Satellite shipborne communication-in-motion satellite-to-satellite semi-physical simulation method and system

Abstract

The invention discloses a satellite shipborne communication-in-motion satellite-aligning semi-physical simulation method and system, and the method comprises the steps: carrying out the satellite ephemeris calculation and satellite trajectory prediction based on a sath toolbox and customized multivariate two-line report TLE data, and generating a target satellite for a satellite-aligning process of communication-in-motion; carrying out ship jolt simulation based on the sea wave power spectrum empirical function corrected by the sea area features, and adding disturbance to satellites for satellite communication in motion; performing simulation fitting on an actually measured inertial navigation data error model and a drift model based on the Airy variance, and feeding back a three-axis angle of the communication-in-moving antenna system in real time; based on cubic spline interpolation of a sensor error model and a drift model correction interpolation coefficient and multi-sensor synchronization of an extrapolation algorithm, simulation authenticity and data reliability are improved. The communication-in-motion semi-physical simulation platform established by the invention effectively restores the satellite communication process of the communication-in-motion antenna, ensures the stability of communication data input and output, supports introduction of actually measured data for semi-physical simulation, and verifies the accuracy of a satellite algorithm.

Description

Technical Field

[0001] The invention belongs to the field of semi-physical simulation of satellites for communication-in-motion antenna systems, and in particular relates to a method and system for semi-physical simulation of satellites for communication-in-motion on a satellite ship. Background Art

[0002] Shipborne mobile communication can help ships communicate with satellites in the far seas. However, the mobile communication system involves multiple modules, and a large amount of marine data collection and measurement is required after production is completed. However, the weather and sea conditions on the sea surface are complex and changeable, and the test data is greatly affected by the sea conditions and difficult to predict, making it difficult to ensure the quality of the test; satellite resources are scarce, and communication satellites need to be coordinated in advance during testing, which is expensive; the mobile communication contains multiple sensors, and the test requires the purchase of sensors of different precisions, which increases the difficulty of testing and the cost of testing. Therefore, physical testing at sea is difficult and costly. At present, there are some simulation platforms for the satellite end, the sea surface end, and the sensor end, but there are some shortcomings: the satellite end simulation system can simulate the motion trajectory of existing satellite data, but does not support custom satellite models and motion characteristics; there are some ocean simulation platforms that can generate wave models based on storm and tidal data, but do not consider the different wave characteristics of near and far seas; some sensor simulation platforms can use virtual components to output sensor data, but cannot achieve the fusion of multi-sensor data and sensor error value fitting, and do not support semi-physical simulation. Summary of the invention

[0003] Purpose of the invention: The present invention provides a method and system for semi-physical simulation of satellite-to-satellite communication on board a satellite, which provides flexible and reliable simulation data for the satellite end, the sea surface end, and the ground sensor end, and supports custom input of satellite and sensor data for semi-physical simulation, thereby assisting the development and design of the communication-in-motion system.

[0004] Invention content: The invention discloses a satellite ship-borne semi-physical simulation method for satellite communication in motion, comprising the following steps:

[0005] (1) Based on the satellite toolbox and the custom multivariate two-line report TLE data, satellite ephemeris solution and satellite trajectory prediction are used to generate target satellites for the on-the-fly tracking process;

[0006] (2) Based on the empirical function of the wave power spectrum corrected by the sea area characteristics, the ship's turbulence is simulated, the interaction between tides, storm surges and waves is simulated, the accuracy of storm surge and wave simulation is improved, the turbulence of ships affected by waves in the sea is simulated, and disturbances are added to the satellite in motion communication;

[0007] (3) The simulation fitting of the measured inertial navigation data error model and the drift model based on the Allan variance supports the custom entry of the gyroscope and accelerometer type, data accuracy, operating frequency and drift error, as well as 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) Based on the sensor error model and drift model, the cubic spline interpolation and extrapolation algorithms for interpolation coefficient correction are used to synchronize multiple sensors, set the simulation step accuracy, and synchronize the interpolation fitting of sensor data with different accuracies in the system to improve the simulation authenticity and data reliability.

