Method for evaluating a lunar space navigation scheme and device therefor

Abstract

The application discloses a kind of method for evaluating earth-moon space navigation scheme and its device, it is related to satellite navigation technical field, wherein, the evaluation method includes: for each navigation scheme, the corresponding multi-source data of navigation scheme is collected, based on multi-source data, determine the original index value of multiple preset indexes, the original index value of each preset index is normalized, and the target index value of each preset index is obtained, for each task phase of target task, based on preset mapping table and the target index value of each preset index, determine the performance evaluation result of navigation scheme under task phase.The application solves the technical problem that the accuracy of related art is lower when evaluating earth-moon space navigation scheme.

Description

Technical Field

[0001] This invention relates to the field of satellite navigation technology, and more specifically, to an evaluation method and apparatus for a lunar space navigation scheme. Background Technology

[0002] As lunar exploration activities deepen, extending the service range of the Global Navigation Satellite System (GNSS) to the Earth-Moon space to provide navigation support for spacecraft has become an important development direction. However, the unique environment of the Earth-Moon space environment results in extremely long GNSS signal transmission distances and significant power attenuation. User terminals mainly receive weak signals from the sidelobes of navigation satellite antennas, leading to low carrier-to-noise ratios, a limited number of visible satellites, and poor observation geometry. Current performance evaluation methods for near-Earth space GNSS are no longer applicable.

[0003] In related technologies, the performance evaluation of GNSS in the Earth-Moon space is mostly focused on isolated analysis or algorithm improvement of specific performance dimensions, mainly including the following methods: (1) Signal visibility and geometric configuration analysis: Through numerical simulation, the percentage of visible time and geometric precision factor (DOP) of GNSS satellites on specific orbits (such as NRHO (Near-Rectilinear Halo Orbit) and DRO (Distant Retrograde Orbit)) are statistically analyzed. (2) High-precision positioning algorithm: Focus on improving the positioning accuracy under adverse conditions such as weak signals, high dynamics, and multipath, for example, by using adaptive filtering, gross error detection and elimination techniques to optimize "accuracy" and "robustness".

[0004] However, the above-mentioned related technologies have the following problems: (1) The evaluation dimensions are fragmented, and most of them only focus on a single or a few indicators such as accuracy or visibility, and fail to incorporate multiple key performance dimensions that affect the success or failure of the mission into a unified framework for overall consideration; (2) There is a lack of quantitative fusion models, and the analysis results of different dimensions are independent of each other, making it impossible to generate an overall performance evaluation conclusion, which is difficult to directly support the comparison and decision-making of multiple schemes; (3) The evaluation is static, and the significant differences in navigation performance requirements of different mission stages (such as cruise, landing, and lunar operations) are not considered. The same set of standards is used to evaluate all stages, and the results may deviate from the actual mission effectiveness.

[0005] Therefore, there is an urgent need for a comprehensive performance evaluation method for GNSS in the Earth-Moon space that can systematically, quantitatively, and dynamically reflect mission requirements.

[0006] There is currently no effective solution to the above problems. Summary of the Invention

[0007] This invention provides a method and apparatus for evaluating Earth-Moon space navigation schemes, thereby addressing at least the technical problem of low accuracy in evaluating Earth-Moon space navigation schemes in related technologies.

[0008] According to one aspect of the present invention, an evaluation method for a lunar space navigation scheme is provided, comprising: for each navigation scheme, collecting multi-source data corresponding to the navigation scheme; determining the original index values ​​of multiple preset indicators based on the multi-source data, wherein the types of preset indicators include at least: coverage indicators, accuracy indicators, availability indicators, continuity indicators, and autonomy indicators; normalizing the original index values ​​of each preset indicator to obtain the target index value of each preset indicator; and for each task stage of the target task, determining the performance evaluation result of the navigation scheme at the task stage based on a preset mapping table and the target index values ​​of each preset indicator, wherein the preset mapping table contains an index weight vector corresponding to each task stage.

[0009] Furthermore, the multi-source data includes at least: spacecraft orbit data, navigation constellation data, receiver data, model data, observation data, and ground support data. The steps for collecting multi-source data corresponding to the navigation scheme include: based on the orbit design strategy in the navigation scheme, collecting the spacecraft's orbital ephemeris during the evaluation period at a preset time resolution to obtain spacecraft orbit data; based on the constellation combination in the navigation scheme, collecting the broadcast ephemeris and clock bias parameters of the constellation, and collecting the phase center and radiation pattern data of the transmitting antenna of each navigation satellite in the constellation to obtain navigation constellation data, wherein the radiation pattern data includes at least: sidelobe gain model data; based on the navigation scheme... The receiver type is determined, and the performance parameters of the receiver used by the spacecraft are collected to obtain receiver data. The performance parameters include at least: receiver sensitivity, antenna gain pattern, noise figure, and loop bandwidth. Model data is collected, including at least: a three-dimensional occlusion model of the Earth and a three-dimensional occlusion model of the Moon. Observation data is received through the receiver, including at least: pseudorange observations, carrier phase observations, carrier-to-noise ratio observations, and signal tracking status. Ground support data is received through the receiver, including at least: ground station uplink injection period, ephemeris and clock error update interval, and autonomous navigation maintenance threshold.

[0010] Furthermore, the step of determining the original index values ​​of multiple preset indicators based on multi-source data includes: when the preset indicator is a coverage indicator, determining the total number of sampling times in the evaluation period; determining the number of visible satellites at each sampling time based on observation data, and determining the visibility indicator function value corresponding to each sampling time by comparing the number of visible satellites with a preset number threshold; determining the position accuracy factor corresponding to each sampling time based on spacecraft orbit data and navigation constellation data; determining the geometric distribution quality factor corresponding to each sampling time based on a preset position accuracy reference value and the position accuracy factor corresponding to each sampling time; and determining the original coverage index value of the coverage indicator based on the visibility indicator function value and the geometric distribution quality factor corresponding to each sampling time.

[0011] Furthermore, the step of determining the position accuracy factor corresponding to each sampling moment based on spacecraft orbit data and navigation constellation data includes: for each sampling moment, determining the spacecraft position at the sampling moment based on the spacecraft orbit data, and determining the satellite position of each visible satellite based on the navigation constellation data; for each visible satellite, constructing a unit observation vector between the spacecraft and the visible satellite based on the spacecraft position and the satellite position of the visible satellite; constructing a geometric matrix based on all unit observation vectors; determining the covariance matrix based on the geometric matrix; and determining the position accuracy factor based on the covariance matrix.

[0012] Furthermore, the step of determining the original index values ​​of multiple preset indicators based on multi-source data also includes: when the preset indicator is a precision indicator, determining the ephemeris error, clock error, and receiver noise error at each sampling time based on navigation constellation data and observation data, wherein the receiver noise error is determined by performing multiple subtraction on pseudorange observations or carrier phase observations; determining the comprehensive error corresponding to each sampling time based on the ephemeris error, clock error, and receiver noise error at each sampling time; determining the average equivalent distance error based on the total number of sampling times and the comprehensive error corresponding to each sampling time; and determining the original precision index value of the precision indicator based on the average equivalent distance error.

[0013] Furthermore, the step of determining the original index values ​​of multiple preset indicators based on multi-source data also includes: when the preset indicator is an availability indicator, for each sampling time, determining whether the number of visible satellites at the sampling time is greater than or equal to a preset number threshold; based on the observation data, obtaining the carrier-to-noise ratio (CNR) observation value corresponding to each sampling time, and based on the receiver data, obtaining the receiver sensitivity; determining whether the minimum CNR observation value among all sampling times is greater than the receiver sensitivity; determining the preset error threshold for each task stage and the task time period for each task stage; for each task stage, based on the navigation constellation data and the observation data, determining each [missing information - likely related to the task time period within the task stage]. The positioning error at the sampling time is determined; it is then determined whether the positioning error is less than the corresponding preset error threshold; if the number of visible satellites at the sampling time is greater than or equal to the preset number threshold, the minimum carrier-to-noise ratio observation is greater than the receiver sensitivity, and the positioning error is less than the corresponding preset error threshold, the service availability indicator function value is determined as the first function value; if the number of visible satellites at the sampling time is less than the preset number threshold, the minimum carrier-to-noise ratio observation is less than or equal to the receiver sensitivity, or the positioning error is greater than or equal to the corresponding preset error threshold, the service availability indicator function value is determined as the second function value; based on the service availability indicator function value corresponding to each sampling time, the original availability index value of the availability index is determined.

[0014] Furthermore, the step of determining the original index values ​​of multiple preset indicators based on multi-source data also includes: when the preset indicator is a continuous indicator, determining the sliding step size based on the preset time resolution, and determining the total number of sliding windows based on the duration of the evaluation period, the sliding step size, and the preset time window length; counting the number of interruption events in each time window based on the observation data, wherein the number of interruption events refers to the number of events in which the continuous service interruption duration exceeds the maximum allowed interruption duration; and determining the original continuous index value of the continuous indicator based on the number of interruption events in all time windows and the total number of sliding windows.

[0015] Furthermore, the step of determining the original index values ​​of multiple preset indicators based on multi-source data also includes: when the preset indicator is an autonomous indicator, determining the longest autonomous duration of the spacecraft in the evaluation period based on ground support data, wherein the autonomous duration refers to the navigation duration of the spacecraft's space system with preset accuracy without relying on the auxiliary information injected by the ground station uplink; and determining the original autonomous index value of the autonomous indicator based on the longest autonomous duration and the navigation duration reference value.

[0016] Furthermore, the step of determining the performance evaluation results of the navigation scheme in each task stage based on the preset mapping table and the target index value of each preset index includes: determining the index weight vector corresponding to each task stage based on the preset mapping table; and performing a weighted summation of the target index values ​​of each preset index based on the index weight vector to obtain the performance evaluation results of the navigation scheme in each task stage.

