Satellite shipborne dinning satellite semi-physical simulation method and system

Abstract

The application discloses a kind of satellite shipborne moving target indication satellite half-physical simulation method and system, the method includes satellite ephemeris solution and satellite trajectory prediction based on satellite toolbox and the satellite ephemeris of two-row report form TLE data of self-defined multivariate, generate target satellite for moving target indication satellite process;Based on the sea wave power spectrum empirical function of sea area feature correction, ship pitching simulation is carried out, disturbance is added for moving target indication satellite;Simulation fitting of measured inertial navigation data error model and drift model based on Allen variance, real-time feedback moving target indication antenna system three-axis angle;Based on the three spline interpolation of sensor error model and drift model correction interpolation coefficient, multi-sensor synchronization of extrapolation algorithm, improve simulation authenticity and data reliability.The moving target indication half-physical simulation platform built by the application effectively restores the satellite communication process of moving target indication antenna, guarantees the stability of communication data input and output, supports the introduction of measured data to carry out half-physical simulation, verifies the accuracy of satellite algorithm.

Description

TECHNICAL FIELD

[0001] The application belongs to the field of satellite on-board moving target indication (MTI) semi-physical simulation, and particularly relates to a satellite on-board MTI semi-physical simulation method and system. BACKGROUND

[0002] The on-board MTI can help the ship to realize communication by means of the satellite in the far sea area, however, the MTI system involves multiple modules, and a large amount of sea data collection and measurement need to be carried out after production. However, the sea weather, sea conditions are complex and changeable, the test data are greatly affected by the sea conditions and are difficult to predict, so that the test quality is difficult to guarantee; the satellite resources are scarce, and the communication satellite needs to be coordinated in advance during the test, which is expensive; the MTI contains multiple sensors, and different precision sensors need to be purchased for testing, which not only increases the testing difficulty, but also increases the testing cost. Therefore, the sea physical test is difficult and costly. At present, there are some simulation platforms on the satellite end, sea surface end and sensor end, but there are some deficiencies: the satellite end simulation system can simulate the motion trajectory of the existing satellite data, but does not support customizing the satellite model and motion characteristics; there are some ocean simulation platforms that can generate sea wave models according to storm and tidal data, but do not consider the different sea wave characteristics in the near and far seas; some sensor simulation platforms can use virtual components to output sensor data, but cannot realize the fusion of multi-element sensor data, the fitting of sensor error values, and the semi-physical simulation. SUMMARY

[0003] The application provides a satellite on-board MTI semi-physical simulation method and system, which provides flexible and reliable simulation data for the satellite end, sea surface end and ground sensor end, and supports customizing input satellite and sensor data for semi-physical simulation, thereby assisting the development and design of the MTI system.

[0004] The application provides a satellite on-board MTI semi-physical simulation method and system, which provides flexible and reliable simulation data for the satellite end, sea surface end and ground sensor end, and supports customizing input satellite and sensor data for semi-physical simulation, thereby assisting the development and design of the MTI system.

[0005] (1) Based on the satellite toolbox and the satellite ephemeris solution and satellite trajectory prediction of the self-defined multi-element two-line report TLE data, a target satellite is generated for the MTI satellite process;

[0006] (2) Based on the sea area feature correction sea wave power spectrum empirical function, the ship pitching simulation is carried out, the interaction between the tide, storm surge and sea wave is simulated, the precision of the storm surge and sea wave simulation is improved, and the pitching of the ship affected by the wave in the sea is simulated, so as to add disturbance to the MTI satellite;

[0007] (3) The simulation fitting of the measured inertial navigation data error model and the drift model based on the Allan variance supports the customization of the type, data accuracy, working frequency, and drift error of the entered gyroscope and accelerometer, and the import of measured data for semi-physical simulation, and real-time feedback of the three-axis angle of the moving antenna system;

[0008] (4) The multi-sensor synchronization of the cubic spline interpolation and extrapolation algorithm based on the sensor error model and drift model correction interpolation coefficient, the simulation step precision setting, and the interpolation synchronization of different precision sensor data in the system improve the simulation authenticity and data reliability.

