84 results about "Orbit prediction" patented technology

The invention relates to the technical field of spacecraft orbit control and space safety, and discloses a space debris orbit prediction and avoidance method, which comprises the following steps that: a ground computing center screens candidate targets and generates enhanced data packets for uploading; the spaceborne computer combines real-time navigation data to carry out geometric feature screening, and an instant high-risk target list is generated; controlling the star sensor to execute optical observation, and resolving an optical measurement relative orbit state by utilizing displacement superposition and Kalman filtering; selecting a high confidence coefficient or a conservative probability threshold according to the optical measurement state acquisition condition, and generating an avoidance instruction when the collision probability exceeds the limit; and the propulsion system responds to the instruction to execute avoidance, verifies the effect by confirming the semi-major axis variation and executes track recovery by selecting an aircraft. According to the method, the ground-air collaborative screening and shift superposition enhancement technology is adopted, the problems that the low-precision ephemeris false alarm rate is high and the detection capability of a satellite-borne sensor on a dark and weak target is insufficient are solved, and the accuracy of evasion decision making and the execution reliability are improved.

The invention provides a two-line element generation method and orbit prediction method adaptive to an analytic propagation model, and the method comprises the steps: obtaining the historical osculating orbit elements at each epoch moment, and carrying out the orbit element optimization step of a plurality of iteration rounds on each historical osculating orbit element through employing the analytic propagation model, obtaining a target osculating orbital element number at each epoch moment, enabling the historical osculating orbital element number to be the same as the target osculating orbital element number, and performing inversion to obtain an average orbital element number; and extracting each average semi-major axis of each average orbital element, fitting each average semi-major axis by adopting a least square method to obtain a semi-major axis attenuation rate, inverting the semi-major axis attenuation rate to obtain an atmospheric resistance parameter, and equivalently converting the atmospheric resistance parameter into a standard resistance term in two rows of elements to obtain the two rows of elements. According to the method, the problems of inconsistent models, insensitive residual errors, unable physical explanation of parameters and the like in the two-line element generation process can be solved, and the ephemeris extrapolation orbit prediction precision is improved.

The invention discloses a practical training platform three-dimensional scene mass data loading optimization method, and relates to the technical field of computer graphics and three-dimensional visualization. Passive response of data loading is converted into active pre-judgment through an orbit prediction model and prospective visible area calculation, and the real-time performance of data loading is improved. The rendering lag caused by the rapid change of the satellite track is obviously reduced; the combination of the predicted position and the camera vision cone ensures that the point location is scheduled before the vision field is switched; secondly, GPU resource utilization is optimized through dynamic attribute increment updating and a dirty marking mechanism; static and dynamic attribute buffer areas are separated, only change data are updated, the CPU-GPU communication overhead is reduced, the frame rate fluctuation problem caused by batch updating is solved, and the system is still kept smooth under ten-thousand-level point location dynamic updating; in addition, the collaborative design with terrain rendering eliminates point location dislocation during terrain detail level switching through monitoring and double-buffer correction, and improves visual consistency.

The invention relates to an RRC (Radio Resource Control) layer enhanced mobile model simulation method and system based on a 3GPP (3rd Generation Partnership Project) NTN (Network Temporary Network) standard. The method comprises the following steps: S1, constructing a simulation environment conforming to the 3GPP NTN standard; s2, constructing an RRC state machine in the NTN scene, wherein the RRC state machine comprises a user RRC state machine and a satellite RRC state machine; s3, configuring a dynamic conversion condition of the RRC state machine and a corresponding behavior logic; s4, scheduling events and injecting NTN parameters, and performing NTN propagation characteristic simulation based on the simulation environment; and S5, counting performance indexes generated in the simulation process, and outputting an NTN scene simulation report. According to the method, the simulation environment conforming to the 3GPP NTN standard is constructed, the RRC state machine in the NTN scene is constructed, the characteristics of long delay, high mobility and orbit prediction of the satellite are adapted, and the problem that a state machine model of a traditional simulation method is not applicable is solved.