[0017] According to another aspect of the present invention, an evaluation device for a lunar space navigation scheme is also provided, comprising: a data acquisition unit for acquiring multi-source data corresponding to each navigation scheme; a first determination unit for determining the original index values ​​of multiple preset indicators based on the multi-source data, wherein the types of preset indicators include at least: coverage indicators, accuracy indicators, availability indicators, continuity indicators, and autonomy indicators; a processing unit for normalizing the original index values ​​of each preset indicator to obtain a target index value for each preset indicator; and a second determination unit for determining the performance evaluation result of the navigation scheme at each task stage of the target task based on a preset mapping table and the target index values ​​of each preset indicator, wherein the preset mapping table contains an index weight vector corresponding to each task stage.

[0018] Furthermore, the multi-source data includes at least: spacecraft orbit data, navigation constellation data, receiver data, model data, observation data, and ground support data. The acquisition unit includes: a first acquisition module, used to acquire the spacecraft's orbital ephemeris during the evaluation period at a preset time resolution based on the orbital design strategy in the navigation scheme, to obtain spacecraft orbit data; a second acquisition module, used to acquire the broadcast ephemeris and clock bias parameters of the constellation based on the constellation combination in the navigation scheme, and to acquire the phase center and radiation pattern data of the transmitting antenna of each navigation satellite in the constellation, to obtain navigation constellation data, wherein the radiation pattern data includes at least: sidelobe gain model data; and a third acquisition module, used to acquire the receiver type in the navigation scheme. The system includes: a first acquisition module for acquiring receiver performance parameters used by the spacecraft, obtaining receiver data, including at least: receiver sensitivity, antenna gain pattern, noise figure, and loop bandwidth; a second acquisition module for acquiring model data, including at least: a three-dimensional occlusion model of the Earth and a three-dimensional occlusion model of the Moon; a third acquisition module for receiving observation data via the receiver, including at least: pseudorange observations, carrier phase observations, carrier-to-noise ratio observations, and signal tracking status; and a fourth acquisition module for receiving ground support data via the receiver, including at least: ground station uplink injection period, ephemeris and clock error update interval, and autonomous navigation maintenance threshold.

[0019] Further, the first determining unit includes: a first determining module, used to determine the total number of sampling times in the evaluation period when the preset indicator is a coverage indicator; a second determining module, used to determine the number of visible satellites at each sampling time based on observation data, and to determine the visibility indicator function value corresponding to each sampling time by comparing the number of visible satellites with a preset number threshold; a third determining module, used to determine the position accuracy factor corresponding to each sampling time based on spacecraft orbit data and navigation constellation data; a fourth determining module, used to determine the geometric distribution quality factor corresponding to each sampling time based on a preset position accuracy reference value and the position accuracy factor corresponding to each sampling time; and a fifth determining module, used to determine the original coverage indicator value of the coverage indicator based on the visibility indicator function value and the geometric distribution quality factor corresponding to each sampling time.

[0020] Furthermore, the third determination module includes: a first determination submodule, used to determine the spacecraft position at each sampling time based on spacecraft orbit data, and to determine the satellite position of each visible satellite based on navigation constellation data; a first construction submodule, used to construct a unit observation vector between the spacecraft and the visible satellite for each visible satellite based on the spacecraft position and the satellite position of the visible satellite; a second construction submodule, used to construct a geometric matrix based on all unit observation vectors; a second determination submodule, used to determine the covariance matrix based on the geometric matrix; and a third determination submodule, used to determine the position accuracy factor based on the covariance matrix.

[0021] Furthermore, the first determining unit also includes: a sixth determining module, used to determine the ephemeris error, clock error, and receiver noise error at each sampling time based on navigation constellation data and observation data, provided that the preset indicator is an accuracy indicator; wherein the receiver noise error is determined by performing multiple subtraction on pseudorange observations or carrier phase observations; a seventh determining module, used to determine the comprehensive error corresponding to each sampling time based on the ephemeris error, clock error, and receiver noise error at each sampling time; an eighth determining module, used to determine the average equivalent distance error based on the total number of sampling times and the comprehensive error corresponding to each sampling time; and a ninth determining module, used to determine the original accuracy indicator value of the accuracy indicator based on the average equivalent distance error.

[0022] Furthermore, the first determining unit also includes: a first judging module, used to determine whether the number of visible satellites at each sampling time is greater than or equal to a preset number threshold, provided that the preset indicator is an availability indicator; a first acquiring module, used to acquire the carrier-to-noise ratio (CNR) observation value corresponding to each sampling time based on observation data, and to acquire the receiver sensitivity based on receiver data; a second judging module, used to determine whether the minimum CNR observation value among all sampling times is greater than the receiver sensitivity; a tenth determining module, used to determine the preset error threshold for each task stage and the task time period for each task stage; and an eleventh determining module, used to determine, for each task stage, the positioning error of each sampling time within the task time period of the task stage, based on navigation constellation data and observation data. The system includes four modules: a third judgment module for determining whether the positioning error is less than the corresponding preset error threshold; a twelfth determination module for determining the service availability indicator function value as the first function value when the number of visible satellites at the sampling time is greater than or equal to a preset number threshold, the minimum carrier-to-noise ratio observation value is greater than the receiver sensitivity, and the positioning error is less than the corresponding preset error threshold; a thirteenth determination module for determining the service availability indicator function value as the second function value when the number of visible satellites at the sampling time is less than a preset number threshold, the minimum carrier-to-noise ratio observation value is less than or equal to the receiver sensitivity, or the positioning error is greater than or equal to the corresponding preset error threshold; and a fourteenth determination module for determining the original availability indicator value of the availability index based on the service availability indicator function value corresponding to each sampling time.

[0023] Furthermore, the first determining unit also includes: a fifteenth determining module, used to determine the sliding step size based on a preset time resolution when the preset indicator is a continuity indicator, and to determine the total number of sliding windows based on the duration of the evaluation period, the sliding step size, and the preset time window length; a first statistics module, used to count the number of interruption events in each time window based on the observation data, wherein the number of interruption events refers to the number of events in which the continuous service interruption duration exceeds the maximum allowed interruption duration; and a sixteenth determining module, used to determine the original continuity indicator value of the continuity indicator based on the number of interruption events in all time windows and the total number of sliding windows.

[0024] Furthermore, the first determining unit also includes: a seventeenth determining module, used to determine the longest autonomous duration of the spacecraft during the evaluation period based on ground support data, when the preset indicator is an autonomous indicator; wherein the autonomous duration refers to the navigation duration of the spacecraft's aerospace system with preset accuracy without relying on the auxiliary information injected by the ground station uplink; and an eighteenth determining module, used to determine the original autonomous indicator value of the autonomous indicator based on the longest autonomous duration and the navigation duration reference value.

[0025] Furthermore, the second determining unit includes: a nineteenth determining module, used to determine the indicator weight vector corresponding to each task stage based on a preset mapping table; and a first weighting module, used to perform a weighted summation of the target indicator values ​​of each preset indicator based on the indicator weight vector, to obtain the performance evaluation results of the navigation scheme in each task stage.

[0026] According to another aspect of the present invention, a computer program product is also provided, including a non-volatile computer-readable storage medium storing a computer program, wherein the computer program, when executed by a processor, implements an evaluation method for a lunar space navigation scheme as described above.

[0027] According to another aspect of the present invention, an electronic device is also provided, including one or more processors and a memory, the memory being used to store one or more programs, wherein when the one or more programs are executed by the one or more processors, the one or more processors cause the one or more processors to implement the evaluation method of any of the above-described lunar space navigation schemes.

[0028] In this invention, for each navigation scheme, multi-source data corresponding to the navigation scheme is collected. Based on the multi-source data, the original index values ​​of multiple preset indicators are determined. The original index values ​​of each preset indicator are normalized to obtain the target index value of each preset indicator. For each task stage of the target task, based on the preset mapping table and the target index value of each preset indicator, the performance evaluation result of the navigation scheme in the task stage is determined, thereby solving the technical problem of low accuracy in evaluating the Earth-Moon space navigation scheme in related technologies.

[0029] In this invention, a multi-dimensional quantitative evaluation and task-stage adaptive fusion approach is adopted. By collecting multi-source data corresponding to navigation schemes and calculating original indicators such as coverage, accuracy, availability, continuity, and autonomy in parallel, and eliminating the influence of dimensions after range normalization, the weight vectors corresponding to each task stage are dynamically loaded and weighted summed in combination with a preset mapping table. This achieves the purpose of systematic, quantitative, and staged comprehensive evaluation of GNSS navigation performance in the Earth-Moon space, thereby realizing the technical effect of comparability and optimization of different navigation schemes in real task scenarios. It also solves the technical problem that the evaluation dimension is single, static, and fixed, and cannot adapt to the dynamic needs of Earth-Moon tasks, resulting in a lack of scientific basis for scheme selection. Attached Figure Description

[0030] The accompanying drawings, which are included to provide a further understanding of the invention and form part of this invention, illustrate exemplary embodiments of the invention and are used to explain the invention, but do not constitute an undue limitation of the invention. In the drawings:

[0031] Figure 1This is a flowchart of an optional evaluation method for a lunar space navigation scheme according to an embodiment of the present invention;

[0032] Figure 2 This is a schematic diagram of an evaluation device for an optional Earth-Moon space navigation scheme according to an embodiment of the present invention;

[0033] Figure 3 This is a hardware structure block diagram of an electronic device (or mobile device) for evaluating a lunar navigation scheme according to an embodiment of the present invention. Detailed Implementation

[0034] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0035] It should be noted that the terms "first," "second," etc., used in this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0036] It should be noted that all related information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, and displayed data) collected and involved in this invention are information and data authorized by the user or fully authorized by all parties. Furthermore, the collection, storage, use, processing, transmission, provision, disclosure, and application of this data comply with the relevant laws, regulations, and standards of the relevant regions, and necessary measures have been taken to ensure compliance with public order and good morals. Corresponding operation entry points are provided for users to choose to authorize or refuse. For example, this system has an interface with relevant users or organizations. Before obtaining relevant information, a request to obtain the information needs to be sent to the aforementioned user or organization through the interface, and the relevant information is obtained only after receiving consent from the aforementioned user or organization.