[0009] Further, the step (1) is implemented as follows:

[0010] Create a satellite scenario object by satelliteScenario and set the total simulation time T and frequency F;

[0011] Import multiple satellite orbit TLE ephemeris data through buttons, or customize TLE data according to the provided ephemeris range, combine real TLE data with customized TLE data, and form a semi-physical simulation;

[0012] After extracting the required data, the two-row report data file of the target satellite will be re-established; at the same time, the satellite toolbox is used to create a satellite model sat, and the satellite trajectory is quickly predicted by combining TLE ephemeris data and the satellite model to solve the satellite trajectory;

[0013] Use the satellite orbit data of the previous day and the prediction error of the previous day as the maximum threshold, when the trajectory error corresponding to the TLE data of the current day is greater than the threshold, correct the orbit elements and re-iterate the loop calculation until the data is less than the threshold, output the final average TLE data, and perform orbit prediction;

[0014] Convert the satellite's ECEF (Earth-Centered, Earth-Fixed) coordinates to the latitude and longitude tangent values at each time through Newton iteration until the result converges, and output the satellite geographic coordinate system latitude and longitude information (lat, lon); use the equatorial radius a, the Earth's flattening f, and the second eccentricity square e 2 Calculate the satellite altitude h, and the satellite trajectory data changes with the simulation time t; the semi-physical simulation platform outputs the satellite trajectory about the time change array (t, lat, lon, h), and generates a satellite trajectory visualization chart.

[0015] Further, the satellite error data is:

[0016]

[0017] Where, V i is the actual speed of the satellite of the previous day, V piThe satellite velocity predicted according to the ephemeris data of the previous day.

[0018] Further, the step (2) is implemented as follows:

[0019] On the basis of the classical bimodal wave, a storm model is integrated for calculating the ocean circulation and a small part of extreme sea conditions, the resolution is reduced in the deep sea area while considering the influence of extreme sea conditions; considering the influence of extreme sea conditions, a bimodal spectral density function S'(ω) = S l '(ω) + S h '(ω) under the influence of mixed factors is obtained; at the same time, in order to improve the calculation efficiency, the resolution of the bimodal spectrum is reduced in the deep sea area where the influence of the terrain is small, when the water depth depth> depth th , the bimodal spectrum is simplified, only the main spectral characteristics are retained, the main peak is retained, the spectral simplification is carried out through the resolution adjustment factor R(ω), the greater the water depth depth, the stronger the influence effect of R(ω), and the modified sea wave spectrum density S"(ω) = S'(ω) x R(ω);

[0020] The self-defined input wind speed V wind , water depth depth, ship characteristic array (length, broad, D, zg) are calculated, and the output sea wave effective wave height h wave , sea wave period T wave , wave speed V wave , wave is taken as the ship disturbance;

[0021] A plurality of directional sea waves are vector superimposed, and the multiple sea wave spectrum has a superposition effect on the ship disturbance;

[0022] The influence of sea wave data, ship data and storm data on the ship disturbance condition is fitted to generate a comprehensive angle change under the superposition of a plurality of sine waves with attenuation.

[0023] Further, the process of vector superimposing a plurality of directional sea waves is as follows:

[0024] The input m sea wave power spectrum is synthesized:

[0025]

[0026] Wherein, j is the wave number, e -αt represents the attenuation factor of the sea wave, and alpha is the damping coefficient of the sea wave, which determines the speed of swing attenuation, and has a relationship with the ship characteristic array (length, broad, D, zg) and the initial amplitude of the sea wave; under the influence of the wave, the ship does a similar sine motion with the same period, and the amplitude is inversely proportional to the swing period.

