To address the problem of poor satellite compatibility in existing methods, which significantly reduces the positioning accuracy of satellite network routing in outdoor scenarios, this invention provides a satellite network routing method and system that can improve the positioning accuracy of satellite network routing in outdoor scenarios.

[0006] A first aspect of this invention provides a satellite networking routing method, comprising: In response to acquiring the elevation angle data and satellite operating parameters of each candidate satellite corresponding to the target device, the first selection factor of each candidate satellite is determined based on the elevation angle data and satellite operating parameters of each candidate satellite; the satellite operating parameters are used to characterize the actual operating performance of the candidate satellite. Based on the first selection factor of each candidate satellite, the target satellite of the target equipment is determined; the target satellite is used to locate the target equipment.

[0007] Furthermore, this invention proposes that the satellite operating parameters include the following: The first weighted mean of the signal pseudorange; The second weighted average of the carrier signal phase; Carrier-to-noise ratio (CNR) is a measure of the ratio between carrier signal power and noise power spectral density. The absolute value of clock skew; the absolute value of clock skew is the absolute value of the clock skew between the atomic clock of the candidate satellite and the local crystal oscillator of the target device; Target number of tasks; the target number of tasks indicates the number of tasks that need to be processed first.

[0008] Furthermore, this invention also proposes determining a first preference factor for each candidate satellite based on its elevation angle data and operating parameters, including: Based on the elevation angle data of each candidate satellite, the first weighted average and the second weighted average of the target candidate satellite, the second selection factor of the target candidate satellite is determined; the target candidate satellite can be any one of the candidate satellites. Based on the second optimization factor and the carrier-to-noise ratio of each candidate satellite, the third optimization factor for the target candidate satellite is determined. Based on the absolute value of the clock deviation of each candidate satellite and the number of target missions, the deviation of the target candidate satellite's operating condition is determined; the deviation of the operating condition is used to characterize the degree to which the actual operating condition performance of the target candidate satellite deviates from the requirements of the target satellite. Based on the third optimization factor and the deviation of operating conditions, the first optimization factor of the target candidate satellite is determined.

[0009] Furthermore, the present invention also proposes determining a second preference factor for the target candidate satellite based on the elevation angle data of each candidate satellite, the first weighted average of the target candidate satellite, and the second weighted average of the target candidate satellite, including: Based on the elevation angle data of each candidate satellite within the target time range closest to the current time, the low elevation angle tendency factor of the target candidate satellite is determined; the low elevation angle tendency factor is used to characterize the degree of signal defect caused by the elevation angle amplitude of the target candidate satellite. Based on the first weighted average and the second weighted average of the target candidate satellites within the target time range, the dual-frequency matching superiority of the target candidate satellites is determined; the dual-frequency matching superiority is used to characterize the matching performance of the pseudorange and carrier signal phase of the target candidate satellites. Based on the low elevation angle tendency factor and the dual-frequency matching priority, the second preferred factor for the target candidate satellite is determined.

[0010] Furthermore, this invention also proposes determining the low elevation angle tendency factor of the target candidate satellite based on the elevation angle data of each candidate satellite within the target time range closest to the current time, including: The average fitting slope is obtained by averaging the slopes of the fitted lines obtained from fitting the elevation angle data of each candidate satellite within the target time range. The average elevation angle data is obtained by averaging the current elevation angle data of each candidate satellite at the current moment. Based on the slope of the target fitting line of the candidate satellite, the target's current elevation angle data, the average fitting slope, and the average elevation angle data, the low elevation angle tendency factor of the candidate satellite is determined.

[0011] Furthermore, the present invention also proposes determining the dual-frequency matching superiority of the target candidate satellite based on the first weighted average and the second weighted average of the target candidate satellite within the target time range, including: Based on the first weighted average of each target candidate satellite within the target time range and the corresponding second weighted average, multiple dual-frequency difference factors of the target candidate satellite within the target time range are determined. The dual-frequency matching priority of the target candidate satellite is determined based on the standard deviation among multiple dual-frequency difference factors.

[0012] Furthermore, the present invention also proposes a third preferred factor for determining the target candidate satellite based on the second preferred factor and the carrier-to-noise ratio of each candidate satellite, including: The average carrier-to-noise ratio is obtained by averaging the current carrier-to-noise ratio of each candidate satellite at the current moment. Based on the difference between the current carrier-to-noise ratio and the average carrier-to-noise ratio of the target candidate satellite at the current moment, and the second optimization factor, the third optimization factor of the target candidate satellite is determined.

[0013] Furthermore, this invention also proposes determining the deviation of the target candidate satellite's operational performance based on the absolute value of the clock deviation of each candidate satellite and the number of target missions, including: The average absolute value of the current clock deviation of each candidate satellite at the current moment is obtained by averaging the current clock deviation absolute values. The clock drift trend of the target candidate satellite at the current moment is determined based on the difference between the absolute value of the target candidate satellite's current clock deviation and the absolute value of the average clock deviation at the current moment. Based on the maximum number of tasks among all target tasks, the number of target tasks for the candidate satellites, and the clock drift trend, the deviation of the target candidate satellites' operating performance is determined.

[0014] Furthermore, the present invention also proposes determining the target satellite of the target device based on a first preference factor for each candidate satellite, including: Divide any four candidate satellites into a group to obtain multiple candidate satellite groups; Based on the mean of the first preference factor and the mean of the Euclidean distance of each candidate satellite group, the routing application preference of each candidate satellite group is determined; the mean of the Euclidean distance is the result obtained by averaging the Euclidean distance between any two candidate satellites in the candidate satellite group. Each candidate satellite in the candidate satellite group with the highest routing application preference is identified as the target satellite for the target device.

[0015] A second aspect of the present invention provides a satellite networking routing system, comprising: The parameter analysis module is used to respond to the acquisition of elevation angle data and satellite operating parameters of each candidate satellite corresponding to the target device. Based on the elevation angle data and satellite operating parameters of each candidate satellite, the first selection factor of each candidate satellite is determined. The satellite operating parameters are used to characterize the actual operating performance of the candidate satellite. The satellite determination module is used to determine the target satellite of the target equipment based on the first preference factor of each candidate satellite; the target satellite is used to locate the target equipment.

[0016] The present invention has the following beneficial effects: In the satellite networking routing method provided by this invention, elevation angle data of candidate satellites and satellite operating condition parameters characterizing the actual operating status of candidate satellites are collected simultaneously. The elevation angle geometric dimension and the actual operating condition performance dimension of the satellite are combined to jointly calculate the first optimization factor for each candidate satellite. This first optimization factor takes into account both the spatial observation geometry of the satellite relative to the terminal and the current operational reliability of the satellite, and no longer relies solely on elevation angle data for unilateral selection. Subsequently, target satellites are screened based on the first optimization factor obtained by combining the two dimensions. This can eliminate satellites with considerable elevation angles but malfunctioning operating conditions or poor signal quality, and retain satellites with excellent geometric observation conditions, stable operating conditions, and accurate observation data as target satellites. This effectively reduces the positioning calculation deviation caused by satellites with abnormal operating conditions, thereby significantly improving the overall positioning accuracy of satellite networking routing in outdoor scenarios. Attached Figure Description

[0017] Figure 1 This is a flowchart illustrating a satellite networking routing method provided in one embodiment of the present invention; Figure 2 This is a schematic diagram of the structure of a satellite networking routing system provided in one embodiment of the present invention. Detailed Implementation

[0018] In satellite network routing technology, traditional solutions rely solely on real-time elevation angle data of satellites relative to terminal devices for selection, directly choosing the four satellites with the highest real-time elevation angles for routing and positioning. This approach fails to incorporate auxiliary analysis based on the actual operational performance of the satellites, resulting in poor adaptability of the selected satellites and consequently reducing the positioning accuracy of satellite network routing in outdoor scenarios.

[0019] Based on this, the present invention provides a specific embodiment of a satellite networking routing method and system, which can improve the positioning accuracy of satellite networking routing in outdoor scenarios.

[0020] It should be noted that the fraction calculation logic used in the embodiments provided below distinguishes between two scenarios to uniformly avoid the calculation failure problem when the denominator of the fraction is zero: For fractions with a risk of the denominator being zero, a very small non-zero constant is introduced as a compensation term and superimposed on the denominator to eliminate the calculation abnormality caused by the denominator being zero. The dimensions and magnitude of the compensation term set must be consistent with those of the data in the denominator; For fractions where the denominator is always greater than zero and there is no possibility of it being zero, no additional compensation term is added, and the original parameters are used directly to complete the fraction calculation. All calculation formulas in the following text follow this differentiated processing rule, and this processing logic will not be repeated for each fraction.