[0037] This invention belongs to the field of aerospace telemetry and control and satellite navigation technology, specifically relating to a method and system for evaluating the navigation performance of a Global Navigation Satellite System (GNSS) in lunar space, which is particularly suitable for navigation scheme design, performance simulation and comparative selection in lunar exploration missions.

[0038] This invention proposes a multi-dimensional comprehensive performance evaluation method and system for GNSS in the Earth-Moon space, addressing the problems of existing evaluation technologies having limited dimensions, lacking systematic quantitative models, and failing to adapt to the special constraints and dynamic requirements of missions in the Earth-Moon space. This method achieves a systematic quantitative characterization of GNSS navigation performance in the Earth-Moon space by constructing an evaluation index system that includes dimensions such as coverage, accuracy, availability, continuity, and autonomy.

[0039] The evaluation method first involves data input and preprocessing, receiving multi-source data including user orbits, GNSS constellations, receiver characteristics, mission profiles, observation data, and ground support parameters. Then, quantitative indicators for each dimension are calculated in parallel: coverage is calculated using a space-time coverage formula, comprehensively considering the number of visible satellites and signal quality; accuracy is characterized by weighted positioning error and geometric accuracy factor degradation coefficient; availability is measured by service availability rate; continuity is assessed based on the probability of interruption within the maximum permissible interruption duration; and autonomy is calculated using corresponding mathematical models. Afterward, the above raw indicators are normalized to eliminate the influence of dimensions. Next, a dynamic weight allocation mechanism based on mission phases is introduced, using the analytic hierarchy process (AHP) to generate differentiated dimensional weight vectors for different mission phases (such as Earth-Moon transfer, lunar orbit, and lunar landing), thus closely linking the evaluation criteria to the actual needs of the mission. Finally, a comprehensive performance score is calculated using a weighted summation model, which can be directly used for performance comparison and ranking of different navigation schemes, receiver configurations, or orbit designs.

[0040] The evaluation system includes modules for data input, indicator calculation, weight management, and comprehensive evaluation, which are used to automate the above processes.

[0041] This invention establishes a systematic, quantifiable, and mission-adaptable comprehensive evaluation framework for GNSS performance in the lunar-Earth space. Its output results can provide scientific, intuitive, and reliable quantitative basis for the design, simulation verification, and scheme decision-making of navigation systems for lunar exploration missions.

[0042] The present invention will now be described in detail with reference to various embodiments.

[0043] Example 1

[0044] According to an embodiment of the present invention, an embodiment of an evaluation method for a lunar space navigation scheme is provided. It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. Furthermore, although a logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in a different order than that shown here.

[0045] Figure 1 This is a flowchart of an optional evaluation method for a lunar space navigation scheme according to an embodiment of the present invention, such as... Figure 1 As shown, the method includes the following steps:

[0046] Step S101: For each navigation scheme, collect multi-source data corresponding to the navigation scheme.

[0047] In this embodiment of the invention, multiple navigation schemes can be constructed first. Each navigation scheme may include the spacecraft's orbital design, the combination of navigation constellations used, and the receivers used.

[0048] First, multi-source data corresponding to each navigation scheme can be collected. This multi-source data contains all the prior parameters and data required for evaluation, including: user orbit data, GNSS constellation data, receiver parameters, environment and mission models, receiver observation data (measured or simulated), and ground support parameters, etc.

[0049] Step S102: Based on multi-source data, determine the original index values ​​of multiple preset indicators. The types of preset indicators include at least: coverage indicators, accuracy indicators, availability indicators, continuity indicators, and autonomy indicators.

[0050] In this embodiment of the invention, raw performance index values ​​for five preset dimensions can be calculated in parallel based on multi-source data. The calculation process follows explicit mathematical formulas to ensure objectivity.

[0051] The types of preset indicators include: coverage indicators, accuracy indicators, availability indicators, continuity indicators, and autonomy indicators. Coverage indicators can be calculated using the trajectory-time weighted coverage formula, and their value is composed of the number of visible satellites and the Position Dilution of Precision (PDOP), reflecting the proportion of overall available time for the signal under conditions of visibility, strength, and geometric quality. Accuracy indicators can be characterized by a weighted positioning error degradation coefficient, the value of which is the mean of the User Equivalent Range Error (UERE) within the evaluation period, which integrates ephemeris error, signal propagation error, and receiver measurement error. Availability indicators can be calculated using service availability rate, and are only 1 when the signal is visible, strength meets the standard, and positioning error is less than the stage alarm limit. Continuity indicators can be assessed by evaluating the interruption probability within the maximum allowable interruption duration, statistically analyzing the frequency of service interruptions exceeding a threshold within a sliding window. Autonomy indicators can be calculated using the normalized value of the average ground injection interval time, reflecting the duration for which the receiver maintains the specified accuracy without ground assistance.

[0052] Step S103: Normalize the original index value of each preset index to obtain the target index value of each preset index.

[0053] In this embodiment of the invention, since the physical meanings and dimensions of the indicators in each dimension are different, normalization is required to eliminate the influence of dimensions and facilitate comprehensive evaluation. For each dimension... i The original index value is set as follows: The range normalization method is used to map the data to the [0,1] interval: ,in, and For the first i The upper and lower limits of each dimension indicator can be set based on theoretical extreme values, historical data statistics, or task requirements. The normalized value (target indicator value) The larger the value, the better the performance of that dimension.

[0054] This step eliminates differences in dimensions and orders of magnitude among different indicators through normalization, ensuring the mathematical rationality of subsequent weighted fusion, preventing a single indicator with a large magnitude from dominating the comprehensive score, and improving the comparability and objectivity of the evaluation results.

[0055] Step S104: For each task stage of the target task, based on the preset mapping table and the target index value of each preset index, determine the performance evaluation result of the navigation scheme under the task stage. The preset mapping table contains the index weight vector corresponding to each task stage.

[0056] In this embodiment of the invention, based on the task phase division file, the weight vector corresponding to the phase in the preset mapping table is called. The weight vector is a five-dimensional normalized vector, and its elements correspond to the relative importance of five indicators: coverage, accuracy, availability, continuity, and autonomy. For example, in the "lunar landing phase," accuracy and continuity have weights higher than 0.3, while autonomy has a weight lower than 0.1; in the "Earth-Moon transfer phase," coverage and autonomy have dominant weights. By linearly weighting and summing the target indicator values ​​with the corresponding weight vectors, the comprehensive performance score for that phase is obtained.

[0057] This step enables dynamic matching between evaluation criteria and task requirements, ensuring that the evaluation results of the same navigation scheme at different stages reflect its true task effectiveness, avoiding decision-making biases caused by static evaluation, and ensuring that the evaluation results serve the engineering goals of task design and scheme selection.

[0058] In summary, a multi-dimensional quantitative evaluation and task-stage adaptive fusion approach can be adopted. By collecting multi-source data corresponding to navigation schemes and calculating raw indicators such as coverage, accuracy, availability, continuity, and autonomy in parallel, and eliminating the influence of dimensions after range normalization, the weight vectors corresponding to each task stage are dynamically loaded into a preset mapping table for weighted summation. This achieves the goal of systematically, quantitatively, and stage-wise comprehensively evaluating the performance of GNSS navigation in the Earth-Moon space. This enables the technical effect of comparability and optimization of different navigation schemes in real task scenarios, and solves the technical problem of single, static, and fixed evaluation dimensions that cannot adapt to the dynamic needs of Earth-Moon tasks, resulting in a lack of scientific basis for scheme selection.

[0059] Optionally, the multi-source data includes at least: spacecraft orbit data, navigation constellation data, receiver data, model data, observation data, and ground support data. To accurately collect multi-source data, in the evaluation method for the Earth-Moon space navigation scheme provided in Embodiment 1 of this application, based on the orbit design strategy in the navigation scheme, the spacecraft's orbital ephemeris during the evaluation period is collected at a preset time resolution to obtain spacecraft orbit data; based on the constellation combination in the navigation scheme, the broadcast ephemeris and clock bias parameters of the constellation are collected, and the phase center and radiation pattern data of the transmitting antenna of each navigation satellite in the constellation are collected to obtain navigation constellation data. The radiation pattern data includes at least: sidelobe gain model data. Based on the receiver type in the navigation scheme, the performance parameters of the receiver used by the spacecraft are collected to obtain receiver data. The performance parameters include at least: receiver sensitivity, antenna gain pattern, noise figure, and loop bandwidth. Model data is collected, including at least: a three-dimensional occlusion model of the Earth and a three-dimensional occlusion model of the Moon. Observation data is received through the receiver, including at least: pseudorange observations, carrier phase observations, carrier-to-noise ratio observations, and signal tracking status. Ground support data is received through the receiver, including at least: ground station uplink injection period, ephemeris and clock error update interval, and autonomous navigation maintenance threshold.

[0060] In this embodiment of the invention, the multi-source data includes: spacecraft orbit data, navigation constellation data, receiver data, model data, observation data, and ground support data, etc.

[0061] Spacecraft orbital data is generated by a high-precision orbital dynamics simulation system or measured ephemeris files. The precise orbital ephemeris of the spacecraft during the evaluation period is recorded with a time resolution of not less than 1 second, including three-dimensional position and velocity, to determine the spatial trajectory of the GNSS signal receiving point.