[0009] Furthermore, the implementation process of step (1) is as follows:

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

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

[0012] After extracting the required data, the two-line 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 and solved by combining the TLE ephemeris data with the satellite model;

[0013] The satellite orbit data of the previous day and the prediction error of the previous day are used as the maximum threshold. When the trajectory error corresponding to the TLE data of the current day is greater than the threshold, the orbital elements are corrected and recalculated iteratively until the data is less than the threshold. The final average TLE data is output for orbit prediction.

[0014] The satellite's geocentric coordinates ECEF are solved by Newton's iteration method to obtain the tangent value of the longitude and latitude at each moment until the result converges, and the longitude and latitude information (lat, lon) of the satellite's geographic coordinate system is output; using the equatorial radius a, the earth's flattening f, and the second eccentricity squared 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 array (t, lat, lon, h) with respect to time changes, and generates a satellite trajectory visualization chart.

[0015] Furthermore, the satellite error data is:

[0016]

[0017] Among them, V i is the actual satellite speed of the previous day, V piis the satellite speed predicted from the ephemeris data for the previous day.

[0018] Furthermore, the implementation process of step (2) is as follows:

[0019] Based on the classic double-peak wave, the storm model is incorporated to calculate the ocean circulation and a few extreme sea conditions. The resolution is reduced in the deep sea area while considering the impact of extreme sea conditions. Considering the impact of extreme sea conditions, the double-peak spectral density function S′(ω)=S under the influence of mixed factors is obtained. l ′(ω)+S h ′(ω); At the same time, in order to improve the calculation efficiency, the bimodal spectrum resolution is reduced in the deep sea area which is less affected by the terrain. th The double-peak spectrum is simplified, only the main spectrum features and main peaks are retained, and the spectrum is simplified by the resolution adjustment factor R(ω). The greater the water depth, the stronger the effect of R(ω). The corrected wave spectrum density S”(ω)=S′(ω)×R(ω);

[0020] Custom input wind speed V in semi-physical simulation platform wind , water depth depth, ship feature array (length,broad,D,zg), after calculation, the output will be the effective wave height h wave , wave period T wave , wave speed V wave , taking waves as ship disturbances;

[0021] The waves in multiple directions are superimposed as vectors, and multiple wave spectra produce superimposed effects on ship disturbances;

[0022] The influence of ocean and wave data on ship disturbance is fitted by using wave data, ship data and storm data, and the comprehensive angle changes under the superposition of several attenuated sinusoidal waves are generated.

[0023] Furthermore, the process of implementing vector superposition of waves in multiple directions is as follows:

[0024] The input m wave power spectra are synthesized:

[0025]

[0026] Where j is the wave number, e -αt represents the attenuation factor of the waves, α is the damping coefficient of the waves, which determines the speed of the swing attenuation and is related to the ship's characteristic array (length, broad, D, zg) and the initial amplitude of the waves; under the influence of the waves, the ship performs a quasi-sinusoidal motion with the same period, and the amplitude Inversely proportional to the swing period.

[0027] Furthermore, the implementation process of step (3) is as follows:

[0028] Customize the simulation start value gry for the gyroscope and accelerometer start ,acc start , acquisition accuracy prec gry 、prec acc , operating frequency f gry 、f acc , bias stability gry 、bias acc , random walk rand gry 、rand acc , the sensor model is used to calculate and output the array of the three-axis data of the inertial navigation device that changes with time (t, gry x ,gry y ,gry z )、(t,acc x ,acc y ,acc z );

[0029] Combine physical inertial navigation data to conduct semi-physical simulation and import measured data;

[0030] Import the working data, and import the device data of the inertial navigation device that has been stationary for a period of time before the antenna works. Use the Allen variance model to fit the sensor noise characteristics, output the Allen variance curve, and read the zero bias stability of the sensor based on the variance curve. gry 、bias acc , random walk rand gry 、rand acc , complete the construction of sensor error model and drift model.