The invention belongs to the technical field of communication satellite measurement and control technologies, and provides a satellite measurement and control system based on multi-modal fusion, which comprises a multi-modal signal fusion module used for receiving measurement and control data, fusing information from a plurality of signal sources, carrying out data time and space alignment and establishing a state estimation equation for prediction, outputting accurate satellite state data; the intelligent orbit prediction module is used for predicting the orbit of the satellite in real time and calculating the future change of the satellite orbit according to the received satellite state data and external information; the satellite control module is used for controlling and adjusting the orbit and attitude of the satellite in real time, and running the satellite according to the preset orbit and attitude through monitoring and control; and the task planning and instruction scheduling module is used for planning and scheduling satellite tasks, generating corresponding instructions according to task requirements, and reasonably allocating resources by scheduling and evaluating various indexes of satellites so as to enable the instructions to be executed on time.

Embodiments of the present application provide a space target orbit prediction method, a prediction device, an electronic device and a medium. The method comprises: obtaining two-line orbital elements of a space target at a current time, space environment data and perturbation coefficients, the perturbation coefficients being coefficients in a space target orbit prediction model; correcting the two-line orbital elements and / or the perturbation coefficients based on a pre-trained parameter correction model, the two-line orbital elements, the perturbation coefficients and the space environment data, to obtain corrected data, the corrected data comprising corrected two-line orbital elements and / or corrected perturbation coefficients; and predicting orbit information of the space target at a preset future time based on the space target orbit prediction model and the corrected data. The method makes the predicted orbit information more accurate.

The invention provides a non-singular semi-analytical method for forecasting a low-thrust perturbation orbit. The method comprises the following steps: firstly, establishing a Gaussian variational equation for describing spring equinox orbital element evolution under small thrust control based on a periodic small thrust hypothesis; thirdly, constructing a long-term equation and a short-period term based on the average spring equinox orbital elements, performing large-step integration on the long-term equation to obtain a flat root, and then performing orbital element correction through the short-period term to finish high-precision orbital forecasting; and finally, establishing an analytic form error estimation model through a semi-major-axis second-order long-term equation, and using the analytic form error estimation model to evaluate orbit forecast errors. The semi-analytic low-thrust orbit forecasting method provided by the invention achieves good balance between calculation efficiency and precision, avoids the problem of singularity, and is suitable for high-precision forecasting of any low-thrust elliptical orbit; the error estimation model provided by the invention can effectively quantify the prediction error and enhance the reliability of the orbit prediction results of most small eccentricity rate on-orbit satellites.

The invention discloses a satellite orbit forecasting method based on in-orbit calibration of model parameters. Belongs to the technical field of remote sensing satellite measurement, operation and control and particularly relates to the technical field of remote sensing satellite orbit forecasting. According to the method, the satellite windward side-to-mass ratio in an atmospheric resistance model is calibrated regularly according to actually measured orbit data, the resistance coefficient in the atmospheric resistance model is calibrated in real time, and more accurate model parameters are used for modeling a perturbative force model in the satellite orbit forecasting process, so that the error of the atmospheric resistance model is reduced, and the orbit forecasting precision is improved. In the time period that the space weather changes gently, the atmospheric resistance modeling precision is improved to 90% or above from about 60%-80% of a traditional method through the method.

The application relates to the technical field of satellite intelligent access, and discloses a multi-satellite intelligent access method, system and equipment based on AI prediction, which comprises the following steps: collecting satellite orbit data and user terminal position data of a multi-satellite system, and performing satellite position prediction through an orbit prediction neural network to obtain satellite predicted positions and communication window information; each intelligent agent perceives communication environment parameters and constructs an environment perception vector; a multi-agent strategy network is driven based on the satellite predicted positions and the environment perception vector to generate a first access strategy; each intelligent agent performs resource conflict processing according to the first access strategy to obtain a second access strategy; the application adopts a multi-agent architecture to replace traditional centralized control, each intelligent agent has independent decision-making capability and realizes cooperation through a distributed negotiation protocol, single-point failure problems are avoided, robustness and scalability are improved, and resource competition and access conflicts are effectively reduced.

The invention discloses an on-orbit failure probability estimation method for a single-particle hard error of a wide bandgap semiconductor device, and aims to support on-orbit prediction of the single-particle hard error failure probability of the wide bandgap semiconductor device. Wide bandgap semiconductor devices such as silicon carbide (SiC), gallium nitride (GaN) and the like are easy to generate hard errors such as single-particle burnout and single-particle gate-through under the radiation of high-energy particles, and the risk of the hard errors in aerospace application needs to be comprehensively predicted and evaluated. Therefore, the invention provides a method for estimating the in-orbit failure probability of the single-particle hard error of the wide bandgap semiconductor device, which considers the influence of a failure section angle and particle distribution characteristics in different orbit environments, and provides data support for model selection.

