HEO Constellation Enhanced Spaceborne GNSS Autonomous Orbit Determination Method and System for Earth-Moon Spacecraft

Abstract

This application relates to the field of autonomous orbit determination technology for spacecraft, and particularly to a method and system for autonomous orbit determination of a lunar spacecraft using HEO constellation enhancement. The method includes: determining the weights of GNSS / HEO data; calculating single-point positioning and velocity measurement results using GNSS / HEO data and HEO enhancement information, and initializing the Kalman filter state variables and covariance; predicting the spacecraft's position and velocity using a simplified dynamic model, and updating the state equations; calculating the GNSS / HEO innovation residual sequence based on the prediction results, and detecting and marking gross errors according to the clock consistency rule; updating the observation data without gross errors by Kalman measurement according to weights, completing the correction of state variables and covariance; and finally outputting the spacecraft's position and velocity information to the navigation and guidance system to achieve autonomous orbit determination and timing. This solves the problems in related technologies where lunar spacecraft cannot adapt to the dynamics and observation conditions of the lunar space, resulting in low positioning accuracy and poor orbit determination and autonomous time maintenance capabilities.

Description

Technical Field

[0001] This application relates to the field of autonomous orbit determination technology for spacecraft, and in particular to an autonomous orbit determination method and system for lunar spacecraft onboard GNSS (Global Navigation Satellite System) enhanced by a HEO (Highly Elliptical Orbit) constellation. Background Technology

[0002] Lunar missions are affected by factors such as Earth's shielding effect and the spacecraft's operating altitude. In the Earth-Moon transfer orbit, the spacecraft can only receive leakage signals from the side lobes of GNSS satellite signals. The weak signal conditions lead to increased pseudorange measurement errors and frequent loss of carrier phase observation values, which limits navigation accuracy and continuity.

[0003] Among related technologies, existing GNSS data fusion and orbit determination algorithms cannot adapt to the dynamic characteristics and observation conditions of lunar space mission scenarios, resulting in problems such as low positioning accuracy and poor orbit determination and autonomous time maintenance capabilities. Summary of the Invention

[0004] This application provides a HEO constellation-enhanced onboard GNSS autonomous orbit determination method and system for Earth-Moon spacecraft, in order to solve the problems of low positioning accuracy, poor orbit determination and autonomous time maintenance capabilities that Earth-Moon spacecraft cannot adapt to the dynamics and observation conditions of Earth-Moon space.

[0005] The first aspect of this application provides a method for autonomous orbit determination of a lunar spacecraft using onboard GNSS, comprising the following steps: acquiring observation data and observation status from multiple satellites, wherein the observation data from multiple satellites includes observation data from GNSS satellites and observation data from HEO constellation satellites; determining the positioning weight of each satellite based on the observation status; determining the initial position and initial velocity of the lunar spacecraft based on the observation data, positioning weight, and augmentation information broadcast by each constellation satellite; at the initial start time, determining the predicted position and predicted velocity of the lunar spacecraft based on the initial position and initial velocity of the lunar spacecraft, and at subsequent times, using the filtered state quantity updated from the previous epoch measurement as the initial position and initial velocity; using the predicted position and predicted velocity as prior information; calculating a residual sequence based on the observation data and prior information; filtering the observation data from multiple satellites based on the residual sequence; updating the filtered state quantity for autonomous orbit determination of the lunar spacecraft based on the filtered observation data from multiple satellites and the positioning weight of each satellite; and realizing autonomous orbit determination and timing of the lunar spacecraft based on the updated filtered state quantity.

[0006] Optionally, the observation status includes the observation half-cycle marker, signal-to-noise ratio, and signal continuous lock time. The lunar spacecraft observation data includes onboard GNSS / HEO single-frequency pseudorange, phase, and Doppler. The augmentation information includes GNSS / HEO constellation orbit and clock bias corrections and user distance accuracy index, broadcast by the HEO constellation.

[0007] Optionally, the positioning weight of each satellite is determined based on the observation status, including: obtaining the user distance accuracy index value; calculating multiple scores for each satellite based on the phase observation value and the user distance accuracy index value; calculating a comprehensive score for each satellite based on the multiple scores; and using the inverse ratio of the comprehensive score as the positioning weight of each satellite.

[0008] Optionally, determining the predicted position and velocity of the Earth-Moon spacecraft based on its initial position and initial velocity includes: acquiring a pre-set simplified dynamics model, wherein the pre-set simplified dynamics model considers the Earth's gravity, solar radiation pressure, and the gravitational forces of the Sun, Moon, and Earth at a preset order; inputting the initial position and initial velocity into the simplified dynamics model, and calculating the predicted position and predicted velocity of the Earth-Moon spacecraft through the simplified dynamics model.

[0009] Optionally, the residual sequence is calculated based on the observation data and prior information, including: calculating the mean and standard deviation based on the predicted location, predicted velocity and observation data; and generating the residual sequence based on the mean and standard deviation.

[0010] Optionally, the observation data of multiple satellites are filtered according to the residual sequence, including: calculating the difference between each value in the residual sequence and the mean; if the difference is greater than a preset standard deviation threshold, it is marked as data with gross errors; if the difference is less than or equal to the preset standard deviation threshold, it is marked as data without gross errors; filtering the observation data with gross errors to obtain the filtered observation data of multiple satellites.

[0011] Optionally, the filtered state variables for autonomous orbit determination of the Earth-Moon spacecraft are updated based on the filtered observation data of multiple satellites and the positioning weight of each satellite, including: updating the Kalman filter based on the filtered observation data of multiple satellites and the positioning weight of each satellite; and updating the filtered state variables for autonomous orbit determination of the Earth-Moon spacecraft based on the updated Kalman filter.

[0012] A second aspect of this application provides an onboard GNSS autonomous orbit determination system for a lunar spacecraft, comprising: a data acquisition module for acquiring observation data and observation status from multiple satellites, wherein the observation data from multiple satellites includes observation data from GNSS satellites and observation data from HEO constellation satellites; a weight and initial value determination module for determining the positioning weight of each satellite based on the observation status, and determining the initial position and initial velocity of the lunar spacecraft based on the observation data, positioning weight, and augmentation information broadcast by each constellation satellite; and a time update module for determining the initial start time based on the initial position and initial velocity of the lunar spacecraft. The initial position and initial velocity of the Earth-Moon spacecraft are determined. At subsequent times, the predicted position and velocity of the Earth-Moon spacecraft are determined using the filtered state variables updated from the previous epoch measurement. These predicted positions and velocities are then used as prior information. A residual sequence is calculated based on the observation data and the prior information, and the observation data from multiple satellites is filtered using this residual sequence. The measurement update module updates the filtered state variables for autonomous orbit determination of the Earth-Moon spacecraft based on the filtered observation data from multiple satellites and the positioning weight of each satellite. The updated filtered state variables are then used to achieve autonomous orbit determination and timing for the Earth-Moon spacecraft.

