Satellite orbit determination method, device and application based on edge computing

Abstract

The present disclosure relates to the technical field of satellite communication, and particularly relates to a satellite orbit determination method based on edge computing, a device and an application, wherein the method comprises calculating the position coordinates and velocity coordinates of a satellite by using the edge computing capability on the satellite, transmitting the position coordinates and velocity coordinates to a ground station, and taking the position coordinates and velocity coordinates as observations by the ground station, and optimizing the observations by a two-step method for precise orbit determination and prediction. Through the embodiments of the present disclosure, the availability of orbit determination and prediction is improved when the satellite-ground measurement and control resources or the operation and control center computing resources are limited.

Description

Technical Field

[0001] This disclosure relates to the field of satellite communication technology, and in particular to a satellite orbit determination method, device and application based on edge computing. Background Technology

[0002] Acquiring precise satellite orbits is fundamental and a prerequisite for providing communication, navigation, and remote sensing services. Currently, the main method for determining and predicting precise satellite orbits involves ground systems processing raw observation data such as pseudorange and carrier phase from satellites and Global Navigation Satellite Systems (GNSS) to achieve precise orbit determination, followed by orbit prediction based on dynamic models. However, when ground-based telemetry, tracking, and command (TT&C) resources are limited (e.g., insufficient regional station coverage or low data transmission rates) or when the operation and control center has limited computing resources, ground systems struggle to acquire complete multi-epoch raw observation data from GNSS satellites. This reduces the usability of traditional ground-based dynamic filtering methods based on raw GNSS satellite observation data.

[0003] How to establish an accurate satellite orbit determination method when space-to-ground tracking and control resources or the computing resources of the operation and control center are limited is an urgent problem to be solved. Summary of the Invention

[0004] To address the problems in the prior art, this disclosure provides a satellite orbit determination method, device, and application based on edge computing, which solves the problem of low efficiency in transmitting observation data via satellite-to-ground links, resulting in low availability of satellite orbit determination.

[0005] This disclosure provides a satellite orbit determination method based on edge computing, which is applied to a target satellite and includes: Determine the equivalent measurement error of the Global Navigation Satellite System (GNSS) satellites associated with the target satellite; wherein the equivalent measurement error includes the equivalent range error and the equivalent velocity error of the GNSS satellites; The first weight matrix of the GNSS satellite is established based on the equivalent measurement error. Based on the first weight matrix and the observations of the GNSS satellite, the current state vector estimate of the target satellite is obtained.

[0006] As a further aspect of this disclosure, determining the equivalent measurement error of the Global Navigation Satellite System (GNSS) satellites associated with the target satellite further includes: Extract pseudorange and Doppler shift observation information from multiple related GNSS satellite signals; Based on the pseudorange observation information and Doppler frequency shift observation information, as well as the information of the target satellite, the equivalent range error and equivalent velocity error of the relevant GNSS satellite are calculated.

[0007] As a further aspect of this disclosure, establishing the first weight matrix of the GNSS satellite based on the equivalent measurement error further includes: Based on the variance of the equivalent distance error and the variance of the equivalent velocity error, an observation error covariance matrix is ​​established. The first weight matrix of the GNSS satellite is generated by inversely generating the observation error covariance matrix.

[0008] As a further aspect of this disclosure, obtaining the current state vector estimate of the target satellite based on the first weight matrix and the observations of the GNSS satellite further includes: Based on the relevant observation matrix and observation data of the GNSS satellite, and the first weight matrix, the current state vector estimate of the target satellite is obtained.

[0009] As another further aspect of this disclosure, the current state vector estimate includes: the current position coordinates and velocity coordinates of the target satellite at the current moment.

[0010] As another further aspect of this disclosure, after obtaining the current state vector estimate of the target satellite, the method further includes: The current state vector estimates are aggregated into multi-epoch state vector estimates and then sent.

[0011] This disclosure also provides a satellite orbit determination method based on edge computing, which is applied to a ground station and includes: The multi-epoch state vector estimate sent by the target satellite is used as the first observation; wherein, the first observation includes the position coordinates and velocity coordinates of the target satellite; Different weights are assigned to the position coordinates and velocity coordinates in the first observation to form the third weight matrix; The state vector estimate of the target satellite is obtained based on the third weight matrix and the first observation.

[0012] As a further aspect of this disclosure, before forming the third weighting matrix by assigning different weights to the position coordinates and velocity coordinates in the first observation, the method further includes: The first observation of the target satellite is updated by removing observations from the first observation by using a second weight matrix to eliminate those from abnormal epochs.

[0013] As a further aspect of this disclosure, before removing observations from anomalous epochs in the first observations using the second weighting matrix, the method further includes: Based on the first observation and the state vector of the target satellite, the first residual and the first observation matrix of each epoch observation are obtained.

[0014] As another further aspect of this disclosure, obtaining the first residual and the first observation matrix for each epoch observation based on the first observation and the state vector of the target satellite further includes: Based on the first observation and the state vector of the i-th iteration theory, the first residual of the current epoch is obtained; The first observation matrix of the current epoch is obtained based on the partial derivative of the state vector of the i-th iteration theory.

[0015] As another further aspect of this disclosure, before removing observations from anomalous epochs in the first observations using the second weighting matrix, the method further includes: Calculate the standardized residual of the first residual; Based on the standardized residuals of the first residual, the confidence level of each epoch observation is obtained; The second weight matrix is ​​generated based on the confidence level of each epoch observation.

[0016] As a further aspect of this disclosure, the confidence level of each epoch observation obtained from the standardized residual of the first residual further includes: The weighting function is obtained based on the standardized residual of the first residual; Based on the weighting function, the weighting factors characterizing the confidence level of each epoch observation are obtained.

[0017] As a further aspect of this disclosure, generating the second weight matrix based on the confidence level of each epoch observation further includes: The second weight matrix is ​​generated based on the weighting factors that characterize the confidence of the observations at each epoch.

[0018] As another further aspect of this disclosure, updating the first observations of the target satellite by removing observations from anomalous epochs in the first observations using a second weighting matrix further includes: The first state vector estimate of the target satellite is calculated based on the state vector, the first residual, the first observation matrix, and the second weight matrix. Determine whether the first state vector estimate has converged: If the first state vector estimate fails to converge, the state vector is updated using the first state vector estimate, and the first state vector estimate of the target satellite is iteratively calculated. When the first state vector estimate converges, the first state vector estimate and the updated first observation are output.

[0019] As another further aspect of this disclosure, the method further includes, before determining whether the first state vector estimate has converged: Based on the set number of iterations, the state vector is updated using the first state vector estimate, and the first state vector estimate of the target satellite is iteratively calculated.

[0020] As a further aspect of this disclosure, assigning different weights to the position coordinates and velocity coordinates in the first observation further includes: Based on the updated first observation and state vector, the second residual and second observation matrix of multiple epoch observations are obtained.

