Ku frequency band communication-in-moving satellite antenna beam control method and system

Abstract

The invention relates to the field of radio transmission, and discloses a Ku-frequency-band communication-in-moving satellite antenna beam control method and a Ku-frequency-band communication-in-moving satellite antenna beam control system. The method comprises the following steps: acquiring a carrier attitude angle and a satellite orbit parameter, calculating an initial beam vector, and establishing a nonlinear coupling model to generate an inclination angle candidate set; the inclination angle set is optimized, the earth curvature and the atmospheric refraction model are fused, and antenna array element amplitude difference distribution is obtained; fusing the power feedback update dip angle set to determine an optimized dip angle, generating a final beam adjustment parameter, and evaluating a link state; extracting an azimuth angle and a pitch angle, calculating an adjustment value, and generating polarization control information and an instruction sequence to update a beam vector; fitting synchronous deviation correction amplitude distribution, and optimizing a nonlinear coupling model to determine a final polarization parameter; and fusing power feedback data and polarization parameters to calculate a fluctuation rate, and comprehensively evaluating a beam stability state. According to the invention, through multi-model fusion and dynamic optimization, the problems of beam pointing lag and link instability are solved, and the stability and accuracy of communication are improved.

Description

Technical Field

[0001] This application relates to the field of radio transmission, and in particular to a beam control method and system for a Ku-band mobile satellite antenna. Background Technology

[0002] With the growth of global communication demands, especially in high-speed mobile environments and weak signal scenarios, maintaining the continuity and reliability of communication links has become a key challenge. The Ku band, due to its moderate bandwidth and high transmission rate, is widely used in satellite communications. However, signals in this band are susceptible to the effects of Earth's curvature, atmospheric refraction, and dramatic changes in the carrier's attitude. Precise beam control is required to ensure communication stability under weak signal links, placing high demands on real-time beam adjustment technology in mobile communication scenarios.

[0003] Existing Ku-band satellite communication on the move beam control methods have significant drawbacks: traditional solutions often rely on static or semi-static beam control logic, failing to adequately consider complex interference factors in highly dynamic environments. On one hand, ignoring the impact of Earth's curvature on beam pointing deviation and assuming the Earth's surface as a plane for beam calculation leads to decreased pointing accuracy for low-Earth orbit satellites or long-distance communications. On the other hand, using fixed parameters to correct atmospheric refraction deviations cannot adapt to dynamic changes in atmospheric density and temperature gradients in real time, easily causing signal power attenuation or link interruption. Furthermore, facing rapid changes in carrier attitude angles (such as UAV turbulence tilting or low-Earth orbit satellite orbit switching), existing technologies lack efficient coupling mechanisms, making it difficult to quickly respond to sudden attitude changes, resulting in beam pointing lag, further reducing communication reliability, and failing to meet the precise control requirements in highly dynamic scenarios.

[0004] To address the aforementioned issues, this invention constructs a nonlinear coupling model by real-time acquisition of attitude angle data and satellite orbital parameters. Combining algorithms such as particle swarm optimization and Kalman filtering, it dynamically corrects the Earth's curvature and atmospheric refraction deviations, and synchronously adjusts the azimuth, pitch, and polarization modes to form a closed-loop optimized beam control logic. This solves the stability, accuracy, and responsiveness deficiencies of existing technologies. Summary of the Invention

[0005] This application proposes a beam control method and system for Ku-band mobile satellite antennas, which is applicable to beam control under weak signal links in high-dynamic communication scenarios. It can solve the problems of existing static / semi-static beam control that are difficult to adapt to drastic changes in carrier attitude, ignore the influence of Earth's curvature, and cause low beam pointing accuracy and unstable communication links due to the inability of fixed parameters to correct atmospheric refraction deviations in real time.

[0006] In a first aspect, this application provides a beam control method for a Ku-band mobile satellite antenna, the method comprising: Step S101: Obtain real-time attitude angle data of the carrier and satellite orbit parameters, and calculate the initial beam pointing vector; use the attitude change threshold to monitor the rate of change of the initial beam pointing vector, then establish a preliminary nonlinear coupling model and generate a set of candidate tilt angle values; Step S102: Optimize the candidate tilt angle values ​​using the particle swarm optimization algorithm, integrate the Earth curvature influence model and the atmospheric refraction deviation correction model, calculate the amplitude distribution data of the antenna array; fit the geometric relationship of the antenna array, calculate the homogenization amplitude compensation coefficient, and obtain the amplitude difference distribution of the antenna array elements after adjusting the amplitude of the antenna array elements. Step S103: Collect signal power feedback data, fuse the homogenization amplitude compensation coefficient to update the tilt angle candidate value set, and determine the optimized tilt angle setting value; extract attitude change trend prediction data based on the optimized tilt angle setting value, generate the final beam adjustment parameters, and evaluate the real-time communication link status; Step S104: Extract the azimuth and elevation data from the final beam adjustment parameters, calculate the dynamic adjustment value of the azimuth and the compensation value of the elevation, generate polarization mode switching control information, construct a real-time sequence of adjustment commands, and update the initial beam pointing vector. Step S105: Fit the synchronization deviation between the updated beam pointing vector and the satellite orbit parameters to correct the amplitude difference distribution; fuse atmospheric refraction deviation correction data to optimize the nonlinear coupling model and determine the final polarization control parameters; Step S106: Collect signal power feedback data, fuse the signal power feedback data with the final polarization control parameters, and calculate the signal power fluctuation rate; comprehensively evaluate the beam stability state through signal power feedback fusion and fluctuation rate threshold judgment.

[0007] Optionally, step S101 includes: The attitude angle data of the carrier is collected in real time by sensors, satellite orbit parameters are obtained from the satellite navigation system, and the initial beam pointing vector is calculated. Determine whether the rate of change of the initial beam pointing vector exceeds a preset attitude change threshold. If so, use the least squares method to fit the deviation between the attitude angle data and the satellite orbit parameters to generate a preliminary nonlinear coupling model. Feature vectors are extracted from the preliminary nonlinear coupling model, and the initial dip angle is adjusted based on the feature vectors to generate a set of dip angle candidate values ​​containing multiple candidate dip angle values.

[0008] Optionally, step S102 includes: The particle swarm optimization algorithm is used to iteratively calculate the set of candidate tilt angle values. During the iterative calculation process, the Earth curvature influence model is integrated, and atmospheric refraction deviation is corrected to obtain the amplitude distribution data of the antenna array. Determine whether the amplitude difference in the amplitude distribution data exceeds a preset first difference threshold. If so, fit the geometric relationship of the antenna array using the least squares method. Based on the fitted geometric relationship of the antenna array, the homogenization amplitude compensation coefficient is calculated; The amplitude of the antenna array elements is adjusted according to the homogenization amplitude compensation coefficient to obtain the amplitude difference distribution of the antenna array elements.

[0009] Optionally, in step S103, determining the optimized tilt angle setting includes: Collect signal power feedback data, and fuse the signal power feedback data with the homogenization amplitude compensation coefficient to generate signal power feedback fused data; Based on the signal power feedback fusion data, the deviation in the preliminary nonlinear coupling model is calculated; The deviation is iteratively optimized using a particle swarm optimization algorithm to update the set of candidate tilt angle values; the signal power fluctuation rate corresponding to each candidate tilt angle value in the updated set of candidate tilt angle values ​​is calculated. The signal power volatility corresponding to each candidate tilt angle value is compared with a preset first volatility threshold, and the candidate tilt angle value with the signal power volatility lower than the first volatility threshold is selected as the optimized tilt angle setting value.