[0021] like Figure 1 As shown, the present invention provides a schematic flowchart of a satellite networking routing method, which can be applied to electronic devices, including the following steps S110 to S120: S110, in response to obtaining the elevation angle data and satellite operating parameters of each candidate satellite corresponding to the target device, the first selection factor of each candidate satellite is determined based on the elevation angle data and satellite operating parameters of each candidate satellite; the satellite operating parameters are used to characterize the actual operating performance of the candidate satellite. S120, based on the first preference factor of each candidate satellite, determine the target satellite of the target equipment; the target satellite is used to locate the target equipment.

[0022] For ease of understanding, the following explains some key terms in this embodiment: The target device refers to the terminal device that needs to obtain location services. This device can be a handheld terminal, an in-vehicle device, or any Internet of Things (IoT) device that requires satellite network support.

[0023] Candidate satellites refer to a set of potential satellites that are within the field of view of the target equipment at a specific time or with which a communication link can be established. These satellites are the objects of subsequent screening and optimization.

[0024] Elevation angle data refers to the angle of a satellite relative to the horizon of a target device. This data is one of the important indicators for evaluating satellite visibility and signal transmission path quality.

[0025] Satellite operating parameters refer to a series of indicators used to characterize the actual operating status and performance of candidate satellites. These parameters can reflect multiple dimensions such as satellite signal quality, clock stability, and mission load, providing a basis for comprehensively evaluating satellite availability.

[0026] The first selection factor is an indicator used to quantitatively evaluate the overall performance of each candidate satellite, calculated based on elevation angle data and satellite operating parameters. A higher value for this factor generally indicates better overall performance of the candidate satellite.

[0027] Target satellites refer to the satellites that, after screening and optimization, are ultimately selected for locating the target device. Typically, at least four target satellites are needed to achieve accurate positioning.

[0028] Equipment positioning refers to the process of determining the precise location information of a target device on Earth by receiving signals transmitted by the target satellite and combining them with a specific positioning algorithm.

[0029] This embodiment provides a satellite networking routing method aimed at improving the accuracy and reliability of device positioning in outdoor scenarios. This method optimizes candidate satellites by comprehensively considering satellite elevation data and actual operating conditions, thereby determining the target satellite for device positioning.

[0030] First, the method responds to obtaining the elevation angle data and satellite operating parameters of each candidate satellite corresponding to the target device. In practical applications, there are several ways to obtain this data. For example, the target device can periodically send requests to the ground station in its area, and the ground station can calculate and transmit back the elevation angle data of each candidate satellite based on the preset satellite orbit information and the target device's position information. Simultaneously, the ground station can also obtain the real-time satellite operating parameters of each candidate satellite from the satellite operation control center.

[0031] Furthermore, based on the elevation angle data and operating parameters of each candidate satellite, a first-optimization factor is determined for each candidate satellite. This step is one of the core aspects of this method, aiming to comprehensively evaluate the performance of each candidate satellite. Specifically, the first-optimization factor can be determined in several ways. For example, weights can be assigned to the elevation angle data and satellite operating parameters, and then the elevation angle data and operating parameters of each candidate satellite can be normalized and then summed with weights to obtain the first-optimization factor of the candidate satellite. As another implementation method, a multi-dimensional evaluation model can be constructed, taking the elevation angle data and satellite operating parameters as input, and outputting the first-optimization factor of each candidate satellite through table lookup or a preset functional relationship.

[0032] Subsequently, based on the first preference factor of each candidate satellite, the target satellite for the target device is determined. This step aims to select the most suitable satellite for device positioning from multiple candidate satellites. One implementation is to sort all candidate satellites by their first preference factors and then select the satellites with the highest first preference factor values ​​as target satellites. For example, if positioning requires at least four satellites, the top four candidate satellites with the highest first preference factors are directly selected as target satellites. It should be noted that the four satellites mentioned here are merely an exemplary number and do not constitute a limitation on the number of target satellites selected. In practice, the number can be flexibly selected based on positioning accuracy, device computing power, and scenario requirements. For example, if accurate positioning can be achieved with just one satellite, only one target satellite can be selected; alternatively, two, three, five, or any other number of satellites can be selected as target satellites.

[0033] This embodiment simultaneously collects elevation angle data of candidate satellites and satellite operating condition parameters characterizing the actual operating status of the candidate satellites. The elevation angle geometric dimension and the actual operating performance dimension of the satellite are combined to jointly calculate the first optimization factor for each candidate satellite. This first optimization factor takes into account both the satellite's spatial observation geometry relative to the terminal and the satellite's current operational reliability, no longer relying solely on elevation angle data for selection. Subsequent selection of target satellites based on the first optimization factor obtained from both dimensions can eliminate satellites with considerable elevation angles but malfunctioning operating conditions or poor signal quality, retaining satellites with excellent geometric observation conditions, stable operating conditions, and accurate observation data as target satellites. This effectively reduces positioning calculation errors caused by satellites with abnormal operating conditions, thereby significantly improving the overall positioning accuracy of satellite network routing in outdoor scenarios.

[0034] In some embodiments, the present invention further proposes that the satellite operating parameters include the following: The first weighted mean of the signal pseudorange; The second weighted mean of the carrier signal phase (referred to as carrier phase); Carrier-to-noise ratio (CNR) is a measure of the ratio between carrier signal power and noise power spectral density. The absolute value of clock skew; the absolute value of clock skew is the absolute value of the clock skew between the atomic clock of the candidate satellite and the local crystal oscillator of the target device; Target number of tasks; the target number of tasks indicates the number of tasks that need to be processed first.

[0035] In this embodiment, it should be noted that all satellite operating parameters and satellite elevation angle data in this invention are synchronously collected based on the same unified sampling timestamp. The entire satellite screening and optimization factor calculation process is executed based on real-time synchronous sampling. In view of the possible problems of inconsistent data update frequencies and misaligned original acquisition times for various parameters such as elevation angle data and carrier-to-noise ratio, this invention further sets up a time alignment calibration mechanism to avoid the distortion of optimization factor calculation caused by misaligned timestamps of different dimension parameters.

[0036] The first weighted mean of the pseudorange signal is the result of weighted averaging of satellite pseudorange observations at different frequencies at the same time. Its purpose is to evaluate the accuracy and stability of satellite signal range measurement. This weighted mean can be obtained by weighting pseudorange observations at different frequencies (e.g., L1 and L2 bands) to eliminate or reduce the influence of ionospheric errors before averaging.

[0037] The second weighted mean of the carrier signal phase is the result of weighted averaging of carrier signal phase observations at different frequencies at the same unified time. It is used to evaluate the accuracy and continuity of satellite signal phase measurements, which is crucial for high-precision positioning. This weighted mean can be used to weight and combine dual-frequency or multi-frequency carrier signal phase observations to improve the accuracy and reliability of the observations.

[0038] The carrier-to-noise ratio (CNR) is a crucial indicator of satellite signal quality, directly reflecting the strength and anti-interference capability of the received satellite signal. Target equipment can measure and calculate the CNR of each satellite signal in real time (i.e., calculate the ratio between carrier signal power and noise power spectral density) and use it as part of the satellite's operating parameters.

[0039] The absolute value of the clock offset is used to evaluate the stability of the satellite clock and its synchronization with the target device's clock. The smaller the clock offset, the higher the positioning accuracy. This value can be calculated by solving the clock parameters in the navigation message broadcast by the satellite to the target device and combining them with local clock information.

[0040] The number of target tasks reflects the satellite's workload and resource allocation priority, and is used to assess the satellite's available resources and response capabilities. The satellite management system can count and broadcast in real time the number of tasks that each satellite currently needs to prioritize (i.e., the number of high-priority tasks) for reference by ground target equipment.

[0041] As a specific implementation of the time alignment mechanism: the target device pre-sets a global fixed sampling period, and uses the timestamp of this fixed sampling period as a reference; for parameters with an update frequency higher than the global sampling period (such as carrier-to-noise ratio and pseudorange observations), the set of observation data closest to the reference timestamp is selected for calculation; for parameters with an update frequency lower than the global sampling period (such as the number of satellite target missions and elevation angle data), linear interpolation and extrapolation compensation algorithms are used to fit the equivalent parameter values ​​corresponding to the reference timestamp, and the time alignment calibration of all-dimensional parameters is completed to ensure that all data participating in the calculation of the first optimization factor correspond to the satellite state at the same time.

