185 results about "Orbit prediction" patented technology

The invention discloses an on-satellite full-autonomous task planning system for an agile remote sensing satellite. Comprising an imaging target database module, a high-precision orbit prediction and orbit calculation module, a target visible arc section calculation module, an imaging task preprocessing module, a subsystem constraint model module, an imaging task planning module, a data transmission task planning module, a solid memory autonomous erasing module, an instruction data block generation module and a satellite state real-time monitoring module. By carrying out constraint modeling on subsystems or single machines such as load, data transmission, control and the like, an on-satellite full-autonomous operation process from receiving of subsystem or single machine instruction blocks from user requirements is established, satellite control is effectively simplified, and remote control uploading efficiency and on-satellite autonomous operation capacity are improved. And through a satellite state real-time monitoring module, the execution condition of tasks on the satellite, satellite state changes and the like are monitored in real time, task execution stopping, whole satellite sun-facing or re-planning are further triggered, task requirements and environment changes are quickly responded, task plans are formulated and adjusted in real time, and the task execution efficiency is improved.

The invention relates to an autonomous navigation method for lunar satellite formation and a second-order filtering navigation architecture, and the autonomous navigation method comprises the steps: obtaining orbit prediction information based on a kinetic model of the lunar satellite formation, and obtaining collaborative theory observation information based on a collaborative observation model, carrying out fusion processing on orbit prediction information, collaborative theory observation information and multi-source observation data of in-orbit observation by using an extended Kalman filtering algorithm, and estimating an absolute orbit state of the lunar satellite formation; establishing a relative motion model of the slave satellite under the local coordinate system of the master satellite, and establishing a relative measurement model; and taking the absolute orbit state of the master satellite as a reference datum, obtaining a preliminary prediction relative orbit state based on the relative motion model of the slave satellite, and obtaining relative measurement data including distance measurement and angle measurement based on the relative measurement model, and fusing the relative measurement data and the preliminarily predicted relative orbit state through an extended Kalman filtering algorithm to estimate the relative orbit state of the slave satellite. Navigation may be independent of ground support.

The invention relates to the technical field of satellite orbit prediction, and discloses a satellite orbit prediction method based on a physical information neural network, a terminal and a medium. The method comprises the following steps: constructing a sample set based on satellite position data, sun position data and moon position data in the same time period; constructing a physical information neural network; the network takes satellite historical position data as input to predict a satellite orbit position at a future moment; training a physical information neural network by using the sample set, and executing a satellite orbit prediction task by using the trained physical information neural network; the trained loss function comprises data loss, physical loss and smoothness loss; the data loss is used for measuring the difference between network prediction output and actual observation data; the physical loss is jointly calculated by the earth aspheric perturbation, the three-body gravitational perturbation and the solar light pressure physical perturbation borne by the satellite; the smoothness loss is used for constraining the smoothness of the predicted satellite orbit. According to the invention, the prediction precision and robustness of the satellite orbit are improved.

The invention discloses a navigation satellite high-precision orbit forecasting method based on a bidirectional long short-term memory network, and relates to the technical field of satellite orbit forecasting, and the method comprises the following steps: (1) obtaining a reference orbit; (2) extrapolating a dynamic orbit; (3) constructing an orbit forecast error sequence; (4) training a network model; (5) predicting orbit forecast errors at future moments; and (6) compensating a forecast result. According to the method, the orbit prediction error sequence is constructed to describe the difference between the dynamic model and the real space environment, and the change rule of the orbit prediction error is captured and learned by adopting the bidirectional long-short-term memory network, so that the orbit error at the future moment is predicted, and the orbit prediction result based on the dynamic model is corrected; therefore, the defects of dynamic modeling in comparison with a real space environment are compensated, the trend that orbit errors are rapidly diverged along with the increase of forecasting time is inhibited, and a high-precision orbit forecasting task of a navigation satellite is effectively realized.