[0013] Optionally, the status observations include observation half-cycle markers, signal-to-noise ratio, and signal continuous lock-on time; the lunar spacecraft observation data include onboard GNSS / HEO single-frequency pseudorange, phase, and Doppler; and the augmentation information includes GNSS / HEO constellation orbit and clock bias corrections and user distance accuracy index, broadcast by the HEO constellation.

[0014] Optionally, the positioning weight of each satellite is determined based on the observation status, including: obtaining the user distance accuracy index value; calculating multiple scores for each satellite based on the phase observation value and the user distance accuracy index value; calculating a comprehensive score for each satellite based on the multiple scores; and using the inverse ratio of the comprehensive score as the positioning weight of each satellite.

[0015] Optionally, determining the predicted position and velocity of the Earth-Moon spacecraft based on its initial position and initial velocity includes: acquiring a pre-set simplified dynamics model, wherein the pre-set simplified dynamics model considers the Earth's gravity, solar radiation pressure, and the gravitational forces of the Sun, Moon, and Earth at a preset order; inputting the initial position and initial velocity into the simplified dynamics model, and calculating the predicted position and predicted velocity of the Earth-Moon spacecraft through the simplified dynamics model.

[0016] Optionally, the residual sequence is calculated based on the observation data and prior information, including: calculating the mean and standard deviation based on the predicted location, predicted velocity and observation data; and generating the residual sequence based on the mean and standard deviation.

[0017] Optionally, the observation data of multiple satellites are filtered according to the residual sequence, including: calculating the difference between each value in the residual sequence and the mean; if the difference is greater than a preset standard deviation threshold, it is marked as data with gross errors; if the difference is less than or equal to the preset standard deviation threshold, it is marked as data without gross errors; filtering the observation data with gross errors to obtain the filtered observation data of multiple satellites.

[0018] Optionally, the filtered state variables for autonomous orbit determination of the Earth-Moon spacecraft are updated based on the filtered observation data of multiple satellites and the positioning weight of each satellite, including: updating the Kalman filter based on the filtered observation data of multiple satellites and the positioning weight of each satellite; and updating the filtered state variables for autonomous orbit determination of the Earth-Moon spacecraft based on the updated Kalman filter.

[0019] Therefore, this application has at least the following beneficial effects:

[0020] By acquiring observation data and phase observations from multiple satellites, the initial position and initial velocity are calculated using GNSS / HEO data, observation data from each satellite, positioning weights, and augmentation information broadcast by each constellation satellite. Based on the initial position and initial velocity of the lunar spacecraft, the state variables and covariance of the Kalman filter are initialized. A simplified dynamic model is used to determine the predicted position and predicted velocity of the lunar spacecraft. Using the predicted position and predicted velocity as prior information, the GNSS / HEO innovation residual sequence is calculated. The observation data from multiple satellites are filtered based on the residual sequence, and the observation data without gross errors are updated by Kalman measurement according to weights. The state variables and covariance are corrected, and finally, the spacecraft's position and velocity information is output to the navigation and guidance system, achieving autonomous orbit determination and timing. The timing accuracy is optimized, ensuring accurate execution of operations such as position determination, time synchronization during rendezvous and docking, and path planning for the rover. This improves the accuracy of orbit determination and timing, enabling autonomous orbit determination and timing for the lunar spacecraft. This solves the problems of low positioning accuracy, poor orbit determination and autonomous time maintenance capabilities that prevent lunar spacecraft from adapting to the dynamics and observation conditions of the lunar space.

[0021] Additional aspects and advantages of this application will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of this application. Attached Figure Description

[0022] The above and / or additional aspects and advantages of this application will become apparent and readily understood from the following description of the embodiments taken in conjunction with the accompanying drawings, wherein:

[0023] Figure 1 This is a flowchart illustrating an autonomous orbit determination method for a lunar spacecraft using onboard GNSS, provided according to an embodiment of this application.

[0024] Figure 2This is a schematic diagram of an onboard GNSS autonomous orbit determination system for a lunar spacecraft according to an embodiment of this application. Detailed Implementation

[0025] The embodiments of this application are described in detail below. Examples of these embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and intended to explain this application, and should not be construed as limiting this application.

[0026] The following describes, with reference to the accompanying drawings, an embodiment of the HEO constellation-enhanced GNSS autonomous orbit determination method and system for a lunar spacecraft. Addressing the problems mentioned in the background art, such as the inability of lunar spacecraft to adapt to the dynamics and observation conditions of the lunar space, resulting in low positioning accuracy and poor orbit determination and autonomous time maintenance capabilities, this application provides a lunar spacecraft-borne GNSS autonomous orbit determination method. In this method, observation data and phase observation values ​​from multiple satellites are acquired. A comprehensive scoring rule is used to determine the weights of the GNSS / HEO data. The initial position and initial velocity are calculated using the GNSS / HEO data, the observation data of each satellite, the positioning weights, and the enhancement information broadcast by each constellation satellite. Based on the initial position and initial velocity of the lunar spacecraft, the Kalman filter state variables and covariance are initialized. To address the shortcomings, a simplified dynamic model was used to determine the predicted position and velocity of the lunar spacecraft. Using this predicted position and velocity as prior information, a GNSS / HEO innovation residual sequence was calculated. Based on this residual sequence, observation data from multiple satellites were filtered, and weighted data without gross errors were sequentially updated using Kalman spectroscopy. This corrected the state variables and covariance, ultimately outputting the spacecraft's position and velocity information to the navigation and guidance system for autonomous orbit determination and timing. This improved timing accuracy, ensuring precise execution of operations such as position determination, time synchronization during rendezvous and docking, and rover path planning. This significantly enhanced the accuracy of orbit determination and timing, enabling autonomous orbit determination and timing for the lunar spacecraft. Therefore, this method solves the problems of low positioning accuracy and poor orbit determination and autonomous time maintenance capabilities that lunar spacecraft cannot adapt to the dynamics and observation conditions of the lunar space.

[0027] Specifically, Figure 1 This is a flowchart illustrating an autonomous orbit determination method for a lunar spacecraft using onboard GNSS, provided in an embodiment of this application.

[0028] like Figure 1 As shown, the autonomous orbit determination method of the Earth-Moon spacecraft's onboard GNSS includes the following steps:

[0029] In step S101, observation data and observation status of multiple satellites are acquired. The observation data of multiple satellites includes observation data of GNSS satellites and observation data of HEO constellation satellites.