[0027] Further, the step (3) is implemented as follows:

[0028] Customize the initial value of the input gyroscope, accelerometer simulation gry start , acc start , collection precision prec gry , prec acc , working frequency f gry , f acc , bias stability bias gry , bias acc , random walk rand gry , rand acc , calculate the output inertial navigation device three-axis data array (t, gry x , gry y , gry z ), (t, acc x , acc y , acc z ) through the sensor model.

[0029] Conduct semi-physical simulation combined with inertial navigation physical data, import measured data;

[0030] Import working data, and import device data of inertial navigation at rest for a period of time before antenna operation, fit sensor noise characteristics using Allan variance model, output Allan variance curve, and read out the sensor bias gry , bias acc , random walk rand gry , rand acc , complete the construction of sensor error model and drift model.

[0031] Further, the import measured data is implemented as follows:

[0032] Real-time acquisition of three-axis data by the inertial navigation module installed at the bottom of the antenna during antenna operation, real-time transmission of data to the semi-physical simulation platform through the RS232 serial port for calculation;

[0033] Or save the data table file during operation, and import the measured data table into the semi-physical simulation platform for calculation.

[0034] Further, the step (4) is implemented as follows:

[0035] Synchronize multiple sensor data and physical data, convert them into data arrays of the same dimension, further extrapolate or interpolate the data according to the simulation time and precision, and output arrays meeting the simulation dimension requirements;

[0036] Among two data points x1 and x2 needing interpolation, interpolation is carried out through an interpolation polynomial S(x)=a0+a1(x-x0)+a2(x-x0) 2 +a3(x-x0) 3 , wherein a0, a1, a2 and a3 are interpolation coefficients, and a smooth connection point is searched for;

[0037] Each sensor error model constructed through the Allan variance model makes real-time correction to the interpolation coefficient, meanwhile, part of data is reserved as verification, when the interpolation data error is less than a threshold τ, the interpolation coefficient is saved; meanwhile, the interpolation function after the correction coefficient is used to predict data.

[0038] The satellite shipborne moving target indication (MTI) satellite pointing semi-physical simulation system comprises a physical model, a sensor model, an algorithm model and a motor control model; wherein:

[0039] The physical model comprises a satellite trajectory model and a ship attitude model; the satellite trajectory model simulates the real motion trajectory of a satellite, and serves as satellite position input information of the MTI satellite pointing process; the ship attitude model simulates real-time changes of ocean waves, and has a periodic influence on the ship attitude angle in the form of approximate sinusoidal motion;

[0040] The sensor model generates sensor data output of each sensor of the MTI, constructs a sensor error model, realizes interpolation or extrapolation of multi-element data, and supports import of measured data to realize semi-physical simulation;

[0041] The algorithm model calculates the satellite pointing algorithm, outputs target angles of two axes, and sends the target angles to the motor control model;

[0042] The motor control model drives the antenna to turn to the target angle by using closed-loop control, and returns the output error angle to the physical model as input to perform calculation of the next cycle.

[0043] Further, the sensor model comprises a gyroscope, an accelerometer, a GNSS model, a received signal strength model, an ephemeris model and an axis angle model.

[0044] Beneficial effects: Compared with the prior art, the beneficial effects of the present application: the present application fills the blank in the field of shipborne dynamic satellite communication semi-physical simulation, restores the sea area actual measurement process of shipborne dynamic satellite communication through reliable full-chain simulation; compared with the traditional simulation platform, the semi-physical data interface is provided for the satellite end, the sea surface end and the sensor end, the sensitivity and reliability of the test are greatly improved, and the multiple data is put into the same platform for calculation and data output; the semi-physical simulation platform proposed in the present application can monitor the data change of each part in the simulation process in real time, and monitor abnormal conditions; at the satellite end, the satellite motion trajectory can be generated by customizing the input satellite data; at the sea surface end, the wave spectrum is generated in combination with the tide, storm surge and off-shore distance; at the sensor end, the sensor error index can be customized, and the measured data can also be imported for calculation. BRIEF DESCRIPTION OF DRAWINGS