The invention relates to the field of satellite communication, and discloses a parabolic antenna pointing tracking method in an inter-satellite communication scene, and the method comprises the following steps: carrying out the attitude initialization, dynamic prediction, error compensation and closed-loop control, and achieving the high-precision satellite tracking of an antenna. The method adapts to large dynamic changes of target satellites and ensures stable and reliable communication; the invention discloses a parabolic antenna pointing tracking system in an inter-satellite communication scene. The system comprises an antenna angle sensor module, a signal receiving module, a prediction module, an error compensation module and a closed-loop control module. According to the method, orbit prediction, an extrapolation algorithm, adaptive control and a closed-loop feedback mechanism are fused, and high-precision and high-robustness antenna satellite tracking is realized. The method can adapt to large dynamic change of a target satellite, reduces dependence on real-time position information, still keeps stable communication under the condition of orbit sudden change or data missing, and improves system reliability and engineering practicability.

The invention discloses a method and system for avoiding frequency interference through AI and digital twinning combination, and relates to the technical field of satellite communication, and the method comprises the steps: building a big data satellite frequency interference avoiding system, and collecting the current orbit information and frequency information of a communication satellite; simulation is performed through a preset interference scene prediction model, a satellite adjustment attitude is determined, the preset interference scene prediction model is obtained by training a normal scene and an interference scene of a satellite system based on an AI algorithm and a digital twin model, and the digital twin model comprises an orbit prediction module and an attitude adjustment prediction module which are connected in sequence. The orbit prediction module is used for outputting an orbit prediction value according to orbit information and frequency information, and the attitude adjustment prediction module outputs a satellite adjustment attitude according to the orbit prediction value; therefore, the interference scene of the communication satellite can be predicted in advance through the preset interference scene prediction model so as to adjust the beam direction, and automatic early warning and processing of the interference of the communication satellite are realized.

A method to predict and fit an APC of a LEO satellite considering attitude information is provided, which includes: transforming a spacecraft body-fixed coordinate system, obtaining a transformation matrix from orbital coordinate system to transformed spacecraft body-fixed coordinate system, obtaining rotation angles during a rotation process, obtaining rotation angles in orbit determination time period to predict rotation angles in orbit prediction time period, obtaining a transformation matrix from spacecraft body-fixed coordinate system to Earth-fixed coordinate system of each prediction moment, obtaining a correction vector from a CoM to a phase center of a downlink signal antenna in the Earth-fixed coordinate system at each prediction moment, and obtaining an orbit of the phase center of the downlink signal antenna of a LEO satellite, and performing an ephemeris parameter fitting on the orbit of the phase center of the downlink signal antenna of the LEO satellite in a fitting time period.

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 discloses a forecasting method and device for an atmospheric resistance coefficient of a low-orbit satellite in the technical field of satellite navigation. The forecasting method comprises the following steps: acquiring satellite orbit determination data and a space environment index in a preset time period before forecasting; according to the satellite orbit determination data, performing simplified dynamic precise orbit determination on a low-orbit satellite, and fitting an atmospheric resistance coefficient in the simplified dynamic precise orbit determination process; performing data preprocessing on the space environment index and the atmospheric resistance coefficient and generating a sample set; constructing an LSTM prediction model, and training, adjusting and evaluating the LSTM prediction model through the sample set; predicting an atmospheric resistance coefficient in a future preset time period according to the finally obtained LSTM prediction model; and applying the atmospheric resistance coefficient in the future preset time period to the atmospheric resistance perturbation model of the low-orbit satellite for orbit forecasting. According to the method, the atmospheric resistance coefficient in the LEO satellite orbit prediction process is predicted in a high-precision manner through the LSTM prediction model, so that the orbit prediction precision is effectively improved.