[0030] Among them, observation data is the raw measurement information related to the satellite, acquired through satellite receiving equipment; observation status is a comprehensive identifier of satellite observation data such as pseudorange / carrier phase of GNSS / HEO; HEO constellation satellites are a satellite network consisting of multiple satellites operating in highly elliptical orbits.

[0031] Understandably, by acquiring observation data from multiple GNSS satellites and HEO constellation satellites, as well as the status of these observations, it is possible to provide core data for navigation with high precision and high reliability. Redundant observations can offset system errors, circumvent the limitations of a single satellite system, and ensure the continuity and stability of positioning services.

[0032] Specifically, the Earth-Moon spacecraft's onboard GNSS autonomous orbit determination system is equipped with a navigation signal observation station and a navigation receiver. The navigation signal observation station can receive GNSS / HEO navigation signals, and the navigation receiver includes a navigation antenna, a baseband signal processing unit, and an onboard embedded computing unit. The navigation receiver is used for GNSS / HEO navigation signal reception, tracking and acquisition, and data processing. The navigation antenna is used for receiving GNSS / HEO navigation signals, and the baseband signal processing unit supports GNSS / HEO signal tracking and acquisition and outputs GNSS / HEO data.

[0033] Furthermore, in the embodiments of this application, the observation status includes the observation half-cycle marker, signal-to-noise ratio, and signal continuous lock time; the lunar spacecraft observation data includes onboard GNSS / HEO single-frequency pseudorange, phase, and Doppler; and the enhancement information includes GNSS / HEO constellation orbit and clock bias corrections and user distance accuracy index, broadcast by the HEO constellation.

[0034] Among them, the half-cycle marker is a half-wavelength deviation marker caused by signal phase jumps in carrier phase observations, which can lead to integer ambiguity calculation errors in phase observation values; the signal-to-noise ratio (SNR) is the ratio of effective signal to noise in the satellite received signal; the signal lock time is the duration for which the receiver continuously tracks and locks onto the signal of a certain satellite; single-frequency pseudorange is the approximate distance between the satellite and the receiver measured by the receiver using a single frequency signal; phase is the phase difference between the satellite's transmitted carrier signal and the receiver's local carrier signal; Doppler is the offset between the received signal frequency and the transmitted frequency due to the relative motion between the satellite and the receiver, used to assist in calculating the velocity of the receiver or satellite; the GNSS / HEO constellation orbit is the set of spatial position and trajectory parameters of all satellites in the HEO navigation constellation in the GNSS / HEO navigation scenario of the Earth-Moon spacecraft; the clock error correction is a parameter used to correct the deviation between the HEO constellation satellite clocks and the system standard time; the user distance accuracy index reflects the impact of ephemeris integration errors on user ranging and can be used as a weight reference index for subsequently determining the participation of GNSS / HEO satellites in positioning and orbit determination.

[0035] Understandably, combining phase observations with half-cycle markers, high signal-to-noise ratio, continuous signal lock time, single-frequency pseudorange, GNSS / HEO orbit and clock error corrections, and user distance accuracy index can mitigate the impact of ephemeris error sources, while improving data reliability, ensuring high-precision positioning in the Earth-Moon space, and enhancing the reliability and adaptability of the navigation system.

[0036] Specifically, as the core data acquisition device of the Earth-Moon spacecraft navigation system, the navigation receiver outputs observation half-cycle markers, signal-to-noise ratio (SNR), and signal lock-on time in real time during continuous tracking of satellite signals. Among these, the phase observation half-cycle markers are used to accurately identify possible half-wavelength jump deviations in carrier phase measurements, providing a direct basis for error correction of subsequent phase observations; the SNR quantifies the ratio of effective signal intensity to background noise in the satellite received signal, intuitively reflecting the signal transmission quality and reception stability; and the signal lock-on time records the duration of continuous tracking of the target satellite signal by the receiver, providing a reference for evaluating the continuity and reliability of the observation data.

[0037] The user distance accuracy index is obtained through GNSS / HEO augmentation information broadcast by a highly elliptical orbit constellation. The orbital layout of the HEO constellation can form efficient coverage of the Earth-Moon space. The augmentation information it broadcasts is systematically processed and integrated, including core correction data such as GNSS / HEO constellation orbit and clock bias, and also provides the user distance accuracy index simultaneously.

[0038] Navigation satellite positions and clock errors serve as the reference for navigation of Earth-Moon spacecraft. First, satellite positions and clock errors with decimeter-to-meter accuracy are calculated using broadcast ephemeris data. Then, orbital and clock error corrections are applied to correct for these positions and clock errors, resulting in high-precision satellite positions and centimeter-level clock errors, thereby improving the navigation accuracy of Earth-Moon spacecraft. By utilizing GNSS / HEO orbital and clock error corrections, the accuracy of satellite positions and clock errors calculated based on GNSS / HEO broadcast ephemeris data can be improved from meter-level to centimeter-level, thus providing a higher spatiotemporal reference for Earth-Moon spacecraft navigation.

[0039] This application embodiment obtains observation data from multiple GNSS satellites and constellation satellites, as well as phase observation values, to provide high-precision and high-reliability core data for navigation. By using redundant observations to offset system errors and avoid the limitations of a single satellite system, it ensures the continuity and stability of positioning services.

[0040] In step S102, the positioning weight of each satellite is determined based on the observation status, and the initial position and initial velocity of the lunar spacecraft are determined based on the observation data, positioning weight, and augmentation information broadcast by each constellation satellite.

[0041] Among them, the positioning weight is a quantitative indicator that reflects the reliability of the observation data of a single satellite, calculated based on the satellite phase observation values; the augmentation information is auxiliary data broadcast by the constellation satellites to the Earth-Moon spacecraft to correct positioning errors and improve the initial positioning accuracy; the initial position is the preliminary spatial coordinates of the Earth-Moon spacecraft at a specific moment, obtained based on the satellite phase observation values, positioning weight, and augmentation information; and the initial velocity is the preliminary spatial motion velocity of the spacecraft at the same observation moment.

[0042] It is understood that the embodiments of this application determine the positioning weight of each satellite by using phase observation values ​​such as half-cycle markers, signal-to-noise ratio, and signal continuous lock time. By using the observation data of each satellite, the positioning weight, and the augmentation information broadcast by each constellation satellite, the initial position and initial velocity of the Earth-Moon spacecraft are located. The weights are dynamically allocated based on the actual accuracy of the observation data, so that the calculation results are closer to the real observation situation, the quality of the observation data is accurately distinguished, the calculation error of the initial position and velocity is controlled within the acceptable range of the mission, and the positioning accuracy of the initial position and initial velocity is improved.