[0062] The navigation constellation data consists of broadcast ephemeris and satellite clock bias files released by various GNSS systems (such as GPS (Global Positioning System), BDS (Beidou Navigation Satellite System), and Galileo (Galileo Satellite Navigation System), as well as the phase center and radiation pattern data of the transmitting antennas of each GNSS satellite. The radiation pattern data includes sidelobe gain model data, which is the radiation intensity distribution parameter outside the main lobe of the satellite antenna, used to calculate the received power of weak signals in the Earth-Moon space.

[0063] Receiver data includes key performance parameters such as receiver sensitivity (acquisition and tracking thresholds, such as 15 dB-Hz and 20 dB-Hz), antenna gain pattern used to correct for received signal strength, noise figure (characterizing the receiver's internal thermal noise level), and loop bandwidth (determining tracking dynamic response capability).

[0064] The model data is constructed from a three-dimensional geometric model of the Earth and Moon, including three-dimensional occlusion models of the Earth and the Moon, to determine whether the signal is blocked by the Earth or the Moon in the propagation path.

[0065] The observation data can be received and recorded by the actual payload of the spacecraft, or it can be obtained through simulation. It includes pseudorange observations (reflecting the signal propagation time delay), carrier phase observations (used for high-precision relative positioning), carrier-to-noise ratio observations (reflecting signal strength and quality), and signal tracking status (indicating whether the signal is stably locked).

[0066] Ground support data is provided by the mission planning system, including the period, update interval, and minimum autonomous time threshold for the receiver to maintain navigation accuracy without external assistance when the ground station injects ephemeris / clock bias into the spacecraft.

[0067] In addition, a mission phase division file can be obtained, which clearly defines the different phases of the mission (e.g., Phase 1 - Earth-Moon transfer cruise phase, Phase 2 - Lunar braking phase, Phase 3 - Lunar orbit phase, Phase 4 - Lunar landing phase, Phase 5 - Lunar operation phase) and the start and end times of each phase.

[0068] In this embodiment, by clearly defining the six types of multi-source data and their engineering acquisition methods, a standardized and executable data input system for GNSS performance evaluation in the Earth-Moon space is constructed. This eliminates the need for assumptions or simplified models in the evaluation process, significantly improving the accuracy, systematicity, and task adaptability of the evaluation results. It also provides reliable, complete, and verifiable input support for subsequent index calculation and comprehensive scoring.

[0069] To improve the accuracy of determining the original coverage index value, in the evaluation method of the Earth-Moon space navigation scheme provided in Embodiment 1 of this application, when the preset index is a coverage index, the total number of sampling times in the evaluation period is determined; based on the observation data, the number of visible satellites at each sampling time is determined, and the visibility indicator function value corresponding to each sampling time is determined by comparing the number of visible satellites with a preset number threshold; based on spacecraft orbit data and navigation constellation data, the position accuracy factor corresponding to each sampling time is determined; based on the preset position accuracy reference value and the position accuracy factor corresponding to each sampling time, the geometric distribution quality factor corresponding to each sampling time is determined; based on the visibility indicator function value and the geometric distribution quality factor corresponding to each sampling time, the original coverage index value of the coverage index is determined.

[0070] In this embodiment of the invention, the start and end times of the evaluation period can be determined first, and the total number of uniformly discrete sampling points within the evaluation period can be calculated based on the sampling time resolution of the spacecraft orbit data. For example, the evaluation period can be discretized along the time axis with a time step of not less than 1 second, and a sampling moment can be recorded at fixed time intervals (e.g., 1 second).

[0071] Here, the evaluation time period refers to a continuous time interval defined for evaluating the performance of a navigation scheme, typically corresponding to a complete flight phase in the mission profile, such as the Earth-Moon transfer phase or the lunar orbit phase. The total number of sampling moments is the number of discrete time points within this time period, divided according to a preset time resolution, used to construct the time baseline for coverage calculations. The sampling moments are discrete time points on the time axis used to evaluate navigation performance point by point; their density is determined by the orbital data resolution to ensure that no dynamic processes are missed.

[0072] By processing the observation data output by the receiver, a list of visible satellites is extracted for each sampling time. The criteria for determining a visible satellite are: at that time, the satellite is within the user's (spacecraft's) field of view and is not obstructed by a three-dimensional occlusion model of the Earth or Moon, and its signal-to-noise ratio is higher than the receiver's acquisition threshold. For each sampling time, the number of satellites meeting the above conditions is counted and denoted as . Then, based on a preset threshold (e.g., 4), if at time t, the number of visible GNSS satellites is... Then the visibility indicator function value It is 1 if it is true, otherwise it is 0.

[0073] Here, the visibility indicator function is a binary function, with a value of 1 indicating that the basic navigation service is available at that moment, and a value of 0 indicating that it is not available.

[0074] At each sampling time, based on the user's three-dimensional position provided by the spacecraft orbit data and the satellite's three-dimensional position provided by the navigation constellation data, a geometric relationship matrix between the user and the visible satellites is constructed, and the position precision factor (PDOP) is calculated.

[0075] Here, the position accuracy factor, also known as the position accuracy attenuation factor (PDOP), is a dimensionless parameter that characterizes the amplification effect of satellite spatial geometry on three-dimensional positioning errors.

[0076] The position precision factor calculated at each sampling time Compared with the preset position accuracy reference value Perform normalization mapping to generate geometric distribution quality factors. .

[0077] Here, the preset position accuracy reference value is the minimum acceptable benchmark for geometric configuration set according to the requirements of GNSS applications in the Earth-Moon space. Its value is determined through mission simulation and historical data statistics.

[0078] The visibility indicator function value and the geometric distribution quality factor are weighted and integrated over time to calculate the original coverage index value. C .

[0079] For example, coverage C Coverage based on trajectory-time weighted Quantification, its calculation formula is:

[0080] ;

[0081] Where T represents the total number of sampling times within the evaluation period. This is a visibility indicator function. At time t, if the number of visible GNSS satellites... ,but Otherwise, it is 0, indicating that the number of satellites can be determined based on the receiver's observation data. The geometric distribution quality factor is calculated using the following formula:

[0082] ;

[0083] in, Let be the position precision factor at time t. This is a set PDOP reference value. When the satellite geometry is good (PDOP value is small), this factor is close to 1; when the geometry is poor (PDOP value is large), this factor decreases, thus reflecting the impact of geometry conditions in coverage assessment.

[0084] The value is between 0 and 1, which comprehensively reflects the proportion of time that the navigation signal satisfies basic visibility and has a good geometric distribution throughout the entire mission timeline.

[0085] In this embodiment, a systematic, computable, and repeatable evaluation of coverage indicators is achieved. The GNSS coverage in the Earth-Moon space is expanded from the traditional single dimension of "number of visible satellites" to a comprehensive model with dual constraints of "visibility + geometric quality". This improves the engineering accuracy and mission adaptability of the evaluation results in weak signal and low geometric configuration environments, and provides a scientific and quantifiable coverage basis for the objective comparison of navigation schemes.

[0086] To improve the accuracy of determining the position accuracy factor, in the evaluation method of the Earth-Moon space navigation scheme provided in Embodiment 1 of this application, for each sampling time, the spacecraft position at the sampling time is determined based on the spacecraft orbit data, and the satellite position of each visible satellite is determined based on the navigation constellation data; for each visible satellite, a unit observation vector between the spacecraft and the visible satellite is constructed based on the spacecraft position and the satellite position of the visible satellite; a geometric matrix is ​​constructed based on all unit observation vectors; a covariance matrix is ​​determined based on the geometric matrix; and the position accuracy factor is determined based on the covariance matrix.

[0087] In this embodiment of the invention, based on spacecraft orbit data, the three-dimensional coordinates of the spacecraft in the geocentric inertial coordinate system are obtained by interpolation according to the sampling time timestamp. At the same time, based on the broadcast ephemeris and clock error parameters of each GNSS system (GPS, BDS, Galileo) in the navigation constellation data, the three-dimensional position of each visible satellite in the same coordinate system is calculated according to the satellite orbit dynamics model.

[0088] For each visible satellite at each sampling time, its line-of-sight vector relative to the spacecraft is calculated and normalized to a unit observation vector. That is, a unit observation vector is constructed based on the user's position and the GNSS satellite positions. ,in, Indicates the satellite position at time t. Indicates the user's location (i.e., the spacecraft's location).

[0089] Here, the unit observation vector is a spatial vector of unit length pointing from the user (spacecraft) to the visible satellite. Its direction reflects the signal propagation path, and its components are used to construct the observation rows of the geometric matrix.

[0090] Based on all visible satellites at the current sampling time, construct a geometric matrix G row by row. , where n is the number of visible satellites.

[0091] The covariance matrix Q of the positioning solution is calculated using matrix operations, and the calculation formula is as follows: The covariance matrix is ​​a measure of the uncertainty in parameter estimation in GNSS positioning, determined by the observation geometry. Its structure is uniquely determined by the geometric matrix, reflecting the mathematical influence of satellite distribution on solution stability.

[0092] Extract the first three rows and first three columns of the submatrix from the covariance matrix Q, calculate the square root of the sum of its main diagonal elements, and thus obtain the positional precision factor. ,in, This represents the j-th diagonal element of matrix Q.

[0093] In this embodiment, a purely geometric, dimensionless, and reproducible calculation of the position accuracy factor is realized. A geometric performance quantification mechanism that is separated from the measured error is established in the GNSS performance evaluation in the Earth-Moon space. This ensures that the evaluation results are no longer affected by signal strength, noise, or receiver performance, and only reflect the inherent potential of the satellite configuration. This enhances the scientific nature and technical objectivity of navigation schemes in orbit design and constellation combination selection.