The invention belongs to the technical field of aerospace, and relates to an unscented batch processing orbit determination method and system for a continuous low-thrust maneuvering satellite. The method comprises the following steps: acquiring a radar observation vector according to radar measured data or satellite ephemeris data in combination with radar site coordinates; obtaining an initial orbit state at a to-be-estimated moment by adopting a Lambert algorithm according to the radar observation vector; generating an initial expansion state quantity according to the initial orbit state at the to-be-estimated moment; a simplified orbit kinetic equation containing earth non-spherical perturbation, atmospheric resistance perturbation and constant small thrust acceleration under an RTN coordinate system is constructed under the ECI coordinate system; based on the simplified orbit kinetic equation, obtaining an expansion state quantity precision value of the to-be-estimated moment through the initial expansion state quantity, the radar observation vector and an unscented batch processing filtering method; and substituting the precision value of the expansion state quantity of the to-be-estimated moment into the simplified orbit kinetic equation to carry out orbit forecasting at other moments. According to the method, the orbit determination precision is remarkably improved, and the influence of single-point observation errors is reduced.

The invention discloses a satellite orbit forecasting method based on deep learning physical constraint loss, and the method comprises the following steps: 1, carrying out the normalization preprocessing of input data, forming a training data set and a test data set, and constructing batch processing training data; and 2, performing dimension expansion on sample data points in each window in the batch processing data formed in the step 1, constructing a multi-dimensional feature space of the sample points, and forming a batch processing input data format capable of being introduced into the model. And 3, performing forward reasoning on the batch data formed in the step 2 by using a model, and obtaining a batch processing orbit prediction value output by the model at the next moment through a CNN lightweight spatial-temporal feature extraction module and a BiLSTM bidirectional time sequence neural network module. And 4, taking the track prediction value obtained in the step 3 and the truth value label in the training set obtained in the step 1 as input, calculating to obtain a loss value of a current training iteration batch through a multi-random learning loss module fusing physical constraints, and performing reverse updating of model parameters to complete model training. And step five, through the steps two to four, performing reasoning verification on the model by using the test set formed in the step one, and comparing with a truth value in the test set to obtain a model test result.

The invention discloses a lunar satellite orbit intelligent prediction method and system. The method comprises the following steps: constructing a basic motion model; constructing a prediction error data set; constructing a deep learning model; training a deep learning model; performing error estimation by using the basic motion model and the deep learning model; and generating a final orbit prediction result according to the estimated error. The system comprises a model construction unit, a data set construction unit, a model training unit, a data acquisition unit, an initial prediction unit, an error prediction unit and a final output unit. By using the method, high-precision orbit prediction data can be provided for the lunar satellite. The method can be widely applied to the field of satellite orbit prediction.

The invention discloses a deep learning-based star chain satellite two-line element orbit prediction error compensation method and system. The prediction error compensation method comprises the following steps of: obtaining to-be-predicted star chain satellite two-line element sample data; the sample data are input into an error compensation forecasting model, a forecasting result is obtained, the error compensation forecasting model is constructed through a neural network and obtained through training of a training set, and the training set is star chain satellite two-line element data. The method solves the randomness of error propagation through the excellent mining capability of a deep learning neural network to data features, carries out forecasting according to at least one periodic time interval by batch star chain TLEs, carries out error compensation by using an actual reference orbit result and a propagation orbit result, and dynamically adjusts an operation strategy according to an error analysis result. Therefore, the stability and applicability of the model in different environment states are ensured.

The invention relates to a low-orbit constellation satellite collection database building and space target orbit forecasting method, and belongs to the field of satellite communication. The method comprises the following steps: firstly, implementing low-orbit constellation satellite collection database construction, including satellite target resource analysis, resource description and resource mining, describing satellite resource individuals to be stored, acquiring multi-dimensional key information, and constructing a comprehensive and accurate low-orbit satellite target resource database; and in combination with a subsequent orbit forecasting algorithm and the parameters of the geographic position, the antenna pitch angle and the beam width of the communication receiving and transmitting end, the quantity and distribution of satellites in the beam intersection area of the receiving and transmitting end are obtained by using the forecasting model, and satellite resources are mined in a resource library. And predicting a satellite orbit by using the SGP4 orbit prediction model, obtaining the instantaneous position and speed of the satellite in-orbit operation, comparing the result of the orbit prediction model with the STK software simulation result, and verifying the prediction model. Technical guarantee is provided for efficient management and utilization of cooperative and non-cooperative giant low-orbit constellations in a complex space environment.