[0043] Specifically, in point positioning and point velocity measurement, the precise orbits and clock errors of GNSS / HEO satellites are first calculated from the broadcast ephemeris of the GNSS / HEO satellites. Then, the orbit and clock error correction information from the GNSS / HEO augmentation information broadcast by the HEO constellation is used to correct these errors. The precise orbits and clock errors of the GNSS / HEO satellites are then substituted into the pseudorange observation equations. Taking into account the weight of the GNSS / HEO observation data, the initial position, velocity, and clock error of the Earth-Moon spacecraft are solved using the least squares method. The formulas for calculating the correction values ​​of the initial position of the Earth-Moon spacecraft and the receiver clock error are shown below:

[0044]

[0045] in, These are correction values ​​for the initial position of the Earth-Moon spacecraft and the receiver clock bias. The observation coefficient matrix, Let it be its transpose matrix. For the residual vector, This represents the weighting ratio of GNSS / HEO observation data.

[0046] Furthermore, in the embodiments of this application, determining the positioning weight of each satellite based on the observation status includes: obtaining the user distance accuracy index value; calculating multiple scores for each satellite based on the phase observation value and the user distance accuracy index value; calculating a comprehensive score for each satellite based on the multiple scores; and using the reciprocal ratio of the comprehensive score as the positioning weight of each satellite.

[0047] Among them, the comprehensive score is a comprehensive index used to quantify a single satellite by fusing multiple dimensions of indicators to determine the satellite positioning weight based on phase observation values.

[0048] It is understood that the embodiments of this application calculate multiple scores for each satellite by using the observed value status and the user distance accuracy index value, calculate the comprehensive score of each satellite, and obtain the positioning weight of each satellite by taking the reciprocal ratio of the comprehensive score. By weighted integration of multi-dimensional scores, the scattered performance indicators are transformed into a unified quantitative benchmark, realizing the overall evaluation of the satellite positioning value. After taking the reciprocal, the satellite with the higher score has a more reasonable reciprocal proportion. Dynamic allocation is realized through ratio calculation.

[0049] Specifically, the weight of each GNSS / HEO satellite observation data is determined by a comprehensive scoring rule based on the half-cycle slip mark of the phase observations, the signal-to-noise ratio or carrier-to-noise ratio, the signal continuous lock time, and the user distance accuracy index.

[0050] First, for each GNSS / HEO satellite's half-cycle marker, if the phase observation has no half-cycle, it is assigned 10 points; otherwise, it is assigned 30 points. For each GNSS / HEO satellite's signal-to-noise ratio (SNR), 100 is subtracted from the SNR to determine the signal strength score. For each GNSS / HEO satellite's continuous signal lock time, if the continuous signal lock time exceeds the time threshold, it is assigned 10 points; otherwise, it is assigned 30 points. For each GNSS / HEO satellite's user distance accuracy index, the user distance accuracy index is used to determine the ranging accuracy score. For each GNSS / HEO satellite considering the influence of orbital altitude, if the orbital altitude is greater than the orbital altitude threshold, it is assigned 30 points; otherwise, it is assigned 10 points. A comprehensive score is then calculated. Finally, all scores are summed to obtain the total comprehensive score. The reciprocal of the total comprehensive score for GNSS / HEO is used as the weighting ratio of the GNSS / HEO observation data.

[0051] Furthermore, in the embodiments of this application, determining the predicted position and predicted velocity of the Earth-Moon spacecraft based on its initial position and initial velocity includes: obtaining a pre-set simplified dynamics model, wherein the pre-set simplified dynamics model considers the Earth's gravity, solar radiation pressure, and the gravitational forces of the Sun, Moon, and Earth at a preset order; inputting the initial position and initial velocity into the simplified dynamics model, and calculating the predicted position and predicted velocity of the Earth-Moon spacecraft through the simplified dynamics model.

[0052] Among them, the simplified dynamics model is a mathematical and physical model designed and constructed based on the complete dynamics model for the Earth-Moon spacecraft orbit prediction scenario; the preset order is the order of the spherical harmonic function expansion artificially set in the Earth gravity field model; the Earth gravity field is the resultant force field of the gravitational force generated by the Earth on surrounding objects and the centrifugal force of rotation; Earth gravity is the universal gravitational force generated by the mass of the Earth; solar radiation pressure is the pressure generated when solar radiation photons hit the surface of the spacecraft; the gravitational force of the Sun, Moon and Sun is the collective term for the gravitational force of the Sun on the spacecraft and the gravitational force of the Moon on the spacecraft.

[0053] Understandably, the simplified dynamics model only simplifies minor disturbance terms. By dynamically linking the preset order of Earth's gravitational field with orbital altitude, and selectively incorporating key influencing factors such as Earth's gravity, solar radiation pressure, and the gravitational forces of the Sun, Moon, and Earth, the initial position and initial velocity are input into the simplified dynamics model to calculate the predicted position and velocity of the Earth-Moon spacecraft. This ensures that short-term prediction errors are controlled within an acceptable range without relying on large ground-based computers, shortens computation time, and enables rapid calculation in the spacecraft processor.

[0054] Specifically, the mathematical models of the dynamic laws governing the orbital motion of spacecraft, such as the orbital calculations of Earth-Moon transfer vehicles, lunar probes, and Earth-Moon space stations, need to be fast and accurate enough. Therefore, only key gravitational forces such as Earth's gravity, solar radiation pressure, and the gravitational forces of the Sun, Moon, and Earth need to be considered, without considering all minute forces.

[0055] Meanwhile, the preset order of the Earth's gravity field is related to the orbital altitude of the Earth-Moon spacecraft. For example, when the orbital altitude of the Earth-Moon spacecraft is less than or equal to 5000 km, the preset order of the Earth's gravity field is 30×30; when the orbital altitude of the Earth-Moon spacecraft is greater than 5000 km and less than or equal to 20000 km, the preset order of the Earth's gravity field is 15×15; when the orbital altitude of the Earth-Moon spacecraft is greater than 20000 km and less than or equal to 100000 km, the preset order of the Earth's gravity field is 10×10; and when the orbital altitude of the Earth-Moon spacecraft is greater than 1000000 km, the preset order of the Earth's gravity field is 8×8.

[0056] If the initial position and initial velocity of the Earth-Moon spacecraft are known at the initial moment, the dynamic equations of the Earth-Moon spacecraft can be solved by numerical integration using the Runge-Kutta integration method. This allows us to obtain information such as the predicted position and velocity of the Earth-Moon spacecraft at subsequent moments, and to complete the time update process of the position and velocity state variables based on the predicted position and velocity information.