[0094] To improve the accuracy of determining the original precision index value, in the evaluation method of the Earth-Moon space navigation scheme provided in Embodiment 1 of this application, when the preset index is the precision index, based on navigation constellation data and observation data, the ephemeris error, clock error, and receiver noise error at each sampling time are determined. The receiver noise error is determined by performing multiple subtraction on pseudorange observations or carrier phase observations. Based on the ephemeris error, clock error, and receiver noise error at each sampling time, the comprehensive error corresponding to each sampling time is determined. Based on the total number of sampling times and the comprehensive error corresponding to each sampling time, the average equivalent distance error is determined. Based on the average equivalent distance error, the original precision index value of the precision index is determined.

[0095] In this embodiment of the invention, the ephemeris and clock error parameters of each GNSS system (GPS, BDS, Galileo) in the navigation constellation data are analyzed, and combined with the time synchronization information of the spacecraft orbit data, the ephemeris error at each sampling moment is calculated. With clock error The ephemeris error is obtained by comparing the three-dimensional positional deviation between the precise ephemeris and the broadcast ephemeris at that moment; the clock error is determined by extracting the difference between the broadcast clock error parameters and the reference clock error standard.

[0096] By processing measured pseudorange or carrier phase observations, double-difference or triple-difference algorithms are used to eliminate common errors (such as ionospheric, tropospheric delays, satellite clock errors, and ephemeris errors), and the residual term contributed only by the receiver's internal measurement noise is retained. The statistical variance of this residual term is the receiver noise error at that moment. This calculation is performed in parallel at sampling times within the index quantization engine.

[0097] Here, ephemeris error refers to the user's equivalent distance error component caused by the deviation between the broadcast ephemeris and the actual orbit; clock error refers to the ranging error component caused by the deviation between the satellite atomic clock and the system time; receiver noise error is the random measurement error introduced by the receiver front-end circuit and the tracking loop, which is extracted from the original observation value through differential processing.

[0098] The ephemeris error, clock error, and receiver noise error at each sampling time are combined by taking the square root of the sum of squares according to the error propagation law to calculate the overall error at that time. ,in, This is due to ephemeris error; This refers to clock error; Receiver noise error (which can be obtained by performing multiple differences on pseudorange or carrier phase observations).

[0099] The combined error of all sampling times within the evaluation period Perform an arithmetic mean to calculate the average equivalent distance error. ,in, This is the time average of the User Equivalent Distance Error (UERE) over the entire evaluation period.

[0100] Based on the average equivalent distance error The original value of the accuracy index is used to calculate the accuracy index. A Precision A Weighted positioning accuracy degradation coefficient Characterization, which is defined as: , The higher the value, the higher the overall positioning accuracy.

[0101] In this embodiment, a precision index calculation system based on real observation and ephemeris data and physical traceability was established in the performance evaluation of GNSS in the Earth-Moon space. This system enables the precision evaluation to truly reflect the comprehensive positioning capability of the system under the combined effects of ephemeris, clock bias and receiver noise, and significantly improves the scientific nature and engineering credibility of the performance comparison of navigation schemes under different hardware configurations and orbital conditions.

[0102] To improve the accuracy of determining the original availability index value, in the evaluation method of the Earth-Moon space navigation scheme provided in Embodiment 1 of this application, when the preset index is the availability index, for each sampling time, it is determined whether the number of visible satellites at the sampling time is greater than or equal to a preset number threshold; based on the observation data, the carrier-to-noise ratio observation value corresponding to each sampling time is obtained, and based on the receiver data, the receiver sensitivity is obtained; it is determined whether the minimum carrier-to-noise ratio observation value among all sampling times is greater than the receiver sensitivity; a preset error threshold for each task stage and the task time period for each task stage are determined; for each task stage, based on the navigation constellation data and the observation data, the task time period in which the task stage is located is determined. The positioning error at each sampling time point within a time interval is determined; it is then determined whether the positioning error is less than the corresponding preset error threshold; if the number of visible satellites at the sampling time point is greater than or equal to the preset number threshold, the minimum carrier-to-noise ratio observation value is greater than the receiver sensitivity, and the positioning error is less than the corresponding preset error threshold, the service availability indicator function value is determined as the first function value; if the number of visible satellites at the sampling time point is less than the preset number threshold, the minimum carrier-to-noise ratio observation value is less than or equal to the receiver sensitivity, or the positioning error is greater than or equal to the corresponding preset error threshold, the service availability indicator function value is determined as the second function value; based on the service availability indicator function value corresponding to each sampling time point, the original availability index value of the availability index is determined.

[0103] In this embodiment of the invention, a list of visible satellites at each sampling time is parsed. This list is calculated jointly from spacecraft orbit data and navigation constellation data, and determined based on a three-dimensional occlusion model and receiver acquisition threshold constraints. For each sampling time, the number of satellites that satisfy geometric visibility (not obscured by the Earth or Moon) and have a signal-to-noise ratio higher than the receiver acquisition threshold is counted, and denoted as . Then, the quantity is compared with a preset quantity threshold (e.g., 4). If... If the condition is met, then the condition is true; otherwise, it is false.

[0104] Extract the carrier-to-noise ratio (C / N0) observations of each visible satellite at each sampling time from the observation data. This value is output by the receiver tracking loop and is in dB-Hz. At the same time, the preset acquisition sensitivity threshold (e.g., 15dB-Hz) and tracking sensitivity threshold (e.g., 20dB-Hz) are read from the receiver parameter configuration file, and the acquisition sensitivity is selected as the minimum signal strength criterion.

[0105] Here, the carrier-to-noise ratio (CNR) observation value is the measured value of the ratio of signal strength to noise power for each visible satellite at the sampling time, reflecting the signal detectability. Receiver sensitivity is the lowest CNR threshold at which the receiver can stably acquire and track GNSS signals. It is determined by the receiver hardware design parameters and is a hard threshold for judging signal availability.

[0106] The carrier-to-noise ratio (CNR) observations at all sampling times within the evaluation period are compared to obtain the observation with the minimum CNR. This minimum value is compared with the receiver sensitivity threshold. Comparison: If If the signal is lost, it is determined that there is no risk of signal loss during the entire assessment period; otherwise, it is determined that there is a risk of signal unavailability.

[0107] By reading the mission phase division file, well-defined phase division information is obtained, including the names of each phase (such as the Earth-Moon transfer phase, the lunar orbit phase, and the lunar landing phase) and their corresponding time intervals (start and end times). For each phase, based on its mission safety level and navigation requirements, a corresponding positioning error threshold is preset (such as 50 meters for the Earth-Moon transfer phase and 3 meters for the lunar landing phase).

[0108] For each mission phase's time interval, based on navigation constellation data and observation data, the positioning error at each sampling moment within that interval is calculated through error propagation. This positioning error combines ephemeris error, clock error, and receiver noise error.

[0109] The positioning error calculated at each sampling time within the corresponding task phase Compare with the preset error threshold for this stage: if the positioning error... If the error is less than the preset error threshold specified for the current task stage, the accuracy constraint is met; otherwise, it is not met.

[0110] For each sampling time, if the following conditions are met simultaneously , Positioning error If the alarm value is less than the alarm limit specified for the current task phase, then the service availability indicator function value... The first function value, 1, indicates that the service is available at that moment. If any condition is not met, the service availability indicator function value becomes the second function value, 0, indicating that the service is unavailable at that moment.

[0111] Service available indicator function values ​​for all sampling times within the evaluation period Perform an arithmetic mean to calculate the raw usability index value. U Availability U By service availability calculate: .

[0112] In this embodiment, a mission-driven, hard-constraint, and measurement-oriented availability quantification model was established in the Earth-Moon space GNSS assessment. This model enables the availability assessment results to truly reflect the multi-dimensional security requirements of navigation services during the mission phase, significantly improving the decision reliability and engineering applicability of navigation schemes in high-reliability mission scenarios (such as lunar landing).

[0113] To improve the accuracy of determining the original continuity index value, in the evaluation method of the Earth-Moon space navigation scheme provided in Embodiment 1 of this application, when the preset index is a continuity index, the sliding step size is determined based on the preset time resolution, and the total number of sliding windows is determined based on the time length of the evaluation period, the sliding step size, and the preset time window length; based on the observation data, the number of interruption events under each time window is counted, where the number of interruption events refers to the number of events where the continuous service interruption duration exceeds the maximum allowable interruption duration; based on the number of interruption events under all time windows and the total number of sliding windows, the original continuity index value of the continuity index is determined.

[0114] In this embodiment of the invention, the sliding step size is determined according to the time resolution (consistent with the orbital data sampling interval, typically 1 second). Set it to be equal to the sampling interval. Then, based on the evaluation time period length T and the sliding step size... and preset time window length Calculate the total number of sliding windows The calculation formula is: Where T is the total observation time and the sliding step size is... It can be set to match the time resolution of the orbital data.

[0115] For each sliding window, based on the time series of service availability indicator function values, identify the time periods within that window where the value is consecutively zero. If the length of a consecutive zero time period (based on the number of sampling moments) exceeds the maximum allowed interruption duration... If a period of time (e.g., 30 seconds) occurs, it is counted as one interrupt event. For each sliding window, all its sampling points are traversed, and the number of interrupt events that meet the above conditions is counted. The output is the interrupt event count for each window. .

[0116] Here, an interruption event refers to an independent event within a single sliding window where the continuous unavailability of the service exceeds the preset maximum allowable interruption duration. The event count is based on the number of events and does not accumulate the interruption duration. The maximum allowable interruption duration is the maximum continuous interruption time threshold that the navigation service can tolerate, defined by the mission phase. It is set according to the mission safety level (e.g., 30 seconds for the lunar orbit phase and 5 seconds for the lunar landing phase), and its value is configured in the mission profile file.