[0031] Furthermore, the implementation process of importing measured data is as follows:

[0032] The inertial navigation module installed at the bottom of the antenna when the antenna is running obtains three-axis data in real time, and transmits the data to the semi-physical simulation platform for calculation in real time through the RS232 serial port;

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

[0034] Furthermore, the implementation process of step (4) is as follows:

[0035] Synchronize multiple sensor data with physical data and convert them into data arrays of the same dimension. According to the simulation time and accuracy, further extrapolate or interpolate the data to output an array that meets the simulation dimension requirements.

[0036] 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) is used. 2 +a3(x-x0) 3 Perform interpolation, where a0, a1, a2, a3 are interpolation coefficients, and find smooth connection points;

[0037] The error models of each sensor constructed by the Allen variance model are used to make real-time corrections to the interpolation coefficients, and some data are 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.

[0038] The semi-physical simulation system for satellite-to-satellite communication in motion carried by a satellite ship of the present invention comprises a physical model, a sensor model, an algorithm model and a motor control model; wherein:

[0039] Physical model: including satellite trajectory model and ship attitude model; the satellite trajectory model simulates the real motion trajectory of the satellite, which serves as the satellite position input information for the tracking process; the ship attitude model simulates the real-time changes of ocean waves, which produces a periodic effect of approximately sinusoidal motion on the ship attitude angle;

[0040] Sensor model: Generates the data output of each sensor of the inertial navigation module in the mobile communication, builds the sensor error model, realizes the interpolation or extrapolation of multivariate data, and supports the import of measured data to realize semi-physical simulation;

[0041] Algorithm model: Calculate the star alignment algorithm, output the target angles of the two axes, and send them to the motor control model;

[0042] Motor control model: Use closed-loop control to drive the antenna to the target angle, and return the output error angle to the physical model as input for the next cycle of calculation.

[0043] Furthermore, the sensor model includes a gyroscope, an accelerometer, a GNSS model, a receiving end 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 invention are as follows: the present invention fills the gap in the field of semi-physical simulation of ship-borne mobile communications, and restores the actual measurement process of ship-borne mobile communications in the sea area through reliable full-chain simulation; compared with traditional simulation platforms, semi-physical data interfaces are provided for the satellite end, the sea surface end, and the sensor end, which greatly improves the sensitivity and reliability of the test, and puts these multivariate data into the same platform for calculation and data output; the semi-physical simulation platform proposed in the present invention can monitor the changes of various parts of the data in the simulation process in real time, and monitor abnormal situations; at the satellite end, the satellite data can be customized to input and the satellite motion trajectory can be generated; at the sea surface end, the wave spectrum is generated by combining tides, storm surges and offshore distances; at the sensor end, the sensor error index can be customized to input, and it also supports the import of measured data for calculation. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] Figure 1 This is a flow chart of the semi-physical simulation method for satellite ship-borne in-motion communication;

[0046] Figure 2 It is a structural block diagram of the semi-physical simulation system of the present invention;

[0047] Figure 3 This is a motion trajectory diagram simulated based on the ephemeris data of Smart Skynet No. 1;

[0048] Figure 4 The sensor data interpolation algorithm of the present invention is used to generate gyroscope error data with a sampling rate of 100 Hz. DETAILED DESCRIPTION

[0049] The present invention will be further described below in conjunction with the accompanying drawings.

[0050] like Figure 1 As shown, the present invention proposes a semi-physical simulation method for satellite ship-borne in-motion communication, which specifically includes the following steps:

[0051] Step 1: Satellite ephemeris solution and satellite trajectory prediction based on two-line report TLE data of satellite toolbox and custom data multivariate.

[0052] like Figure 2 As shown, the satellite trajectory model first inputs the total simulation time T and simulation frequency F in this simulation process to determine the accuracy of the input and output arrays. Create a satellite scenario object through satelliteScenario, including satellites and ground stations. Import two lines of satellite report data, or directly input the key information of two lines of report data, print TLE data in the console and verify the correctness, combine the correctly input TLE data with the satellite scenario sc, and cyclically calculate the position and velocity of the satellite in the simulation time, one point every 1 / F seconds, and store the satellite's position and velocity data in p0.