[0057] The simplified dynamic model calculation formula is as follows:

[0058]

[0059] in, The acceleration of the Earth-Moon spacecraft's position. Due to Earth's gravity, The acceleration due to solar radiation pressure perturbation. Let N be the gravitational perturbation acceleration considering only the Sun and Moon.

[0060] This application embodiment determines the positioning weight of each satellite by using phase observation values ​​such as half-cycle markers, signal-to-noise ratio, and signal continuous lock time. By using the observation data of each satellite, the positioning weight, and the augmentation information broadcast by each constellation satellite, the initial position and initial velocity of the Earth-Moon spacecraft are located. The weights are dynamically allocated based on the actual accuracy of the observation data, making the calculation results more consistent with the actual observation situation, accurately distinguishing the quality of the observation data, and controlling the calculation error of the initial position and velocity within the acceptable range of the mission, thereby improving the positioning accuracy of the initial position and initial velocity.

[0061] In step S103, at the initial start time, the initial position and initial velocity of the lunar spacecraft are determined based on the initial position and initial velocity of the location. At subsequent times, the filtered state quantity updated by the measurement of the previous epoch is used as the initial position and initial velocity to determine the predicted position and predicted velocity of the lunar spacecraft. The predicted position and predicted velocity are used as prior information. The residual sequence is calculated based on the observation data and the prior information. The observation data of multiple satellites are filtered based on the residual sequence.

[0062] Among them, the predicted position is the theoretical spatial coordinates of the spacecraft at a specific future moment, obtained through numerical integration or analytical calculation based on the initial position of the Earth-Moon spacecraft; the predicted velocity is the theoretical motion vector at a specific future moment, calculated based on the initial velocity of the spacecraft; the prior information is the pre-cognition reference information of the spacecraft's state based on the predicted position and predicted velocity; and the residual sequence is a set of difference values ​​formed by calculating the vector difference between the actual observation data of the spacecraft from multiple satellites and the prior information at the corresponding moment, and arranging them in chronological order.

[0063] Understandably, by using the initial position and initial velocity of the Earth-Moon spacecraft to determine its predicted position and velocity, and using the predicted position and velocity as prior information, the residual sequence is calculated. Observational data from multiple satellites are filtered to identify and remove observational data with excessively large residuals. This avoids interference from abnormal data in subsequent data processing, ensures that the retained observational data conforms to the actual orbital evolution characteristics of the Earth-Moon spacecraft, improves data quality, and guarantees navigation accuracy.

[0064] Specifically, observational data from the Earth-Moon spacecraft are susceptible to special environmental factors such as lunar obstruction causing satellite signal interruptions and excessively long observation distances at apogees, resulting in a higher proportion of gross errors. Directly incorporating these data into orbit calculations could lead to divergent orbit estimates and mission decision-making errors. Therefore, the initial position and velocity of the Earth-Moon spacecraft serve as the starting point for orbit calculations. These prior information is used to derive predicted position and velocity, providing a theoretical reference benchmark for the observational data. By combining prior information with multi-satellite observational data, residual sequences are calculated to capture the differences between theoretical predictions and actual observations. This process transforms abstract data quality into quantifiable deviation indicators. The residual sequences are then used to filter out data without gross errors, eliminate abnormal data caused by obstruction, interference, or equipment malfunctions, and retain only valid data containing normal noise and minor model errors. This ensures that the observational data accurately reflects the actual orbital state of the spacecraft and enhances the system's adaptability to the complex Earth-Moon environment.

[0065] Furthermore, in the embodiments of this application, calculating the residual sequence based on observation data and prior information includes: calculating the mean and standard deviation based on the predicted location, predicted velocity, and observation data; and generating the residual sequence based on the mean and standard deviation.

[0066] It is understood that the embodiments of this application calculate the mean and standard deviation by using the predicted position, predicted velocity and observation data. By calculating the mean of the difference between the observation data and the predicted value, the overall offset trend can be quantified. When generating the residual sequence, it is corrected based on the mean, so that the final residual sequence is closer to the random error of the spacecraft orbit, quantifying the dispersion of the data and improving the reliability of the data.

[0067] Specifically, in scenarios such as GNSS / HEO joint navigation orbit state estimation, the predicted position, predicted velocity, and observation data are compared with the transformed predicted observations at corresponding times. The difference between the actual observation and the predicted observation at each time is used to compare each residual in the original residual sequence with a set screening threshold. Abnormal residuals that exceed the threshold range are eliminated, and residual data that fall within a reasonable range are retained to form a preliminary optimized residual sequence, which intuitively reflects the initial deviation between the prior prediction and the actual observation.

[0068] Furthermore, in the embodiments of this application, filtering the observation data of multiple satellites based on the residual sequence includes: calculating the difference between each value in the residual sequence and the mean; if the difference is greater than a preset standard deviation threshold, it is marked as data with gross errors; if the difference is less than or equal to the preset standard deviation threshold, it is marked as data without gross errors; filtering the observation data with gross errors to obtain the filtered observation data of multiple satellites.

[0069] Among them, the standard deviation threshold is a pre-set anomaly judgment boundary based on the statistical characteristics of the residual sequence, used to distinguish between normal random fluctuations of the residuals and significant abnormal deviations; data with outliers are observation data in multi-satellite observation data where the difference between the corresponding residual and the residual mean exceeds the preset standard deviation threshold; data without outliers are observation data in multi-satellite observation data where the difference between the corresponding residual and the residual mean does not exceed the preset standard deviation threshold.

[0070] It is understood that the embodiments of this application distinguish between data without gross errors and data with gross errors by comparing the difference between each value in the residual sequence and the mean with a pre-set standard deviation threshold, and filter the data with gross errors to obtain the observation data of multiple satellites after filtering. This effectively removes the gross error data generated by abnormal factors such as electromagnetic interference, equipment failure, and transmission errors in satellite observations, providing a real and reliable residual basis for the verification and optimization of observation models and prediction models, and ensuring the accuracy and stability of multi-satellite data fusion and state estimation.

[0071] Specifically, the predicted position and velocity of the Earth-Moon spacecraft are used as prior information and substituted into the GNSS / HEO pseudorange observation equation to calculate the innovation residual sequence. The innovation residual sequence is used to detect gross errors in GNSS / HEO data according to a preset clock consistency rule. The standard deviation threshold is set to 3 times the standard deviation. The mean and standard deviation of the innovation residual sequence of a single system GNSS / HEO are calculated separately according to the navigation system type. The difference between the innovation residual and the mean is compared. GNSS / HEO data with innovation residuals that deviate from 3 times the standard deviation are considered to not meet the clock consistency rule and are marked as having gross errors. GNSS / HEO data marked with gross errors do not participate in the subsequent autonomous orbit determination Kalman measurement update process.