[0117] Based on the number of interrupt events across all time windows and the total number of sliding windows Calculate the original value of the continuity index. Ct Continuity Ct Based on the maximum allowable interruption probability Evaluate: ,in, Statistics show that within the sliding time window, the continuous interruption time of navigation services exceeded the maximum allowable interruption duration specified for that task phase. The number of events; This represents the total number of sliding windows.

[0118] In this embodiment, a continuity assessment mechanism based on time window statistics, mission-oriented thresholds, and event counting is established in the GNSS assessment of the Earth-Moon space. This enables the continuity assessment to accurately reflect the risk of long-term service interruption that threatens mission execution, and significantly improves the identification of service continuity assurance capabilities and engineering decision-making value of navigation schemes in high-reliability missions (such as lunar braking and lunar landing).

[0119] To improve the accuracy of determining the original autonomous index value, in the evaluation method of the Earth-Moon space navigation scheme provided in Embodiment 1 of this application, when the preset index is an autonomous index, the longest autonomous duration of the spacecraft in the evaluation time period is determined based on ground support data. Here, the autonomous duration refers to the navigation duration of the spacecraft's space system with preset accuracy without relying on the auxiliary information injected by the ground station uplink. Based on the longest autonomous duration and the navigation duration reference value, the original autonomous index value of the autonomous index is determined.

[0120] In this embodiment of the invention, by analyzing ground support data in the mission operation log, including the timing records of uplink commands such as ephemeris injection, clock correction, and orbit updates, the duration interval during which the spacecraft continuously did not receive any ground station auxiliary information within the evaluation period is identified. During this interval, the spacecraft navigation system relies solely on signals provided by satellites for autonomous navigation. By comparing high-precision post-hoc orbit data, if it is verified that the positioning error is consistently less than a preset accuracy threshold (e.g., 50 meters) during this period, then the navigation within this interval is determined to be autonomous navigation. The longest autonomous duration is determined as the longest autonomous duration. .

[0121] Here, autonomous duration refers to the continuous time during which a spacecraft maintains navigation accuracy no lower than a preset threshold, relying on locally stored parameters and autonomous navigation algorithms, without relying on uplink auxiliary information from ground stations. The preset accuracy threshold is the minimum accuracy requirement for defining "effective autonomous navigation," set according to the mission phase (e.g., 50 meters for the lunar orbit phase), ensuring that navigation capabilities in autonomous mode meet basic mission requirements.

[0122] The longest autonomous duration Compared with the preset navigation duration reference value (e.g., 24 hours) After normalization, the original autonomy index value Au is calculated. The autonomy Au is quantified by ground support dependence, specifically the normalized value of the average ground injection interval time, and its calculation formula is as follows: ,in, The evaluation period is the longest time during which a user's spacecraft's navigation system can autonomously maintain a specified navigation accuracy without relying on external auxiliary information such as ephemeris, clock bias, and orbital parameters injected from ground stations, or the average time interval obtained through statistics. This is calculated from the ground injection period and the threshold for autonomously maintaining navigation accuracy.

[0123] Here, navigation duration reference value The autonomous capability benchmarks set for mission-level missions are based on the requirements of typical lunar exploration missions. For example, 24 hours represents the requirement for navigation capability without ground intervention for at least one consecutive day.

[0124] The longer, The larger the value, the stronger the system autonomy. For a "fully autonomous" mode that relies entirely on GNSS direct acquisition signals and requires no ground injection, a setting can be made... For infinity or a number much larger than the task duration, such that Reach the maximum.

[0125] In this embodiment, an autonomous quantification model based on "no injection + accuracy achievement" as dual criteria was established in the GNSS performance evaluation in the Earth-Moon space. This model enables the autonomous indicators to truly reflect the navigation capabilities of spacecraft operating independently in the deep space environment, and significantly improves the navigation system's ability to objectively evaluate autonomous operation capabilities and optimize solutions in long-distance, low-communication-frequency mission scenarios.

[0126] To improve the accuracy of performance evaluation results of navigation schemes at different mission stages, the evaluation method for lunar space navigation schemes provided in Embodiment 1 of this application determines the index weight vector corresponding to each mission stage based on a preset mapping table; based on the index weight vector, the target index values ​​of each preset index are weighted and summed to obtain the performance evaluation results of the navigation scheme at each mission stage.

[0127] In this embodiment of the invention, weights are dynamically allocated to ensure that the evaluation results closely match the actual task. The evaluation weights are not fixed but dynamically allocated based on the task stage. A weight vector is preset for each task stage. ,satisfy ,in, These represent the weights for coverage, accuracy, availability, continuity, and autonomy, respectively. For the firsti Each weight.

[0128] Here, the weights are determined using the analytic hierarchy process (AHP) combined with expert experience. For example, the "Earth-Moon transfer cruise segment" may be assigned higher weights to coverage and autonomy, as it is necessary to ensure signal availability and autonomous operation capabilities during extended periods away from Earth. The "precise lunar landing segment," on the other hand, requires significantly higher weights to accuracy, integrity, and continuity, as this phase demands extremely high levels of safety and reliability for navigation.

[0129] The evaluation system has a built-in default phase-weight mapping table for typical lunar exploration missions, and allows users to customize it according to specific mission requirements.

[0130] The evaluation system uses a pre-defined mapping table built into it. This mapping table is indexed by mission phase and stores the weight vector of the five-dimensional indicators for each phase in advance. For example, the weight vector mapped to the "Earth-Moon transfer cruise phase" is (0.30, 0.15, 0.15, 0.10, 0.30), corresponding to coverage, accuracy, availability, continuity, and autonomy, respectively; the weight vector mapped to the "lunar landing phase" is (0.10, 0.40, 0.20, 0.20, 0.10).

[0131] Obtain the normalized target index values ​​of the five preset indicators for the current task stage (i.e., the normalized values ​​of coverage, accuracy, availability, continuity, and autonomy, all located in the [0,1] interval), and assign the corresponding index weight vectors. Perform a dot product operation with the target indicator value to calculate the overall performance score. ,in, It is the first in the current task phase i Weights of each dimension This is the normalized value for this dimension. S is a scalar value between 0 and 1; the higher the value, the better the overall performance of the evaluated GNSS navigation scheme from the perspective of the current mission phase. This score can serve as a direct basis for quantitative comparison and ranking between different schemes (such as those using different receivers, different orbital designs, or different constellation combinations).

[0132] In this embodiment, the evaluation criteria and mission phase requirements are coupled and linked in the GNSS performance evaluation in the Earth-Moon space, so that the comprehensive performance score can truly reflect the actual effectiveness of the navigation scheme in different mission phases, and significantly improve the guidance, objectivity and engineering practicality of the evaluation results in mission planning, system design and scheme selection.

[0133] In this embodiment of the invention, an evaluation system for implementing the above-described evaluation method is also provided. The system includes: a data input and preprocessing module, an indicator quantification calculation engine, a weight configuration and management module, a comprehensive evaluation calculation module, and a result output module. These modules work collaboratively to automatically complete the entire process from data input to comprehensive score output.

[0134] This invention proposes a multi-dimensional performance quantification evaluation index system and fusion calculation method for GNSS in the Earth-Moon space. It comprehensively constructs a GNSS performance evaluation index system covering five dimensions: coverage, accuracy, availability, continuity, and autonomy, and provides a computable quantitative mathematical model for each dimension. Furthermore, it proposes a fusion calculation method that normalizes the quantification results of each dimension and obtains a single comprehensive performance score through weighted summation, solving the problems of existing technologies having one-sided evaluation dimensions, isolated results, and an inability to provide overall performance conclusions. In addition, it proposes a performance evaluation weight allocation mechanism based on dynamic adjustment of mission phases. This introduces mission phase information into the evaluation process, pre-defining or dynamically generating matching dimension weight vectors to address the differentiated navigation performance requirements of different phases of the Earth-Moon exploration mission (such as transfer, lunar orbit, and landing), and applying them to the comprehensive score calculation. This ensures that the performance evaluation results of the same navigation scheme at different mission phases accurately reflect its actual utility value at that phase, achieving dynamic adaptation of evaluation standards to mission scenarios and overcoming the limitations of static evaluation.

[0135] In this embodiment of the invention, in view of the fact that the existing technology only evaluates from a single dimension such as accuracy or visibility, which cannot reflect the whole picture of the system, a five-dimensional system is constructed to comprehensively characterize the system from signal reception, positioning calculation, service quality to system characteristics. This system can systematically reveal the performance shortcomings and bottlenecks of the navigation scheme, point out the direction for global optimization, and avoid the risk of sacrificing other key performances due to the one-sided pursuit of a certain high index.

[0136] Since existing technologies mostly provide discrete data points or qualitative descriptions, which are difficult to use directly for multi-scheme comparison, this paper uses rigorous mathematical formulas and normalization and weighted fusion processes to output a quantitative comprehensive performance score. This score enables intuitive and objective numerical comparison and ranking of different receiver models, orbital design schemes or constellation usage strategies, greatly improving the scientific nature and efficiency of the scheme selection process.

[0137] Since existing evaluation methods are often decoupled from specific mission phases, and the evaluation conclusions may not reflect the actual mission requirements, a dynamic weight allocation mechanism is used to enable the evaluation criteria to be automatically adjusted according to the changes in the core requirements of mission phases (such as cruise and landing). As a result, the evaluation results can better reflect the true effectiveness of the navigation scheme in actual mission execution and have stronger practical guiding value for mission planning and system design.

[0138] The following is a detailed description with reference to another embodiment.

[0139] Example 2

[0140] The evaluation device for a lunar space navigation scheme provided in this embodiment includes multiple implementation units, each of which corresponds to a specific implementation step in Embodiment 1 above.