[0053] Using the satellite orbit data of the previous day and the prediction error of the previous day as the maximum threshold, when the orbital elements in the TLE data of the current day are greater than this threshold, re-iterate and loop the calculation until the data is less than the threshold, output the final average TLE data, and perform orbit prediction. The satellite error data predicted the previous day stored in the hardware-in-the-loop simulation platform as the maximum error threshold, where V i is the actual satellite speed of the previous day, and V pi is the satellite speed predicted according to the ephemeris data of the previous day. Calculate the ephemeris error Δto of the current day in the same way and compare it with Δyest. When Δto > Δyest, based on the orbital elements of the previous day's ephemeris, correct the orbital element data of the current day and recalculate the error until Δto ≤ Δyest to ensure the accuracy of the satellite's motion trajectory on the current day.

[0054] After ensuring the correctness of the predicted satellite data, the Earth-centered Earth-fixed coordinates (ECEF) of the satellite stored in p0 are used to solve the tangent values of latitude and longitude at each simulation moment through Newton's iterative method until the result converges. At this time, the latitude and longitude information (lat, lon) of the satellite's geographic coordinate system is output; using the equatorial radius a, the flattening f of the Earth, and the square of the second eccentricity e 2 calculate the satellite altitude h, and all these satellite trajectory data change with the simulation time t (t < T). Finally, the hardware-in-the-loop simulation platform outputs an array of satellite trajectories varying with time (t, lat, lon, h) and generates a visual chart of the satellite trajectory. Compared with the traditional satellite trajectory prediction technology, this satellite orbit model provides a hardware-in-the-loop simulation interface and uses the previous day's satellite data as an error constraint, improving the accuracy of satellite trajectory prediction and enabling real-time output of arrays and orbit data.

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

[0056] Considering that the vast majority of the application scenarios of on-ship satellite communication are in the deep sea area far from the coast, at this time the wave fluctuation state is less affected by the terrain and is more likely to be affected by extreme sea conditions such as ocean circulation or storms. Therefore, the present invention makes improvements on the basis of the classic double-peak wave, incorporates the ADCIRC model to calculate ocean circulation and a very small part of extreme sea conditions, and takes into account the influence of extreme sea conditions while appropriately reducing the resolution in the deep sea area, which not only improves the calculation efficiency of the platform but also makes the wave spectrum more in line with the actual situation than the original one.

[0057] The wind speed V can be customized in the hardware-in-the-loop simulation platform wind, water depth depth, ship feature array (length,broad,D,zg), after model calculation, the output will be the effective wave height h wave , wave period T wave , wave speed V wave As a ship disturbance. You can input wave conditions in multiple directions and make vector superposition. These multiple wave spectra will have a superposition effect on ship disturbance. Use wave data, ship data and storm data to fit the impact of ocean and wave data on ship disturbance, and generate comprehensive angle changes under the superposition of several attenuated sinusoidal waves.

[0058] The ship attitude model modified based on the sea area characteristics is used to determine the low-frequency spectrum S of the sea conditions in the simulation area through the simulation platform interface. l (ω), high frequency spectrum S h (ω); you can also input the wind speed V by your own custom wind , water depth depth, ship feature array (length,broad,D,zg), after model calculation, the output will be the effective wave height h wave , wave period T wave , wave speed V wave The output of the wave model is used as the input information of the ADCIRC model to simulate the ocean circulation and storm surge numerical model. After outputting the wind force, water level and ocean current information, the surge spectrum and wind wave spectrum are corrected respectively to obtain S l ′(ω) and S h ′(ω), and perform the next cycle calculation.

[0059] On the basis of the traditional double-peak wave model, the influence of extreme sea conditions such as ocean circulation and storm surge is considered, and the double-peak spectral density function S′(ω)=S under the influence of mixed factors is obtained. l ′(ω)+S h ′(ω). At the same time, in order to improve the calculation efficiency, the bimodal spectrum resolution is reduced in the deep sea area which is less affected by the terrain. th The double-peak spectrum is simplified, only the main spectrum features and main peaks are retained, and the spectrum is simplified by the resolution adjustment factor R(ω). The greater the water depth, the stronger the effect of R(ω), and the corrected wave spectrum density S”(ω)=S′(ω)×R(ω).