[0010] Optionally, in step S103, generating the final beam adjustment parameters and evaluating the real-time communication link status includes: Based on the optimized tilt angle setting, attitude change trend prediction data are extracted; Collect the signal power feedback data corresponding to the optimized tilt angle setting value, and calculate the signal power fluctuation rate of the signal power feedback data; The attitude change trend prediction data is fused with the corresponding signal power fluctuation data to form a comprehensive dataset; The signal power fluctuation rate in the comprehensive dataset is compared with a preset second fluctuation threshold to determine whether the former is lower than the latter. If so, the Kalman filter algorithm is used to fuse the attitude change trend prediction data with the optimized tilt angle setting value to generate the final beam adjustment parameters. If not, the process returns to step S103 to redetermine the new optimized tilt angle setting value. Based on the final beam adjustment parameters, the antenna array elements are controlled to adjust the beam direction, and the adjusted communication link signal data is collected to evaluate the real-time communication link status.

[0011] Optionally, step S104 includes: The fast response triggering logic is triggered by the attitude change threshold monitoring to start the analysis process of the final beam adjustment parameters; Extract azimuth and elevation data from the final beam adjustment parameters, and calculate the dynamic azimuth adjustment value and elevation compensation value. Based on the azimuth angle dynamic adjustment value and the elevation angle compensation value, combined with the amplitude difference distribution of the antenna array elements, polarization mode switching control information is generated; Collect real-time communication link status data, calculate the volatility of the real-time communication link status, determine whether it exceeds a preset third volatility threshold, and if so, generate a real-time sequence of adjustment instructions based on the polarization mode switching control information. According to the real-time sequence of adjustment instructions, azimuth adjustment, elevation compensation and polarization mode switching operations are executed sequentially to update the initial beam pointing vector.

[0012] Optionally, step S105 includes: The updated initial beam pointing vector is fitted to the satellite orbital parameters using the least squares method, and the synchronization deviation between the two is calculated to generate a deviation correction model. The amplitude difference distribution of the antenna array elements is updated based on the deviation correction model to obtain the updated amplitude difference distribution of the antenna array elements. The corrected atmospheric refraction deviation data is fused with the updated antenna array element amplitude difference distribution to optimize the parameters of the preliminary nonlinear coupling model, resulting in an optimized nonlinear coupling model. Based on the optimized nonlinear coupling model, the signal power fluctuation rate and amplitude uniformity under different polarization modes are predicted. The polarization mode is selected where the predicted signal power fluctuation rate is lower than the preset third fluctuation threshold and the amplitude uniformity is higher than the preset uniformity threshold, and the final polarization mode control parameters are determined.

[0013] Optionally, step S106 includes: Real-time signal power feedback data is collected, and the signal power feedback data is fused with the final polarization control parameters. The signal power fluctuation rate is calculated based on the fused data. Determine whether the signal power fluctuation rate of the fused data exceeds the preset fourth fluctuation rate threshold. If so, use the particle swarm optimization algorithm, combine the final polarization mode control parameters with the Earth curvature influence to integrate the data, and iteratively adjust the details of the dynamic adjustment value of the azimuth angle calculated in step S104 until the signal power fluctuation rate of the fused data is lower than the preset fourth fluctuation rate threshold. By combining the signal power feedback fusion result and the fourth volatility threshold judgment result, the updated beam pointing vector and the corrected antenna element amplitude difference distribution are further correlated to comprehensively evaluate the beam stability.

[0014] Secondly, this application provides a Ku-band on-the-move satellite antenna beam control system, the system comprising: The tilt angle candidate value acquisition module is used to acquire real-time attitude angle data of the carrier and satellite orbital parameters, calculate the initial beam pointing vector, monitor the rate of change of attitude change threshold, establish a preliminary nonlinear coupling model, and generate a set of tilt angle candidate values. The amplitude difference acquisition module is used to optimize the set of candidate tilt angle values ​​using the particle swarm optimization algorithm, integrate the influence of Earth curvature and the atmospheric refraction deviation correction model, calculate the amplitude distribution data of the antenna array, fit the geometric relationship, calculate the homogenization amplitude compensation coefficient, and obtain the amplitude difference distribution after adjusting the array element amplitude. The beam adjustment parameter acquisition module is used to collect signal power feedback data, fuse the homogenization amplitude compensation coefficient to update the tilt angle candidate value set, and determine the optimized tilt angle setting value; based on this value, it extracts attitude change trend prediction data, generates the final beam adjustment parameters, and evaluates the real-time communication link status; The real-time command sequence generation module is used to extract azimuth and elevation data from the final beam adjustment parameters, calculate the dynamic adjustment value of azimuth and the compensation value of elevation, generate polarization mode switching control information, construct the real-time sequence of adjustment commands, and update the initial beam pointing vector. The final polarization control parameter generation module is used to fit the synchronization deviation between the updated beam pointing vector and the satellite orbit parameters, correct the amplitude difference distribution, fuse atmospheric refraction deviation correction data, optimize the nonlinear coupling model, and determine the final polarization control parameters. The beam stability assessment module is used to collect signal power feedback data, fuse it with the final polarization control parameters, and calculate the signal power fluctuation rate. Through signal power feedback fusion and fluctuation threshold judgment, the beam stability is comprehensively assessed.

[0015] This application proposes a beam control method and system for a Ku-band satellite antenna on the move, applicable to beam control under weak signal links in high-dynamic communication scenarios. It solves problems in existing technologies such as the difficulty of adapting to drastic changes in carrier attitude, neglecting the influence of Earth's curvature, and the inability of fixed parameters to correct atmospheric refraction deviations in real time, resulting in low beam pointing accuracy and unstable communication links. Compared with existing technologies, the beneficial effects of this application's technical solution are at least as follows: First, by collecting attitude angle data in real time through sensors and fusing it with satellite orbit parameter synchronization information, and combining it with attitude change threshold monitoring to trigger nonlinear coupling relationship modeling, it can dynamically capture rapid changes in the carrier's attitude, solving the beam pointing lag problem caused by the lack of an efficient coupling modeling mechanism in existing technologies.

[0016] Secondly, the particle swarm optimization algorithm is used to iteratively calculate the candidate tilt angle values, and the atmospheric refraction deviation is corrected by integrating the Earth curvature influence model. At the same time, the homogenization amplitude compensation coefficient is obtained by fitting the antenna array geometry through the least squares method. This not only considers the influence of Earth curvature on beam pointing deviation, but also realizes real-time dynamic correction of atmospheric refraction deviation. This solves the problems of decreased pointing accuracy and signal power attenuation caused by the traditional scheme assuming the Earth is flat and using fixed parameters to correct atmospheric refraction, thus improving the stability of antenna array signal transmission.

[0017] Third, the tilt candidate value set is iteratively updated by fusing signal power feedback data, and the final beam adjustment parameters are generated by fusing attitude change trend prediction data with Kalman filtering algorithm. At the same time, the azimuth, pitch and polarization are adjusted based on the real-time communication link status triggering fast response logic, forming a closed-loop optimized beam control logic. This solves the problem of easy communication link interruption caused by the inability of existing technology to dynamically optimize beam parameters, ensuring the continuity and reliability of weak signal links in highly dynamic environments, and meeting the communication stability requirements of key tasks such as navigation, remote sensing and emergency communication.

[0018] Fourth, by fitting the initial beam pointing vector and the satellite orbit parameter synchronization deviation using the least squares method, the amplitude difference distribution of the antenna array elements is updated and the final polarization control parameters are obtained, further optimizing the nonlinear coupling relationship model. At the same time, the beam stability state is determined by combining signal power feedback fusion and volatility threshold judgment, which solves the problems of incomplete beam parameter optimization and inaccurate stability state judgment in the existing technology. Attached Figure Description

[0019] To more clearly illustrate the technical solutions of the embodiments of this application, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0020] Figure 1 This is a flowchart illustrating a beam control method for a Ku-band mobile satellite antenna according to this application. Figure 2 This is a flowchart illustrating the process of determining the optimal tilt angle setting value during beam control of a Ku-band mobile satellite antenna in this application. Figure 3 This is a performance verification diagram of a Ku-band on-the-move satellite antenna beam control method according to this application; Figure 4 This is a structural diagram of a Ku-band mobile satellite antenna beam control system according to this application. Detailed Implementation

[0021] This application provides a Ku-band on-the-move satellite antenna beam control method and system. The terms "first," "second," "third," "fourth," etc. (if present) in the specification, claims, and accompanying drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments described herein can be implemented in a sequence other than that illustrated or described herein. Furthermore, the terms "comprising" or "having" and any variations thereof are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or device that includes a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or devices.