The invention discloses a satellite internet of things intelligent access device based on orbit prediction and multi-satellite cooperation, which belongs to the field of low orbit satellite internet of things communication and comprises a main control and calculation unit, a multi-satellite access and radio frequency unit, an ephemeris storage and orbit prediction unit, a positioning and time service unit and a data buffering and service adaptation unit. Wherein the multi-satellite access and radio frequency unit is integrated with at least two sets of independent radio frequency front-end channels and supports parallel processing of signals of a plurality of satellites; and the ephemeris storage and orbit prediction unit is used for calculating accurate orbit parameters of future visible satellites. The core of the device is that the main control and calculation unit can actively generate an access and switching plan according to a track prediction result and service requirements, and control multiple radio frequency channels to cooperatively work in a main-standby or load balancing mode. According to the invention, the problem of unstable connection of the low-orbit satellite Internet of Things terminal caused by high-speed movement of the satellite is fundamentally solved, and the communication continuity, reliability and system efficiency are remarkably improved.

The application relates to a target over-screen detection method of a V-shaped electronic fence, and belongs to the technical field of space target monitoring. According to the motion law of a target, sufficient conditions of target over-screen detection are set, when a target simultaneously satisfies a longitude constraint of a vertical projection screen, a azimuth angle constraint of passing through the vertical projection screen and a maximum latitude constraint that can be reached by a target subsolar point, the target can be considered to simultaneously pass through the V-shaped fence screen. The method does not need to directly search whether the target can pass through the screen, avoids the compromise selection of operation time and orbit prediction accuracy, and effectively improves the calculation efficiency of target over-screen detection.

The invention relates to the technical field of space imaging, and provides a space target space-based microwave imaging system design method and device. The method comprises the following steps: firstly, determining an intersection process of an observation satellite and a space target based on an orbit dynamics simulation method, and further determining an imaging observation window; determining beam scanning geometry by combining relative motion characteristics of an observation satellite and a space target in the imaging observation window and the maximum scanning capability of the phased-array antenna, and generating a beam scanning strategy; secondly, realizing accurate capturing and locking of a wave beam by optimizing an orbit forecast error of a space target; and finally, according to the beam scanning strategy and the optimized orbit parameters of the space target, high-precision imaging of the space target is realized in combination with a synthetic aperture imaging algorithm and error compensation, double-high-speed motion imaging feasible position determination can be realized, a stable and efficient phased array beam scanning strategy is constructed, and the imaging accuracy is improved. High-precision beam pointing of a non-cooperative target is realized, and the precision and reliability of space target imaging are improved.

The invention relates to a lunar satellite formation orbit forecasting method based on a physical information neural network model, and the method comprises the steps: building a non-spherical perturbation acceleration model for a physical loss function under the inertial coordinates of the lunar center; designing a physical information neural network model: selecting a full-connection deep neural network as a network architecture of the model, and using a composite loss function containing a data loss function and a physical loss function as a loss function of the full-connection deep neural network; training a physical information neural network model; and using the physical information neural network model to predict the orbit state of the satellite formation. The physical information neural network model can output a high-precision orbit state within milliseconds through forward propagation, error accumulation of traditional integration is avoided, calculation complexity is remarkably reduced, and efficient, stable and high-precision online orbit state forecasting can be achieved.

The application belongs to the technical field of aerospace, and relates to a distance-keeping game control framework and method for a maneuvering non-cooperative target. The framework comprises a rolling control window, an event perception window arranged in each rolling control window, and an event triggering mechanism arranged in each event perception window. The event perception window predicts an orbit state through a two-body dynamics model, the event triggering mechanism calculates a relative distance between an active satellite and a target satellite at each discrete point according to an orbit prediction result, and judges whether the relative distance constraint is met. If the relative distance constraint is met, an impulse maneuver strategy of the active satellite as a decision output is calculated through a game strategy optimization algorithm. Simulation results show that the application can realize long-time distance keeping, and the success rate reaches 98% in a 12-hour maintenance task, which is increased by 308.3% compared with a traditional algorithm.