The invention relates to the technical field of low-orbit satellite communication, in particular to an orbit optimization method and system for dynamically and autonomously tracking a low-orbit satellite based on a ground station, and the method comprises the steps: collecting TLE orbit element data, constructing an SGP4 orbit prediction model, and obtaining a prediction result; low-earth-orbit satellite orbits with different inclination angles are determined through STK software modeling based on a ground station, and corresponding standard results are obtained; comparing the prediction result with a standard result to obtain an error result; dynamically optimizing the Bstar parameter based on an error result by adopting a genetic algorithm, and determining an optimal Bstar parameter; adjusting the SGP4 orbit prediction model based on the optimal Bstar parameter, and obtaining an optimization result through the adjusted SGP4 orbit prediction model; according to the method, optimization is carried out based on an existing SGP4 model, optimal Bstar parameters are dynamically searched through a genetic algorithm, accumulative errors caused by the fact that TLE data are not updated in time are compensated, stable tracking of a ground station is guaranteed, and the precision of medium and long term orbit forecasting is 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 satellite group collaborative prediction method based on Lie group differential manifold and covariant optimization, and mainly solves the problems of inaccurate disturbance modeling, poor physical interpretation and large prediction error in the prior art. According to the scheme, the method comprises the following steps: mapping the position and attitude of a satellite to an SE (3) manifold, and describing the movement speed of the satellite by using Lie algebra; establishing a multi-level Lie algebra disturbance decomposition mechanism including absolute perturbation, relative coupling and observation noise; constructing a Riemannian loss function by using the orbit state after Lie group manifold embedding and the orbit state deviation obtained by disturbance decomposition; defining an orbital state updating rule by using a covariant derivative of the function, and iteratively solving the orbital state updating rule on the SE (3) manifold to output an orbital state prediction result of the satellite; for an abnormal state of inter-satellite link interruption, a differential geometric connection form is defined, and virtual relative position observation of an integral generation orbit state is carried out. According to the method, the physical interpretability and long-term prediction stability of orbit prediction can be improved, prediction errors are reduced, and the method can be used for low orbit communication constellation deployment of frequency band inter-satellite links.

The invention relates to the technical field of unmanned aerial vehicle trapping, and discloses an unmanned aerial vehicle trapping method for emitting a navigation deception signal, which comprises the following steps: step 1, generating a navigation deception signal synchronized with the time reference of a satellite navigation system through an atomic clock synchronization mechanism; step 2, injecting dynamically predicted satellite orbit parameters based on the navigation deception signal; 3, subcarrier pseudo-arbitrary frequency hopping control is carried out on the navigation deception signal with the injected parameters; and 4, collecting multi-source data of the radar and the optical sensor, and fusing the multi-source data to generate a motion track of the unmanned aerial vehicle. According to the invention, an atomic clock synchronization mechanism is adopted to generate a high-precision time reference, a technical scheme of dynamic orbit prediction and pseudo-random frequency hopping control is combined, time synchronization with a satellite system is realized through an atomic clock, satellite orbit parameters are dynamically corrected through a long and short-term memory network, and rapid hopping modulation of a subcarrier frequency band is realized. The technical effects of high space-time consistency of deception signals and dynamic disguising of spectrum characteristics are achieved.

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 discloses a neural network method for forecasting a spacecraft orbit, which belongs to the field of spacecraft orbits, and comprises the following steps: S1, predicting a TLE initial value by using a deep learning neural network model and a feedback neural network model; s2, using a deep learning neural network model to improve the precision of the SGP4 model; s3, comparing the predicted value in the S1 with a true value, and evaluating the accuracy of TLE initial value prediction; and S4, comparing the corrected model in the step S2 with a true value, and evaluating the improvement degree of the precision of the SGP4 model. By adopting the neural network method for spacecraft orbit forecasting, the problem of balance between precision and data volume in spacecraft orbit forecasting is solved, the precision of a common SGP4 is improved, and meanwhile, the problem of precision of a TLE initial value for spacecraft orbit indirect forecasting is also solved.