[0045] Figure 1 The satellite shipborne dynamic satellite communication satellite semi-physical simulation method flow chart;

[0046] Figure 2 The semi-physical simulation system structure block diagram of the present application;

[0047] Figure 3 The motion trajectory graph based on the ephemeris data simulation of wisdom Tianwang 1 No. 01 satellite;

[0048] Figure 4 The gyro error data with a sampling rate of 100 Hz generated by the sensor data interpolation algorithm of the present application. DETAILED DESCRIPTION

[0049] The present application will be further described below in combination with the drawings.

[0050] As shown in the drawings, Figure 1 the present application proposes a satellite shipborne dynamic satellite communication satellite semi-physical simulation method, which specifically includes the following steps:

[0051] Step 1: satellite ephemeris calculation and satellite trajectory prediction based on satellite toolbox and self-defined data multiple two-line report TLE data.

[0052] As shown in the drawings, Figure 2 the satellite trajectory model first inputs the total simulation time T and the simulation frequency F in the simulation process to determine the input and output array precision. The satellite scenario object is created through satelliteScenario, including the satellite and the ground station. The two-line report data of the satellite is imported, or the key information of the two-line report data is directly input, the TLE data is printed in the console and the correctness is verified, the position and speed of the satellite at the simulation time are calculated in combination with the correctly input TLE data and the satellite scenario sc, one point every 1 / F second, and the position and speed data of the satellite are stored in p0.

[0053] The satellite orbit data of the previous day and the prediction error of the previous day are used as the maximum threshold value, and when the orbit root number in the TLE data of the current day is greater than the threshold value, the iterative loop calculation is restarted until the data is less than the threshold value, and the final average TLE data is output for orbit prediction. The satellite error data predicted by the previous day stored in the semi-physical simulation platform as the maximum error threshold, wherein V i is the actual speed of the satellite of the previous day, V pi is the predicted satellite speed according to the ephemeris data of the previous day. In the same way, the ephemeris error Δto of the current day is calculated and compared with Δyest, and when Δto>Δyest, the orbit root number of the current day is corrected based on the orbit root number of the previous day ephemeris, and the error is recalculated until Δto≤Δyest, to ensure the accuracy of the satellite motion trajectory of the current day.

[0054] After ensuring the correctness of the predicted satellite data, the satellite's Earth-Centered Earth-Fixed coordinates (ECEF) stored in p0 are solved by the Newton iteration method to obtain the tangent value of the latitude and longitude at each simulation time until the result converges, at which time the geographic coordinate system latitude and longitude information (lat, lon) of the satellite is output; using the equatorial radius a, the Earth's flattening f, the second eccentricity square e 2 to calculate the satellite altitude h, and these satellite trajectory data vary with the simulation time t (t

[0055] Step 2: Ship pitching simulation based on the coupling model of the sea area feature correction sea wave power spectrum empirical function and ADCIRC.

[0056] Considering that most application scenarios of shipborne mobile satellite communication systems are in deep sea areas far from the coast, at this time the sea wave fluctuation state is less affected by the terrain, and is more likely to be affected by circulation or storms and other extreme sea conditions, therefore, the invention makes improvements on the basis of the classic bimodal wave, and integrates the ADCIRC model for calculating ocean circulation and a small part of extreme sea conditions, appropriately reduces the resolution in deep sea areas while considering the influence of extreme sea conditions, which not only improves the calculation efficiency of the platform, but also makes the original sea wave spectrum more realistic.

[0057] The wind speed V wind, water depth depth, ship characteristic array (length, broad, D, zg), after model calculation will output sea wave effective wave height h wave , sea wave period T wave , wave speed V wave As the ship disturbance interference. Multiple direction sea wave conditions can be input, and vector superposition is performed. These multiple sea wave spectra will have a superposition effect on the ship disturbance. The sea wave data, ship data and storm data are fitted to generate a comprehensive angle change under the superposition of several sinusoidal waves with attenuation.