The invention relates to the technical field of space imaging, and provides a space target space-based microwave imaging parameter optimization method and device. According to the method, through predicting a target intersection section of a detection satellite and a space target, an average relative speed, a minimum intersection distance and an orbit prediction error component of the detection satellite and the space target are extracted from the target intersection section; determining the optimized detection time length and the optimized detection frequency of the detection satellite at least based on the minimum intersection distance and the orbit forecast error component, and determining the optimized imaging time length of the detection satellite at least based on the average relative speed and the minimum intersection distance, and multi-constraint cooperative solution is carried out on the pulse repetition frequency PRF of the detection satellite at least based on the average relative speed and the minimum intersection distance to obtain the optimized PRF, microwave imaging parameter optimization design of quantitative model and actual scene constraint is realized, the efficiency is high, the application scene is wide, and the method is suitable for large-scale popularization and application. And powerful support is provided for precision improvement and engineering landing of a space-based microwave imaging technology.

This invention relates to the field of collision warning technology, specifically to a spacecraft collision warning method for a large number of space targets. The method includes: selecting a multi-data parallel screening warning approach; screening secondary target orbits within the TLE data set of the complete collision target orbits using set pre-screening thresholds for date expiration, apogee / perigee pre-screening safety margins, orbital intersection pre-screening thresholds, and time pre-screening thresholds; performing SGP4 orbit prediction on the primary target orbit and the initially screened secondary target orbits; obtaining the minimum spatial distance and collision probability between spacecraft within the primary and secondary target orbits; summarizing the primary and secondary target orbit numbering information, as well as the minimum spatial distance, collision probability, and the time corresponding to the minimum spatial distance, into a collision warning result; and summarizing all collision warning results to output a collision warning set information. This invention can filter out targets that are unlikely to intersect with the spacecraft to the greatest extent possible, reducing the computational load of subsequent orbit prediction.

The application relates to the technical field of space imaging, and provides a space target space-based microwave imaging system design method and device. The method firstly determines the intersection course of an observation satellite and a space target based on an orbit dynamics simulation method, and then determines an imaging observation window; then, the relative motion characteristics of the observation satellite and the space target in the imaging observation window and the maximum scanning capability of a phased array antenna are combined to determine a beam scanning geometry and generate a beam scanning strategy; next, precise acquisition and locking of the beam are realized by optimizing the orbit prediction error of the space target; finally, high-precision imaging of the space target is realized by combining a synthetic aperture imaging algorithm and error compensation according to the beam scanning strategy and the optimized orbit parameters of the space target, double high-speed motion imaging feasible position determination is realized, a stable and efficient phased array beam scanning strategy is constructed, high-precision beam pointing of a non-cooperative target is realized, and the precision and reliability of space target imaging are improved.

The application provides an autonomous star observation method for super low orbit satellites, comprising: setting a whole satellite mode word; judging whether an autonomous working condition trigger enable flag is enabled according to the whole satellite mode word; if yes, judging an orbit height decision working condition, and starting a star observation task according to the working condition; if no, setting a working condition through a ground instruction, and starting a star observation task according to the working condition. Meanwhile, an autonomous star observation system for super low orbit satellites is provided. The application can adapt to star observation task requirements under super low orbit height conditions, can overcome large aerodynamic interference moments under super low orbit conditions and large rotation moments caused by a turntable motor, can autonomously switch super low orbit star observation working conditions according to orbit heights, can reduce errors caused by ground orbit prediction, and can retain the ability of ground intervention whole satellite tasks, and can switch whole satellite autonomous observation and ground forced observation at any time.

The application discloses a TLE target library matching method and system for radar observation data, and belongs to the field of space target monitoring; the method comprises the following specific steps: S1, using an SGP4 model to derive and store position information of all targets in a TLE library; S2, converting radar measurement data into position coordinates of targets in an inertial system; S3, using an extended Laplace algorithm to perform initial orbit determination on a measurement arc segment; S4, screening targets in the TLE library according to the orbit determination result; S5, performing coarse matching of an observation arc segment and a space target by taking position mean square error as a criterion, and obtaining candidate targets; and S6, considering observation error and TLE orbit error, and determining a matching result according to covariance analysis; the application reduces the calculation amount of orbit prediction by using Lagrange interpolation technology, and screens TLE by using initial orbit determination results, so that unnecessary matching calculation can be avoided, coarse matching and fine matching are combined, the target matching speed can be ensured, and the matching precision can also be ensured.