[0021] As another further aspect of this disclosure, forming the third weight matrix further includes: Based on the second residual and the weights of the position coordinates and velocity coordinates in the first observation, the unit weight variance of the position coordinates and the unit weight variance of the velocity coordinates are obtained. The third weight matrix is ​​obtained based on the unit weight variance of the position coordinate observation and the unit weight variance of the velocity coordinate observation.

[0022] As a further aspect of this disclosure, obtaining the state vector estimate of the target satellite based on the third weight matrix and the first observation further includes: The second state vector estimate of the target satellite is calculated based on the state vector, the second residual, the second observation matrix, and the third weight matrix. Determine whether the second state vector estimate has converged: If the second state vector estimate fails to converge, the state vector is updated using the second state vector estimate, and the second state vector estimate of the target satellite is iteratively calculated. When the second state vector estimate converges, the second state vector estimate is used as the state vector estimate of the target satellite.

[0023] As another further aspect of this disclosure, the method further includes, before determining whether the second state vector estimate has converged: Based on the set number of iterations, the state vector is updated using the second state vector estimate, and the second state vector estimate of the target satellite is iteratively calculated.

[0024] This disclosure also provides a satellite orbit prediction method, which predicts the future orbit of the target satellite based on the state vector estimate of the target satellite obtained by the above method.

[0025] This disclosure also provides a satellite, including: At least one processor; and At least one memory storing instructions that, when executed individually or jointly by the at least one processor, cause the satellite to perform the methods described above.

[0026] This disclosure also provides a network-side device, including: At least one processor; and At least one memory storing instructions that, when executed individually or jointly by the at least one processor, cause the network-side device to perform the method described above.

[0027] This disclosure also provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the method described above.

[0028] This disclosure also provides a computer-readable storage medium storing computer instructions that, when executed by a processor, implement the above-described method.

[0029] This disclosure also provides a computer program product, which includes a computer program that, when executed by a processor, implements the above-described method.

[0030] Using the embodiments of this disclosure, the satellite's position and velocity can be calculated using the satellite's on-board edge computing resources, and the position and velocity coordinates can be transmitted to the ground station. The ground station can then use the received satellite position and velocity coordinates as initial observations to perform orbit determination calculations for the satellite. This can improve the availability of orbit determination and prediction when satellite-ground tracking and control resources or the computing resources of the operation and control center are limited. Attached Figure Description

[0031] To more clearly illustrate the technical solutions in the embodiments of this disclosure or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this disclosure. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0032] Figure 1 The diagram shown is a schematic representation of a satellite orbit determination system according to an embodiment of this disclosure. Figure 2 The diagram shown is a flowchart of a satellite orbit determination method based on edge computing according to an embodiment of this disclosure; Figure 3 The diagram shown is a flowchart of a satellite orbit determination method based on edge computing according to an embodiment of this disclosure; Figure 4 The diagram shown is a flowchart of a satellite orbit determination method according to an embodiment of this disclosure; Figure 5 The diagram shown is a structural schematic of a satellite orbit determination device based on edge computing according to an embodiment of this disclosure; Figure 6 The diagram shown is a structural schematic of a satellite orbit determination device based on edge computing according to an embodiment of this disclosure; Figure 7 The diagram shown is a schematic representation of the network device according to an embodiment of this disclosure. Detailed Implementation

[0033] To enable those skilled in the art to better understand the technical solutions in this disclosure, the technical solutions in the embodiments of this disclosure will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this disclosure, and not all embodiments. Based on the embodiments in this disclosure, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of this disclosure.

[0034] In the description of this disclosure, unless otherwise stated, "and / or" is a term describing the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A alone, A and B simultaneously, and B alone, where A and B can be singular or plural. Furthermore, in the description of this disclosure, unless otherwise stated, "multiple" means two or more. "At least one of the following" or similar expressions refer to any combination of these items, including any combination of single or plural items. For example, at least one of a, b, or c can represent: a, b, c, ab, ac, bc, or abc, where a, b, and c can be single or multiple.

[0035] In this disclosure, expressions such as "greater than" or "less than" may be used to determine whether a specific condition is met, but this is merely for illustrative purposes and is not intended to exclude expressions of "above" or "below". A condition expressed as "above" may be replaced by "greater than", a condition expressed as "below" may be replaced by "less than", and a condition expressed as "above and less than" may be replaced by "greater than and below". Furthermore, hereinafter, "A" to "B" represent at least one of the elements from A (inclusive) to B (inclusive).

[0036] In the embodiments of this disclosure, the singular forms "a," "the," etc., including the plural forms, should be broadly understood as "a kind" or "a class" rather than limited to the meaning of "an"; furthermore, the term "the" should be understood to include both the singular and plural forms, unless the context clearly indicates otherwise. Additionally, the term "according to" should be understood as "at least partially based on…," and the term "based on" should be understood as "at least partially based on…," unless the context clearly indicates otherwise.

[0037] This disclosure provides the operational steps of the methods described in the embodiments or flowcharts, but based on conventional or non-inventive labor, more or fewer operational steps may be included. The order of steps listed in the embodiments is merely one possible execution order among many and does not represent the only execution order. In actual system or device products, the methods shown in the embodiments or drawings can be executed sequentially or in parallel.

[0038] It should be noted that in the embodiments disclosed herein, certain software, components, models, and other existing solutions in the industry may be mentioned. These should be considered as exemplary and are intended only to illustrate the feasibility of implementing the technical solutions disclosed herein. However, they do not mean that the applicant has used or necessarily used such solutions.

[0039] This disclosure uses terminology used in some communication specifications (e.g., the 3rd Generation Partnership Project, 3GPP, the European Telecommunications Standards Institute, ETSI, Extensible Radio Access Network, ERAN, and Open-Radio Access Network, O-RAN) to illustrate various embodiments, but this is merely illustrative. The various embodiments of this disclosure can be readily modified and applied in other communication systems.

[0040] In the embodiments of this disclosure, communication between devices in the communication system can be carried out according to communication protocols at any stage, such as including but not limited to the following communication protocols: 1G (generation), 2G, 2.5G, 2.75G, 3G, 4G, 4.5G and 5G, New Radio (NR), etc., and / or other currently known or future communication protocols.

[0041] For ease of understanding, the technical terms involved in the embodiments of this disclosure will be explained below.