[0058] The ship attitude model is corrected based on the characteristics of the sea area. The sea state low frequency spectrum S l (ω) and the high frequency spectrum S h (ω) of the simulation area are determined through the simulation platform interface. The wind speed V wind , water depth depth, ship characteristic array (length, broad, D, zg) can also be input. After model calculation, the sea wave effective wave height h wave , sea wave period T wave , wave speed V wave will be output. The output of the sea wave model is used as the input information of the ADCIRC model. The ocean circulation and storm surge numerical model is simulated. After outputting the wind force, water level and current information, the swell spectrum and wind wave spectrum are corrected to obtain S l '(ω) and S h '(ω), and the next cycle of calculation is performed.

[0059] Based on the traditional bimodal wave model, the influence of ocean circulation and storm surge and other extreme sea conditions is considered, and the bimodal spectrum density function S'(ω) = S l '(ω) + S h '(ω) under the influence of mixed factors is obtained. At the same time, in order to improve the calculation efficiency, the resolution of the bimodal spectrum is reduced in the deep sea area which is less affected by the topography. When the water depth depth> depth th , the bimodal spectrum is simplified, only the main spectral characteristics are retained, the main peak is retained, the spectral simplification is performed through the resolution adjustment factor R(ω), the greater the water depth depth, the stronger the influence effect of R(ω), and the corrected sea wave spectrum density S"(ω) = S'(ω) x R(ω).

[0060] In actual sea conditions, sea waves are the result of superposition of multiple direction sea waves. m sea wave power spectra are input for synthesis:

[0061]

[0062] Where k is the wave number, e -αtThe attenuation factor, a, represents the damping coefficient of the sea wave, determines the speed of the swing attenuation, and is related to the ship characteristic array (length, broad, D, zg), and the initial amplitude of the sea wave. Under the influence of the wave, the ship does the same period of sine-like motion, and the amplitude is inversely proportional to the swing period.

[0063] Step 3: Simulation fitting of the measured inertial navigation data error model and drift model based on Allan variance, supporting custom entering the type of gyroscope, accelerometer, data precision, working frequency and drift error, and importing measured data for semi-physical simulation, real-time feedback of three-axis angle of moving antenna system.

[0064] The simulation platform first generates the sensor true value, that is, the real data without error, and then fits the sensor error model based on the true value data, adds the sensor error data, and the obtained data is the measured data of the sensor. Custom input the simulation starting value gry start , acc start , collection precision prec gry , prec acc , working frequency f gry , f acc , bias stability bias gry , bias acc , random walk rand gry , rand acc , calculate the output of the inertial navigation device three-axis data array (t, gry x , gry y , gry z ), (t, acc x , acc y , acc z ) through the sensor model, which contains actual data and sensor error, and the measured data of the inertial navigation device is high in restoration degree; It also supports semi-physical simulation combined with inertial navigation physical data. Measured data can be imported in two ways: ① Real-time acquisition of three-axis data by the inertial navigation module installed at the bottom of the antenna during antenna operation, and real-time transmission of data into the semi-physical simulation platform through the RS232 serial port for calculation, ② Save the data table.xls file during operation, and import the measured data table into the semi-physical simulation platform for calculation.

[0065] The imported working data also need to import the device data of the inertial navigation system before the antenna works and is stationary for a period of time, use the Allan variance model to fit the sensor noise characteristics, draw the Allan variance curve of the sensor, and read out the zero bias bias gry , bias acc , random walk randgry rand acc , the sensor error model and the drift model are completed. After synchronizing the final sensor output data, it is used as the input data of the moving target indication algorithm module, reflecting the real-time change of the antenna's attitude angle and the swing angle of the antenna base with the ship.