[0060] In actual sea conditions, waves are the result of the superposition of waves in multiple directions. The input m wave power spectra are synthesized:

[0061]

[0062] Where k is the wave number, e -αtrepresents the attenuation factor of the waves, α is the damping coefficient of the waves, which determines the speed of the swing attenuation and is related to the ship's characteristic array (length,broad,D,zg) and the initial amplitude of the waves. Under the influence of the waves, the ship performs a quasi-sine motion with the same period, and the amplitude Inversely proportional to the swing period.

[0063] Step 3: The simulation fitting of the measured inertial navigation data error model and the drift model based on the Allan variance supports the custom entry of the gyroscope and accelerometer type, data accuracy, operating frequency and drift error, as well as the import of measured data for semi-physical simulation, and real-time feedback of the three-axis angle of the moving antenna system.

[0064] The simulation platform first generates the true value of the sensor, 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 resulting data is the actual measured data of the sensor. start ,acc start , acquisition accuracy prec gry 、prec acc , operating frequency f gry 、f acc , bias stability gry 、bias acc , random walk rand gry 、rand acc , the sensor model is used to calculate and output the array of the three-axis data of the inertial navigation device that changes with time (t, gry x ,gry y ,gry z )、(t,acc x ,acc y ,acc z ), these arrays contain actual data and sensor errors, and have a high degree of restoration of the measured data of the inertial navigation device; they also support semi-physical simulation combined with physical inertial navigation data. The measured data can be imported through two schemes: ① When the antenna is running, the inertial navigation module installed at the bottom of the antenna obtains three-axis data in real time, and transmits the data to the semi-physical simulation platform in real time 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] When importing the working data, you also need to import the device data of the inertial navigation system that has been stationary for a period of time before the antenna works. Use the Allen variance model to fit the sensor noise characteristics, draw the Allen variance curve of the sensor, and read the zero bias stability of the sensor based on the variance curve. gry 、bias acc , random walk randgry 、rand acc , complete the construction of sensor error model and drift model. After the final sensor output data is synchronized, it is used as the input data of the moving communication algorithm module to reflect the real-time satellite attitude angle change of the antenna and the swing angle of the antenna base with the ship.

[0066] Step 4: Multi-sensor synchronization based on cubic spline interpolation and extrapolation algorithms with corrected interpolation coefficients based on sensor error model and drift model.

[0067] The mobile communication system involves data from multiple sensor devices such as gyroscopes, accelerometers, and GNSS. Before all arrays are input into the algorithm model for calculation, the data accuracy of these arrays needs to be synchronized to ensure that after entering the algorithm module, all data have the same corresponding time to ensure the authenticity of the data. Therefore, it is necessary to further extrapolate or interpolate the data according to the simulation time and accuracy set by the simulation platform to output an array that meets 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) is used. 2 +a3(x-x0) 3 Interpolation is performed, where a0, a1, a2, and a3 are interpolation coefficients, and smooth connection points are found. The error models of each sensor constructed by the Allen variance model in the previous step are used to make real-time corrections to the interpolation coefficients, 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] like Figure 2As shown, the present invention also proposes a semi-physical simulation system for satellite ship-borne satellite alignment in motion, including a physical model, a sensor model, an algorithm model and a motor control model; different models can be divided into several sub-models, each of which can import key parameter information, provide a physical data import interface, and output corresponding array information. These information are used as input information of the satellite alignment algorithm in motion to verify the feasibility of the satellite alignment algorithm in motion. The physical model mainly inputs real physical world data, including ship attitude and position data, satellite trajectory and satellite-to-ground link data, and mechanical platform data at the bottom of the satellite alignment in motion. The sensor model includes a gyroscope, an accelerometer, a GNSS model, a receiving end signal strength model, and an ephemeris model and an axis angle model. After completing the input of the physical model and the sensor model, the calculated data is passed into the algorithm model interface to perform the calculation of the alignment algorithm and output the alignment angle of the two axes; finally, the alignment angle is passed into the motor control model, and the motor control model uses closed-loop control to drive the antenna 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. The four major models in the semi-physical simulation platform all have simulation and measured data interfaces, which support the combination of virtual components and measured data and restore real offshore 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, the present invention generates a satellite motion trajectory array in real time based on the input Smart Skynet 1 No. 01 ephemeris data, and simulates the motion trajectory diagram, thereby realizing satellite motion simulation and prediction. Figure 4 As shown in FIG. 1 , after generating the gyroscope raw data, the sensor data interpolation algorithm of the present invention is used to expand the original data with a data sampling rate of 10 Hz to generate gyroscope error data with a sampling rate of 100 Hz. Figure 4 It can be seen that the high sampling rate data generated after interpolation not only reflects the changing trend of the original data well, but also realizes the expansion of sensor data accuracy, making it suitable for satellite simulation with high precision requirements.