[0042] (1) Terminal: refers to a device that has wireless transceiver function and can cooperate with network-side equipment to provide communication services to users. Such a terminal can receive services from non-Geostationary Orbit (NGSO) satellite base stations. That is, the terminal connects to the NGSO satellite base station within the service cell of the NGSO satellite base station and receives the services of the NGSO satellite base station. The terminal is not connected to the GEO satellite system. When the terminal is in the avoidance area where the NGSO satellite base station causes interference to the geostationary orbit (GEO) satellite system, it will be affected by the interruption of services by the NGSO satellite base station. For example, terminal devices can be mobile phones, tablets, laptops, wearable devices, virtual reality (VR) devices, augmented reality (AR) devices, wireless communication devices, wireless terminals in industrial control, wireless terminals in self-driving, wireless terminals in remote medical surgery, wireless terminals in smart grids, wireless terminals in transportation safety, wireless terminals in smart cities, wireless terminals in smart homes, Internet of Things (IoT) devices, narrowband Internet of Things (NB-IoT) devices, vehicle-to-everything (V2X) devices, devices in device-to-device communication (D2D), enhanced machine-type communication (eMTC) devices, and reduced-capacity devices. Capability (RedCap), cellular phones, cordless phones, Session Initiation Protocol (SIP) phones, Wireless Local Loop (WLL) stations, Personal Digital Assistants (PDAs), clients, handheld devices with wireless communication capabilities, vehicle-mounted devices, or shipboard devices, etc.

[0043] In scenarios such as the Internet of Things (IoT), the terminal can also be a machine or device for monitoring or measurement, such as including but not limited to: machine-type communication (MTC) terminals, vehicle communication terminals, device-to-device terminals, machine-to-machine (M2M) terminals, and so on.

[0044] (2) Network equipment: refers to network-side equipment capable of communicating with terminal equipment. Network equipment can be located on NGSO satellites, GEO satellites, or on the ground. Network equipment can also be called space base station, satellite-borne base station, satellite communication node, satellite network terminal equipment, satellite communication module, communication module payload, or base station, etc. This network-side equipment can also be called access network equipment or wireless access network equipment. Network-side equipment can be a base station (BTS) in a satellite-borne Global System for Mobile Communication (GSM) or Code Division Multiple Access (CDMA) communication system; a base station (NodeB, NB) in a satellite-borne Wideband Code Division Multiple Access (WCDMA) system; an evolved base station (eNB, eNodeB) in a satellite-borne LTE system; a base station in a terrestrial network or non-terrestrial network (NTN), such as a base station (gNB) in a satellite-borne 5G network; a base station in a future network (e.g., 6G) after 5G, carried by satellite; a base station in a future evolved Public Land Mobile Network (PLMN) network, carried by satellite; a Transmission Reception Point (TRP) carried by satellite; or a Cloud Radio Access Network carried by satellite. In the context of Networks (CRAN), wireless controllers can also be satellite-borne city base stations, micro base stations, pico base stations, or femtobase stations. Base stations can also be ground-based base stations capable of satellite communication, and can be referred to as Access Points (APs), 5G nodes (5th generation nodes), wireless points, or Transmission / Reception Points (TRPs), the latter having equivalent technical meanings. Network equipment can also refer to base station equipment carried by High Altitude Platform Stations (HAPS) with loiter capabilities, such as large balloons or airships, base station equipment in Roadside Units (RSUs), or base station equipment in vehicle-to-everything (V2X) networks.

[0045] Both terminal devices and network devices can perform beamforming, but this disclosure is not limited to this. In some embodiments, the terminal may or may not perform beamforming. Similarly, the network device may or may not perform beamforming. That is, only one of the terminal and network device may perform beamforming, or neither the terminal nor the network device may perform beamforming. In this disclosure, a beam refers to the spatial flow of signals in a wireless channel, formed by one or more antennas or antenna elements; such a formation process can be called beamforming.

[0046] Furthermore, the term "network side" or "network equipment side" refers to one side of the network, which can be a base station or include one or more network devices as described above. The term "terminal side" or "terminal equipment side" refers to the side of the user or terminal, which can be a UE or include one or more terminal devices as described above.

[0047] This disclosure provides a satellite orbit determination method and an orbit determination method based on edge computing, such as... Figure 1 The diagram shows a satellite orbit determination system according to an embodiment of this disclosure. Satellite 101 acts as an onboard edge node, receiving signals from its associated GNSS satellite 102 via its onboard GNSS receiver to acquire corresponding observations (pseudorange and Doppler shift). The edge node module on satellite 101 performs real-time (single epoch) position and velocity calculations for the satellite and stores multi-epoch data (e.g., from t0 to t). N The calculation results are as follows: When the satellite passes over a ground station (e.g., gateway station 103), gateway station 103 receives the multi-epoch state vector estimate of satellite 101 (i.e., the position and velocity coordinates calculated by the on-board edge computing module) via the satellite-to-ground link and forwards it to the operations control center 104. The operations control center 104 uses the received position and velocity coordinates of satellite 101 as the initial observations and state vector of the satellite to perform orbit determination calculations for the target satellite. After obtaining the precise state vector estimate for the satellite's orbit determination, the precise orbit determination of the satellite can be completed. This precise state vector estimate can also be used as an initial value for orbit extrapolation through a dynamic model, thereby realizing the determination of the satellite's future orbit (e.g., t). N+1 Time, t N+2 High-precision prediction of orbit (time) or inversion of satellite physical characteristics, etc.

[0048] The satellites in this disclosure may include, for example, low Earth orbit (LEO) satellites, medium Earth orbit (MEO) satellites, geostationary Earth orbit (GEO) satellites, inclined geosynchronous orbit (IGSO) satellites, highly elliptical orbit (HEO) satellites, and highly eccentric orbit (HEO) spacecraft.

[0049] like Figure 2 The diagram shows a flowchart of a satellite orbit determination method based on edge computing according to an embodiment of this disclosure. The diagram describes the process of calculating the position and velocity coordinates of the target satellite based on received observations from related GNSS satellites on the target satellite's edge computing module. A first weight matrix is ​​established based on the equivalent measurement error of the GNSS satellites, thereby identifying the contribution of different GNSS satellites to the calculation of the satellite's position and velocity coordinates. This allows for more accurate calculations and fully utilizes the satellite's edge computing capabilities. When the satellite passes overhead (or during communication between the satellite and the ground station), the ground station transmits the satellite's observations directly, instead of transmitting the massive amount of raw multi-epoch GNSS satellite observations. This saves bandwidth on the satellite-to-ground link and shortens data transmission time, resulting in more accurate orbit determination results from the ground station. The method includes: Step 201: Determine the equivalent measurement error of the Global Navigation Satellite System (GNSS) satellites associated with the target satellite; wherein the equivalent measurement error includes the equivalent distance error and the equivalent velocity error of the GNSS satellites; Step 202: Establish the first weight matrix of the GNSS satellite based on the equivalent measurement error; Step 203: Based on the first weight matrix and the observations of the GNSS satellite, obtain the estimated current state vector of the target satellite.

[0050] The method in this embodiment can be used to characterize the reliability of each GNSS satellite associated with the satellite using a first weight matrix, filter out the observations of GNSS satellites with low reliability, and use the existing computing power on the target satellite to calculate its own position and velocity coordinates based on the observations of GNSS satellites with high reliability. When the target satellite transmits the observations to the ground station, it directly transmits the calculated position and velocity coordinates, thereby saving transmission bandwidth and improving transmission efficiency, so that the ground station can calculate the orbit determination parameters of the target satellite more accurately.