[0072] This application embodiment uses the initial position and initial velocity of the Earth-Moon spacecraft to determine its predicted position and velocity. The predicted position and velocity are used as prior information to calculate the residual sequence. The observation data from multiple satellites are filtered to identify and remove observation data with excessive residuals, thus avoiding interference from abnormal data in subsequent data processing. This ensures that the retained observation data conforms to the actual orbital evolution characteristics of the Earth-Moon spacecraft, improves data quality, and guarantees navigation accuracy.

[0073] In step S104, the filtered state variables for autonomous orbit determination of the Earth-Moon spacecraft are updated based on the filtered observation data of multiple satellites and the positioning weight of each satellite. The autonomous orbit determination and timing of the Earth-Moon spacecraft are then achieved based on the updated filtered state variables.

[0074] Among them, the filtered state variables are a set of parameters describing the core state of the spacecraft that are estimated and updated in real time through filtering algorithms during the autonomous orbit determination of the Earth-Moon spacecraft; autonomous orbit determination is a technology in which the Earth-Moon spacecraft does not rely on real-time commands and data support from ground control stations, but only obtains observation data through onboard equipment, and combines a preset orbital dynamics model and filtering algorithm to autonomously complete the estimation and updating of orbital state.

[0075] It is understood that the embodiments of this application use onboard autonomous processing of filtered multi-satellite observation data, combined with positioning weights to update the filtered state quantity. The updated filtered state quantity can truly reflect the actual state of the spacecraft, providing accurate parameters for orbit determination, and simultaneously optimizing timing accuracy. This ensures that operations such as position determination, time synchronization during rendezvous and docking, and path planning of the rover are accurately implemented, thereby improving the accuracy of orbit determination and timing, and realizing autonomous orbit determination and timing of the Earth-Moon spacecraft.

[0076] Specifically, in the complex Earth-Moon orbit environment, the Earth-Moon spacecraft filters satellite observation data based on methods such as the 3σ criterion and residual testing, eliminating outliers and idiosyncrasies affected by perturbation interference and noise, while retaining reliable observation information such as distance, angular position, and Doppler shift. Then, combined with the positioning weight of each satellite, algorithms adapted to nonlinear orbit characteristics, such as extended Kalman filtering and unscented Kalman filtering, are used to achieve dynamic updates of the filtered state variables.

[0077] The lunar spacecraft first predicts prior estimates of parameters such as position, velocity, and attitude using state equations. It then compares the weighted and integrated multi-source observation data with the prior estimates, calculates the observation residuals, and solves for the filter gain by combining the observation noise covariance and the state error covariance. The product of the gain and the residuals is used to correct the prior state variables, resulting in more accurate posterior state variables. At the same time, the state error covariance matrix is ​​updated to quantify the accuracy level of the new state.

[0078] Furthermore, in the embodiments of this application, updating the filtered state quantity for autonomous orbit determination of the Earth-Moon spacecraft based on the filtered observation data of multiple satellites and the positioning weight of each satellite includes: updating the Kalman filter based on the filtered observation data of multiple satellites and the positioning weight of each satellite; and updating the filtered state quantity for autonomous orbit determination of the Earth-Moon spacecraft based on the updated Kalman filter.

[0079] Kalman filtering, in particular, combines prior knowledge with real-time observation data to output the optimal estimate of state variables in the presence of measurement noise and system errors.

[0080] It is understood that the embodiments of this application update the Kalman filter by using the observation data of multiple satellites after filtering and the positioning weight of each satellite, and update the filtered state quantity of the autonomous orbit determination of the Earth-Moon spacecraft. By iteratively updating the state covariance through Kalman filtering, the uncertainty is dynamically quantified, making the orbit determination results more adaptable to environmental changes and data fluctuations, enhancing the robustness of orbit determination, and achieving high-precision autonomous orbit determination.

[0081] Specifically, taking the GPS / BDS dual system as an example, GNSS uses the dual-frequency pseudorange and Doppler observation data of the Earth-Moon spacecraft's GPS / BDS / HEO to perform single-point positioning and single-point velocity measurement, respectively, to obtain the position and velocity of the Earth-Moon spacecraft in the Earth-centered Earth-solid system, as well as the receiver's GPS / BDS / HEO clock bias and clock drift parameters. It also obtains the RMS value of the pseudorange and Doppler observation residuals in the positioning and velocity measurement.

[0082] Using the above information, we construct the state variables and their state error covariance matrix of the autonomous orbit determination filter for the Earth-Moon spacecraft. The state variables of the autonomous orbit determination filter for the Earth-Moon spacecraft are:

[0083]

[0084] in, For the state variables of the autonomous orbit determination filter of the Earth-Moon spacecraft, The three-axis positions of the Earth-Moon spacecraft to be estimated in the geocentric inertial frame. The three-axis velocities of the Earth-Moon spacecraft to be estimated in the geocentric inertial frame are: The clock difference of the receiver relative to GPS. The clock bias of the receiver relative to the BDS. These represent the clock bias of the receiver relative to the HEO. For receiver clock speed, This is the solar radiation pressure coefficient. This refers to the compensating accelerations in the radial, tangential, and normal directions of the track.

[0085] The initial values ​​of the state error covariance matrix of the autonomous orbit determination filter for the Earth-Moon spacecraft are:

[0086]

[0087] in, Indicates a diagonal matrix. Let be the initial values ​​of the error covariance of the three-axis position of the Earth-Moon spacecraft. Let be the initial value of the error covariance of the three-axis velocities of the Earth-Moon spacecraft. This is the initial value of the error covariance of the GPS receiver clock bias. Let be the initial value of the error covariance of the BDS receiver clock bias. Let be the initial value of the error covariance of the HEO receiver clock bias. The initial value of the error covariance of the receiver clock rate. Let be the initial variance of the solar radiation pressure coefficient. The initial variances of the radial, tangential, and normal compensating accelerations of the track.

[0088] The initial value is set to 1. Initial values ​​are set with different ratios in the radial, tangential, and normal directions, for example, R:A:C= in, .

[0089] The update formula for Kalman filtering is:

[0090]

[0091] in, Let K be the Kalman gain matrix corresponding to the i-th Earth-Moon spacecraft at time k. For the observation matrix, To observe the noise covariance matrix, Measure the updated filter state parameters for the i-th GNSS / HEO satellite. This represents the filtered state quantity updated at time k. The carrier phase observation of the i-th GNSS / HEO satellite is either smoothed pseudorange or the original pseudorange observation. Let k be the state error covariance matrix after time update. This is the updated state error covariance matrix measured at time k.