[0071] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principle of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. A semi-physical simulation method for satellite ship-borne in-motion communication, characterized in that: The following steps are involved: (1) Based on the satellite toolbox and the custom multivariate two-line report TLE data, satellite ephemeris solution and satellite trajectory prediction are used to generate target satellites for the on-the-fly tracking process; (2) Based on the empirical function of the wave power spectrum corrected by the sea area characteristics, the ship's turbulence is simulated, the interaction between tides, storm surges and waves is simulated, the accuracy of storm surge and wave simulation is improved, the turbulence of ships affected by waves in the sea is simulated, and disturbances are added to the satellite in motion communication; (3) The simulation fitting of the measured inertial navigation data error model and the drift model based on the Allan variance supports the custom entry of the gyroscope and accelerometer type, data accuracy, operating frequency and drift error, as well as the import of measured data for semi-physical simulation, and real-time feedback of the three-axis angle of the moving antenna system; (4) Based on the sensor error model and drift model, the cubic spline interpolation and extrapolation algorithms for interpolation coefficient correction are used to synchronize multiple sensors, set the simulation step accuracy, and synchronize the interpolation fitting of sensor data with different accuracies in the system to improve the simulation authenticity and data reliability.

2. The method for semi-physical simulation of satellite communication in motion carried by a satellite ship according to claim 1, characterized in that: The implementation process of step (1) is as follows: Create a satellite scenario object through satelliteScenario and set the total simulation time T and frequency F; Import multiple satellite trajectory TLE ephemeris data through buttons, or customize input TLE data according to the provided ephemeris range, and combine real TLE data with customized TLE data to form a semi-physical simulation; After extracting the required data, the two-line 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 and solved by combining the TLE ephemeris data with the satellite model; The satellite orbit data of the previous day and the prediction error of the previous day are used as the maximum threshold. When the trajectory error corresponding to the TLE data of the current day is greater than the threshold, the orbital elements are corrected and recalculated iteratively until the data is less than the threshold. The final average TLE data is output for orbit prediction. The satellite's geocentric coordinates ECEF are solved by Newton's iteration method to obtain the tangent value of the longitude and latitude at each moment until the result converges, and the longitude and latitude information (lat, lon) of the satellite's geographic coordinate system is output; using the equatorial radius a, the earth's flattening f, and the second eccentricity squared 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 array (t, lat, lon, h) with respect to time changes and generates a satellite trajectory visualization chart.

3. The method for semi-physical simulation of satellite communication in motion carried by a satellite ship according to claim 2, characterized in that: The satellite error data is: Among them, V i is the actual satellite speed of the previous day, V pi is the satellite speed predicted from the ephemeris data for the previous day.

4. The method for semi-physical simulation of satellite communication in motion carried by a satellite ship according to claim 1, characterized in that: The implementation process of step (2) is as follows: Based on the classic double-peak wave, the storm model is incorporated to calculate the ocean circulation and a few extreme sea conditions. The resolution is reduced in the deep sea area while considering the impact of extreme sea conditions. Considering the impact of extreme sea conditions, the double-peak spectral density function S′(ω)=S under the influence of mixed factors is obtained. l ′(ω)+S h ′(ω); At the same time, in order to improve the calculation efficiency, the bimodal spectrum resolution is reduced in the deep sea area which is less affected by the terrain. th The double-peak spectrum is simplified, only the main spectrum features and main peaks are retained, and the spectrum is simplified by the resolution adjustment factor R(ω). The greater the water depth, the stronger the effect of R(ω). The corrected wave spectrum density S″(ω)=S′(ω)×R(ω); Custom input wind speed V in semi-physical simulation platform wind , water depth, ship feature array (length, broad, D, zg), after calculation, the output will be the effective wave height h wave , wave period T wave , wave speed V wave , taking waves as ship disturbances; The waves in multiple directions are superimposed as vectors, and multiple wave spectra produce superimposed effects on ship disturbances; The influence of ocean and wave data on ship disturbance is fitted by using wave data, ship data and storm data, and the comprehensive angle changes under the superposition of several attenuated sinusoidal waves are generated.