[0051] In this embodiment of the disclosure, determining the equivalent measurement error of the Global Navigation Satellite System (GNSS) satellite associated with the target satellite further includes: Extract pseudorange and Doppler shift observation information from multiple related GNSS satellite signals; Based on the pseudorange observation information and Doppler frequency shift observation information, as well as the information of the target satellite, the equivalent range error and equivalent velocity error of the relevant GNSS satellite are calculated.

[0052] In this embodiment, the GNSS satellites associated with the target satellite include those whose GNSS satellite broadcast navigation signals can be received by the receiver of the target satellite. The GNSS satellite broadcast navigation signals may include, for example, ranging codes, carrier waves, navigation messages, and other information. The target satellite can obtain pseudorange observation information, Doppler shift observation information, and other information required to calculate equivalent measurement errors based on the GNSS satellite broadcast navigation signals.

[0053] In the embodiments of this disclosure, ignoring errors such as antenna phase center, phase entanglement, and relativistic effects, the pseudorange and Doppler observation models considering equivalent measurement errors are as follows: , Where the superscript k represents the k-th GNSS satellite, the subscript r represents the target satellite, and PC and These are pseudorange observation information and Doppler shift observation information without ionosphere, respectively. r is the relative distance between the target satellite and the GNSS satellite, v is the relative velocity between the target satellite and the GNSS satellite, and c represents the speed of light. It is the clock bias of the target satellite. It is the clock bias of the k-th GNSS satellite. It is the equivalent distance error of GNSS ephemeris error. and These are pseudorange observation noise and Doppler observation noise, respectively. It is the clock drift of the target satellite. It is the clock drift of the k-th GNSS satellite. It is the equivalent velocity error of GNSS ephemeris error.

[0054] The orbital and velocity errors of GNSS broadcast ephemeris are converted into equivalent range and velocity errors for the target satellite receiver. Specifically, this is achieved by calculating the gradients of the relative distance and relative velocity between the GNSS satellite and the target satellite, thus establishing the orbital error and equivalent range error. and equivalent velocity error Mapping relationship: , , in, For the three-dimensional position error of GNSS satellite orbit, This represents the three-dimensional velocity error of the GNSS satellite orbit.

[0055] The equivalent distance error can be calculated in a three-dimensional spherical coordinate system. and equivalent velocity error With the Earth's center as the origin of the coordinate system, the line connecting the Earth's center and the GNSS satellite is the Z-axis, the GNSS satellite's motion direction at this moment is the X-axis, and the Y-axis is perpendicular to the XOZ plane. The equivalent distance error and equivalent velocity error in the spherical coordinate system are as follows: , , In this context, the subscript 'r' indicates radial direction, and the subscript 't' indicates the direction of motion. It refers to the position error of the GNSS satellite in the radial and motion directions in the spherical coordinate system. It refers to the radial and directional velocity errors of GNSS satellites in spherical coordinates. and It is a coefficient, and its specific form is as follows:

[0056] Where r is the relative distance between the target satellite and the GNSS satellite, r e The distance from the Earth's surface to its center. and V represents the polar angle (the physical meaning of the polar angle is the angle between the satellite's position and the Earth's center) and the azimuth angle (the physical meaning of the azimuth angle is the angle between the satellite's position projected onto the orbital plane and the velocity direction), respectively. x V represents the velocity component of a GNSS satellite along the X-axis (its own direction of motion, tangential to its orbit). z It is the velocity component of a GNSS satellite along the Z-axis (the direction of the line connecting the Earth's center and the radial direction of the orbit).

[0057] By mapping the GNSS satellite orbit error into an equivalent distance error and an equivalent velocity error, the orbit error can be compensated, thereby improving the calculation accuracy of the estimated value of the target satellite state vector.

[0058] In the embodiment of the present disclosure, the first orbit determination information includes: further including establishing the first weight matrix of the GNSS satellite according to the equivalent measurement error: Establish an observation error covariance matrix according to the standard deviation of the equivalent distance error and the standard deviation of the equivalent velocity error; Generate the first weight matrix of the relevant GNSS satellite by taking the inverse of the observation error covariance matrix.

[0059] In this embodiment, the equivalent distance error and the equivalent velocity error caused by the GNSS satellite orbit error are modeled as independent errors, and the variance of the equivalent distance error is and the variance of the equivalent velocity error is Then, the covariance matrix Σ of the observation error is:

[0060] [[ID=2G]]where Σ ρ and Σ f represent the pseudorange observation error covariance matrix and the Doppler observation error covariance matrix respectively, where: , , where K is the total number of GNSS satellites for positioning and speed determination (i.e., the total number of relevant GNSS satellites), 1 < k < K, and the operator is used to convert a vector into a diagonal matrix. Specifically, for the vector , represents an N×N square matrix, whose main diagonal elements are successively , and the rest of the elements are all zero, represents the variance of the equivalent distance error of the m-th GNSS satellite, represents the variance of the equivalent velocity error of the m-th GNSS satellite, 1 ≤ m ≤ K.

[0061] According to the weighted least squares principle, the first weight matrix W is the inverse of the covariance matrix Σ. The first weight matrix W characterizes the credibility weights of different GNSS satellite observables and is determined by the equivalent measurement error (the larger the error, the lower the weight), that is: , where: , .

[0062] Using the above method, the first weight matrix established based on the equivalent measurement error of GNSS satellites can set the weights (importance) of different GNSS satellite observations in the calculation process of the target satellite's position and velocity coordinates according to the orbital error of GNSS satellites, thereby making the calculation results more accurate.

[0063] In this embodiment of the disclosure, obtaining the current state vector estimate of the target satellite based on the first weight matrix and the observations of the GNSS satellite further includes: Based on the relevant observation matrix and observation data of the GNSS satellite, and the first weight matrix, the current state vector estimate of the target satellite is obtained.

[0064] In this embodiment, based on the first weight matrix obtained in the preceding steps, the estimated state vector of the target satellite can be calculated using the on-board edge computing module and the following formula. The estimated state vector may, for example, include [x, y, z, ...]. ,v x ,v y ,v z , ] T Where x, y, z, ,v x ,v y ,v z , These represent the target satellite's three-dimensional position coordinates, relative clock error, three-dimensional velocity coordinates, and relative clock drift, respectively. , Among them, X s The current state vector estimate is calculated by the on-board edge computing module. This estimate is based on the observations of the GNSS satellite at the current time and the first weight matrix; H s Y is the observation matrix; W is the first weight matrix; s For GNSS satellite observations.

[0065] The Y s H s and X s Satisfy: Y s =H s X s +ε s Y s The observations consist of pseudorange and Doppler shift of the GNSS satellite, ε represents the observation error (including pseudorange observation error and Doppler shift observation error), and the superscript s represents the target satellite. For X... sTaking the partial derivative yields H s , .