[0066] Step 4: Based on the sensor error model and the drift model, the interpolation coefficient is corrected, and the three-spline interpolation and extrapolation algorithm of the multi-sensor synchronization is carried out.

[0067] In the moving target indication system, the data of gyroscopes, accelerometers, GNSS and other sensors are involved. Before all the arrays are input into the algorithm model for calculation, the data accuracy of these arrays needs to be synchronized to ensure that all the data correspond to the same time after inputting into the algorithm module, and the authenticity of the data is guaranteed. Therefore, according to the simulation time and accuracy set by the simulation platform, the data needs to be further extrapolated or interpolated to output arrays that meet the simulation dimension requirements.

[0068] In the two data points x1 and x2 that need to be interpolated, the interpolation polynomial S(x) = a0 + a1(x-x0) + a2(x-x0) 2 +a3(x-x0) 3 is used for interpolation, where a0, a1, a2, a3 are interpolation coefficients, and the smooth connection point is found. The interpolation coefficient is corrected in real time by using the error model of each sensor constructed by the Allen variance model in the previous step, and 5% of the data is reserved for verification. When the interpolation data error is less than the threshold τ, the interpolation coefficient is saved. At the same time, the interpolation function after the correction coefficient is used to predict the data.

[0069] As Figure 2As shown, this invention also proposes a satellite-borne on-the-move satellite-synchronization semi-physical simulation system, including a physical model, a sensor model, an algorithm model, and a motor control model. Each model can be further divided into several sub-models, each of which can import key parameter information and provides a physical data import interface, outputting corresponding array information. This information serves as input to the on-the-move satellite-synchronization algorithm, verifying its feasibility. The physical model primarily inputs real-world data, including ship attitude and position data, satellite trajectory and satellite-to-ground link data, and data from the mechanical platform at the bottom of the on-the-move satellite. The sensor model includes gyroscopes, accelerometers, a GNSS model, a receiver signal strength model, and ephemeris and axis angle models. After inputting data from the physical and sensor models, the calculated data is passed to the algorithm model interface for satellite-synchronization algorithm calculation, outputting the alignment angles of the two axes. Finally, the alignment angles are passed to the motor control model, which uses closed-loop control to drive the antenna to the target angle and returns the output error angle back to the physical model as input for the next cycle of calculation. The hardware-in-the-loop simulation platform includes four models, each with interfaces for simulation and measured data. This allows for the integration of virtual components and measured data, recreating realistic sea test data. Finally, the feasibility of the algorithm model is verified based on real-time monitoring of input and output data.

[0070] like Figure 3 As shown, this invention generates a satellite motion trajectory array in real time based on the input ephemeris data of Smart SkyNet-1 01, and simulates the motion trajectory diagram, thus realizing satellite motion simulation and prediction. Figure 4 As shown, after generating the original gyroscope data, the sensor data interpolation algorithm of this invention is used to augment the original data with a sampling rate of 10Hz, generating gyroscope error data with a sampling rate of 100Hz. Figure 4 As can be seen, the high sampling rate data generated after interpolation not only reflects the changing trend of the original data well, but also expands the accuracy of the sensor data, making it suitable for high-precision satellite simulation.