5. The method for semi-physical simulation of satellite communication in motion carried by a satellite ship according to claim 4, characterized in that: The process of implementing vector superposition of waves in multiple directions is as follows: The input m wave power spectra are synthesized: Where k is the wave number, e -αt represents the attenuation factor of the waves, α is the damping coefficient of the waves, which determines the speed of the swing attenuation and is related to the ship's characteristic array (length, broad, D, zg) and the initial amplitude of the waves; under the influence of the waves, the ship performs a quasi-sinusoidal motion with the same period, and the amplitude Inversely proportional to the swing period.

6. The method for semi-physical simulation of satellite communication in motion carried by a satellite ship according to claim 1, characterized in that: The implementation process of step (3) is as follows: Customize the simulation start value gry for the gyroscope and accelerometer start ,acc start , acquisition accuracy prec gry 、prec acc , operating frequency f gry 、f acc , bias stability gry 、bias acc ,random walk rand gry 、rand acc , the sensor model is used to calculate and output the array of the three-axis data of the inertial navigation device that changes with time (t, gry x , gry y , gry z )、(t,acc x ,acc y ,acc z ); Combine physical inertial navigation data to conduct semi-physical simulation and import measured data; Import the working data and the device data of the inertial navigation system that has been stationary for a period of time before the antenna works. Use the Allan variance model to fit the sensor noise characteristics, output the Allan variance curve, and read the bias stability of the sensor based on the variance curve. gry 、bias acc ,random walk rand gry 、rand acc , complete the construction of sensor error model and drift model.

7. A satellite ship-borne semi-physical simulation method for satellite communication in motion according to claim 6, characterized in that: The process of importing measured data is as follows: The inertial navigation module installed at the bottom of the antenna when the antenna is running obtains three-axis data in real time, and transmits the data to the semi-physical simulation platform for calculation in real time through the RS232 serial port; Alternatively, save the data table file during operation and import the measured data table into the semi-physical simulation platform for calculation.

8. The method for semi-physical simulation of satellite communication in motion carried by a satellite ship according to claim 1, characterized in that: The implementation process of step (4) is as follows: Synchronize multiple sensor data with physical data and convert them into data arrays of the same dimension. According to the simulation time and accuracy, further extrapolate or interpolate the data to output an array that meets the simulation dimension requirements. 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) 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 by the Allen variance model are used to make real-time corrections to the interpolation coefficients, and some data are 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.

9. A satellite ship-borne semi-physical simulation system for in-motion communication with satellites using the method according to any one of claims 1 to 8, characterized in that: It includes physical model, sensor model, algorithm model and motor control model; among which: Physical model: including satellite trajectory model and ship attitude model; the satellite trajectory model simulates the real motion trajectory of the satellite, which serves as the satellite position input information for the tracking process; the ship attitude model simulates the real-time changes of ocean waves, which produces a periodic effect of approximately sinusoidal motion on the ship attitude angle; Sensor model: Generates the data output of each sensor of the inertial navigation module in the mobile communication, builds 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: Calculate the star alignment algorithm, output the target angles of the two axes, and send them to the motor control model; Motor control model: Use closed-loop control to drive the antenna to the target angle, and return the output error angle to the physical model as input for the next cycle of calculation.

10. The satellite ship-borne semi-physical simulation system for satellite communication in motion according to claim 9, characterized in that: The sensor model includes a gyroscope, an accelerometer, a GNSS model, a receiving end signal strength model, an ephemeris model and an axis angle model.

Images