[0042] The present invention refines satellite operating parameters into a first weighted average of signal pseudorange, a second weighted average of carrier signal phase, carrier-to-noise ratio, absolute value of clock offset, and number of target missions, making the evaluation of the actual operating performance of candidate satellites more comprehensive, accurate, and quantitative. These parameters do not exist in isolation but complement and corroborate each other. When these refined satellite operating parameters are combined with satellite elevation data, the system can more comprehensively and accurately calculate the first selection factor for each candidate satellite. This multi-dimensional and refined parameter evaluation mechanism allows for the consideration of not only the satellite's geometric visibility (elevation angle) but also its inherent signal quality, time synchronization performance, and service load when determining the target satellite for a target device, thereby significantly improving the accuracy and reliability of target satellite selection.

[0043] The above technical solution refines satellite operating parameters into a first weighted average of signal pseudorange, a second weighted average of carrier signal phase, carrier-to-noise ratio, absolute value of clock offset, and number of target missions. This makes the evaluation of the actual operating performance of candidate satellites more comprehensive, accurate, and quantitative. This overcomes the problem of inaccurate or insufficient evaluation that may result from using only general operating parameters.

[0044] In some embodiments, the present invention further proposes that the process of determining the first preference factor for each candidate satellite based on the elevation angle data and satellite operating parameters in step S110 above specifically includes: Based on the elevation angle data of each candidate satellite, the first weighted average and the second weighted average of the target candidate satellite, the second selection factor of the target candidate satellite is determined; the target candidate satellite can be any one of the candidate satellites. Based on the second optimization factor and the carrier-to-noise ratio of each candidate satellite, the third optimization factor for the target candidate satellite is determined. Based on the absolute value of the clock deviation of each candidate satellite and the number of target missions, the deviation of the target candidate satellite's operating condition is determined; the deviation of the operating condition is used to characterize the degree to which the actual operating condition performance of the target candidate satellite deviates from the requirements of the target satellite. Based on the third optimization factor and the deviation of operating conditions, the first optimization factor of the target candidate satellite is determined.

[0045] In this embodiment, considering that satellite signals are easily affected by terrain obstruction, foliage attenuation, or circuit interference when traveling 20,000 kilometers from space to reach the ground, resulting in extremely weak power, and that the health performance of different satellite signals varies due to different obstruction conditions, which can be reflected by the signal-to-noise ratio, a third optimal factor is obtained based on the second optimal factor and the analysis of the signal-to-noise ratio. At the same time, the operational feedback performance of different satellites affects the satellite selection and evaluation analysis. For example, the lower the satellite's clock drift performance and the fewer concurrent congested tasks in real time, the more suitable it is for optimal analysis. Therefore, the first optimal factor is obtained by combining the satellite's operational matching degree and the satellite's refined feedback performance.

[0046] The target candidate satellite is any one of the candidate satellites whose first selection factor needs to be calculated. For example, if there are three candidate satellites, satellite A, satellite B, and satellite C, satellite B is selected as the target candidate satellite after calculating its first selection factor.

[0047] The second selection factor is an indicator used to comprehensively evaluate the preliminary selection degree of candidate satellites in terms of signal quality and geometric configuration. It can be determined by fuzzy logic reasoning or machine learning model calculations on elevation angle data, the first weighted average, and the second weighted average. One implementation method is to use these parameters as inputs, calculate them through a pre-trained neural network model, and output the second selection factor.

[0048] The third optimization factor is an indicator that further considers the impact of the carrier-to-noise ratio (CNR) on satellite performance, building upon the second optimization factor. The CNR directly reflects the strength of the received signal and the noise level, and is a key parameter for measuring signal quality. In practice, methods such as table lookup can be used to search a preset table based on the second optimization factor and the CNR to determine the third optimization factor.

[0049] Operating performance deviation is an indicator that measures the degree of deviation between the actual operating status of a candidate satellite and the positioning requirements of the target equipment. The absolute value of the clock deviation reflects the stability and accuracy of the satellite clock, directly affecting positioning accuracy; the number of target tasks reflects the satellite's workload, which may affect its response speed and resource allocation. Operating performance deviation can be determined by normalizing the absolute value of the clock deviation and the number of target tasks, followed by a weighted sum, or by calculating using a multi-factor comprehensive evaluation model. For example, the larger the clock deviation and the greater the number of target tasks, the higher the operating performance deviation.

[0050] The first selection factor is a key indicator ultimately used to evaluate the overall performance of candidate satellites and select the target satellite accordingly. It integrates signal quality, geometric configuration, signal strength, and the degree of deviation from the satellite's own operational status. The first selection factor can be determined by weighting the third selection factor with the deviation from the operational performance or by using a decision tree model.

[0051] As an example, the first preference factor for the target candidate satellite can be determined using the following formula 1:

[0052] In Formula 1, U is used to characterize the first preference factor for the target candidate satellite. The third selection factor used to characterize the target candidate satellites. Used to characterize the deviation of target candidate satellites from their operational performance.

[0053] Among them, if the third priority factor of the target candidate satellite in the current analysis is larger and the deviation of the operating condition is smaller, it reflects that the selection priority of the satellite is higher, that is, the first priority factor is larger.

[0054] This invention employs a layered, progressive approach to refine the performance indicators of candidate satellites. First, based on elevation angle data, a first weighted average, and a second weighted average, the signal quality and geometric configuration of the target candidate satellite are initially assessed, yielding a second selection factor. Subsequently, based on the second selection factor, a carrier-to-noise ratio parameter is introduced to further refine the consideration of signal strength, resulting in a third selection factor. Simultaneously, the internal operating status of the target candidate satellite is independently evaluated, using the absolute value of clock deviation and the number of target tasks to determine its operational performance deviation, reflecting the satellite's health and workload. Finally, the third selection factor, refined through signal quality and strength evaluation, is combined with the operational performance deviation reflecting the satellite's internal operating status to obtain a more comprehensive and accurate first selection factor. This multi-dimensional, phased evaluation mechanism ensures that the selection of candidate satellites is not merely a simple aggregation of parameters, but rather a logical correlation and weighted allocation of various influencing factors. This guarantees that the ultimately selected target satellite not only performs excellently in external signal conditions but also exhibits greater stability and reliability in its internal operating status, thereby providing higher-quality positioning services for the target equipment.

[0055] Through the above technical solution, this invention can more comprehensively and precisely evaluate the overall performance of candidate satellites, avoiding the limitations caused by evaluation based on a single or simple combination of parameters. By calculating the second and third optimal factors and the deviation from operational performance step by step, and finally synthesizing them to obtain the first optimal factor, the satellite selection process considers not only the geometric conditions and strength of the signal, but also the operational health status and mission load of the satellite itself. This helps to identify satellites that perform excellently in terms of signal quality, signal strength, and internal operational stability, thereby significantly improving the accuracy, stability, and reliability of target equipment positioning, and effectively solving the problem of inaccurate satellite selection under complex operating conditions using traditional methods.

[0056] In some embodiments, the present invention further proposes determining a second preference factor for the target candidate satellite based on the elevation angle data of each candidate satellite, a first weighted average of the target candidate satellites, and a second weighted average of the target candidate satellites, specifically including: Based on the elevation angle data of each candidate satellite within the target time range closest to the current time, the low elevation angle tendency factor of the target candidate satellite is determined; the low elevation angle tendency factor is used to characterize the degree of signal defect caused by the elevation angle amplitude of the target candidate satellite. Based on the first weighted average and the second weighted average of the target candidate satellites within the target time range, the dual-frequency matching superiority of the target candidate satellites is determined; the dual-frequency matching superiority is used to characterize the matching performance of the pseudorange and carrier signal phase of the target candidate satellites. Based on the low elevation angle tendency factor and the dual-frequency matching priority, the second preferred factor for the target candidate satellite is determined.

[0057] In this embodiment, when the satellite is at a low elevation angle, i.e., close to the horizon, the satellite signal needs to penetrate a thicker layer of atmosphere at an inclined angle to reach the ground. The thicker the atmosphere through which the satellite signal passes, the more severe the attenuation and multipath effects become, resulting in a larger calculation error for the satellite. This phenomenon reduces the accuracy of satellite screening and analysis. At the same time, the satellite signal may experience severe disturbances or abnormal refraction when passing through the ionosphere, which is reflected in the change in the matching of the signal pseudorange and carrier signal phase. Therefore, the dual-frequency variation of the signal can be used as an auxiliary analysis during satellite screening. Thus, this step combines the dynamic elevation angle performance of the satellite with the matching of the signal pseudorange and carrier signal phase to obtain the real-time second selection factor for each visible candidate satellite within the horizon range.