[0071] The above description is only a preferred embodiment of the present invention. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principle of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A satellite-borne, on-the-move, hardware-in-the-loop simulation method for satellite-to-satellite communication, characterized in that, Includes the following steps: (1) Satellite ephemeris calculation and satellite trajectory prediction based on satellite toolbox and custom multivariate two-line report TLE data to generate target satellites for satellite tracking process in motion; (2) Based on the empirical function of the power spectrum of ocean waves modified by the characteristics of the sea area, ship turbulence simulation is carried out to simulate the interaction between tides, storm surges and ocean waves, improve the accuracy of storm surge and ocean wave simulation, simulate the turbulence of ships affected by waves in the sea, and add disturbance to the satellite communication in motion. (3) Simulation fitting of measured inertial navigation data error model and drift model based on Allen variance, supporting custom input of gyroscope, accelerometer type, data accuracy, working frequency and drift error, as well as importing measured data for semi-physical simulation, and real-time feedback of three-axis angle of mobile antenna system; (4) Based on the sensor error model and drift model, the cubic spline interpolation and extrapolation algorithm is used to synchronize multiple sensors, set the simulation step size accuracy, and synchronize the interpolation fitting of sensor data with different accuracy in the system to improve the simulation realism and data reliability. The implementation process of step (3) is as follows: Custom input of simulation starting values ​​gry for gyroscope and accelerometer start acc start Acquisition accuracy prec gry ,prec acc Operating frequency f gry f acc Zero bias stability bias gry bias acc Random walk rand gry rand acc The array (t, gry) of the three-axis data of the inertial navigation device as a function of time is calculated and output through the sensor model. x ,gry y ,gry z ), (t,acc x ,acc y ,acc z ); Perform hardware-in-the-loop simulation by combining physical inertial navigation data and importing measured data; Import the working data, and simultaneously import the device data from the inertial navigation system after it has been stationary for a period of time before the antenna is put into operation. Use the Allen variance model to fit the sensor noise characteristics, output the Allen variance curve, and read the zero-bias stability bias of the sensor from the variance curve. gry bias acc Random walk rand gry rand acc Complete the construction of sensor error model and drift model; The implementation process of step (4) is as follows: Multiple sensor data and physical data are synchronized and converted into data arrays of the same dimension. Based on the simulation time and accuracy, the data are further extrapolated or interpolated to output an array that meets the simulation dimension requirements. For the two data points x1 and x2 that need interpolation, the interpolation polynomial S(x) = a0 + a1(x - x0) + a2(x - x0) is used. 2 +a3(x-x0) 3 Perform interpolation, where a0, a1, a2, and a3 are interpolation coefficients, and find smooth connection points; The error models of each sensor constructed using the Allen variance model are used to make real-time corrections to the interpolation coefficients, while reserving some data for verification. When the interpolation error is less than the threshold τ, the interpolation coefficient is saved. At the same time, the interpolation function after the correction coefficient is used to predict the data.

2. The satellite-borne, on-the-move, hardware-in-the-loop simulation method for satellite communication according to claim 1, characterized in that, The implementation process of step (1) is as follows: Create a satellite scene object using satelliteScenario and set the total simulation time T and frequency F; Import multiple satellite trajectory TLE ephemeris data via a button, or customize TLE data according to the provided ephemeris range. Combine real TLE data with custom TLE data to form a semi-physical simulation. After extracting the required data, a two-line report data file for the target satellite will be recreated; at the same time, the satellite model SAT will be created using the satellite toolbox, and the satellite trajectory will be quickly predicted by combining the TLE ephemeris data and the satellite model to solve the satellite trajectory. Using the previous day's satellite orbit data and the previous day's prediction error as the maximum threshold, when the trajectory error corresponding to today's TLE data is greater than this threshold, the orbital elements are corrected and the calculation is iterated again until the data is less than the threshold. Finally, the average TLE data is output for orbit prediction. The geocentric coordinates ECEF of the satellite are used to solve for the latitude and longitude tangents at each moment using Newton's iterative method until the results converge, outputting the satellite's geographic coordinate system latitude and longitude information (lat, lon); using the equatorial radius a, Earth's oblateness f, and the square of the second eccentricity e 2 Calculate the satellite altitude h and the change of satellite trajectory data with simulation time t; The hardware-in-the-loop simulation platform outputs an array (t,lat,lon,h) of satellite trajectory changes over time and generates a visualization chart of the satellite trajectory.

3. The satellite-borne mobile communication hardware-in-the-loop simulation method according to claim 2, characterized in that, The satellite error data is as follows: Among them, V i V is the actual velocity of the satellite the previous day. pi This is the satellite velocity predicted based on ephemeris data from the previous day.