[0092] This application embodiment uses onboard autonomous processing of filtered multi-satellite observation data, combined with positioning weights to update the filtered state quantity. The updated filtered state quantity can accurately reflect the actual state of the spacecraft, providing precise parameters for orbit determination, and simultaneously optimizing timing accuracy. This ensures the accurate implementation of operations such as position determination, time synchronization during rendezvous and docking, and path planning for the rover, thereby improving the accuracy of orbit determination and timing, and realizing autonomous orbit determination and timing for the Earth-Moon spacecraft.

[0093] In summary, the onboard GNSS autonomous orbit determination method for a lunar spacecraft proposed in this application acquires observation data and phase observations from multiple satellites. It calculates the initial position and velocity using GNSS / HEO data, observation data from each satellite, positioning weights, and augmentation information broadcast by each constellation satellite. Based on the initial position and velocity of the lunar spacecraft, it initializes the Kalman filter state variables and covariance. A simplified dynamic model is used to determine the predicted position and velocity of the lunar spacecraft. Using the predicted position and velocity as prior information, it calculates the GNSS / HEO innovation residual sequence. Based on the residual sequence, it filters the observation data from multiple satellites, updating the observation data without gross errors sequentially according to weights using Kalman measurements. This completes the correction of the state variables and covariance, and finally outputs the spacecraft's position and velocity information to the navigation and guidance system, achieving autonomous orbit determination and timing. This optimizes timing accuracy, ensuring precise execution of operations such as position determination, time synchronization during rendezvous and docking, and rover path planning, thus improving orbit determination and timing accuracy and realizing autonomous orbit determination and timing for the lunar spacecraft.

[0094] Figure 2 This is a schematic diagram of the Earth-Moon spacecraft onboard GNSS autonomous orbit determination system provided in the embodiments of this application;

[0095] like Figure 2 As shown, the Earth-Moon spacecraft-borne GNSS autonomous orbit determination system 200 includes: a data acquisition module 201, a weight and initial value determination module 202, a time update module 203, and a measurement update module 204.

[0096] The system includes the following modules: a data acquisition module 201, which acquires observation data and observation status from multiple satellites, including observation data from GNSS satellites and HEO constellation satellites; a weight and initial value determination module 202, which determines the positioning weight of each satellite based on the observation status, and determines the initial position and initial velocity of the lunar spacecraft based on the observation data, positioning weight, and augmentation information broadcast by each constellation satellite; and a time update module 203, which determines the initial startup time based on the initial position of the lunar spacecraft. With initial velocity, and at subsequent times, the filtered state quantity updated from the previous epoch measurement is used as the initial position and initial velocity to determine the predicted position and predicted velocity of the Earth-Moon spacecraft. The predicted position and predicted velocity are used as prior information. The residual sequence is calculated based on the observation data and the prior information. The observation data of multiple satellites is filtered based on the residual sequence. The measurement update module 204 is used to update the filtered state quantity for autonomous orbit determination of the Earth-Moon spacecraft based on the filtered observation data of multiple satellites and the positioning weight of each satellite. The autonomous orbit determination and timing of the Earth-Moon spacecraft are realized based on the updated filtered state quantity.

[0097] Furthermore, in the embodiments of this application, the observation status includes the observation half-cycle marker, signal-to-noise ratio, and signal continuous lock time; the lunar spacecraft observation data includes onboard GNSS / HEO single-frequency pseudorange, phase, and Doppler; and the enhancement information includes GNSS / HEO constellation orbit and constellation clock bias corrections and user distance accuracy index, broadcast by the HEO constellation.

[0098] Furthermore, in the embodiments of this application, the weight and initial value determination module 202 is further used to obtain the user distance accuracy index value; calculate multiple scores for each satellite based on the phase observation value and the user distance accuracy index value; calculate the comprehensive score for each satellite based on the multiple scores; and use the reciprocal ratio of the comprehensive score as the positioning weight of each satellite.

[0099] Furthermore, in the embodiments of this application, the weight and initial value determination module 202 is further used to obtain a pre-set simplified dynamic model, wherein the pre-set simplified dynamic model considers the Earth's gravity, solar radiation pressure, and the gravitational force of the Sun, Moon, and Earth's gravitational fields of a preset order; the initial position and initial velocity are input into the simplified dynamic model, and the predicted position and predicted velocity of the Earth-Moon spacecraft are calculated through the simplified dynamic model.

[0100] Furthermore, in the embodiments of this application, the time update module 203 is further used to calculate the mean and standard deviation based on the predicted position, predicted velocity and observation data; and generate a residual sequence based on the mean and standard deviation.

[0101] Furthermore, in the embodiments of this application, the time update module 203 is further used to calculate the difference between each value in the residual sequence and the mean; if the difference is greater than a preset standard deviation threshold, it is marked as data with gross errors; if the difference is less than or equal to the preset standard deviation threshold, it is marked as data without gross errors; the data with gross errors in the observation data are filtered to obtain the filtered observation data of multiple satellites.

[0102] Furthermore, in the embodiments of this application, the measurement update module 204 is further used to update the Kalman filter based on the filtered observation data of multiple satellites and the positioning weight of each satellite; and to update the filtered state quantity of the autonomous orbit determination of the Earth-Moon spacecraft based on the updated Kalman filter.

[0103] It should be noted that the specific description of the Earth-Moon spacecraft onboard GNSS autonomous orbit determination system in this application embodiment refers to the above-described Earth-Moon spacecraft onboard GNSS autonomous orbit determination method, and will not be repeated here.

[0104] In summary, the lunar-Earth spacecraft-borne GNSS autonomous orbit determination system proposed in this application acquires observation data and phase observations from multiple satellites. It calculates the initial position and initial velocity using GNSS / HEO data, the observation data of each satellite, positioning weights, and augmentation information broadcast by each constellation satellite. Based on the initial position and initial velocity of the lunar-Earth spacecraft, it initializes the Kalman filter state variables and covariance. A simplified dynamic model is used to determine the predicted position and predicted velocity of the lunar-Earth spacecraft. Using the predicted position and predicted velocity as prior information, it calculates the GNSS / HEO innovation residual sequence. Based on the residual sequence, it filters the observation data from multiple satellites, updating the observation data without gross errors sequentially according to weights using Kalman measurements. This completes the correction of the state variables and covariance, and finally outputs the spacecraft's position and velocity information to the navigation and guidance system, achieving autonomous orbit determination and timing. This optimizes timing accuracy, ensuring precise execution of operations such as position determination, time synchronization during rendezvous and docking, and rover path planning, thus improving orbit determination and timing accuracy and realizing autonomous orbit determination and timing for the lunar-Earth spacecraft.