[0022] For ease of understanding, the specific process of the embodiments of this application is described below. Please refer to [link / reference]. Figure 1 One embodiment of a Ku-band on-the-move satellite antenna beam control method in this application includes: Step S101: Obtain real-time attitude angle data of the carrier and satellite orbit parameters, and calculate the initial beam pointing vector; use the attitude change threshold to monitor the rate of change of the initial beam pointing vector, then establish a preliminary nonlinear coupling model and generate a set of candidate tilt angle values.

[0023] In one specific embodiment, step S101 may specifically include the following steps: The attitude angle data of the carrier is collected in real time by sensors, satellite orbit parameters are obtained from the satellite navigation system, and the initial beam pointing vector is calculated. Determine whether the rate of change of the initial beam pointing vector exceeds a preset attitude change threshold. If so, use the least squares method to fit the deviation between the attitude angle data and the satellite orbit parameters to generate a preliminary nonlinear coupling model. Feature vectors are extracted from the preliminary nonlinear coupling model, and the initial dip angle is adjusted based on the feature vectors to generate a set of dip angle candidate values ​​containing multiple candidate dip angle values.

[0024] Specifically, attitude angle data, including roll, pitch, and yaw angles, is collected by sensors such as gyroscopes and accelerometers mounted on the carrier, at a frequency of 10 times per second. Satellite orbit parameters, including satellite position, velocity, and time synchronization information, are obtained from satellite navigation systems (such as GPS and BeiDou) at a frequency of once per second. The attitude angle data is converted into a direction vector in the carrier coordinate system and added to the satellite orbit parameters to obtain the initial beam pointing vector, which represents the initial direction of the antenna pointing at the satellite. The difference between the initial beam pointing vectors at two consecutive time points is calculated, and the difference is divided by the time interval (e.g., 0.1 seconds) to obtain the rate of change of the initial beam pointing vector. A preset attitude change threshold is set at 3 degrees per second for low-Earth orbit satellite scenarios and 7 degrees per second for high-Earth orbit satellite scenarios. The calculated rate of change is compared with the preset attitude change threshold. If the rate of change does not exceed the attitude change threshold, the initial beam pointing vector is maintained; if it exceeds the threshold, the nonlinear coupling model construction process is triggered.

[0025] Using attitude angle data as the independent variable and satellite orbital parameter deviation as the dependent variable, a design matrix A and an observation vector B are constructed. Design matrix A contains attitude angle polynomial terms, and observation vector B represents the measured values ​​of satellite orbital parameter deviations. This is achieved through the formula... Solving for the fitted parameters ,in For the polynomial coefficients. Based on the fitted parameters, a preliminary nonlinear coupling model is constructed, and the model form can be set as follows: ,in, For satellite orbital parameter deviation, For roll angle, For pitch angle, Yaw angle a , b , c , d These are the fitting parameters. This model is used to describe the nonlinear relationship between attitude angles and satellite orbital parameters, solving the problem that traditional linear models cannot capture parameter coupling relationships under drastic attitude changes, and avoiding beam pointing deviations caused by insufficient model adaptability.

[0026] Extracting fitting parameters from the initial nonlinear coupling model a , b , c , d This forms an eigenvector. Using the initial dip angle (e.g., 10 degrees) as a baseline, the initial dip angle is adjusted according to the weights of each parameter in the eigenvector. aA positive and large value indicates that the roll angle has a significant impact on orbital parameter deviations, requiring a corresponding increase in the inclination adjustment range. This necessitates generating a set of multiple candidate inclination angle values, such as {8 degrees, 10 degrees, 12 degrees} for low-Earth orbit satellites and {15 degrees, 18 degrees, 21 degrees} for high-Earth orbit satellites. This set provides multiple inclination angle alternatives for subsequent processing of Earth's curvature and atmospheric refraction deviations, addressing the problem that a single inclination angle value is insufficient to adapt to dynamic environmental changes and reducing the risk of signal power attenuation or link interruption due to a fixed inclination angle.

[0027] Step S102: Optimize the candidate tilt angle values ​​using the particle swarm optimization algorithm, integrate the Earth curvature influence model and the atmospheric refraction deviation correction model, calculate the amplitude distribution data of the antenna array; fit the geometric relationship of the antenna array, calculate the homogenization amplitude compensation coefficient, and obtain the amplitude difference distribution of the antenna array elements after adjusting the amplitude of the antenna array elements.

[0028] In one specific embodiment, step S102, determining the dynamic changes in bacterial community structure, may specifically include the following steps: The particle swarm optimization algorithm is used to iteratively calculate the set of candidate tilt angle values. During the iterative calculation process, the Earth curvature influence model is integrated, and atmospheric refraction deviation is corrected to obtain the amplitude distribution data of the antenna array. Determine whether the amplitude difference in the amplitude distribution data exceeds a preset first difference threshold. If so, fit the geometric relationship of the antenna array using the least squares method. Based on the fitted geometric relationship of the antenna array, the homogenization amplitude compensation coefficient is calculated; The amplitude of the antenna array elements is adjusted according to the homogenization amplitude compensation coefficient to obtain the amplitude difference distribution of the antenna array elements.

[0029] Specifically, the generated set of candidate tilt angle values ​​is used as the initial particle swarm for the particle swarm optimization algorithm. The algorithm parameters are set as follows: population size is set to 30, maximum number of iterations is set to 50, initial inertia weight is set to 0.9, and it decreases linearly to 0.4 during iteration. The learning factor is... c 1 and c All values ​​are set to 2. During iterative calculations, each candidate tilt angle value is used as the particle position, and the fitness function is to maximize the received signal power of the antenna array. In each iteration, the particle updates its velocity and position based on its individual optimal position and the global optimal position. The velocity update formula is as follows: ,in For the first i Particles t Velocity at +1 moment For inertial weights, and A random number in the range [0,1]. This represents the optimal position for an individual particle. To be the globally optimal position For the first i Particles t Time position, position update formula set as .

[0030] The iteration process incorporates a model of the influence of Earth's curvature. The model inputs are the latitude of the carrier (e.g., 30°N), altitude (e.g., 10km), and satellite orbital altitude (500km for low-Earth orbit satellites and 35786km for high-Earth orbit satellites). This is achieved through formulas... Calculate the deviation angle, where, The beam pointing deviation angle is caused by the curvature of the Earth. R The Earth's radius is 6371 km. h The altitude of the carrier is used as the reference point. The atmospheric refraction deviation correction model is then simultaneously input, based on real-time collected atmospheric temperature (e.g., 25℃), humidity (e.g., 60%), and air pressure (e.g., 101kPa) data, using the formula... Calculate the correction value, where δ This is the atmospheric refraction deviation value. k For correction factor, N Atmospheric refractive index , T Absolute temperature P For air pressure, e The water vapor pressure is used. The deviation angle and the correction value are superimposed on the particle position update process. After each iteration, the antenna array amplitude distribution data at the corresponding tilt angle is output. This data contains the amplitude value of each array element (such as discrete values ​​in the range of -3dB to 3dB), which solves the problem of reduced pointing accuracy caused by ignoring the curvature of the earth and using fixed parameters to correct atmospheric refraction in traditional schemes.