[0066] In other embodiments, recursive least squares (RLS) may be used, employing a recursive form rather than batch matrix operations, thus avoiding the need to recalculate [(H s ) T WH s ] - ¹, and can use the solution result of the previous epoch as the initial value, resulting in faster convergence.

[0067] Filtering methods can also be used, such as the Extended Kalman Filter (EKF), which introduces the orbital dynamics model as the state transition equation, transforming the single-epoch independent solution into a recursive filter; or the Unscented Kalman Filter (UKF), which uses unscented transformation to handle nonlinear problems, avoiding the linearization error of the EKF.

[0068] In some embodiments, the current state vector estimate of the target satellite may include only position and velocity coordinates. After accumulating state vector estimates over multiple epochs, the multi-epoch state vector estimates (multi-epoch position and velocity coordinates) can be sent to the ground station when the target satellite communicates with the ground station.

[0069] By calculating the position and velocity coordinates of the satellite at multiple epochs using the on-board edge computing in this embodiment and transmitting them to the ground station during communication, significant bandwidth can be saved and transmission efficiency improved. For example, when the satellite transmits observation data from eight related GNSS satellites to the ground station via the satellite-to-ground link, the data scale of the observations from the eight GNSS satellites at multiple epochs (including pseudorange observations, Doppler shift observations, carrier phase observations, navigation message data, etc.) is much larger than the estimated state vector values ​​(position and velocity coordinates) of the satellite at multiple epochs. The amount of data transmitted between the satellite and the ground link can be significantly reduced, improving transmission efficiency. By fully utilizing on-board edge computing resources and rationally allocating on-board and ground resources, the computing and storage pressure on the ground segment is reduced, thereby reducing the construction and maintenance costs of the ground system. The system's autonomous operation capability is enhanced under conditions of limited satellite-to-ground links, improving system reliability.

[0070] like Figure 3 The diagram shows a flowchart of a satellite orbit determination method based on edge computing according to an embodiment of this disclosure. The diagram describes how, after the ground station receives the multi-epoch state vector estimate transmitted from the target satellite, it uses it as the first observation and assigns overall weights to the position and velocity coordinates within it. This adjusts the different errors in the first observation based on position and velocity, thereby enabling more accurate orbit determination of the target satellite by combining the edge calculations of the position and velocity coordinates on-board. The method specifically includes: Step 301: The estimated value of the multi-epoch state vector sent by the target satellite is used as the first observation; wherein the first observation includes the position coordinates and velocity coordinates of the target satellite.

[0071] Step 302: For the position coordinates and velocity coordinates in the first observation, different weights are assigned to form the third weight matrix.

[0072] Step 303: Based on the third weight matrix and the first observation, obtain the estimated value of the state vector of the target satellite.

[0073] The method of this disclosure can acquire more epochs of observation data by using less satellite-to-ground link bandwidth. Since the error characteristics of position and velocity observations calculated on the satellite are different, the weight ratio of heterogeneous position and velocity data in the observations is optimized by the third weight matrix to reflect the statistical characteristics of heterogeneous observations and to reflect the contribution of position and velocity to orbit determination in subsequent calculations, thereby performing orbit determination calculations for the target satellite more accurately.

[0074] In this embodiment of the disclosure, before assigning different weights to the position coordinates and velocity coordinates in the first observation to form the third weight matrix, the method further includes: The first observation of the target satellite is updated by removing observations from the first observation by using a second weight matrix to eliminate those from abnormal epochs.

[0075] In this embodiment, for the multi-epoch state vector estimates received by the ground station (e.g., the gateway station) from the target satellite, there may be some abnormal epoch state vector estimates due to various errors such as GNSS satellite broadcast ephemeris error, target satellite receiver noise, and multipath effect. The abnormal epoch observations can be removed from the first observations by using the second weight matrix.

[0076] In this embodiment, the second weighting matrix filters out position and velocity coordinates with significant anomalies in the multi-epoch first observations, updating them to those with high reliability. This first step improves the accuracy of the target satellite state vector estimation. Then, the third weighting matrix distinguishes the importance of different types of observations (position and velocity), further enhancing the accuracy of the target satellite state vector estimation. This two-step processing of the received first observations can be closely integrated with the on-board edge calculation of position and velocity coordinates. This leverages the target satellite's edge computing capabilities, improves the utilization of the satellite-to-ground link, and optimizes the calculated state vector estimation, ensuring the accuracy of the ground-based calculation of the target satellite's orbit determination parameters.

[0077] like Figure 4 The diagram shows a flowchart of a satellite orbit determination method according to an embodiment of this disclosure. The diagram describes how, after the satellite transmits multi-epoch state vector estimates, the ground station uses these estimates as the first observation and iteratively processes them using a second and third weighting matrix to finally calculate and output the satellite state vector estimate. This embodiment reduces the occupancy of the satellite-to-ground link, allowing the ground station to receive multi-epoch state vector estimates (position and velocity coordinates) from the satellite in a short time. These estimates are then used as the first observation, and optimization is performed on them to make the satellite state vector estimate calculated by the ground station more accurate. The method includes: Step 401: Initialize the state vector and the second weight matrix.

[0078] In this step, the state vector is set as... The first 0 in the subscript represents time t0 (i.e., the current epoch), and i is the iteration number, initialized to i=0. This represents the estimated value of the second weight matrix in the i-th iteration.

[0079] The received position and velocity coordinates transmitted from the satellite are used as the first observation. The initial orbital information of the satellite is obtained by performing second-order polynomial fitting on the multi-epoch position and velocity coordinate sequences in the first observation. The force model parameters (atmospheric drag coefficient) for the satellite orbital perturbation are then used. and solar radiation pressure coefficient The value is set to an empirical value of 2.2, thus forming the satellite's state vector. .

[0080] The second weight matrix is ​​set. It is an identity matrix.

[0081] Step 402: Calculate the first residual and the first observation matrix for each epoch observation based on the first observation and the state vector.

[0082] In this step, the first residual corresponding to the i-th iteration is calculated using the first observation (the sequence of position and velocity coordinates from multiple epochs transmitted by the satellite) and the state vector of the current iteration number. ,in, It is the first observation. This is the total dynamic mapping function, used to extrapolate the state vector at the reference time to the i-th observation epoch, obtaining the theoretical position and velocity coordinates for that epoch. First observation matrix. ,in, For the state vector Find the Jacobian matrix with partial derivatives, which represents the degree of influence of small changes in the state vector on the observations.

[0083] Step 403: Calculate the weight function using the first residual.