[0058] The target time range refers to a continuous sliding time window formed by backtracking a preset fixed duration from the current sampling time as the time endpoint. This preset fixed duration can be flexibly configured according to the positioning scenario, with typical values ​​being 30 seconds, 1 minute, 5 minutes, etc. In this invention, the calculation of the low elevation angle tendency factor and the dual-frequency matching superiority are all uniformly based on the synchronous observation data collected within the same target time window to ensure time reference alignment.

[0059] The low elevation angle tendency factor is used to characterize the degree of signal defects caused by the elevation angle amplitude of a target candidate satellite. For example, by establishing an empirical model between elevation angle and signal quality, the probability or severity of signal defects can be predicted based on the satellite's current or historical elevation angle distribution.

[0060] Dual-frequency matching superiority is used to characterize the matching performance of pseudorange and carrier signal phase of a target candidate satellite. This factor aims to evaluate the consistency and reliability between satellite dual-frequency measurement data (pseudorange and carrier signal phase), which is crucial for high-precision positioning. For example, the matching of dual-frequency data can be evaluated by analyzing the differences in pseudorange and carrier signal phase measurements at different frequencies, such as calculating the deviation between pseudorange or carrier signal phase at different frequencies.

[0061] Based on the elevation angle data of each candidate satellite within the target time range closest to the current moment, the low elevation angle trend factor of the target candidate satellite is determined. This step aims to quantify the potential risk of signal defects by analyzing the satellite's recent elevation angle changes. For example, elevation angle data of the satellite over the past few minutes can be collected, and trend analysis can be performed on this data, such as using linear regression or multinomial fitting, to predict elevation angle changes.

[0062] The dual-frequency matching superiority of the target candidate satellites is determined based on the first weighted average and the second weighted average over the target time range. This step aims to assess the consistency of the pseudorange and carrier signal phase matching by analyzing the satellites' recent dual-frequency measurement data. For example, the matching can be comprehensively evaluated by calculating the average difference between the first and second weighted averages over the target time range and incorporating the volatility of the difference (such as variance).

[0063] Based on the low elevation angle tendency factor and the dual-frequency matching superiority, a second selection factor for the target candidate satellite is determined. This step aims to comprehensively consider the satellite's low elevation angle signal defect risk and the quality of its dual-frequency measurement data, thereby more comprehensively evaluating its suitability as a target satellite. This can be achieved by constructing a multi-dimensional evaluation model, such as using fuzzy logic or a neural network, taking the low elevation angle tendency factor and the dual-frequency matching superiority as inputs, and outputting the second selection factor.

[0064] As an example, the second preference factor for the target candidate satellite can be determined using the following formula 2:

[0065] In Formula 2, F is used to characterize the second preference factor for the target candidate satellite. Low elevation angle tendency factor used to characterize target candidate satellites Used to characterize the dual-frequency matching priority of target candidate satellites.

[0066] Among them, if the low elevation angle tendency factor of the target candidate satellite currently being analyzed... The lower the value, the better the dual-frequency matching performance. The higher the value, the greater its second-best-choice factor.

[0067] The present invention optimizes the process of determining the second optimal factor for target candidate satellites by introducing a low elevation angle tendency factor and a dual-frequency matching superiority factor. Specifically, when determining the second optimal factor, it no longer relies solely on the original elevation angle data, the first weighted average, and the second weighted average, but first performs a deeper analysis and refinement of these original data. By analyzing the elevation angle data of each candidate satellite within the target time range closest to the current time, the low elevation angle tendency factor can be calculated. This factor can effectively quantify the degree of signal defects that may occur at low elevation angles, thereby predicting potential risks to signal quality. Simultaneously, by analyzing the first and second weighted averages of the target candidate satellites within the same target time range, the dual-frequency matching superiority factor can be calculated. This factor can accurately characterize the consistency between pseudorange and carrier signal phase measurements, reflecting the reliability of dual-frequency data. Finally, by comprehensively considering these two more representative factors—the low elevation angle tendency factor and the dual-frequency matching superiority factor—the second optimal factor for the target candidate satellite is determined. This method makes the calculation of the second optimal factor more precise and comprehensive, and can more accurately reflect the actual signal quality and measurement reliability of the satellite. It avoids evaluation bias caused by the use of simple parameters, thus providing a more solid foundation for the determination of the subsequent third and first optimal factors.

[0068] By introducing a low elevation angle tendency factor and dual-frequency matching dominance, this scheme can more comprehensively and accurately evaluate the signal quality and measurement reliability of candidate satellites. The low elevation angle tendency factor effectively quantifies the signal defects that may occur at low elevation angles, while the dual-frequency matching dominance ensures the consistency of pseudorange and carrier signal phase measurement data. Therefore, when determining the second optimization factor, these key factors can be fully considered, resulting in selected target satellites with more stable signal reception capabilities and higher data reliability, thereby significantly improving the positioning accuracy and stability of the target equipment.

[0069] In some embodiments, the present invention further proposes determining a low elevation angle tendency factor for the target candidate satellite based on elevation angle data of each candidate satellite within the target time range closest to the current time, specifically including: The average fitting slope is obtained by averaging the slopes of the fitted lines obtained from fitting the elevation angle data of each candidate satellite within the target time range. The average elevation angle data is obtained by averaging the current elevation angle data of each candidate satellite at the current moment. Based on the slope of the target fitting line of the candidate satellite, the target's current elevation angle data, the average fitting slope, and the average elevation angle data, the low elevation angle tendency factor of the candidate satellite is determined.

[0070] In this embodiment, the slope of the target fitted line specifically refers to the slope of the fitted line obtained by fitting the elevation angle data of the target candidate satellite within the target time range, and the target current elevation angle data specifically refers to the current elevation angle data of the target candidate satellite at the current moment.

[0071] The average fitted slope is obtained by averaging the slopes of the fitted lines obtained from the elevation angle data of each candidate satellite within the target time range. This technique aims to analyze the elevation angle change trend of each candidate satellite over a specific time period, rather than relying solely on instantaneous elevation angle values. The slope of the fitted line can quantify the rate and direction of elevation angle change over time; for example, a positive slope indicates that the elevation angle is increasing, and a negative slope indicates that the elevation angle is decreasing. In particular, when a satellite experiences a sustained deterioration in long-range elevation angle and maintains a negative slope trend for an extended period, linear regression is prone to generating negative intercept offsets, forming slope disturbances that interfere with trend judgment. Therefore, this invention can identify and correct such slope disturbances in advance during the calibration process.

[0072] Specifically, before performing linear regression fitting on the elevation angle data, a long-range elevation angle deterioration slope disturbance verification step can be added: traverse all elevation angle sampling points within the target time window to identify negative intercept slope abnormal disturbances caused by the satellite's long-term continuous downward movement; if multiple consecutive sets of sampling data show a continuous downward long-range deterioration trend and the regression intercept shows a negative value shift, then the slope compensation correction logic is activated to correct the shift in the original fitted slope, avoiding calculation distortion caused by long-range elevation angle deterioration.

[0073] During the calibration process, the elevation angle data within the window is first uniformly normalized to a dimensionless value, and the regression intercept obtained is only a dimensionless offset coefficient. The upper limit of the normalized elevation angle interval is selected as a fixed compensation benchmark constant. The original regression intercept with negative offset is added to this benchmark constant to complete the negative intercept baseline lifting, thus obtaining the intercept offset correction amount. Then, this correction amount is divided by the compensation benchmark constant to obtain the scaling correction coefficient. The original fitted slope is multiplied by this correction coefficient to finally obtain the corrected slope after intercept offset compensation.

[0074] By averaging the slopes of these fitted lines, a reference value reflecting the overall trend of elevation angle changes in the satellite constellation can be obtained, which can then be used for subsequent comparisons with the trends of individual target candidate satellites. One approach is to collect a series of elevation angle data points within the target time frame for each candidate satellite, then perform linear regression on these data points using the least squares method to obtain a best-fit line and extract its slope. Subsequently, the slopes of the fitted lines for all candidate satellites are arithmetically averaged.