[0031] A first difference threshold is preset, set to 2dB for low-Earth orbit satellite scenarios and 1.5dB for high-Earth orbit satellite scenarios. The difference between the maximum and minimum amplitudes in the amplitude distribution data is calculated and compared with the first difference threshold. If the difference exceeds the threshold, the antenna array element coordinates (assuming the elements are linearly arranged with a spacing of 1 / 2 of the Ku-band wavelength, such as 7.5mm, with coordinates (0,0), (7.5mm,0), (15mm,0)...) are constructed as the independent variable, and the amplitude distribution data is the dependent variable. A design matrix C and an observation vector D are constructed. The design matrix C contains first-order and second-order terms of the element coordinates, and the least squares formula is used. Solving for the fitting coefficients Based on the fitting coefficients, a geometric relationship model of the antenna array is constructed, such as... , F Let z be the amplitude, and z be the x-coordinate of the array element. , and Let be the fitting coefficient. Based on the fitted geometric relationship model, calculate the homogenization amplitude compensation coefficient, and set the formula as follows: ,in, For the first i The compensation coefficient of each array element, This represents the average value of the amplitude distribution data. For the first i The original amplitude of each array element is calculated. The original amplitude of each array element is multiplied by the corresponding compensation coefficient to obtain the adjusted array element amplitude. The difference between the amplitudes of each array element after adjustment is calculated to form the distribution of antenna array element amplitude differences (e.g., the difference after adjustment is concentrated in the range of 0.5dB to 1dB). This solves the problem of signal power attenuation caused by excessive amplitude differences and provides a uniform amplitude basis for subsequent beam stabilization control.

[0032] Step S103: Collect signal power feedback data, fuse the homogenization amplitude compensation coefficient to update the tilt angle candidate value set, and determine the optimized tilt angle setting value; extract attitude change trend prediction data based on the optimized tilt angle setting value, generate the final beam adjustment parameters, and evaluate the real-time communication link status.

[0033] In one specific embodiment, step S103, determining the optimized tilt angle setting value, may specifically include the following steps: Collect signal power feedback data, and fuse the signal power feedback data with the homogenization amplitude compensation coefficient to generate signal power feedback fused data; Based on the signal power feedback fusion data, the deviation in the preliminary nonlinear coupling model is calculated; The deviation is iteratively optimized using a particle swarm optimization algorithm to update the set of candidate tilt angle values; the signal power fluctuation rate corresponding to each candidate tilt angle value in the updated set of candidate tilt angle values ​​is calculated. The signal power volatility corresponding to each candidate tilt angle value is compared with a preset first volatility threshold, and the candidate tilt angle value with the signal power volatility lower than the first volatility threshold is selected as the optimized tilt angle setting value.

[0034] Specifically, please refer to Figure 2 Signal power feedback data is collected via a power sensor at the antenna array receiver. The collection frequency is consistent with the attitude angle data collection frequency in step S101, set to 10 times per second, with a data range of -85dBm to -30dBm. The signal power feedback data is then fused with the homogenization amplitude compensation coefficient calculated in step S102 using a weighted summation method, with the formula set as follows: ,in, H For signal power feedback fusion data, This is the weighting coefficient, with a value of 0.6. For signal power feedback data, To homogenize the amplitude compensation coefficients, each array element corresponds to a set of fused data, forming a signal power feedback fused dataset. Based on the signal power feedback fused data, the deviation in the preliminary nonlinear coupling model is calculated. The deviation calculation is based on the difference between the model prediction value and the fused data, and the formula is set as follows: ,in, This is the deviation amount. The fused value of the predicted signal power output from the preliminary nonlinear coupling model. The actual calculated signal power is fed back to the fused data. The fused data of all array elements is traversed to obtain the set of deviations.

[0035] The particle swarm optimization algorithm is used to iteratively optimize the deviation. The algorithm parameters are consistent with those in step S102, wherein the population size is set to 30, the maximum number of iterations is set to 50, the initial value of the inertia weight is set to 0.9, and it decreases linearly to 0.4 during the iteration. The learning factor is... c 1 and c All values ​​are set to 2. During iterative calculations, each candidate tilt angle value is used as the particle position, and the fitness function is the minimization of the deviation. In each iteration, the particle updates its velocity and position based on its individual optimal position and the global optimal position. The velocity update formula is: ,in For the first i Particles t Velocity at +1 moment For inertial weights, and A random number in the range [0,1]. This represents the optimal position for an individual particle. To be the globally optimal position For the first i Particles t Time position, position update formula set as After each iteration, the candidate tilt values ​​are adjusted based on the optimized deviation, and the set of candidate tilt values ​​generated in step S101 is updated. For example, in the case of a low-Earth orbit satellite, the set can be updated to {7.8 degrees, 9.9 degrees, 12.1 degrees}.

[0036] Calculate the signal power volatility for each candidate tilt angle value in the updated candidate tilt angle value set. The volatility calculation is based on signal power feedback data from 10 consecutive acquisition cycles, and the formula is set as follows: ,in, For signal power fluctuation, For the first k Signal power feedback data for each acquisition cycle This is the average of 10 periods of data. The data collection period is 10, and each candidate tilt angle value corresponds to a volatility value, forming a volatility set.

[0037] A first volatility threshold is preset: 0.8 dB for low-Earth orbit (LEO) satellite scenarios and 0.5 dB for high-Earth orbit (HEO) satellite scenarios. The volatility corresponding to each candidate tilt angle value is compared with the first volatility threshold. Candidate tilt angle values ​​with volatility lower than the first volatility threshold are selected as the optimized tilt angle setting value. If multiple candidate values ​​meet the criteria, the candidate value with the highest average signal power is selected. For example, in the LEO satellite scenario, 78 degrees corresponds to a volatility of 0.7 dB, 9.9 degrees to 0.6 dB, and 12.1 degrees to 0.9 dB; therefore, 9.9 degrees is selected as the optimized tilt angle setting value. This process solves the problem of easy communication link interruption caused by the inability of traditional technologies to dynamically optimize beam parameters. Through real-time feedback and iterative optimization, it ensures stable signal power in highly dynamic environments, meeting the communication needs of weak signal links.

[0038] In one specific embodiment, step S103, generating the final beam adjustment parameters and evaluating the real-time communication link status, may specifically include the following steps: Based on the optimized tilt angle setting, attitude change trend prediction data are extracted; Collect the signal power feedback data corresponding to the optimized tilt angle setting value, and calculate the signal power fluctuation rate of the signal power feedback data; The attitude change trend prediction data is fused with the corresponding signal power fluctuation data to form a comprehensive dataset; The signal power fluctuation rate in the comprehensive dataset is compared with a preset second fluctuation threshold to determine whether the former is lower than the latter. If so, the Kalman filter algorithm is used to fuse the attitude change trend prediction data with the optimized tilt angle setting value to generate the final beam adjustment parameters. If not, the process returns to step S103 to redetermine the new optimized tilt angle setting value. Based on the final beam adjustment parameters, the antenna array elements are controlled to adjust the beam direction, and the adjusted communication link signal data is collected to evaluate the real-time communication link status.

[0039] Specifically, based on a defined optimized tilt angle setting, such as 9.9 degrees for low-Earth orbit satellites and 17.8 degrees for high-Earth orbit satellites, attitude angle data for 20 consecutive cycles (10 times per second) is collected using attitude sensors on the carrier. A linear regression algorithm is then used to extract attitude change trend prediction data, and the prediction formula is set as follows: ,in, To predict attitude angles, The trend slope To predict the time step, Using the intercept as the output, the predicted attitude angle values ​​for the next 5 cycles are generated to form an attitude change trend prediction dataset.

[0040] Collect signal power feedback data corresponding to the optimized tilt angle setpoint. The acquisition period is consistent with the attitude angle data. Collect power data for 15 consecutive periods (range -85dBm to -30dBm), and then calculate the power data according to the formula. Calculate the signal power fluctuation rate here. For volatility, For the first k Power data for each cycle, This is the average of 15 periods of data. The sampling period is set to 15, and each optimized tilt angle setting corresponds to one volatility data point. The attitude change trend prediction data is fused with the corresponding signal power volatility data. The fusion method is data concatenation, meaning that the attitude angle prediction value for each prediction time step corresponds one-to-one with the volatility data, forming a comprehensive dataset. For example, in a low-Earth orbit satellite scenario, the comprehensive dataset can be represented as follows: .