[0084] In this step, the first residual is used Standardized residuals are obtained after calculating the unit weighted variance. The weight function is generated using the standardized residuals. The weight function can be, for example, IGG3 or Huber. In this embodiment, IGG3 will be used as an example for explanation: The IGG3 weight function is divided into three segments, considering the normal stage, the doubtful stage, and the elimination stage. The formula for the IGG3 weight function is shown below:

[0085] in, The equivalent weight factor represents the observations at the nth epoch, characterizing the confidence level of the position and velocity coordinates at the nth epoch. A larger value indicates higher confidence; a value of 0 indicates that the observations at that epoch are gross errors and should be discarded. It is the absolute value of the standardized residual. These are model parameters, where standardized residuals are... , It is the first residual of the nth epoch. Unit weighted variance factor , This represents the median of the absolute values ​​of the residuals; k1 and k0 are harmonic coefficients, ranging from 1.5 to 3.2 and from 2.5 to 8.0, respectively. This indicates taking the absolute value.

[0086] Step 404: Update the second weight matrix.

[0087] In this step, equivalent weighting factors are used. Determine the elements in the second weight matrix : , Where 0 represents a matrix consisting of zero elements. The superscript 1 indicates the second weight matrix in the first robust estimation step, and i+1 represents the (i+1)th iteration. The elements in the second weight matrix... This represents the weight value of the nth epoch in the i-th iteration.

[0088] Step 405: Calculate the first state vector estimate of the target satellite based on the state vector, the first residual, the first observation matrix, and the second weight matrix.

[0089] In this step, the first state vector estimate of the target satellite can be calculated according to the following formula: , in, This is the estimated value of the first state vector obtained in the (i+1)th iteration. The estimated value of the first state vector obtained in the i-th iteration (that is, the state vector of the i-th iteration). This is the first observation matrix. For the first residual, The updated second weight matrix is ​​used to remove observations from outlier epochs.

[0090] Step 406: Determine whether to iterate. If it continues to iterate, return to step 402. If it stops iterating, proceed to step 407.

[0091] In this step, the conditions for determining whether to iterate may include the number of iterations and / or the root mean square of the difference between the position coordinates obtained from two iterations being less than a preset threshold.

[0092] For example, first determine whether the current iteration number is less than the iteration threshold. If the current iteration number is less than the iteration threshold, update the state vector with the first state vector estimate obtained in the current iteration, and determine whether the root mean square of the difference between the first state vector estimates obtained in the last two iterations is less than a preset threshold. If it is less, proceed to step 407; otherwise, return to step 402 for the next iteration to obtain the first state vector estimate of the corresponding iteration. If the number of iterations is greater than the iteration threshold, proceed to step 407.

[0093] In other embodiments, a convergence judgment condition may be selectively executed to determine whether the root mean square of the difference between the estimated values ​​of the first state vector obtained in the two most recent iterations is less than a preset threshold. If it is less, proceed to step 407; otherwise, update the state vector with the estimated value of the first state vector obtained in the current iteration and return to step 402.

[0094] Step 407: Initialize the state vector and the third weight matrix using the first state vector estimate output from the previous steps.

[0095] In this step, the state vector is set. The first state vector estimate output from step 406 above is used, where the first 0 in the index represents time t0 (i.e., the current epoch), the iteration number is i, and i is initialized to 0. Using... and The weight matrices representing the position and velocity coordinates of the i-th iteration are respectively used to form the third weight matrix, which is used to adjust the contribution ratio of the two types of heterogeneous observations to the final orbit determination result. and Set it to a unit array.

[0096] Step 408: Based on the updated first observation and state vector, obtain the second residual and second observation matrix of multiple epoch observations.

[0097] In this step, the second residual corresponding to the i-th iteration is calculated using the first observation after removing outlier epoch observations and the state vector updated in the aforementioned steps. Second observation matrix .

[0098] Step 409: Calculate the unit weight variance of position coordinates and the unit weight variance of velocity coordinates.

[0099] In this step, the unit weight variance of the position coordinates after the i-th iteration normalization is estimated using the second residual. and velocity coordinate observation unit weight variance Unit weight variance and The estimation formula is: , , in, and K represents the position and velocity coordinate residuals of the i-th iteration, respectively. P and K V It is the number of epochs for position and velocity coordinates, m P and m V These are the dimensions of the state vectors corresponding to orbit determination using position coordinates and orbit determination using velocity coordinates, respectively, which are 8 in this embodiment.

[0100] In other embodiments, the unit weight variance of the position coordinate observations can also be obtained using minimum norm quadratic unbiased estimation. and velocity coordinate observation unit weight variance : , , in, and M represents the position and velocity coordinate residuals of the i-th iteration, respectively. P,i Let be the projection matrix of the position observations in the i-th iteration. P i The weight matrix for the i-th iteration M is composed of a position coordinate weight matrix and a velocity weight matrix. V,i Let be the projection matrix of the velocity observations in the i-th iteration. tr() is the trace operation of a matrix, which is the sum of all elements on the main diagonal of the matrix.

[0101] Step 410: Update the third weight matrix.

[0102] In this step, the unit weight variance is observed using the aforementioned location coordinates. and velocity coordinate observation unit weight variance Determine the third weight matrix ,in, The superscript 2 indicates the third weight matrix for the second step of heterogeneity estimation. and Determined by the following formula: , .

[0103] Step 411: Calculate the estimated value of the second state vector of the target satellite based on the state vector, the second residual, the second observation matrix, and the third weight matrix.

[0104] In this step, the second state vector estimate of the target satellite can be calculated according to the following formula: , in, This is the estimated value of the second state vector obtained in the (i+1)th iteration. The estimated value of the second state vector obtained in the i-th iteration (that is, the state vector of the i-th iteration). This is the second observation matrix. For the second residual, The updated third weighting matrix assigns different weights to different types of observations (position coordinates and velocity coordinates).

[0105] Step 412: Determine whether to iterate. If it continues to iterate, return to step 408; otherwise, proceed to step 413.

[0106] In this step, the conditions for determining whether to iterate may include the number of iterations and / or the root mean square of the difference between the position coordinates obtained from two iterations being less than a preset threshold.

[0107] For example, first determine whether the current iteration number is less than the iteration threshold. If the current iteration number is less than the iteration threshold, update the state vector with the second state vector estimate obtained in the current iteration, and determine whether the root mean square of the difference between the second state vector estimates obtained in the last two iterations is less than the preset threshold. If it is less, proceed to step 413; otherwise, return to step 408. If the number of iterations exceeds the iteration threshold, proceed to step 413.

[0108] In other embodiments, a convergence judgment condition may be selectively executed to determine whether the root mean square of the difference between the second state vector estimates obtained in the two most recent iterations is less than a preset threshold. If it is less, proceed to step 413; otherwise, update the state vector with the second state vector estimate obtained in the current iteration and return to step 408.

[0109] Step 413: Output the second state vector estimate as the state vector estimate of the target satellite.

[0110] In subsequent steps, the future orbit of the target satellite can be predicted based on the final estimated state vector value of the target satellite.