The invention discloses a space target long-time-sequence intelligent collision early warning method, and belongs to the technical field of space. The implementation method comprises the steps of constructing a double-branch multi-scale time convolution network long-time-sequence orbit forecasting model, and on the basis of a traditional time convolution network, establishing a relation between the model and perturbation of different time scales by establishing two branch expansion convolution structures and setting different expansion coefficients, so that the forecasting precision of the model is improved. On the basis, a voxel hash mapping rapid screening method is established, a Gaussian negative logarithm likelihood loss function is introduced, statistical characteristics of forecast errors are utilized, a voxel cube is constructed, only voxels where satellites are located and the distance between targets in neighborhoods of the voxels and the satellites need to be calculated during screening, the screening calculation scale is greatly reduced, and the screening speed is increased. The method does not depend on a high-precision dynamic model, can sense and extract the features of different time scales, improves the track forecasting precision and the collision risk screening efficiency, and accelerates the collision early warning speed.

This application relates to a satellite orbit prediction method. The method includes: constructing an expression for the gravitational force acting on the satellite based on its mass and distance from Earth; constructing a differential equation for the satellite's acceleration based on Newton's second law and the expression for the gravitational force; solving the differential equation based on the conservative force field characteristics of mechanics, calculating the gravitational potential energy function and its derivative with respect to time; transforming the derivative expression based on the mechanical conservation law that the sum of the derivatives of kinetic energy and gravitational potential energy with respect to time is zero, to obtain the derivative expression for acceleration; converting the time relationship in the derivative expression of acceleration to a radial position relationship based on the principle of conservation of angular momentum; integrating the radial position relationship to obtain the satellite's orbit equation. This method enables satellite orbit prediction.

The invention relates to the technical field of space debris orbit forecasting, in particular to a space debris orbit forecasting correction method based on machine learning and triangular characteristic decomposition. The azimuth angle and the pitch angle are converted into six-dimensional feature vectors through triangular feature decomposition, and a real-time mapping model of orbit forecast deviation is established in combination with a neural network. The two bottleneck problems on which traditional image tracking depends are solved: 1) in a strong laser interference scene, tracking failure caused by background light saturation of an optical camera is avoided, and real-time image feedback is replaced by deviation prediction driven by historical echo data; 2) when space debris in an earth shadow area is invisible, constructing a training set by using deviation data captured at the moment of successful laser ranging, and realizing autonomous correction of forecast errors under a non-illumination condition; the method gets rid of dependence on space debris optical visibility, realizes all-day and all-arc telescope pointing deviation correction, and improves the reliability and continuity of space debris orbit prediction under complex working conditions.

A method and system for optimizing on-board orbit mission sequences based on real-time windward area is disclosed. The system includes a data configuration module, an area mapping module, a high-precision prediction module, and an autonomous feedback optimization module. The data configuration module receives mission sequences and space environment characteristic parameters, and synchronizes the satellite's real-time orbit vector. The area mapping module analyzes the target attitude based on the attitude parameters in the mission sequence and calculates the equivalent windward area corresponding to each mission time period in real time by combining the velocity vector. The high-precision prediction module executes the attitude-coupled variable area HPOP algorithm, incorporating the dynamically calculated equivalent windward area as a key perturbation term into the atmospheric drag calculation, and outputs orbit prediction results reflecting the attitude adjustment impact. The autonomous feedback optimization module performs consistency judgment on the difference between two adjacent recursive results. If the difference does not meet the preset requirements, it autonomously adjusts the mission execution time and mission execution parameters in the mission sequence and feeds them back to the area mapping module for iterative calculation.

The application provides a satellite orbit prediction method, system and device based on revisiting nominal orbit, relates to the technical field of satellite orbit prediction, and the prediction method comprises the following steps: acquiring on-satellite GPS telemetry data of a satellite; obtaining a descending node time and a descending node longitude of an actual orbit of the satellite within a preset time period according to the on-satellite GPS telemetry data; matching the descending node longitude of the actual orbit with a plurality of descending node longitudes of a revisiting nominal orbit stored in advance, selecting a descending node state corresponding to the descending node longitude of the revisiting nominal orbit with the minimum deviation from the descending node longitude of the actual orbit as a matching nominal orbit state; and inputting the descending node time of the actual orbit and the matching nominal orbit state into an improved high-precision orbit dynamics model for orbit recursion to obtain predicted orbit data. The application can realize long-time and high-precision orbit prediction, and significantly improves the reliability and precision of satellite mission planning.