The invention discloses a space target luminosity dynamic measurement ground experiment system, and the system comprises a multi-degree-of-freedom controllable rotary table which is connected with a satellite scaling model and is used for driving the satellite scaling model to rotate and adjust at multiple angles; the visible light detection unit is positioned in the reflection light path direction of the satellite scaling model and is used for imaging the satellite scaling model and generating a continuous observation image sequence; the light source unit is used for simulating a parallel light source of the sun, and the light source directly irradiates the satellite scaling model; and a master control unit. And the master control unit is used for controlling the intensity of the light source, the motion parameters of the rotary table and image acquisition, and carrying out downsampling processing on image data to generate a luminosity sequence for simulating long-distance observation. The technical scheme provided by the invention can provide reliable technical support for space target identification, orbit prediction and stealth technical verification, and has innovativeness, practicability and economical efficiency.

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 provides a space target orbit maneuver identification method and system based on adaptive LSM, and the method comprises the steps: obtaining the orbit data of a space target, calculating the six orbit elements based on the orbit data, and coding the six orbit elements into a pulse sequence; and inputting the pulse sequence into an improved adaptive LSM for detection to obtain an orbit maneuver behavior identification result of the space target. According to the method provided by the invention, six orbital elements are calculated through the speed and the position of the orbital, then the orbital parameters are input into an improved LSM, and the improved LSM adopts an adaptive threshold neuron as a calculation unit, so that the threshold can be dynamically adjusted according to the current pulse response and attenuated along with time; meanwhile, an improved particle swarm optimization algorithm is combined to carry out optimization setting on an initial value of a neuron, so that the initial performance of the model is improved; and a learnable neuron connection weight based on an attention mechanism is introduced, so that the accuracy and reliability of orbit prediction are improved.

The invention provides an automatic planning device for an on-orbit observation mode of a solar reflection band hyperspectral imager, and relates to the technical field of satellite remote sensing, and the device comprises a track forecast analysis module, an observation mode priority ranking module, an observation target combination module, a control instruction generation module and an instruction verification and uploading module. The method comprises the following steps: reading and analyzing a satellite orbit forecasting file to obtain orbit forecasting information of a satellite under a geocentric inertial system, and calculating a view field angle and a position parameter of an observation target in combination with an instrument installation coordinate; dynamically adjusting the priority of an observation task according to the function composition of a preset earth-moon observation mode and a self-calibration mode, an observation period and a satellite sudden interruption event; and calling a preset observation target combination rule, generating an instrument maneuvering control instruction and an observation duration control instruction, performing integrity verification on an instruction sequence, and injecting the verified instruction into an instrument execution controller through an instruction upper injector, so that automatic arrangement and generation of the instruction sequence can be realized.

The invention relates to an ephemeris orbit calculation method suitable for orbit extrapolation on an elliptical orbit satellite. The ephemeris orbit calculation method comprises the following steps: calculating ephemeris orbit base point parameters on the ground; ephemeris orbit base point parameters which are injected from the ground are provided; and performing orbit extrapolation by using the ephemeris orbit base point parameters to calculate the position and the speed of the satellite in the inertial coordinate system. According to the method, orbit extrapolation is carried out on the basis of ephemeris orbit base point parameters injected on the ground, the calculation amount is low, the method is suitable for satellite-borne calculation, the calculation precision is high, the high-precision requirement of real-time orbit forecasting in the elliptic orbit satellite inter-satellite communication period can be met, the calculation capacity constraint of a satellite service system can be met, and the completion degree of an inter-satellite communication task is improved.

A database (102) of a rocket launch assistance device records orbit forecast information of a mega-constellation satellite group (301) that is acquired from a space information recorder included in a mega-constellation business device and space object information that is acquired from a rocket launch business device. The orbit forecast information of the mega-constellation satellite group (301) is composed of a prediction value of an orbit of at least one representative satellite (331) out of the mega-constellation satellite group (301) and a prediction value of an orbit of a constituent satellite (332), which is a value relative to the prediction value of the orbit of the representative satellite (331).