[0111] In some embodiments, after obtaining the state vector estimate for satellite orbit determination to complete the satellite orbit determination, the state vector estimate can also be used as an initial value to generate a high-precision predicted orbit for future periods through dynamic extrapolation. This can directly support on-orbit operational applications such as orbital maneuvering, collision avoidance, satellite-to-ground / inter-satellite link plan generation, inversion of satellite physical parameters, initial link alignment, and beam pointing planning.

[0112] like Figure 5 The diagram shown is a schematic representation of a satellite orbit determination device based on edge computing according to an embodiment of this disclosure. The diagram illustrates the logical component structure for the target satellite to execute the aforementioned satellite-side orbit determination method. The device in this embodiment can be implemented by an on-board edge computing module or by an on-board partial edge computing module. The logical component implementing the aforementioned method in this embodiment can be implemented by a general-purpose processor or a specially configured processor. Specifically, the device includes: The receiving unit 501 is configured to determine the equivalent measurement error of a Global Navigation Satellite System (GNSS) satellite associated with the target satellite; wherein the equivalent measurement error includes the equivalent distance error and the equivalent velocity error of the GNSS satellite.

[0113] The first weighting unit 502 is configured to establish a first weighting matrix for the GNSS satellite based on the equivalent measurement error.

[0114] The calculation unit 503 is configured to obtain an estimate of the current state vector of the target satellite based on the first weight matrix and the observations of the GNSS satellite.

[0115] like Figure 6 The diagram shown is a schematic representation of a satellite orbit determination device based on edge computing according to an embodiment of this disclosure. The diagram illustrates the logical component structure of the network-side device executing the aforementioned network-side satellite orbit determination method. The network-side device may include, for example, a gateway station and an operations control center in a ground station. In this embodiment, the logical component implementing the above method can be implemented by a general-purpose processor or a specially configured processor. Specifically, the device includes: The acquisition unit 601 is configured to use the multi-epoch state vector estimate sent by the target satellite as the first observation; wherein the first observation includes the position coordinates and velocity coordinates of the target satellite.

[0116] The third weighting unit 602 is configured to assign different weights to the position coordinates and velocity coordinates in the first observation, forming the third weighting matrix.

[0117] The orbit determination unit 603 is configured to obtain an estimate of the state vector of the target satellite based on the third weight matrix and the first observation.

[0118] The apparatus described in the above-described embodiments of the present disclosure can fully utilize on-board edge computing capabilities, reduce the occupation of satellite-to-ground links, improve the efficiency of data transmission, and enable more accurate satellite orbit determination, resulting in more accurate predictions of subsequent satellite orbits.

[0119] For ease of description, the above apparatus is described by dividing it into various functional units. Of course, in implementing this disclosure, the functions of each unit can be implemented in one or more software and / or hardware.

[0120] Figure 7 The diagram shown is a schematic representation of the network device according to an embodiment of this disclosure. The structure of the satellite and ground station in this disclosure can be referenced. Figure 7 The network device 700 may include a processor 710 (e.g., a central processing unit CPU) and a memory 720; the memory 720 is coupled to the processor 710. The memory 720 may store various types of data; it also stores an information processing program 730, and executes the program 730 under the control of the processor 710.

[0121] For example, processor 710 can be configured to execute a program to implement the satellite orbit determination method as described in the previous embodiment. For example, processor 710 can be configured to perform the following control: determine the equivalent measurement error of a Global Navigation Satellite System (GNSS) satellite associated with the target satellite; wherein the equivalent measurement error includes the equivalent range error and equivalent velocity error of the GNSS satellite; establish a first weight matrix for the GNSS satellite based on the equivalent measurement error; and obtain an estimate of the current state vector of the target satellite based on the first weight matrix and the observations of the GNSS satellite.

[0122] Alternatively, the processor 710 of the network device in this embodiment can also be configured to use the multi-epoch state vector estimate value sent by the target satellite as the first observation; wherein the first observation value includes the position coordinates and velocity coordinates of the target satellite; different weights are assigned to the position coordinates and velocity coordinates in the first observation value to form the third weight matrix; and the state vector estimate value of the target satellite is obtained according to the third weight matrix and the first observation value.

[0123] In addition, such as Figure 7 As shown, network device 700 may also include: transceiver 740 and antenna 750, etc.; the functions of the above components are similar to those in the prior art, and will not be described in detail here. It is worth noting that network device 700 is not necessarily required to include... Figure 7 All components shown; in addition, network device 700 may also include Figure 7 For components not shown, please refer to existing technologies.

[0124] This disclosure is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), computer-readable storage media, and computer program products of embodiments. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processor to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processor, create a machine for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0125] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processor to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0126] These computer program instructions may also be loaded onto a computer or other programmable data processor, causing a series of operational steps to be performed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable device for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0127] In a typical configuration, a computer device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0128] Memory may include non-persistent storage in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM. Memory is an example of computer-readable media.

[0129] Computer-readable media, including both permanent and non-permanent, removable and non-removable media, can store information using any method or technology. Information can be computer-readable instructions, data structures, program modules, or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, disk storage or other magnetic storage devices, or any other non-transferable medium that can be used to store information accessible by computer equipment. As defined in this disclosure, computer-readable media does not include transient computer-readable media, such as modulated data signals and carrier waves.

[0130] Those skilled in the art will understand that embodiments of this disclosure can be provided as methods, systems, or computer program products. Therefore, embodiments of this disclosure can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, embodiments of this disclosure can be implemented as a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0131] Embodiments of this disclosure can be described in the general context of computer-executable instructions, such as program modules, that are executed by a computer. Generally, program modules include routines, programs, objects, components, data structures, etc., that perform a specific task or implement a specific abstract data type. Embodiments of this disclosure can also be practiced in distributed computing environments where tasks are performed by remote processors connected via a communication network. In distributed computing environments, program modules can reside in local and remote computer storage media, including storage devices.

[0132] It should also be understood that, in the embodiments of this disclosure, the term "and / or" is merely a description of the relationship between associated objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, and B existing alone. Additionally, the character " / " in this document generally indicates that the preceding and following associated objects have an "or" relationship.

[0133] The various embodiments in this disclosure are described in a progressive manner. Similar or identical parts between embodiments can be referred to mutually. Each embodiment focuses on describing the differences from other embodiments. In particular, the system embodiments are basically similar to the method embodiments, so the description is relatively simple; relevant parts can be referred to the descriptions of the method embodiments.

[0134] In the description of this disclosure, references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the embodiments of this disclosure. In this disclosure, 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 a suitable manner in any 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 disclosure, as well as the features of different embodiments or examples.

[0135] The above description is merely an embodiment of this disclosure and is not intended to limit the scope of this disclosure. Various modifications and variations can be made to this disclosure by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this disclosure should be included within the scope of the claims of this disclosure.