[0105] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., refer to specific features, structures, materials, or characteristics described in connection with that embodiment or example, which are included in the embodiments or examples of this application. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0106] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of this application, "N" means at least two, such as two, three, etc., unless otherwise explicitly specified.

[0107] Any process or method described in the flowchart or otherwise herein can be understood as representing a module, segment, or portion of code comprising one or N executable instructions for implementing custom logic functions or processes, and the scope of the preferred embodiments of this application includes additional implementations in which functions may be performed not in the order shown or discussed, including substantially simultaneously or in reverse order depending on the functions involved, as should be understood by those skilled in the art to which embodiments of this application pertain.

[0108] It should be understood that the various parts of this application can be implemented using hardware, software, firmware, or a combination thereof. In the above embodiments, the N steps or methods can be implemented using software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, it can be implemented using any one or more of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (FPGAs), field-programmable gate arrays (FPGAs), etc.

[0109] Those skilled in the art will understand that all or part of the steps of the methods in the above embodiments can be implemented by a program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, the program includes one or a combination of the steps of the method embodiments.

Claims

1. A method for autonomous orbit determination using onboard GNSS on a lunar spacecraft, characterized in that, Includes the following steps: Acquire observation data and observation status from multiple satellites, wherein the observation data from multiple satellites includes observation data from GNSS satellites of the Global Navigation Satellite System and observation data from HEO constellation satellites; The positioning weight of each satellite is determined based on the observed data. The initial position and initial velocity of the Earth-Moon spacecraft are determined based on the observed data, positioning weight, and augmentation information broadcast by each constellation satellite. At the initial startup moment, based on the initial position and initial velocity of the lunar spacecraft, and at subsequent moments, based on the filtered state quantity updated after the measurement of the previous epoch as the initial position and initial velocity, the predicted position and predicted velocity of the lunar spacecraft are determined. The predicted position and predicted velocity are used as prior information. Based on the observation data and the prior information, a residual sequence is calculated. The observation data of multiple satellites are filtered based on the residual sequence. Based on the filtered observation data from multiple satellites and the positioning weight of each satellite, the filtered state variables for autonomous orbit determination of the Earth-Moon spacecraft are updated, and the autonomous orbit determination and timing of the Earth-Moon spacecraft are realized based on the updated filtered state variables.

2. The method for autonomous orbit determination of a lunar spacecraft onboard GNSS according to claim 1, characterized in that, The observation status includes the observation half-cycle marker, signal-to-noise ratio, and signal continuous lock time. The lunar spacecraft observation data includes onboard GNSS / HEO single-frequency pseudorange, phase, and Doppler. The enhancement information includes GNSS / HEO constellation orbit and clock bias corrections, as well as user distance accuracy index, broadcast by the HEO constellation.

3. The method for autonomous orbit determination by a spacecraft-borne GNSS on Earth and Moon as described in claim 1, characterized in that, The step of determining the positioning weight of each satellite based on the observed state includes: Obtain the user's distance accuracy index value; Calculate multiple scores for each satellite based on the observed status and the user distance accuracy index value, and calculate a comprehensive score for each satellite based on the multiple scores; The inverse ratio of the comprehensive score is used as the positioning weight for each satellite.

4. The method for autonomous orbit determination by a spacecraft-borne GNSS on Earth and Moon as described in claim 1, characterized in that, The step of determining the predicted position and predicted velocity of the Earth-Moon spacecraft based on its initial position and initial velocity includes: Obtain a pre-set simplified dynamic model, wherein the pre-set simplified dynamic model considers the Earth's gravity, solar radiation pressure, and gravitational forces of the Sun, Moon, and Earth's gravitational fields of a preset order; The initial position and initial velocity are input into the simplified dynamics model, and the predicted position and predicted velocity of the Earth-Moon spacecraft are calculated through the simplified dynamics model.

5. The method for autonomous orbit determination of a lunar-Earth spacecraft using onboard GNSS according to claim 1, characterized in that, Calculating the residual sequence based on the observed data and the prior information includes: Calculate the mean and standard deviation based on the predicted location, the predicted velocity, and the observation data; The residual sequence is generated based on the mean and the standard deviation.

6. The method for autonomous orbit determination of a lunar-Earth spacecraft using onboard GNSS according to claim 5, characterized in that, The filtering of observation data from multiple satellites based on the residual sequence includes: Calculate the difference between each value in the residual sequence and the mean; If the difference is greater than the preset standard deviation threshold, it is marked as having gross error. If the difference is less than or equal to the preset standard deviation threshold, it is marked as data with no gross errors; By filtering out data with gross errors from the observation data, we obtain the filtered observation data from multiple satellites.

7. The method for autonomous orbit determination by a spacecraft-borne GNSS on Earth and Moon as described in claim 1, characterized in that, The step of updating the filtered state variables for autonomous orbit determination of the Earth-Moon spacecraft based on the filtered observation data from multiple satellites and the positioning weight of each satellite includes: The Kalman filter is updated based on the filtered observation data from multiple satellites and the positioning weight of each satellite. The filtered state variables for autonomous orbit determination of the Earth-Moon spacecraft are updated based on the updated Kalman filter.

8. A spaceborne GNSS autonomous orbit determination system for a lunar spacecraft, characterized in that, include: The data acquisition module acquires observation data and observation status from multiple satellites, including observation data from GNSS satellites and HEO constellation satellites. The weight and initial value determination module determines the positioning weight of each satellite based on the observed value status, and determines the initial position and initial velocity of the Earth-Moon spacecraft based on the observed data, positioning weight, and augmentation information broadcast by each constellation satellite. The time update module, at the initial startup time, determines the predicted position and velocity of the lunar spacecraft based on the initial position and initial velocity of the spacecraft. At subsequent times, it uses the filtered state quantity updated after the measurement of the previous epoch as the initial position and initial velocity to determine the predicted position and predicted velocity of the lunar spacecraft. The predicted position and predicted velocity are used as prior information. The module calculates the residual sequence based on the observation data and the prior information, and filters the observation data of multiple satellites based on the residual sequence. The measurement update module updates the filtered state variables for autonomous orbit determination of the Earth-Moon spacecraft based on the filtered observation data from multiple satellites and the positioning weight of each satellite. The updated filtered state variables enable autonomous orbit determination and timing of the Earth-Moon spacecraft.

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