[0041] A second volatility threshold is preset: 0.6 dB for low-Earth orbit (LEO) satellites and 0.4 dB for high-Earth orbit (HEO) satellites. The signal power volatility in the integrated dataset is compared with this second volatility threshold. If the volatility is lower than the second volatility threshold, a Kalman filter algorithm is used to fuse the attitude change trend prediction data with the optimized tilt angle setting. During the filtering process, the state equation is set as follows: ,in for k The time-state vector contains the optimized tilt angle and the predicted attitude angle; E is the state transition matrix, which is set as the identity matrix; To represent process noise, the variance is set to 0.01; the observation equation is set to... ,in, The observation vector is the optimized tilt angle setpoint. Let [1,0] be the observation matrix. To minimize noise, the variance is set to 0.02. The filter gain is calculated using the Kalman filter recursive formula, the state estimate is updated, and the final beam adjustment parameters are generated. These parameters include azimuth, elevation, and polarization angles. For example, in a low-Earth orbit satellite scenario, the output azimuth is 30.2°, elevation is 25.6°, and polarization is 4.8°. If the volatility exceeds the second volatility threshold, the process returns to step S103 to determine a new optimized tilt angle setting and re-executes the signal power feedback data acquisition, deviation calculation, and iterative optimization process.

[0042] Based on the final beam adjustment parameters, the antenna control module drives the antenna elements to adjust the beam pointing, while the link monitoring module collects the adjusted communication link signal data, including the signal bit error rate (range). to The signal-to-noise ratio (5dB to 15dB) and signal strength (-80dBm to -35dBm) range, if the bit error rate is lower than If the signal-to-noise ratio (SNR) is higher than 8dB and the signal strength is higher than -70dBm, the real-time communication link is considered stable; otherwise, it is considered unstable, triggering a parameter fine-tuning command. This process solves the problem of insufficient communication reliability caused by the lack of closed-loop evaluation of link status in traditional technologies. Through dynamic feedback and filtering optimization, it ensures beam pointing accuracy and link stability in high-dynamic scenarios, adapting to the communication needs of scenarios such as aerospace and mobile platforms.

[0043] Step S104: Extract the azimuth and elevation data from the final beam adjustment parameters, calculate the dynamic adjustment value of the azimuth and the compensation value of the elevation, generate polarization mode switching control information, construct a real-time sequence of adjustment commands, and update the initial beam pointing vector.

[0044] In one specific embodiment, step S104 may specifically include the following steps: The fast response triggering logic is triggered by the attitude change threshold monitoring to start the analysis process of the final beam adjustment parameters; Extract azimuth and elevation data from the final beam adjustment parameters, and calculate the dynamic azimuth adjustment value and elevation compensation value. Based on the azimuth angle dynamic adjustment value and the elevation angle compensation value, combined with the amplitude difference distribution of the antenna array elements, polarization mode switching control information is generated; Collect real-time communication link status data, calculate the volatility of the real-time communication link status, determine whether it exceeds a preset third volatility threshold, and if so, generate a real-time sequence of adjustment instructions based on the polarization mode switching control information. According to the real-time sequence of adjustment instructions, azimuth adjustment, elevation compensation and polarization mode switching operations are executed sequentially to update the initial beam pointing vector.

[0045] Specifically, the preset attitude change threshold (3 degrees per second for low-Earth orbit satellites and 7 degrees per second for high-Earth orbit satellites) in step S101 is invoked. The attitude monitoring module compares the current carrier attitude angle change rate with the attitude change threshold in real time. If the change rate reaches the attitude change threshold, the fast response trigger logic is triggered. This logic starts the parameter parsing module to parse the final beam adjustment parameters generated in step S103. During the parsing process, azimuth angle data (e.g., 30.2° for low-Earth orbit satellites and 45.6° for high-Earth orbit satellites) and pitch angle data (e.g., 25.6° for low-Earth orbit satellites and 32.8° for high-Earth orbit satellites) are extracted according to the parameter identifier. The polarization angle data is discarded and temporarily stored in the cache unit.

[0046] The formula for calculating the dynamic adjustment value of the azimuth angle is set as follows: ,in This is the dynamic adjustment value for the azimuth angle. This refers to the azimuth data in the final beam adjustment parameters. For the azimuth component in the initial beam pointing vector calculated in step S101, such as an initial azimuth component of 28.5° and a final azimuth data of 30.2°, then... ; Calculate the pitch angle compensation value, the formula is set as follows ,in This is the pitch angle compensation value. The elevation angle data is used in the final beam adjustment parameters. The elevation component in the initial beam pointing vector. The deviation angle calculated by the Earth curvature influence model in step S102 is, for example, if the initial pitch angle component is 23.8°, the final pitch angle data is 25.6°, and the deviation angle is 0.5°, then... =2.3°. The amplitude difference distribution of the antenna array elements generated in step S102 (e.g., the adjusted difference is concentrated in the 0.5dB to 1dB range) is retrieved. Using the azimuth dynamic adjustment value and elevation compensation value as input, polarization mode switching control information is generated through the polarization mode mapping model. The model rule is: when... The absolute value is ≤2° and When the absolute value is ≤2°, the polarization mode remains linear polarization (horizontal / vertical); when The absolute value is >2° or When the absolute value is greater than 2°, switch to circular polarization (left-handed / right-handed), and mark the polarization switching timing (e.g., switching delay ≤ 50ms) to form control information containing polarization type and switching time.

[0047] Real-time communication link status data is collected through the link monitoring module. The data includes the signal error rate and signal-to-noise ratio for 10 consecutive cycles, calculated according to the formula... Calculate the link state volatility, where, For link volatility, Let be the bit error rate in the k-th cycle. This is the average bit error rate. The signal-to-noise ratio for the k-th cycle is... This represents the average signal-to-noise ratio. The sampling period is 10. A third volatility threshold is preset: 0.15 for low-Earth orbit satellites and 0.1 for high-Earth orbit satellites. If the threshold is exceeded, the polarization mode switching control information, azimuth dynamic adjustment value, and pitch compensation value will be sorted in time sequence to generate a real-time sequence of adjustment commands. The sequence format is "[azimuth adjustment command ( =1.7°, time → Pitch angle compensation command ( =2.3°, time → Polarization switching command (circular polarization left-hand rotation, time) )]",in - =30ms - =20ms.

[0048] The antenna drive module executes operations sequentially according to the real-time sequence of adjustment commands: The azimuth motor is constantly driven to rotate. This adjusts the antenna azimuth angle from its initial value to the target value. The pitch motor is constantly driven to rotate. To compensate for pitch deviations caused by changes in the Earth's curvature and attitude; The polarization switching switch is triggered at all times to complete the polarization mode switch. After the operation is executed, the initial beam pointing vector is recalculated, and the new vector components are "adjusted azimuth = initial azimuth + " Adjusted pitch angle = initial pitch angle + The process involves setting the polarization mode to the type after switching to update the initial beam pointing vector. This addresses the issues of delayed beam adjustment response and untimely parameter updates in traditional technologies. By using rapid response logic and time-sequential commands, it ensures that the beam pointing adapts in real time to changes in the relative positions of the carrier and satellite in highly dynamic scenarios, thereby improving the stability of the communication link.

[0049] Step S105: Fit the synchronization deviation between the updated beam pointing vector and the satellite orbit parameters to correct the amplitude difference distribution; fuse atmospheric refraction deviation correction data to optimize the nonlinear coupling model and determine the final polarization control parameters.