Claims

1. A satellite orbit determination method based on edge computing, characterized in that... This method is applied to target satellites, including: Determine the equivalent measurement error of the Global Navigation Satellite System (GNSS) satellites associated with the target satellite; wherein the equivalent measurement error includes the equivalent range error and the equivalent velocity error of the GNSS satellites; The first weight matrix of the GNSS satellite is established based on the equivalent measurement error. Based on the first weight matrix and the observations of the GNSS satellite, the current state vector estimate of the target satellite is obtained.

2. The method according to claim 1, characterized in that, The determination of the equivalent measurement error of the Global Navigation Satellite System (GNSS) satellites associated with the target satellite further includes: Extract pseudorange observation information and Doppler frequency shift observation information from multiple related GNSS satellites; Based on the pseudorange observation information and Doppler frequency shift observation information, as well as the information of the target satellite, the equivalent range error and equivalent velocity error of the relevant GNSS satellite are calculated.

3. The method according to claim 2, characterized in that, Establishing the first weight matrix of the GNSS satellite based on the equivalent measurement error further includes: Based on the variance of the equivalent distance error and the variance of the equivalent velocity error, an observation error covariance matrix is ​​established. The first weight matrix of the GNSS satellite is generated by inversely generating the observation error covariance matrix.

4. The method according to claim 1, characterized in that, The estimation of the current state vector of the target satellite, based on the first weight matrix and the observations of the GNSS satellite, further includes: Based on the relevant observation matrix and observation data of the GNSS satellite, and the first weight matrix, the current state vector estimate of the target satellite is obtained.

5. The method according to claim 1, characterized in that, The estimated current state vector includes the target satellite's current position coordinates and velocity coordinates.

6. The method according to claim 1, characterized in that, After obtaining the current state vector estimate of the target satellite, the process also includes: The current state vector estimates are aggregated into multi-epoch state vector estimates and then sent.

7. A satellite orbit determination method based on edge computing, characterized in that... This method is applied to ground stations, including: The multi-epoch state vector estimate sent by the target satellite is used as the first observation; wherein, the first observation includes the position coordinates and velocity coordinates of the target satellite; Different weights are assigned to the position coordinates and velocity coordinates in the first observation, forming a third weight matrix; The state vector estimate of the target satellite is obtained based on the third weight matrix and the first observation.

8. The method according to claim 7, characterized in that, Before forming the third weighting matrix by assigning different weights to the position and velocity coordinates in the first observation, the following steps are also included: The first observation of the target satellite is updated by removing observations from the first observation by using a second weighting matrix to eliminate those from abnormal epochs.

9. The method according to claim 8, characterized in that, Before removing outlier observations from the first observations using the second weighting matrix, the process also includes: Based on the first observation and the state vector of the target satellite, the first residual and the first observation matrix of each epoch observation are obtained.

10. The method according to claim 9, characterized in that, Based on the first observation and the state vector of the target satellite, the first residual and the first observation matrix for each epoch observation are further obtained, including: Based on the first observation and the state vector of the i-th iteration theory, the first residual of the current epoch is obtained; The first observation matrix of the current epoch is obtained based on the partial derivative of the state vector of the i-th iteration theory.

11. The method according to claim 9, characterized in that, Before removing outlier observations from the first observations using the second weighting matrix, the process also includes: Calculate the standardized residual of the first residual; Based on the standardized residuals of the first residual, the confidence level of each epoch observation is obtained; The second weight matrix is ​​generated based on the confidence level of each epoch observation.

12. The method according to claim 11, characterized in that, The confidence level of each epoch observation obtained from the standardized residual of the first residual further includes: The weighting function is obtained based on the standardized residual of the first residual; Based on the weighting function, the weighting factors characterizing the confidence level of each epoch observation are obtained.

13. The method according to claim 12, characterized in that, Generating the second weight matrix based on the confidence level of each epoch observation further includes: The second weight matrix is ​​generated based on the weighting factors that characterize the confidence of the observations at each epoch.

14. The method according to claim 9, characterized in that, Updating the first observations of the target satellite by removing observations from anomalous epochs in the first observations using a second weighting matrix further includes: The first state vector estimate of the target satellite is calculated based on the state vector, the first residual, the first observation matrix, and the second weight matrix. Determine whether the first state vector estimate has converged: If the first state vector estimate fails to converge, the state vector is updated using the first state vector estimate, and the first state vector estimate of the target satellite is iteratively calculated. When the first state vector estimate converges, the first state vector estimate and the updated first observation are output.

15. The method according to claim 14, characterized in that, Before determining whether the first state vector estimate has converged, the following steps are also included: Based on the set number of iterations, the state vector is updated using the first state vector estimate, and the first state vector estimate of the target satellite is iteratively calculated.

16. The method according to claim 15, characterized in that, For the position coordinates and velocity coordinates in the first observation, different weights are assigned, further including: Based on the updated first observation and state vector, the second residual and second observation matrix of multiple epoch observations are obtained.

17. The method according to claim 16, characterized in that, Forming the third weight matrix further includes: Based on the second residual and the weights of the position coordinates and velocity coordinates in the first observation, the unit weight variance of the position coordinates and the unit weight variance of the velocity coordinates are obtained. The third weight matrix is ​​obtained based on the unit weight variance of the position coordinate observation and the unit weight variance of the velocity coordinate observation.

18. The method according to claim 16, characterized in that, The estimated state vector of the target satellite, obtained based on the third weight matrix and the first observation, further includes: The second state vector estimate of the target satellite is calculated based on the state vector, the second residual, the second observation matrix, and the third weight matrix. Determine whether the second state vector estimate has converged: If the second state vector estimate fails to converge, the state vector is updated using the second state vector estimate, and the second state vector estimate of the target satellite is iteratively calculated. When the second state vector estimate converges, the second state vector estimate is used as the state vector estimate of the target satellite.

19. The method according to claim 18, characterized in that, Before determining whether the second state vector estimate has converged, the following steps are also included: Based on the set number of iterations, the state vector is updated using the second state vector estimate, and the second state vector estimate of the target satellite is iteratively calculated.

20. A satellite orbit prediction method, characterized in that... The state vector estimate of the target satellite obtained by the method according to any one of claims 7-19 is used to predict the future orbit of the target satellite.

21. A satellite, characterized in that, include: At least one processor; as well as At least one memory storing instructions that, when executed individually or jointly by the at least one processor, cause the satellite to perform the method according to any one of claims 1 to 6.

22. A network-side device, characterized in that, include: At least one processor; as well as At least one memory storing instructions that, when executed individually or jointly by the at least one processor, cause the network-side device to perform the method according to any one of claims 7 to 19.

23. A computer storage medium storing instructions thereon, characterized in that, When the instructions are executed individually or jointly by at least one processor of a computer device, the computer device performs the method according to any one of claims 1 to 6 or any one of claims 7 to 19.

24. A computer program product, comprising instructions, characterized in that, When the instructions are executed individually or jointly by at least one processor of a computer device, the computer device performs the method according to any one of claims 1 to 6 or any one of claims 7 to 19.

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