4. The satellite-borne, on-the-move, hardware-in-the-loop simulation method for satellite communication according to claim 1, characterized in that, The implementation process of step (2) is as follows: Based on the classic bimodal wave model, a storm model is incorporated to calculate ocean circulation and a few extreme sea states. While reducing resolution in the deep sea region, the influence of extreme sea states is considered. Taking into account the influence of extreme sea states, the bimodal spectral density function S'(ω) = S under the influence of mixed factors is obtained. l '(ω)+S h '(ω); Meanwhile, to improve computational efficiency, the resolution of the bimodal spectrum was reduced in deep-sea areas less affected by topography, when depth > depth th The bimodal spectrum is simplified by retaining only the main spectral features and the main peaks. The spectrum is simplified by adjusting the resolution factor R(ω). The greater the water depth, the stronger the effect of R(ω). The corrected wave spectral density S””ω)=S'(ω)×R(ω). Custom input wind speed V in a hardware-in-the-loop simulation platform wind The calculation will output the significant wave height h, calculated from the water depth (depth) and the ship feature array (length, breadth, D, zg). wave Wave cycle T wave Wave speed V wave The waves are used as ship disturbances; By superimposing ocean waves from multiple directions into vectors, the multiple wave spectra produce a superposition effect on ship disturbance; By fitting ocean and wave data, ship data and storm data to the impact of ocean and wave data on ship disturbance, a comprehensive angle change under the superposition of several attenuated sinusoidal waves is generated.

5. A satellite-borne, on-the-move, hardware-in-the-loop simulation method for satellite communication according to claim 4, characterized in that, The process of vector superposition of ocean waves from multiple directions is as follows: Synthesize m input wave power spectra: Where k is the wave number, e -αt The damping factor represents the wave's attenuation, α is the wave's damping coefficient, which determines the rate of oscillation attenuation and is related to the ship's characteristic array (length, breadth, D, zg) and the initial wave amplitude. Under the influence of the waves, the ship undergoes a sinusoidal motion with the same period, and the amplitude... It is inversely proportional to the oscillation period.

6. A satellite-borne, on-the-move, hardware-in-the-loop simulation method for satellite communication according to claim 1, characterized in that, The process of importing measured data is as follows: The inertial navigation module installed at the bottom of the antenna acquires triaxial data in real time during antenna operation, and transmits the data to the hardware-in-the-loop simulation platform for calculation in real time via RS232 serial port. Alternatively, the data table file can be saved during operation and imported into the hardware-in-the-loop simulation platform for calculation.

7. A satellite-borne onboard hardware-in-the-loop simulation system for satellite-to-satellite communication using the method described in any one of claims 1 to 6, characterized in that, This includes a physical model, a sensor model, an algorithm model, and a motor control model; among which: Physical models include satellite trajectory models and ship attitude models. The satellite trajectory model simulates the actual motion trajectory of the satellite and serves as the satellite position input information for the satellite alignment process. The ship attitude model simulates the real-time changes in ocean wind and waves, generating a periodic effect on the ship's attitude angles that approximates a sinusoidal motion. Sensor Model: Generates the data output of each sensor in the inertial navigation module during the flight, constructs the sensor error model, realizes the interpolation or extrapolation of multivariate data, and supports the import of measured data to realize semi-physical simulation; Algorithm model: Perform star alignment algorithm calculation, output the target angles of the two axes, and send them to the motor control model; Motor control model: Using closed-loop control, the antenna is driven to turn to the target angle, and the output error angle is returned to the physical model as input for the next cycle of calculation.

8. A satellite-borne mobile communication hardware-in-the-loop simulation system for satellite communication according to claim 7, characterized in that, The sensor model includes a gyroscope, an accelerometer, a GNSS model, a receiver signal strength model, an ephemeris model, and an axis angle model.

Images