[0141] Figure 2 This is a schematic diagram of an evaluation device for an optional Earth-Moon space navigation scheme according to an embodiment of the present invention, as shown below. Figure 2 As shown, the evaluation device may include: a data acquisition unit 20, a first determination unit 21, a processing unit 22, and a second determination unit 23.

[0142] Among them, the acquisition unit 20 is used to acquire multi-source data corresponding to each navigation scheme;

[0143] The first determining unit 21 is used to determine the original indicator values ​​of multiple preset indicators based on multi-source data. The types of preset indicators include at least: coverage indicators, accuracy indicators, availability indicators, continuity indicators, and autonomy indicators.

[0144] The processing unit 22 is used to normalize the original index value of each preset index to obtain the target index value of each preset index.

[0145] The second determining unit 23 is used to determine the performance evaluation result of the navigation scheme in each task stage of the target task based on a preset mapping table and the target index value of each preset index. The preset mapping table contains the index weight vector corresponding to each task stage.

[0146] The aforementioned evaluation device can adopt a multi-dimensional quantitative evaluation and task-stage adaptive fusion approach. By collecting multi-source data corresponding to navigation schemes and calculating raw indicators such as coverage, accuracy, availability, continuity, and autonomy in parallel, and eliminating the influence of dimensions after range normalization, it combines a preset mapping table to dynamically load the weight vectors corresponding to each task stage for weighted summation. This achieves the goal of systematically, quantitatively, and stage-wise comprehensively evaluating the performance of GNSS navigation in the Earth-Moon space. This enables the technical effect of comparability and optimization of different navigation schemes in real task scenarios, and solves the technical problem of single, static, and fixed evaluation dimensions that cannot adapt to the dynamic needs of Earth-Moon missions, resulting in a lack of scientific basis for scheme selection.

[0147] Optionally, the multi-source data includes at least: spacecraft orbit data, navigation constellation data, receiver data, model data, observation data, and ground support data. The acquisition unit includes: a first acquisition module, used to acquire the spacecraft's orbital ephemeris during the evaluation period at a preset time resolution based on the orbital design strategy in the navigation scheme, to obtain spacecraft orbit data; a second acquisition module, used to acquire the broadcast ephemeris and clock bias parameters of the constellation based on the constellation combination in the navigation scheme, and to acquire the phase center and radiation pattern data of the transmitting antenna of each navigation satellite in the constellation, to obtain navigation constellation data, wherein the radiation pattern data includes at least: sidelobe gain model data; and a third acquisition module, used to acquire the receiver type in the navigation scheme, The system acquires the performance parameters of the receiver used by the spacecraft to obtain receiver data. These performance parameters include at least: receiver sensitivity, antenna gain pattern, noise figure, and loop bandwidth. A fourth acquisition module acquires model data, which includes at least: a three-dimensional occlusion model of the Earth and a three-dimensional occlusion model of the Moon. A first receiving module receives observation data via the receiver, which includes at least: pseudorange observations, carrier phase observations, carrier-to-noise ratio observations, and signal tracking status. A second receiving module receives ground support data via the receiver, which includes at least: ground station uplink injection period, ephemeris and clock error update interval, and autonomous navigation maintenance threshold.

[0148] Optionally, the first determining unit includes: a first determining module, used to determine the total number of sampling times in the evaluation period when the preset indicator is a coverage indicator; a second determining module, used to determine the number of visible satellites at each sampling time based on observation data, and to determine the visibility indicator function value corresponding to each sampling time by comparing the number of visible satellites with a preset number threshold; a third determining module, used to determine the position accuracy factor corresponding to each sampling time based on spacecraft orbit data and navigation constellation data; a fourth determining module, used to determine the geometric distribution quality factor corresponding to each sampling time based on a preset position accuracy reference value and the position accuracy factor corresponding to each sampling time; and a fifth determining module, used to determine the original coverage indicator value of the coverage indicator based on the visibility indicator function value and the geometric distribution quality factor corresponding to each sampling time.

[0149] Optionally, the third determining module includes: a first determining submodule, used to determine the spacecraft position at each sampling time based on spacecraft orbit data, and to determine the satellite position of each visible satellite based on navigation constellation data; a first constructing submodule, used to construct a unit observation vector between the spacecraft and the visible satellite for each visible satellite based on the spacecraft position and the satellite position of the visible satellite; a second constructing submodule, used to construct a geometric matrix based on all unit observation vectors; a second determining submodule, used to determine the covariance matrix based on the geometric matrix; and a third determining submodule, used to determine the position accuracy factor based on the covariance matrix.

[0150] Optionally, the first determining unit further includes: a sixth determining module, used to determine the ephemeris error, clock error, and receiver noise error at each sampling time based on navigation constellation data and observation data, provided that the preset indicator is an accuracy indicator; wherein the receiver noise error is determined by performing multiple subtraction on pseudorange observations or carrier phase observations; a seventh determining module, used to determine the comprehensive error corresponding to each sampling time based on the ephemeris error, clock error, and receiver noise error at each sampling time; an eighth determining module, used to determine the average equivalent distance error based on the total number of sampling times and the comprehensive error corresponding to each sampling time; and a ninth determining module, used to determine the original accuracy indicator value of the accuracy indicator based on the average equivalent distance error.

[0151] Optionally, the first determining unit further includes: a first judging module, used to determine whether the number of visible satellites at each sampling time is greater than or equal to a preset number threshold, provided that the preset indicator is an availability indicator; a first acquiring module, used to acquire the carrier-to-noise ratio (CNR) observation value corresponding to each sampling time based on observation data, and to acquire the receiver sensitivity based on receiver data; a second judging module, used to determine whether the minimum CNR observation value among all sampling times is greater than the receiver sensitivity; a tenth determining module, used to determine the preset error threshold for each task stage and the task time period for each task stage; and an eleventh determining module, used to determine, for each task stage, the positioning error of each sampling time within the task time period of the task stage, based on navigation constellation data and observation data. The system includes four modules: a third judgment module for determining whether the positioning error is less than the corresponding preset error threshold; a twelfth determination module for determining the service availability indicator function value as the first function value when the number of visible satellites at the sampling time is greater than or equal to a preset number threshold, the minimum carrier-to-noise ratio observation value is greater than the receiver sensitivity, and the positioning error is less than the corresponding preset error threshold; a thirteenth determination module for determining the service availability indicator function value as the second function value when the number of visible satellites at the sampling time is less than a preset number threshold, the minimum carrier-to-noise ratio observation value is less than or equal to the receiver sensitivity, or the positioning error is greater than or equal to the corresponding preset error threshold; and a fourteenth determination module for determining the original availability indicator value of the availability index based on the service availability indicator function value corresponding to each sampling time.

[0152] Optionally, the first determining unit further includes: a fifteenth determining module, used to determine the sliding step size based on a preset time resolution when the preset indicator is a continuous indicator, and to determine the total number of sliding windows based on the duration of the evaluation period, the sliding step size, and the preset time window length; a first statistics module, used to count the number of interruption events in each time window based on the observation data, wherein the number of interruption events refers to the number of events in which the continuous service interruption duration exceeds the maximum allowed interruption duration; and a sixteenth determining module, used to determine the original continuous indicator value of the continuous indicator based on the number of interruption events in all time windows and the total number of sliding windows.

[0153] Optionally, the first determining unit further includes: a seventeenth determining module, used to determine the longest autonomous duration of the spacecraft in the evaluation period based on ground support data when the preset indicator is an autonomous indicator, wherein the autonomous duration refers to the navigation duration of the spacecraft's space system with preset accuracy without relying on the auxiliary information injected by the ground station uplink; and an eighteenth determining module, used to determine the original autonomous indicator value of the autonomous indicator based on the longest autonomous duration and the navigation duration reference value.

[0154] Optionally, the second determining unit includes: a nineteenth determining module, used to determine the indicator weight vector corresponding to each task stage based on a preset mapping table; and a first weighting module, used to perform a weighted summation of the target indicator values ​​of each preset indicator based on the indicator weight vector, to obtain the performance evaluation results of the navigation scheme in each task stage.

[0155] The aforementioned evaluation device may also include a processor and a memory. The aforementioned acquisition unit 20, first determination unit 21, processing unit 22, second determination unit 23, etc., are all stored in the memory as program units, and the processor executes the aforementioned program units stored in the memory to realize the corresponding functions.

[0156] The aforementioned processor contains a kernel, which retrieves the corresponding program units from memory. One or more kernels can be configured, and by adjusting kernel parameters, the performance evaluation results of the navigation scheme during the mission phase are determined based on a preset mapping table and the target value of each preset metric.

[0157] The aforementioned memory may include non-permanent memory in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM, and the memory includes at least one memory chip.

[0158] The present invention also provides a computer program product, which, when executed on a data processing device, is suitable for executing an initialization program with the following method steps: for each navigation scheme, collecting multi-source data corresponding to the navigation scheme; based on the multi-source data, determining the original index values ​​of multiple preset indicators; normalizing the original index values ​​of each preset indicator to obtain the target index value of each preset indicator; and for each task stage of the target task, determining the performance evaluation result of the navigation scheme in the task stage based on a preset mapping table and the target index value of each preset indicator.

[0159] According to another aspect of the present invention, a computer program product is also provided, including a non-volatile computer-readable storage medium storing a computer program, wherein the computer program, when executed by a processor, implements an evaluation method for a lunar space navigation scheme as described above.

[0160] According to another aspect of the present invention, an electronic device is also provided, including one or more processors and a memory, the memory being used to store one or more programs, wherein when the one or more programs are executed by the one or more processors, the one or more processors cause the one or more processors to implement the above-described evaluation method for the Earth-Moon space navigation scheme.