The invention provides a space debris orbit prediction model training method and a space debris orbit prediction method, relates to the technical field of space debris orbit prediction, and is used for solving the technical problem that in the prior art, target information is difficult to effectively extract from sparse data, so that space situation changes cannot be accurately perceived. The space debris orbit prediction model training method comprises the following steps: acquiring historical orbit data of space debris; according to historical orbit data, extracting position residual errors between the prediction state vectors and the reference state vector of the space debris at different moments; generating a multi-dimensional feature data set according to the position residual error; performing data dimension reduction processing on the multi-dimensional feature data set to obtain a sample data set after dimension reduction; and training an initial random forest model through a machine learning algorithm by using the sample data set after dimension reduction, so that the initial random forest model establishes a mapping relationship between the orbit parameters of the space debris and future states, and a space debris orbit prediction model is obtained.

The invention relates to an autonomous measurement and control orbit determination method and system for a high-orbit satellite. The method comprises the following steps: acquiring satellite-borne GNSS broadcast ephemeris data and single-frequency pseudo-range observation data of a high-orbit satellite; based on the GNSS broadcast ephemeris data and the single-frequency pseudo-range observation data, coarse orbit data of the high-orbit satellite are generated through a dynamic pseudo-range orbit determination algorithm; performing outlier elimination preprocessing on the coarse orbit data to obtain orbit data after outlier elimination; performing smooth fitting and equal-interval sampling on the orbit data after outlier elimination to obtain smooth orbit data; determining an orbit determination arc section according to the scheduling time and the orbit maneuvering time; and using the motion equation and the state transition matrix of the high-orbit satellite to process least square iteration in batches to obtain optimal orbit data, and executing orbit forecasting. The method provided by the invention does not depend on information transmission of ground measurement and control resources, realizes on-satellite autonomous orbit determination, reduces the complexity of data fusion calculation, and is small in calculation amount.

The invention discloses a calculation method for an orbit of a near-circle medium-and-low-orbit satellite, and relates to the technical field of satellite orbit calculation. Real-time acquisition and classification processing of disturbance data are realized through parallel calculation and a multi-disturbance-factor preprocessing mechanism, so that orbit calculation can quickly respond to environmental changes, and the calculation accuracy is improved. The parallel computing framework independently processes the influence of different disturbance factors on each node, and quickly forms an orbit deviation trend, so that the satellite can perform adaptive adjustment more quickly when the orbit deviates, and meanwhile, an adaptive compensation model is adopted to dynamically adjust a compensation coefficient according to the actual orbit deviation, so that the adaptive compensation is realized. Adaptive adjustment is carried out on different disturbance coefficients, and automatic optimization compensation is carried out according to real-time orbit data, so that orbit prediction can adapt to rapidly changing disturbance conditions; when short-term disturbance is large, satellite orbit calculation precision can still be guaranteed, deviation accumulation is greatly reduced, extended Kalman filtering (EKF) is executed on each node, and orbit state estimation precision is improved.

The invention discloses a calculation method for determining a visible time period between a satellite and a ground station through TLE data, and relates to the technical field of spaceflight measurement and control and satellite communication scheduling. Through a dynamic sampling mechanism of cluster perception, the time resolution is automatically improved in a dense transit period, the sampling density is reduced in a non-cluster period, the redundancy calculation is remarkably reduced, and the calculation efficiency is improved. The calculation power peak congestion is avoided; a boundary detection algorithm based on elevation angle change rate symbol switching can capture a rapid jump point of a satellite elevation angle, the error merging phenomenon of short interval windows is eliminated, and the precision of an output timestamp reaches a sub-second level; the confidence identification dynamically reflects TLE data timeliness and cluster complexity, a user can intuitively evaluate the window time credibility, and the communication interruption risk caused by orbit prediction errors is reduced.