[0075] The average elevation angle data of each candidate satellite at the current moment is obtained by averaging the current elevation angle data. This technical feature is used to obtain the average elevation angle data of each candidate satellite at the current moment. The current elevation angle data reflects the instantaneous position of the satellite at a specific moment. By averaging the current elevation angle data of all candidate satellites, a benchmark value representing the overall elevation angle level of the current satellite constellation can be obtained, which facilitates the evaluation of the current elevation angle of an individual target candidate satellite relative to the overall level. One implementation method is to directly sum the elevation angle data of all candidate satellites at the current moment and then divide by the number of candidate satellites to obtain the arithmetic mean.

[0076] Based on the slope of the target fitted line, the current elevation angle data, the average fitted slope, and the average elevation angle data of the target candidate satellite, a low elevation angle tendency factor is determined. This technique comprehensively utilizes the dynamic change trend (slope of the target fitted line), instantaneous state (current elevation angle data) of the target candidate satellite itself, as well as the average dynamic trend (average fitted slope) and average instantaneous state (average elevation angle data) of the entire satellite constellation, to quantify the probability or severity of the target candidate satellite entering a low elevation angle state. One implementation method is to construct a mathematical model or function that takes the slope of the target fitted line, the current elevation angle data, the average fitted slope, and the average elevation angle data as input parameters and outputs the low elevation angle tendency factor. Another implementation method is to use a predefined rule set or decision tree to determine and assign the low elevation angle tendency factor based on different combinations of these four parameters and thresholds.

[0077] As an example, the low elevation angle tendency factor of a target candidate satellite can be determined using the following formula 3:

[0078] In formula 3, Low elevation angle tendency factor used to characterize target candidate satellites Used to characterize average elevation angle data Used to characterize the average fitted slope Used to characterize the target's current elevation angle data. Used to characterize the slope of the target fitted line. This represents the function that takes the absolute value.

[0079] Among them, if the real-time elevation angle of the target candidate satellite to the target equipment is smaller and the trend of the elevation angle decreasing is more significant, it reflects that the screening priority of the target candidate satellite is lower, that is, the low elevation angle tendency factor of the target candidate satellite is larger.

[0080] The present invention introduces a method to fit the elevation angle data of each candidate satellite within a target time range, obtaining the slope of the fitted straight line representing the elevation angle change trend, and further calculating the average fitted slope. Simultaneously, the current elevation angle data of each candidate satellite is averaged to obtain the average elevation angle data. By comprehensively considering the fitted straight line slope and current elevation angle data of the target candidate satellite itself, along with the average fitted slope and average elevation angle data of the entire candidate satellite group, a more comprehensive and dynamic assessment of the target candidate satellite's low elevation angle trend can be achieved. This method not only considers the instantaneous position of the satellite but also incorporates its historical trajectory and overall trend, thereby accurately predicting the degree of signal defect and avoiding misjudgments caused by insufficient consideration of the elevation angle change trend.

[0081] Through the above technical solution, this invention can more accurately evaluate the low elevation angle tendency factor of target candidate satellites. By introducing the slope of the fitted line and the average elevation angle data, it not only considers the instantaneous elevation angle of the satellite, but also incorporates its dynamic change trend over a period of time and its overall performance relative to the entire satellite constellation. This makes the judgment on the degree of satellite signal defects more comprehensive and accurate, effectively avoiding misjudgments that may be caused by relying solely on instantaneous elevation angle data.

[0082] In some embodiments, the present invention further proposes determining the dual-frequency matching superiority of the target candidate satellites based on the first weighted average and the second weighted average of the target candidate satellites within the target time range, including: Based on the first weighted average of each target candidate satellite within the target time range and the corresponding second weighted average, multiple dual-frequency difference factors of the target candidate satellite within the target time range are determined. The dual-frequency matching priority of the target candidate satellite is determined based on the standard deviation among multiple dual-frequency difference factors.

[0083] In this embodiment, when a satellite signal passes through the ionosphere, there may be severe disturbances or abnormal refractions. This disturbance phenomenon will cause a significant change in the total electron content of the ionosphere in a short period of time. Since the signal delay is proportional to the electron density, it will further lead to a huge deviation between the distance calculated by the receiver and the actual geometric distance. In actual scenarios, the ionosphere is a dispersive medium, and its influence on the pseudorange and carrier signal phase is equal. Therefore, the matching performance of the pseudorange and carrier signal phases in the satellite signal can reflect the ionizing interference. Therefore, in order to further improve the analysis accuracy of satellite selection, it is necessary to combine the analysis of the dual-frequency matching performance of the satellite signal.

[0084] Based on the first weighted average and the corresponding second weighted average of the target candidate satellite within the target time range, multiple dual-frequency difference factors of the target candidate satellite within the target time range are determined. This step aims to quantify the degree of consistency or difference between the pseudorange measurement (characterized by the first weighted average) and the carrier signal phase measurement (characterized by the second weighted average) of the target candidate satellite within a specific time range. Specifically, a unified dimension conversion process is first performed on the two types of observation data, the first weighted mean and the second weighted mean, which have different physical properties and dimensions. This maps the observed values ​​in the pseudorange dimension and the carrier signal phase dimension to the same dimensionless standard interval. (Specifically: Preset upper and lower limits for the pseudorange are determined separately. These limits can be obtained through statistical analysis of historical pseudorange data, or from the upper and lower limits of the preset standard numerical range of the pseudorange. The pseudorange is then normalized to its maximum and minimum values ​​based on these limits. Similarly, preset upper and lower limits for the carrier signal phase are determined separately. These limits can be obtained through statistical analysis of historical pseudorange data, or from the upper and lower limits of the preset standard numerical range of the pseudorange.) Historical data is statistically obtained, or the upper and lower limits of the preset standard value range of the carrier signal phase are used. The carrier signal phase is normalized to its maximum and minimum values ​​based on the preset upper and lower limits. In addition, if the normalized object is greater than the corresponding preset upper limit in extreme cases, the normalization result is set to 1; if the normalized object is less than the corresponding preset lower limit in extreme cases, the normalization result is set to 0. This eliminates the computational distortion caused by the inconsistency of physical units between the two types of data. Then, the difference between the first weighted average of each target candidate satellite within the target time range after uniform dimensional conversion and the corresponding second weighted average is calculated to obtain multiple dual-frequency difference factors of the target candidate satellite within the target time range.

[0085] Pseudorange and carrier signal phase are two important observations in satellite navigation, each with different characteristics and error sources. Ideally, they should exhibit a high degree of consistency. By calculating their difference, a "dual-frequency difference factor" reflecting the quality of dual-frequency signal matching can be obtained. These factors reveal the relative deviations of pseudorange and carrier signal phase observations at different times, providing fundamental data for subsequent signal quality assessment. Specifically, the dual-frequency difference factor can be obtained by subtracting the corresponding second-weighted mean of the carrier signal phase from the first-weighted mean of the pseudorange of the target candidate satellite at each sampling time; alternatively, a series of dual-frequency difference factors can be generated by subtracting the sequence of the first-weighted mean of the pseudorange of the target candidate satellite within the target time range from the sequence of the second-weighted mean of the carrier signal phase point by point.

[0086] The dual-frequency matching superiority of a target candidate satellite is determined based on the standard deviations among multiple dual-frequency difference factors. Standard deviation is a statistic that measures the dispersion of data. Here, by calculating the standard deviations among multiple dual-frequency difference factors, the volatility or stability of these difference factors within the target time range can be assessed. A smaller standard deviation indicates high and stable consistency between pseudorange and carrier signal phase measurements, i.e., excellent dual-frequency matching performance; a larger standard deviation may indicate unstable signal quality or large errors. Therefore, standard deviation can effectively characterize the dual-frequency matching superiority of a target candidate satellite, providing a reliable basis for satellite selection. Specifically, the corresponding dual-frequency matching superiority can be obtained by calculating the standard deviations of the calculated multiple dual-frequency difference factors and then taking the reciprocal. It should be noted that due to the permanent existence of interference such as ionospheric disturbances and multipath effects during actual satellite observation, the dual-frequency difference factors always exhibit small fluctuations. The final calculated standard deviation value is always greater than zero, and the denominator of this reciprocal operation cannot possibly be zero, so no additional compensation term is needed.