[0161] Figure 3This is a hardware structure block diagram of an electronic device (or mobile device) for an evaluation method of a lunar space navigation scheme according to an embodiment of the present invention. Figure 3 As shown, an electronic device may include one or more processors (e.g., Figure 3 The processors 302a, 302b, ..., 302n, etc., may include, but are not limited to, processing devices such as microprocessors (MCUs) or programmable logic devices (FPGAs), and a memory 304 for storing data. In addition, it may include: a display, an input / output interface (I / O interface), a universal serial bus (USB) port (which may be included as one of the ports in the I / O interface), a network interface, a keyboard, a power supply, and / or a camera. Those skilled in the art will understand that... Figure 3 The structure shown is for illustrative purposes only and does not limit the structure of the electronic device described above. For example, the electronic device may also include components that are more... Figure 3 The more or fewer components shown, or having the same Figure 3 The different configurations shown.

[0162] The sequence numbers of the above embodiments of the present invention are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments.

[0163] The embodiments or examples disclosed herein are not exhaustive, but merely illustrative of some embodiments or examples, and are not intended to limit the scope of protection of this disclosure. Unless otherwise specified, each step in a particular embodiment or example can be implemented as an independent embodiment, and the steps can be arbitrarily combined. For example, a solution after removing some steps in a particular embodiment or example can also be implemented as an independent embodiment, and the order of the steps in a particular embodiment or example can be arbitrarily interchanged. Furthermore, optional methods or examples in a particular embodiment or example can be arbitrarily combined; moreover, embodiments or examples can be arbitrarily combined. For example, some or all steps of different embodiments or examples can be arbitrarily combined, and a particular embodiment or example can be arbitrarily combined with optional methods or examples of other embodiments or examples.

[0164] In the above embodiments of the present invention, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0165] In the several embodiments provided by this invention, it should be understood that the disclosed technical content can be implemented in other ways. The device embodiments described above are merely illustrative; for example, the division of units can be a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the displayed or discussed mutual coupling, direct coupling, or communication connection can be through some interfaces; the indirect coupling or communication connection of units or modules can be electrical or other forms.

[0166] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0167] Furthermore, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0168] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, read-only memory (ROM), random access memory (RAM), portable hard drives, magnetic disks, or optical disks.

[0169] The above description is only a preferred embodiment of the present invention. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principle of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A method for evaluating a lunar space navigation scheme, characterized in that, include: For each navigation scheme, collect multi-source data corresponding to the navigation scheme; Based on the multi-source data, the original index values ​​of multiple preset indicators are determined, wherein the types of the preset indicators include at least: coverage indicators, accuracy indicators, availability indicators, continuity indicators, and autonomy indicators. The original index value of each preset index is normalized to obtain the target index value of each preset index. For each task stage of the target task, the performance evaluation result of the navigation scheme under the task stage is determined based on the preset mapping table and the target index value of each preset index, wherein the preset mapping table contains the index weight vector corresponding to each task stage.

2. The evaluation method according to claim 1, characterized in that, The multi-source data includes at least: spacecraft orbit data, navigation constellation data, receiver data, model data, observation data, and ground support data. The steps for collecting the multi-source data corresponding to the navigation scheme include: Based on the orbit design strategy in the navigation scheme, the spacecraft's orbital ephemeris is collected at a preset time resolution during the evaluation period to obtain the spacecraft's orbital data; Based on the constellation combination in the navigation scheme, the broadcast ephemeris and clock error parameters of the constellation are collected, and the phase center and radiation pattern data of the transmitting antenna of each navigation satellite in the constellation are collected to obtain the navigation constellation data. The radiation pattern data includes at least: sidelobe gain model data. Based on the receiver type in the navigation scheme, the performance parameters of the receiver used by the spacecraft are collected to obtain the receiver data. The performance parameters include at least: receiver sensitivity, antenna gain pattern, noise figure, and loop bandwidth. The model data is collected, wherein the model data includes at least: a three-dimensional occlusion model of the Earth and a three-dimensional occlusion model of the Moon; The observation data is received by the receiver, wherein the observation data includes at least: pseudorange observation value, carrier phase observation value, carrier-to-noise ratio observation value, and signal tracking status; The receiver receives the ground support data, which includes at least: the ground station uplink injection cycle, the ephemeris and clock error update interval, and the autonomous navigation maintenance threshold.

3. The evaluation method according to claim 2, characterized in that, The step of determining the original indicator values ​​of multiple preset indicators based on the multi-source data includes: If the preset indicator is the coverage indicator, determine the total number of sampling times for the evaluation period; Based on the observation data, the number of visible satellites at each sampling time is determined, and the visibility indicator function value corresponding to each sampling time is determined by comparing the number of visible satellites with a preset number threshold. Based on the spacecraft orbit data and the navigation constellation data, determine the position accuracy factor corresponding to each sampling moment; Based on the preset position accuracy reference value and the position accuracy factor corresponding to each sampling time, the geometric distribution quality factor corresponding to each sampling time is determined. Based on the visibility indicator function value corresponding to each sampling time and the geometric distribution quality factor, the original coverage index value of the coverage index is determined.

4. The evaluation method according to claim 3, characterized in that, The step of determining the position accuracy factor corresponding to each sampling moment based on the spacecraft orbit data and the navigation constellation data includes: For each sampling time, the spacecraft position at the sampling time is determined based on the spacecraft orbit data, and the satellite position of each visible satellite is determined based on the navigation constellation data; For each of the visible satellites, a unit observation vector between the spacecraft and the visible satellite is constructed based on the spacecraft's position and the satellite's position. Construct a geometric matrix based on all the unit observation vectors described above; Based on the geometric matrix, determine the covariance matrix; The position accuracy factor is determined based on the covariance matrix.

5. The evaluation method according to claim 2, characterized in that, The step of determining the original indicator values ​​of multiple preset indicators based on the multi-source data further includes: When the preset index is the accuracy index, based on the navigation constellation data and the observation data, the ephemeris error, clock error and receiver noise error at each sampling time are determined, wherein the receiver noise error is determined by performing multiple subtraction on pseudorange observations or carrier phase observations. Based on the ephemeris error, clock error, and receiver noise error at each sampling time, determine the comprehensive error corresponding to each sampling time. Based on the total number of sampling times and the comprehensive error corresponding to each sampling time, the average equivalent distance error is determined; Based on the average equivalent distance error, the original accuracy index value of the accuracy index is determined.

6. The evaluation method according to claim 2, characterized in that, The step of determining the original indicator values ​​of multiple preset indicators based on the multi-source data further includes: When the preset indicator is the availability indicator, for each sampling time, it is determined whether the number of visible satellites at the sampling time is greater than or equal to a preset number threshold; Based on the observation data, the carrier-to-noise ratio observation value corresponding to each sampling time is obtained, and based on the receiver data, the receiver sensitivity is obtained; Determine whether the minimum carrier-to-noise ratio observation value among all the sampling times is greater than the receiver sensitivity; Determine the preset error threshold for each task stage and the task time period for each task stage; For each of the mission phases, based on the navigation constellation data and the observation data, the positioning error at each of the sampling times within the mission time period in which the mission phase is located is determined; Determine whether the positioning error is less than the corresponding preset error threshold; If the number of visible satellites at the sampling time is greater than or equal to the preset number threshold, the minimum carrier-to-noise ratio observation value is greater than the receiver sensitivity, and the positioning error is less than the corresponding preset error threshold, the service availability indication function value is determined to be the first function value. If the number of visible satellites at the sampling time is less than the preset number threshold, the minimum carrier-to-noise ratio observation value is less than or equal to the receiver sensitivity, or the positioning error is greater than or equal to the corresponding preset error threshold, the service availability indication function value is determined to be the second function value. Based on the service availability indication function value corresponding to each sampling time, the original availability index value of the availability index is determined.

7. The evaluation method according to claim 2, characterized in that, The step of determining the original indicator values ​​of multiple preset indicators based on the multi-source data further includes: When the preset indicator is the continuity indicator, the sliding step size is determined based on the preset time resolution, and the total number of sliding windows is determined based on the time length of the evaluation time period, the sliding step size, and the preset time window length. Based on the observation data, the number of interruption events in each time window is counted, where the number of interruption events refers to the number of events in which the continuous service interruption duration exceeds the maximum allowed interruption duration; The original continuity index value is determined based on the number of interruption events under all the time windows and the total number of sliding windows.

8. The evaluation method according to claim 2, characterized in that, The step of determining the original indicator values ​​of multiple preset indicators based on the multi-source data further includes: When the preset indicator is the autonomy indicator, the longest autonomous duration of the spacecraft in the evaluation time period is determined based on the ground support data. The autonomous duration refers to the navigation duration of the spacecraft's space system with preset accuracy without relying on the auxiliary information injected by the ground station uplink. Based on the longest autonomous duration and the navigation duration reference value, the original autonomous indicator value of the autonomy index is determined.

9. The evaluation method according to claim 1, characterized in that, The step of determining the performance evaluation result of the navigation scheme in the task phase based on a preset mapping table and the target index value of each preset index includes: Based on the preset mapping table, determine the indicator weight vector corresponding to each task stage; Based on the index weight vector, the target index value of each preset index is weighted and summed to obtain the performance evaluation result of the navigation scheme in each task stage.

10. An evaluation device for a lunar space navigation scheme, characterized in that, include: The acquisition unit is used to acquire multi-source data corresponding to each navigation scheme. The first determining unit is used to determine the original index values ​​of multiple preset indicators based on the multi-source data, wherein the types of the preset indicators include at least: coverage indicators, accuracy indicators, availability indicators, continuity indicators, and autonomy indicators. The processing unit is used to normalize the original index value of each preset index to obtain the target index value of each preset index. The second determining unit is used to determine the performance evaluation result of the navigation scheme at each task stage of the target task, based on a preset mapping table and the target index value of each preset index, wherein the preset mapping table contains the index weight vector corresponding to each task stage.

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