[0050] In one specific embodiment, step S105 may specifically include the following steps: The updated initial beam pointing vector is fitted to the satellite orbital parameters using the least squares method, and the synchronization deviation between the two is calculated to generate a deviation correction model. The amplitude difference distribution of the antenna array elements is updated based on the deviation correction model to obtain the updated amplitude difference distribution of the antenna array elements. The corrected atmospheric refraction deviation data is fused with the updated antenna array element amplitude difference distribution to optimize the parameters of the preliminary nonlinear coupling model, resulting in an optimized nonlinear coupling model. Based on the optimized nonlinear coupling model, the signal power fluctuation rate and amplitude uniformity under different polarization modes are predicted. The polarization mode is selected where the predicted signal power fluctuation rate is lower than the preset third fluctuation threshold and the amplitude uniformity is higher than the preset uniformity threshold, and the final polarization mode control parameters are determined.

[0051] Specifically, the updated initial beam pointing vector from step S104 is retrieved. This vector includes the adjusted azimuth angle (e.g., 28.5° + 1.7° = 30.2° for a low-Earth orbit satellite scenario), elevation angle (23.8° + 2.3° = 26.1°), and polarization mode (e.g., circular polarization left-handed). Simultaneously, real-time satellite orbit parameters (including satellite position coordinates) are obtained from the satellite navigation system. , , orbital angular velocity The updated beam pointing vector component (azimuth) is used. Pitch angle With as the dependent variable and satellite orbital parameters as the independent variables, construct a design matrix. With observation vector Design matrix Includes a first-order term for satellite position coordinates, a first-order term for orbital angular velocity, and an observation vector. The measured values ​​of the beam pointing vector components are obtained using the least squares method according to the formula. Solving for the fitted parameters ( (These are polynomial coefficients). A bias correction model is generated based on the fitted parameters, and the model form is set as follows: ( For synchronization deviation, , and (These are the fitting parameters), and the synchronization deviation values ​​are calculated (e.g., 0.3° for low-Earth orbit satellite scenarios and 0.15° for high-Earth orbit satellite scenarios). Based on the deviation correction model, the amplitude difference distribution of the antenna array elements generated in step S102 is updated. The update method is to proportionally distribute the synchronization deviation value to the amplitude difference of each array element. The formula is set as updated array element amplitude difference = original amplitude difference + allocation coefficient × synchronization deviation. For example, if the original amplitude difference is 0.8dB and the synchronization deviation is 0.3°, then the updated array element amplitude difference = 0.8dB + allocation coefficient × 0.3°, forming the updated antenna array element amplitude difference distribution (e.g., concentrated in the 0.8dB to 0.9dB range in the low-orbit satellite scenario). The corrected atmospheric refraction deviation data from step S102 is retrieved and fused with the updated antenna array element amplitude difference distribution. The fusion method is weighted summation. Using the fused data as input, the preliminary nonlinear coupling model constructed in step S101 is applied. Parameter optimization is performed by adjusting the model coefficients using the gradient descent algorithm. a , b , c, d This minimizes the mean square error between the model output and the fused data, resulting in an optimized nonlinear coupled model.

[0052] Based on the optimized nonlinear coupling model, the parameters corresponding to different polarization modes (horizontal linear polarization, vertical linear polarization, left-handed circular polarization, and right-handed circular polarization) are input to predict the signal power volatility and amplitude uniformity under each polarization mode (calculated using the formula amplitude uniformity = 1 - (maximum amplitude difference - minimum amplitude difference) / average amplitude difference), forming prediction datasets for four polarization modes. A third volatility threshold (0.7dB for LEO satellite scenarios and 0.4dB for GEO satellite scenarios) and a uniformity threshold (0.85 for LEO satellite scenarios and 0.9 for GEO satellite scenarios) are preset. The predicted signal power volatility for each polarization mode is compared with the third volatility threshold, and the predicted amplitude uniformity is compared with the uniformity threshold. The process selects a polarization mode that simultaneously satisfies the conditions of volatility below a threshold and uniformity above a threshold. If multiple polarization modes meet the criteria, the type with the highest average predicted signal power is chosen. For example, left-hand circular polarization corresponds to a volatility of 0.6 dB and a uniformity of 0.88 (in the case of low-Earth orbit satellites). This type is then determined as the final polarization mode, generating final polarization control parameters that include polarization type, switching timing, and control voltage (e.g., 5V for linear polarization switching and 12V for circular polarization switching). This process addresses the insufficient polarization control accuracy caused by traditional techniques neglecting synchronization deviations and lacking dynamic model optimization. Through multi-dimensional prediction and screening, it ensures that the polarization mode adapts to the real-time environment, further improving the stability of the communication link.

[0053] Step S106: Collect signal power feedback data, fuse the signal power feedback data with the final polarization control parameters, and calculate the signal power fluctuation rate; comprehensively evaluate the beam stability state through signal power feedback fusion and fluctuation rate threshold judgment.

[0054] In one specific embodiment, step S106 may specifically include the following steps: Real-time signal power feedback data is collected, and the signal power feedback data is fused with the final polarization control parameters. The signal power fluctuation rate is calculated based on the fused data. Determine whether the signal power fluctuation rate of the fused data exceeds the preset fourth fluctuation rate threshold. If so, use the particle swarm optimization algorithm, combine the final polarization mode control parameters with the Earth curvature influence to integrate the data, and iteratively adjust the details of the dynamic adjustment value of the azimuth angle calculated in step S104 until the signal power fluctuation rate of the fused data is lower than the preset fourth fluctuation rate threshold. By combining the signal power feedback fusion result and the fourth volatility threshold judgment result, the updated beam pointing vector and the corrected antenna element amplitude difference distribution are further correlated to comprehensively evaluate the beam stability.

[0055] Specifically, real-time signal power feedback data is collected through the power sensor at the antenna array receiver. The collection frequency is consistent with the previous attitude angle data collection frequency, set to 10 times per second, with a data range covering -85dBm to -30dBm. 20 consecutive cycles are collected to ensure data validity. The final polarization control parameters generated in step S105 are retrieved. These parameters include polarization type (e.g., circular polarization left-handed), switching timing (e.g., switching delay ≤50ms), and control voltage (e.g., 12V). The signal power feedback data is fused with these control parameters. The fusion method involves correcting the power data according to the signal attenuation coefficient corresponding to the polarization type (0.95 in the circular polarization left-handed scenario). That is, the power data of each cycle is multiplied by the attenuation coefficient to obtain the fused data. Signal power volatility is calculated based on the fused data. The calculation first takes the average of 20 periods of fused data, then calculates the deviation of each period's data from the average. The volatility is derived from the dispersion of this deviation (the calculated result may be 0.6dB to 0.8dB for low-Earth orbit (LEO) satellites and 0.3dB to 0.5dB for high-Earth orbit (HEO) satellites). A fourth volatility threshold is preset and dynamically set according to the scenario: 0.7dB for LEO satellites due to more frequent attitude changes, and 0.4dB for HEO satellites with relatively stable orbits. The calculated signal power volatility is compared with the fourth volatility threshold to determine if it exceeds the threshold range.

[0056] If the volatility exceeds the threshold, the particle swarm optimization algorithm is initiated to adjust the parameters. The algorithm uses the dynamically adjusted azimuth angle calculated in step S104 (e.g., 1.7° in a low-Earth orbit satellite scenario) as the optimization target. The initial population size is set to 30, the maximum number of iterations is set to 50, the inertia weight is linearly decreased from 0.9 to 0.4, and the learning factor is set to 2 for all values. During the optimization process, the final polarization mode control parameters are input and the data on the influence of Earth curvature in step S102 are integrated (e.g., a deviation angle of 0.5° in a low-Earth orbit satellite scenario). The fitness function is to minimize the volatility. In each iteration, the dynamically adjusted azimuth angle value is updated based on the individual optimal and global optimal positions until the volatility of the fused data drops below the threshold (e.g., the dynamically adjusted azimuth angle value in a low-Earth orbit satellite scenario may become 1.8°, and the volatility drops to 0.65dB).