The invention relates to a method and a system for forecasting the position of a medium-high orbit equatorial orbit satellite, and belongs to the technical field of satellites, the method for forecasting the position of the medium-high orbit equatorial orbit satellite comprises the following steps: preprocessing multi-source observation data, determining initial orbit parameters, constructing an equatorial orbit adaptive orbit dynamics model, and determining the position of the medium-high orbit equatorial orbit satellite; and orbit forecasting calculation based on adaptive numerical integration, dynamic error compensation and multi-source data fusion optimization, forecasting result output and precision verification. The core innovation of the invention focuses on deep adaptation of orbit characteristics of medium-high orbit equatorial orbit satellites, and the precision bottleneck of the existing method in equatorial orbit application is solved through the full-process technical innovation of model optimization, integral improvement, error compensation and data fusion.

This invention discloses a fitting method, device, and medium for improving the accuracy of ephemeris orbit prediction based on TLE (Time-Based Echo) data. The method includes: reading the epoch times of a set of TLE elements in chronological order; determining the ephemeris prediction time interval for each TLE element; using the SGP4 / SDP4 analytical model to predict the ephemeris orbit and obtain ephemeris orbit parameters; selecting an orbit perturbation model to predict the original ephemeris orbit and using the least squares principle to estimate the ephemeris orbit parameters; using the estimated ephemeris orbit parameters to predict the ephemeris orbit and outputting the final result. This invention, by fusing TLE data and using the least squares principle to iteratively fit the orbit parameters, effectively reduces the prediction divergence problem caused by model errors (SGP4 / SDP4) and orbit determination errors in TLE data, and significantly improves the accuracy of orbit prediction.

The application relates to a low-orbit satellite orbit determination method, device and medium based on a multi-thread parallel processing architecture, and belongs to the technical field of satellite orbit determination. The method comprises the following steps: adopting a multi-thread parallel architecture to modularly decouple a satellite orbit determination business process, splitting the satellite orbit determination business process into three types of independent threads, i.e., a positioning thread, an orbit integration thread and an orbit prediction thread, and configuring the three types of threads in turn from high to low in priority: the positioning thread has the highest priority, the orbit prediction thread is in the middle, and the orbit integration thread has the lowest priority; meanwhile, relying on a hierarchical priority scheduling mechanism, GNSS observation data is preferentially guaranteed to be processed in real time in the positioning thread, and the latest effective orbit determination basic observation information is continuously output; the second priority is guaranteed to enable the orbit prediction thread to quickly respond to user requests and data retrieval requirements of an inter-satellite link; and finally, the kinetic integration with a large amount of calculation is processed, so that the real-time performance and business continuity of the orbit determination are effectively improved.

The invention discloses an earth-moon space orbit long-term forecasting method considering light pressure and jet unloading disturbance, and belongs to the technical field of spacecraft orbit forecasting and navigation. The method comprises the following steps: establishing a sunlight pressure and light pressure moment model considering parameter uncertainty; constructing a spacecraft attitude dynamic model containing a flywheel; establishing a flywheel jet unloading model, and calculating disturbing force generated in the unloading process; coupling unloading disturbing force into the orbit dynamic model, and constructing an attitude and orbit integrated model; the influence of parameter uncertainty on orbit evolution is analyzed through Monte Carlo simulation, a key event detection mechanism is introduced, and the risk that the orbit collides with the earth, collides with the moon or escapes from the earth-moon system is identified. According to the method, the long-term orbit behavior of the earth-moon spacecraft under the disturbance of light pressure and jet unloading can be predicted more accurately, and the reliability of task planning and navigation control is improved.

The application provides a satellite orbit prediction method and device based on orbit data of a spherical satellite. The method comprises determining a target atmospheric density model according to a target space environment condition of a satellite to be predicted, wherein the target atmospheric density model is obtained by correcting a preset atmospheric density model according to orbit data and a dynamic model of the spherical satellite in the target space environment condition; identifying an atmospheric drag coefficient of the satellite to be predicted based on the target atmospheric density model; and performing orbit prediction on the satellite to be predicted according to the target atmospheric density model and the atmospheric drag coefficient. According to the embodiment of the application, the spherical satellite maintains a stable and high-precision surface value ratio and atmospheric drag coefficient when operating in an orbit, the orbit data of the spherical satellite can be used to better achieve high-precision correction of the preset atmospheric density model, and the atmospheric drag coefficient of the satellite to be predicted can be more accurately identified based on the corrected atmospheric density model, thereby improving the orbit prediction accuracy of the satellite to be predicted.