[0087] The present invention obtains a series of dual-frequency difference factors by subtracting the first weighted mean and the corresponding second weighted mean of each target candidate satellite within the target time range. These dual-frequency difference factors intuitively reflect the relative deviation between pseudorange measurement and carrier signal phase measurement at different times. Subsequently, the present invention further determines the dual-frequency matching superiority of the target candidate satellite based on the standard deviation among these dual-frequency difference factors. The standard deviation, as a statistic measuring the degree of data dispersion, can effectively quantify the volatility of the dual-frequency difference factors. When the standard deviation is small, it indicates that the consistency between pseudorange and carrier signal phase measurement is high and stable, i.e., the dual-frequency matching performance is excellent; conversely, a large standard deviation indicates that the dual-frequency matching performance is unstable or has a large error.

[0088] Through the above technical solution, this invention effectively solves the problem that relying solely on instantaneous or simple average values ​​may lead to unstable evaluation results when assessing the quality of dual-frequency satellite signals. By calculating the dual-frequency difference factor between the first weighted mean of pseudorange and the second weighted mean of carrier signal phase, and further analyzing the standard deviation of these dual-frequency difference factors, the stability and consistency of the dual-frequency signal matching of the target candidate satellite can be accurately quantified. This method based on fluctuation analysis can more comprehensively and reliably reflect the actual signal quality of the satellite, thus providing more accurate input for the subsequent calculation of the second and even the first optimal factors. This enables the more accurate identification of candidate satellites with excellent dual-frequency signal matching performance under complex and variable satellite operating conditions, thereby improving the accuracy and reliability of target equipment positioning.

[0089] In some embodiments, the present invention further proposes determining a third preference factor for the target candidate satellite based on a second preference factor and the carrier-to-noise ratio of each candidate satellite, specifically including: The average carrier-to-noise ratio is obtained by averaging the current carrier-to-noise ratio of each candidate satellite at the current moment. Based on the difference between the current carrier-to-noise ratio and the average carrier-to-noise ratio of the target candidate satellite at the current moment, and the second optimization factor, the third optimization factor of the target candidate satellite is determined.

[0090] In this embodiment, the target current carrier-to-noise ratio refers to the current carrier-to-noise ratio of the target candidate satellite at the current moment.

[0091] The average carrier-to-noise ratio (CNR) is obtained by averaging the current CNR values ​​of each candidate satellite at the current moment. This step aims to obtain a benchmark value to measure the CNR performance of an individual candidate satellite relative to the overall candidate satellite constellation. By statistically averaging the CNR data of all candidate satellites at the current moment, an average CNR reflecting the overall satellite signal quality level under the current environment can be obtained. This helps to eliminate the accidental influence of environmental factors or equipment differences on the CNR evaluation of a single satellite, providing a more valuable benchmark for comparison. This averaging process can be performed by summing the current CNR values ​​of all candidate satellites and then dividing by the total number of candidate satellites to calculate the arithmetic mean.

[0092] Based on the difference between the current carrier-to-noise ratio (CNR) and the average CNR of the target candidate satellite at the current moment, and the second optimization factor, a third optimization factor for the target candidate satellite is determined. This step compares the CNR performance of the target candidate satellite with the average level of its environment and, combined with the existing second optimization factor, comprehensively evaluates the satellite's optimization level. By calculating the difference, the signal quality of the target candidate satellite can be quantified as being better or worse than the average level, thus reflecting its signal strength and stability more precisely. Combining this difference with the second optimization factor ensures that the third optimization factor not only considers factors such as elevation angle and dual-frequency matching (reflected by the second optimization factor) but also fully considers real-time signal quality (reflected by the CNR difference).

[0093] As an example, the third preference factor for the target candidate satellite can be determined using the following formula 4:

[0094] In formula 4, The third preferred factor is used to characterize the target candidate satellite, and F is used to characterize the second preferred factor for the target candidate satellite. Used to characterize the target candidate satellite's current carrier-to-noise ratio at the current moment. Used to characterize the average carrier-to-noise ratio. Used to characterize the hyperbolic tangent function, and used to normalize the internal values ​​to the interval between -1 and +1.

[0095] In determining the third optimal factor for target candidate satellites, the present invention first averages the current carrier-to-noise ratio (CNR) of each candidate satellite at the current moment to obtain an average CNR. This average CNR serves as a benchmark reference for the signal quality of all candidate satellites under the current environment. Subsequently, the difference between the target candidate satellite's current CNR and this average CNR is calculated. This difference objectively reflects the signal quality of the target candidate satellite relative to the overall satellite constellation, effectively avoiding potential instantaneous fluctuations or environmental interference from single raw CNR data. Finally, this relative CNR difference is combined with the previously determined second optimal factor to jointly determine the third optimal factor for the target candidate satellites. The second optimal factor comprehensively considers factors such as elevation angle data, the first weighted average, and the second weighted average, reflecting the satellite's geometric characteristics and signal matching degree. By combining real-time relative signal quality (CNR difference) with these inherent characteristics, the third optimal factor can more comprehensively and accurately evaluate the overall performance of the target candidate satellites, thus providing a more reliable basis for the subsequent determination of the first optimal factor and the selection of target satellites.

[0096] By employing the aforementioned technical solution, the determination of the third optimization factor for a target candidate satellite no longer relies solely on its original carrier-to-noise ratio (CNR) data. Instead, it incorporates the difference between the CNR and the average CNR of all current candidate satellites. This approach effectively eliminates the interference of transient factors such as environmental noise and differences in receiving equipment on the CNR evaluation of a single satellite, making the CNR assessment more relative and objective. Combining this relative CNR performance with the second optimization factor allows for a more accurate and comprehensive reflection of the real-time signal quality and overall optimization level of the target candidate satellite. This avoids misjudgments caused by local or transient signal fluctuations, thereby improving the robustness and accuracy of the third optimization factor.

[0097] In some embodiments, the present invention further proposes determining the deviation of the target candidate satellite's operational performance based on the absolute value of the clock deviation of each candidate satellite and the number of target missions, specifically including: The average absolute value of the current clock deviation of each candidate satellite at the current moment is obtained by averaging the current clock deviation absolute values. The clock drift trend of the target candidate satellite at the current moment is determined based on the difference between the absolute value of the target candidate satellite's current clock deviation and the absolute value of the average clock deviation at the current moment. Based on the maximum number of tasks among all target tasks, the number of target tasks for the candidate satellites, and the clock drift trend, the deviation of the target candidate satellites' operating performance is determined.

[0098] In this embodiment, the absolute value of the target's current clock deviation specifically refers to the absolute value of the target candidate satellite's current clock deviation at the current moment.

[0099] As satellites operate in space for longer periods, the atomic gas used to generate frequencies may gradually adhere to the walls of the resonant cavity, causing slight changes in the physical properties of the cavity itself. This is reflected in the satellite's atomic clock drifting. In other words, the greater the abnormal deviation of a satellite's atomic clock from the local crystal oscillator at the terminal, the stronger the trend of clock drifting, and the greater the calculation error during subsequent equipment positioning. Therefore, it is necessary to analyze the clock drift performance of each satellite in combination.

[0100] Furthermore, satellites constantly need to process massive amounts of data requests. Therefore, satellites will prioritize responding to tasks with earlier timing and higher urgency. These tasks are usually identified as high-priority tasks by the satellite system and scheduled for priority processing. Even if a satellite has good signal and clock stability, if it has too many high-priority tasks to process, its search selectivity should be appropriately reduced. Therefore, it is necessary to analyze the satellite's performance in conjunction with its high-priority task performance.

[0101] The absolute value of the current clock deviation for each candidate satellite at the current moment is averaged to obtain the average absolute clock deviation. This step aims to obtain an overall reference level for the absolute clock deviations of all candidate satellites at the current moment. The absolute clock deviation is an important indicator for measuring the difference between the internal clock of a satellite and a standard time source. Averaging this deviation can effectively smooth out the instantaneous fluctuations of individual satellites, providing a more representative benchmark for group performance. This averaging can be done using an arithmetic mean, which is a simple summation of the absolute values ​​of the current clock deviations of all candidate satellites divided by the number of satellites.

[0102] Based on the difference between the absolute value of the target candidate satellite's current clock deviation and the absolute value of the average clock deviation at the current moment, the clock drift trend of the target candidate satellite at the current moment is determined. This step is used to quantify the degree of deviation of the clock performance of a single target candidate satellite relative to the entire candidate satellite group. The clock drift trend reflects the relative stability of the target candidate satellite's clock and can be determined using the following formula 5:

[0103] In Formula 5, W is used to characterize the clock drift trend of the target candidate satellite at the current moment. Used to characterize the absolute value of the target candidate satellite's current clock deviation at the current moment. The function is used to characterize the absolute value of the average clock deviation, and norm() is used to characterize the normalization process.