[0057] If the volatility does not exceed the threshold, the beam stability assessment proceeds directly. During the assessment, the signal power feedback fusion result (e.g., average power -55dBm, volatility 0.6dB) is first correlated with the fourth volatility threshold judgment result (not exceeding the limit). Then, the updated beam pointing vector from step S104 (e.g., azimuth 30.3°, elevation 26.1°) and the corrected antenna element amplitude difference distribution from step S105 (e.g., 0.8dB to 0.86dB in a low-Earth orbit satellite scenario) are retrieved to form a multi-dimensional assessment dataset. The assessment criteria are set as follows: average power after fusion is higher than -70dBm, volatility is lower than the fourth threshold, and amplitude difference distribution range is less than 0.5dB. If all criteria are met, the beam is determined to be in a stable state; if any criterion is not met, the beam is determined to be unstable and a re-optimization instruction is triggered.

[0058] This process addresses the problem of beam stability misjudgment caused by the lack of real-time power feedback and multi-parameter linkage evaluation in traditional technologies. Through dynamic optimization and comprehensive evaluation, it ensures that the beam always adapts to the relative position of the carrier and the satellite in highly dynamic scenarios, avoiding communication link fluctuations caused by single parameter deviations, and further improving the communication reliability of Ku-band mobile satellite antennas.

[0059] Please see Figure 3 , Figure 3 This is a performance verification diagram of the beam control method for Ku-band mobile satellite antennas. Figure 3 (a) is a comparison of the signal power fluctuation rate changes of the Ku-band satellite antenna beam control method at different time points, showing the difference in power fluctuation between the two methods: the power fluctuation rate curve of the method in this patent is generally smoother, with an average fluctuation rate of only 0.604 dB, while the average fluctuation rate of the traditional method reaches 0.777 dB. The fluctuation rate of the method in this patent is reduced by 0.173 dB compared with the traditional method. This verifies that the method in this patent can effectively suppress signal power fluctuations through multi-model fusion and dynamic optimization, solve the power instability problem caused by static control logic in the traditional method, and improve the power stability of beam control. Figure 3 (b) is a visual comparison of the stable state ratio between the method of this patent and the traditional method. It shows that the stable state ratio of the method of this patent is as high as 86.0%, while that of the traditional method is only 55.6%, which is 30.4% higher. This verifies that the method of this patent can significantly extend the stable operation time of the beam through closed-loop optimization logic, solve the problem of low stable state ratio caused by ignoring dynamic interference factors in the traditional method, and enhance the continuous reliability of the communication link. Figure 3(c) is a comparison chart of the comprehensive performance scores of the patented method and the traditional method in multiple dimensions. From each dimension, the patented method scores significantly higher than the traditional method in core dimensions such as stability, power quality, and link reliability. Overall, the patented method improves the comprehensive performance by 17.8% compared to the traditional method. This verifies that the patented method, through a full-process control system of "dynamic perception - model optimization - parameter fine-tuning - state closed loop," achieves a synergistic improvement in the multi-dimensional performance of beam control. It not only overcomes the limitations of single-performance optimization in traditional methods but also meets the comprehensive requirements for communication stability, accuracy, and responsiveness in highly dynamic scenarios.

[0060] Please see Figure 4 The following describes the Ku-band mobile satellite antenna beam control system 400 in the embodiments of this application, including: The tilt angle candidate value acquisition module 401 is used to acquire real-time attitude angle data of the carrier and satellite orbit parameters, calculate the initial beam pointing vector, monitor the rate of change of attitude change threshold, establish a preliminary nonlinear coupling model, and generate a set of tilt angle candidate values. The amplitude difference acquisition module 402 is used to optimize the set of candidate tilt angle values ​​using the particle swarm algorithm, integrate the influence of Earth curvature and the atmospheric refraction deviation correction model, calculate the amplitude distribution data of the antenna array, fit the geometric relationship, calculate the homogenization amplitude compensation coefficient, and obtain the amplitude difference distribution after adjusting the array element amplitude. The beam adjustment parameter acquisition module 403 is used to collect signal power feedback data, fuse homogenization amplitude compensation coefficient to update the tilt angle candidate value set, determine the optimized tilt angle setting value; based on the value, extract attitude change trend prediction data, generate the final beam adjustment parameters, and evaluate the real-time communication link status. The real-time command sequence generation module 404 is used to extract the azimuth and elevation data from the final beam adjustment parameters, calculate the dynamic adjustment value of the azimuth and the compensation value of the elevation, generate polarization mode switching control information, construct the real-time sequence of adjustment commands, and update the initial beam pointing vector. The final polarization control parameter generation module 405 is used to fit the synchronization deviation between the updated beam pointing vector and the satellite orbit parameters, correct the amplitude difference distribution, fuse atmospheric refraction deviation correction data, optimize the nonlinear coupling model, and determine the final polarization control parameters. The beam stability assessment module 406 is used to collect signal power feedback data, fuse it with the final polarization control parameters, and calculate the signal power fluctuation rate; through signal power feedback fusion and fluctuation rate threshold judgment, the beam stability is comprehensively assessed.

[0061] Through the collaborative efforts of the aforementioned components, the system constructs a comprehensive beam control system for Ku-band mobile satellite antennas, encompassing "dynamic perception, model optimization, parameter fine-tuning, and state closed-loop management." This system achieves automated and highly adaptable control from carrier attitude and satellite orbit data acquisition to continuous beam stability assurance. Specifically, the tilt candidate value acquisition module 401 provides initial beam pointing and tilt candidate data for subsequent modules. Its output tilt candidate value set directly serves as the optimization input for the amplitude difference acquisition module 402. The connection between the two through "initial parameters and optimization processing" solves the problem that traditional static tilt angles cannot adapt to dynamic environments. The uniform amplitude compensation coefficient calculated by the amplitude difference acquisition module 402 is synchronously transmitted to the beam adjustment parameter acquisition module 403 to update the tilt candidate values, and the generated amplitude difference distribution provides polarization information for the real-time command sequence generation module 404. The switching of beam pattern is based on the linkage between amplitude optimization and beam pointing adjustment. The final beam adjustment parameters generated by the beam adjustment parameter acquisition module 403 are the core basis for the command real-time sequence generation module 404 to calculate the azimuth and elevation angle adjustment values. The beam pointing vector updated by the command real-time sequence generation module 404 provides basic data for the final polarization mode control parameter generation module 405 to fit the synchronization deviation, forming a progressive logic of "parameter generation - vector update - deviation correction". The polarization parameters determined by the final polarization mode control parameter generation module 405 are sent to the beam stability state evaluation module 406 for fusion of signal power data. If the beam stability state evaluation module 406 determines that the beam is unstable, it will also trigger the preceding modules to re-optimize the tilt angle, amplitude, and beam parameters, forming a closed-loop control, and ultimately ensuring the continuity and reliability of the communication link in the high dynamic scenario of the Ku band.

[0062] The above embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application.

Claims

1. A Ku-band mobile satellite antenna beam control method, characterized in that, Comprising the following steps: Step S101, acquiring carrier real-time attitude angle data and satellite orbit parameters, calculating initial beam pointing vector; using attitude sudden change threshold to monitor the change rate of the initial beam pointing vector, then establishing a preliminary nonlinear coupling model to generate a candidate inclination angle set; Step S102, using particle swarm optimization algorithm to optimize the candidate inclination angle set, fusing the earth curvature influence model and the atmospheric refraction deviation correction model to calculate the amplitude distribution data of the antenna array; fitting the geometric relationship of the antenna array, calculating the uniform amplitude compensation coefficient, and obtaining the antenna element amplitude difference distribution after adjusting the antenna element amplitude; Step S103, collecting signal power feedback data, updating the candidate inclination angle set by fusing the uniform amplitude compensation coefficient, and determining the optimized inclination angle setting value; based on the optimized inclination angle setting value, extracting attitude change trend prediction data, generating final beam adjustment parameters, and evaluating real-time communication link state; Step S104, extracting azimuth and elevation angle data from the final beam adjustment parameters, calculating azimuth dynamic adjustment value and elevation compensation value, generating polarization mode switching control information, constructing adjustment instruction real-time sequence, and updating the initial beam pointing vector; Step S105, fitting the synchronization deviation between the updated beam pointing vector and the satellite orbit parameters, and correcting the amplitude difference distribution; fusing atmospheric refraction deviation correction data, optimizing the nonlinear coupling model, and determining the final polarization mode control parameter; Step S106, collecting signal power feedback data, fusing the signal power feedback data and the final polarization mode control parameter, and calculating signal power fluctuation rate; through signal power feedback fusion and fluctuation rate threshold judgment, comprehensively evaluating the beam stability state.