[0104] In this invention, a minimum-maximum normalization method is used to perform the aforementioned normalization operation. The specific index determination and calculation process is as follows: First, all candidate satellites are traversed, and the difference between the absolute value of each candidate satellite's own clock deviation and the absolute value of the group's average clock deviation is calculated. The maximum value among all differences is extracted as the upper limit index of normalization, and the minimum value is extracted as the lower limit index of normalization. Then, the difference corresponding to the target candidate satellite is subtracted from the lower limit index of normalization, and the result is divided by the difference between the upper and lower limits of normalization, finally mapping to the standard dimensionless interval of 0 to 1, completing the normalization process. This normalization method can unify the numerical magnitude of the clock deviation differences between different satellites, eliminating the interference caused by the difference in the original numerical span on the assessment of clock drift trend.

[0105] Based on the maximum number of tasks among all target tasks, the number of target tasks for candidate satellites, and the clock drift trend, the deviation degree of the target candidate satellite's operational performance is determined. This step comprehensively considers the satellite's task load and clock stability to fully assess the degree of deviation between its operational performance and target requirements. The maximum number of tasks can serve as a reference for the maximum task load the system can currently bear, used to normalize the number of target tasks for candidate satellites, thereby objectively reflecting the relative level of their task load. The deviation degree of operational performance can be determined in various ways. For example, a multi-dimensional evaluation model can be constructed, using fuzzy logic or machine learning algorithms for comprehensive judgment. Its core lies in combining the satellite's intrinsic performance (clock stability) with its extrinsic load (number of tasks) to form a unified indicator that reflects its service capability and reliability. As an example, the deviation degree of the target candidate satellite's operational performance can be determined using the following formula 6:

[0106] In formula 6, Used to characterize the deviation of target candidate satellites from their operational performance. Used to characterize the number of target missions for candidate satellites. W is used to characterize the maximum number of tasks, and W is used to characterize the clock drift trend of the target candidate satellite at the current moment.

[0107] This invention first establishes an average benchmark for the clock performance of all candidate satellites at the current moment by averaging the absolute values ​​of satellite clock deviations. Based on this, the clock drift trend of a single satellite is precisely quantified by calculating the difference between the current absolute value of the target candidate satellite's clock deviation and this average benchmark. This relativistic evaluation method allows the assessment of satellite clock stability to move beyond its absolute value and dynamically reflect its relative performance within the entire satellite constellation, thus more accurately identifying potential performance issues or advantages. Subsequently, this clock drift trend is combined with the maximum number of tasks among all target missions and the number of target missions for each target candidate satellite to determine the deviation from the target candidate satellite's operational performance. The maximum number of tasks provides a system-level upper limit for mission load, allowing the number of target missions to be effectively normalized, thereby objectively assessing the mission load of a single satellite. By comprehensively considering the clock drift trend (reflecting intrinsic performance stability) and mission load (reflecting external service pressure), this solution generates a more comprehensive, dynamic, and refined operational performance deviation index. This indicator not only considers the satellite's own health condition but also takes into account its actual workload. This allows for a more accurate assessment of the satellite's comprehensive service capabilities and reliability when determining the first priority factor, thereby selecting the most suitable target satellite for equipment positioning and effectively avoiding positioning performance degradation or service interruption caused by inaccurate satellite condition assessment.

[0108] Through the above technical solution, this invention can more accurately and comprehensively evaluate the deviation of target candidate satellites from their operational performance. By introducing the mean value of the absolute value of the current clock deviation and calculating the clock drift trend based on this, the evaluation of satellite clock performance is transformed from a single absolute value judgment to a relative and dynamic trend analysis, thereby enabling more sensitive capture of the actual operating status of the satellite clock. Simultaneously, incorporating the maximum number of missions into the consideration makes the evaluation of the target mission quantity more systematic and objective, avoiding biases caused by viewing mission load in isolation. This method, which comprehensively considers both the satellite's intrinsic performance (clock stability) and extrinsic load (number of missions), ensures that the determined deviation of operational performance more realistically reflects the satellite's service capability and reliability.

[0109] In some embodiments, the present invention further proposes that the process of determining the target satellite of the target device based on the first preference factor of each candidate satellite in step S120 above specifically includes: Divide any four candidate satellites into a group to obtain multiple candidate satellite groups; Based on the mean of the first preference factor and the mean of the Euclidean distance of each candidate satellite group, the routing application preference of each candidate satellite group is determined; the mean of the Euclidean distance is the result obtained by averaging the Euclidean distance between any two candidate satellites in the candidate satellite group. Each candidate satellite in the candidate satellite group with the highest routing application preference is identified as the target satellite for the target device.

[0110] In this embodiment, for a set of multiple satellites, even if the signal performance of these multiple satellites is good, if they are all crowded in the same corner of the sky, that is, if the congestion of the satellite set is too high, the configuration stability between the satellites is poor, and the calculated positioning error will be greatly amplified. Therefore, in order to further screen and evaluate each candidate satellite set more accurately, it is necessary to combine the analysis of the geometric spatial configuration of the satellite set.

[0111] Grouping any four candidate satellites into a single group results in multiple candidate satellite groups. This step aims to group a large number of candidate satellites to assess their potential for collaborative operation. Its purpose is to organize previously independent satellites into a collection with cooperative positioning capabilities.

[0112] Based on the mean of the first-optimization factor and the mean of the Euclidean distance for each candidate satellite group, the routing application optimization degree of each candidate satellite group is determined. This step is used to comprehensively evaluate the overall performance and geometric configuration of each candidate satellite group to quantify its suitability as a target satellite group for routing applications. The mean of the first-optimization factor is the result of averaging the first-optimization factors of each of the four candidate satellites in the group. It characterizes the average individual performance of all satellites in the group and reflects the overall quality of the satellites within the group. The mean of the Euclidean distance is the result of averaging the Euclidean distance between any two candidate satellites in the group. The Euclidean distance characterizes the spatial distribution of satellites, and its mean reflects the geometric configuration of the satellite group.

[0113] The final decision-making process involves selecting the optimal satellite group from multiple candidate satellite groups and designating its satellites as target satellites for the target device. This ensures that the selected target satellites not only have excellent individual performance but also provide the best positioning service when working collaboratively. The system can maintain a list recording all candidate satellite groups and their corresponding routing application optimization scores. After calculation, the system iterates through this list, identifies the satellite group with the highest routing application optimization score, and designates its member satellites as target satellites.

[0114] As an example, the routing preference for candidate satellite groups can be determined using the following formula 7:

[0115] In formula 7, Used to characterize the routing application preference of the candidate satellite group in the current analysis. The mean of the first preferred factor used to characterize the candidate satellite group in the current analysis. The mean Euclidean distance used to characterize the candidate satellite group in the current analysis. The average of the first preferred factor means used to characterize each candidate satellite group. The average value used to characterize the mean Euclidean distance of each candidate satellite group.

[0116] It should be noted that before determining the routing application preference of the candidate satellite group, the target device continuously receives the broadcast ephemeris data stream sent by the satellite navigation system as the source information input stream. Based on the complete ephemeris parameters, the three-dimensional coordinate data of each candidate satellite in the geocentric three-dimensional coordinate system is calculated in real time. All satellite spatial distance and geometric configuration related calculations (such as the calculation of Euclidean distance) are completed based on the three-dimensional coordinate data obtained by ephemeris calculation.

[0117] The present invention first divides all available candidate satellites into groups of four, forming multiple candidate satellite groups. This grouping aims to shift from single-satellite evaluation to multi-satellite collaborative evaluation, recognizing that in practical positioning applications, multiple satellites are typically needed to provide services to improve positioning accuracy and reliability. After forming these candidate satellite groups, the system performs a comprehensive evaluation of each group, specifically by calculating the group's "routing application preference." The calculation of the routing application preference comprehensively considers two key factors: first, the average individual performance of each candidate satellite within the group, i.e., the "mean of the first preference factor," which reflects the overall quality of the group; and second, the geometric configuration of the satellites within the group, i.e., the "mean of the Euclidean distance," which characterizes the satellites' distribution in space. By combining the mean of the first preference factor with the mean of the Euclidean distance, the potential performance and stability of a satellite group in providing positioning services can be comprehensively measured. Finally, the system selects the candidate satellite group with the highest routing application preference and identifies all candidate satellites within that group as target satellites for the target device. This selection mechanism ensures that the selected target satellites not only perform well individually, but more importantly, as a whole, they can provide optimal cooperative positioning services for the target equipment.

[0118] Through the above technical solution, this invention, when determining the target satellite for a target device, no longer limits itself to the performance evaluation of a single satellite, but introduces the consideration of multi-satellite cooperative positioning. By dividing candidate satellites into multiple four-satellite groups and comprehensively evaluating the average individual performance (mean value of the first selection factor) and geometric configuration (mean value of Euclidean distance) of each satellite group, the overall service capability of the satellite group can be reflected more comprehensively and accurately. Finally, the satellite group with the highest routing application optimization degree is selected as the target satellite, ensuring that the target device can obtain the optimal cooperative positioning service, significantly improving the positioning accuracy, stability, and reliability. It is especially suitable for scenarios with high requirements for positioning service quality, thus effectively solving the limitations that may exist in selecting target satellites based solely on single-satellite optimization factors.

[0119] Based on the specific embodiments of the satellite networking routing method provided above by the present invention, the present invention further provides a specific embodiment of a satellite networking routing system.

[0120] like Figure 2 As shown, a schematic diagram of a satellite networking routing system is provided, which includes: The parameter analysis module is used to respond to the acquisition of elevation angle data and satellite operating parameters of each candidate satellite corresponding to the target device. Based on the elevation angle data and satellite operating parameters of each candidate satellite, the first selection factor of each candidate satellite is determined. The satellite operating parameters are used to characterize the actual operating performance of the candidate satellite. The satellite determination module is used to determine the target satellite of the target equipment based on the first preference factor of each candidate satellite; the target satellite is used to locate the target equipment.

[0121] This embodiment simultaneously collects elevation angle data of candidate satellites and satellite operating condition parameters characterizing the actual operating status of the candidate satellites. The elevation angle geometric dimension and the actual operating performance dimension of the satellite are combined to jointly calculate the first optimization factor for each candidate satellite. This first optimization factor takes into account both the satellite's spatial observation geometry relative to the terminal and the satellite's current operational reliability, no longer relying solely on elevation angle data for selection. Subsequent selection of target satellites based on the first optimization factor obtained from both dimensions can eliminate satellites with considerable elevation angles but malfunctioning operating conditions or poor signal quality, retaining satellites with excellent geometric observation conditions, stable operating conditions, and accurate observation data as target satellites. This effectively reduces positioning calculation errors caused by satellites with abnormal operating conditions, thereby significantly improving the overall positioning accuracy of satellite network routing in outdoor scenarios.

Claims

1. A satellite networking routing method, characterized in that, include: In response to obtaining the elevation angle data and satellite operating parameters of each candidate satellite corresponding to the target device, a first preference factor for each candidate satellite is determined based on the elevation angle data and satellite operating parameters of each candidate satellite; the satellite operating parameters are used to characterize the actual operating performance of the candidate satellite. Based on the first preference factor of each of the candidate satellites, the target satellite of the target device is determined; the target satellite is used to locate the target device.

2. The satellite networking routing method according to claim 1, characterized in that, The satellite operating parameters include the following: The first weighted mean of the signal pseudorange; The second weighted average of the carrier signal phase; Carrier-to-noise ratio; the carrier-to-noise ratio is used to characterize the ratio between carrier signal power and noise power spectral density; The absolute value of the clock deviation; the absolute value of the clock deviation is the absolute value of the clock deviation between the atomic clock of the candidate satellite and the local crystal oscillator of the target device; The target number of tasks; the target number of tasks is the number of tasks that need to be processed first, as indicated by task priority.

3. The satellite networking routing method according to claim 2, characterized in that, The determination of a first preference factor for each candidate satellite based on its elevation angle data and operating parameters includes: Based on the elevation angle data of each candidate satellite, the first weighted average of the target candidate satellite, and the second weighted average of the target candidate satellite, a second preference factor for the target candidate satellite is determined; the target candidate satellite can be any one of the candidate satellites. Based on the second preference factor and the carrier-to-noise ratio of each of the candidate satellites, a third preference factor for the target candidate satellite is determined; Based on the absolute value of the clock deviation of each candidate satellite and the number of target missions, the deviation of the operating condition of the target candidate satellite is determined; the deviation of the operating condition is used to characterize the degree to which the actual operating condition performance of the target candidate satellite deviates from the requirements of the target satellite. Based on the third optimization factor and the deviation of the operating condition, the first optimization factor of the target candidate satellite is determined.

4. The satellite networking routing method according to claim 3, characterized in that, The determination of the second preference factor for the target candidate satellite based on the elevation angle data of each candidate satellite, the first weighted average of the target candidate satellite, and the second weighted average of the target candidate satellite includes: Based on the elevation angle data of each candidate satellite within the target time range closest to the current time, a low elevation angle tendency factor of the target candidate satellite is determined; the low elevation angle tendency factor is used to characterize the degree of signal defect caused by the elevation angle amplitude of the target candidate satellite. Based on the first weighted average and the second weighted average of the target candidate satellite within the target time range, the dual-frequency matching superiority of the target candidate satellite is determined; the dual-frequency matching superiority is used to characterize the matching performance of the pseudorange and carrier signal phase of the target candidate satellite. Based on the low elevation angle tendency factor and the dual-frequency matching preference, the second preferred factor for the target candidate satellite is determined.

5. The satellite networking routing method according to claim 4, characterized in that, The step of determining the low elevation angle tendency factor of the target candidate satellite based on the elevation angle data of each of the candidate satellites within the target time range closest to the current time includes: The average fitting slope is obtained by averaging the slopes of the fitted straight lines obtained by fitting the elevation angle data of each candidate satellite within the target time range. The average elevation angle data is obtained by averaging the current elevation angle data of each candidate satellite at the current moment. Based on the slope of the target fitted line of the target candidate satellite, the target current elevation angle data, the average fitted slope, and the average elevation angle data, the low elevation angle tendency factor of the target candidate satellite is determined.

6. The satellite networking routing method according to claim 4, characterized in that, The step of determining the dual-frequency matching priority of the target candidate satellite based on the first weighted average and the second weighted average of the target candidate satellite within the target time range includes: Based on the first weighted average of each target candidate satellite within the target time range and the corresponding second weighted average, multiple dual-frequency difference factors of the target candidate satellite within the target time range are determined. The dual-frequency matching priority of the target candidate satellite is determined based on the standard deviation among the multiple dual-frequency difference factors.

7. The satellite networking routing method according to claim 3, characterized in that, The step of determining the third preference factor for the target candidate satellite based on the second preference factor and the carrier-to-noise ratio of each candidate satellite includes: The average carrier-to-noise ratio is obtained by averaging the current carrier-to-noise ratio of each candidate satellite at the current moment. Based on the difference between the target candidate satellite's current carrier-to-noise ratio and the average carrier-to-noise ratio at the current moment, and the second preference factor, a third preference factor for the target candidate satellite is determined.

8. The satellite networking routing method according to claim 3, characterized in that, The determination of the operational performance deviation of the target candidate satellites based on the absolute value of the clock deviation of each candidate satellite and the number of target missions includes: The absolute value of the current clock deviation of each candidate satellite at the current time is averaged to obtain the average absolute value of the clock deviation. The clock drift trend of the target candidate satellite at the current moment is determined based on the difference between the absolute value of the target current clock deviation and the absolute value of the average clock deviation at the current moment. Based on the maximum number of tasks among the target tasks, the number of target tasks of the target candidate satellite, and the clock drift trend, the deviation of the operating condition of the target candidate satellite is determined.

9. The satellite networking routing method according to claim 1, characterized in that, The step of determining the target satellite for the target device based on a first preference factor for each of the candidate satellites includes: Any four candidate satellites are grouped together to obtain multiple candidate satellite groups; Based on the mean of the first preference factor and the mean of the Euclidean distance of each candidate satellite group, the routing application preference of each candidate satellite group is determined; the mean of the Euclidean distance is the result obtained by averaging the Euclidean distance between any two candidate satellites in the candidate satellite group. Each candidate satellite in the candidate satellite group with the highest routing application preference is determined as the target satellite of the target device.

10. A satellite networking routing system, characterized in that, include: The parameter analysis module is used to determine the first preference factor for each candidate satellite based on the elevation angle data and satellite operating parameters of each candidate satellite in response to the acquisition of elevation angle data and satellite operating parameters of each candidate satellite. The satellite operating parameters are used to characterize the actual operating performance of the candidate satellite; The satellite determination module is used to determine the target satellite of the target device based on a first preference factor of each of the candidate satellites; the target satellite is used to locate the target device.

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