2. The method of claim 1, wherein, The step S101 comprises: Real-time acquisition of carrier attitude angle data through sensors, acquisition of satellite orbit parameters from satellite navigation system, and calculation of initial beam pointing vector; Judging whether the change rate of the initial beam pointing vector exceeds the preset attitude sudden change threshold, if yes, fitting the deviation of the attitude angle data and the satellite orbit parameters by least square method to generate a preliminary nonlinear coupling model; Extracting feature vectors from the preliminary nonlinear coupling model, adjusting the initial inclination angle based on the feature vectors, and generating an inclination angle candidate value set containing multiple candidate inclination angle values.

3. The method of claim 1, wherein, The step S102 comprises: Iterative calculation of the inclination angle candidate value set using particle swarm optimization algorithm, fusing the earth curvature influence model in the iterative calculation process, and correcting the atmospheric refraction deviation to obtain the amplitude distribution data of the antenna array; Judging whether the amplitude difference in the amplitude distribution data exceeds the preset first difference threshold, if yes, fitting the geometric relationship of the antenna array by least square method; Based on the fitted geometric relationship of the antenna array, calculating the uniform amplitude compensation coefficient; Adjusting the antenna element amplitude according to the uniform amplitude compensation coefficient to obtain the antenna element amplitude difference distribution.

4. The method of claim 1, wherein, In the step S103, determining the optimized inclination angle setting value comprises: Collect signal power feedback data, fuse the signal power feedback data with the homogenization amplitude compensation coefficient, and generate signal power feedback fusion data; Based on the signal power feedback fusion data, calculate the deviation amount in the preliminary nonlinear coupling model; Use a particle swarm optimization algorithm to iteratively optimize the deviation amount, update the candidate inclination value set, and calculate the signal power fluctuation rate corresponding to each candidate inclination value in the updated candidate inclination value set; Compare the signal power fluctuation rate corresponding to each candidate inclination value with a preset first fluctuation rate threshold, and select the candidate inclination value whose signal power fluctuation rate is lower than the first fluctuation rate threshold as the optimized inclination setting value.

5. The method of claim 4, wherein, In step S103, generate the final beam adjustment parameter and evaluate the real-time communication link state, including: Extract the attitude change trend prediction data based on the optimized inclination setting value; Collect the signal power feedback data corresponding to the optimized inclination setting value, and calculate the signal power fluctuation rate of the signal power feedback data; Fuse the attitude change trend prediction data and the corresponding signal power fluctuation rate data to form a comprehensive data set; Compare the signal power fluctuation rate in the comprehensive data set with a preset second fluctuation rate threshold, and determine whether the former is lower than the latter. If yes, fuse the attitude change trend prediction data and the optimized inclination setting value using a Kalman filtering algorithm to generate the final beam adjustment parameter. If no, return to step S103 to determine a new optimized inclination setting value; Based on the final beam adjustment parameter, control the antenna array element to adjust the beam pointing direction, collect the adjusted communication link signal data, and evaluate the real-time communication link state.

6. The method of claim 1, wherein, Step S104 includes: Trigger the fast response trigger logic through the attitude sudden change threshold monitor, and start the analysis process of the final beam adjustment parameter; Extract the azimuth angle data and the elevation angle data from the final beam adjustment parameter, calculate the azimuth angle dynamic adjustment value and the elevation angle compensation value; According to the azimuth angle dynamic adjustment value and the elevation angle compensation value, combine the antenna array element amplitude difference distribution to generate polarization mode switching control information; Collect real-time communication link state data, calculate the fluctuation rate of the real-time communication link state, and determine whether it exceeds a preset third fluctuation rate threshold. If yes, generate an adjustment instruction real-time sequence based on the polarization mode switching control information; According to the adjustment instruction real-time sequence, sequentially perform azimuth angle adjustment, elevation angle compensation, and polarization mode switching operations, and update the initial beam pointing vector.

7. The method of claim 1, wherein, Step S105 includes: Use the least squares method to fit the updated initial beam pointing vector and the satellite orbit parameters, calculate the synchronization deviation between them, generate a deviation correction model, and update the antenna array element amplitude difference distribution based on the deviation correction model to obtain the updated antenna array element amplitude difference distribution. ​ Fuse the corrected atmospheric refraction deviation correction data with the updated antenna array element amplitude difference distribution, optimize the parameters of the preliminary nonlinear coupling model, and obtain an optimized nonlinear coupling model; Based on the optimized nonlinear coupling model, predict the signal power fluctuation rate and amplitude uniformity under different polarization modes; Select the polarization mode whose predicted signal power fluctuation rate is lower than a preset third fluctuation threshold and whose amplitude uniformity is higher than a preset uniformity threshold, and determine the final polarization mode control parameter.

8. The method of claim 1, wherein, The step S106 comprises: Collect real-time signal power feedback data, fuse the signal power feedback data with the final polarization mode control parameter, and calculate the signal power fluctuation rate based on the fused data; Determine whether the signal power fluctuation rate of the fused data exceeds a preset fourth fluctuation rate threshold, if yes, use the particle swarm optimization algorithm, combine the final polarization mode control parameter and the earth curvature influence integrated data, and iteratively adjust the azimuth dynamic adjustment value calculated in step S104, until the signal power fluctuation rate of the fused data is lower than the preset fourth fluctuation rate threshold; Combine the signal power feedback fusion result and the fourth fluctuation rate threshold determination result, further associate the updated beam pointing vector and the corrected antenna array element amplitude difference distribution, and comprehensively evaluate the beam stability state.

9. A Ku-band mobile satellite antenna beam control system, characterized in that, The Ku frequency band mobile satellite antenna beam control system for implementing the Ku frequency band mobile satellite antenna beam control method according to any one of claims 1-8 comprises: An inclination candidate value acquisition module, configured to acquire carrier real-time attitude angle data and satellite orbit parameters, calculate an initial beam pointing vector, monitor the change rate thereof through an attitude change threshold, establish a preliminary nonlinear coupling model, and generate an inclination candidate value set; An amplitude difference acquisition module, configured to optimize the inclination candidate value set using a particle swarm algorithm, fuse an earth curvature influence and an atmospheric refraction deviation correction model, calculate antenna array amplitude distribution data, fit a geometric relationship, calculate a uniformized amplitude compensation coefficient, and acquire amplitude difference distribution after adjusting the element amplitude; A beam adjustment parameter acquisition module, configured to collect signal power feedback data, fuse the uniformized amplitude compensation coefficient to update the inclination candidate value set, determine an optimized inclination setting value, extract attitude change trend prediction data based on the value, generate final beam adjustment parameters, and evaluate real-time communication link state; An instruction real-time sequence generation module, configured to extract azimuth and elevation angle data in the final beam adjustment parameters, calculate azimuth dynamic adjustment value and elevation compensation value, generate polarization mode switching control information, construct adjustment instruction real-time sequence, and update the initial beam pointing vector; A final polarization mode control parameter generation module, configured to fit the synchronization deviation between the updated beam pointing vector and the satellite orbit parameters, correct the amplitude difference distribution, fuse atmospheric refraction deviation correction data, optimize the nonlinear coupling model, and determine the final polarization mode control parameter. The beam stability state evaluation module is used for collecting signal power feedback data, fusing the signal power feedback data with the final polarization mode control parameter, calculating a signal power fluctuation rate, judging through signal power feedback fusion and the fluctuation rate threshold, and comprehensively evaluating the beam stability state.

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