An adaptive beam tracking control method for a low-orbit satellite broadband communication phased array antenna

Abstract

The application relates to a kind of low-orbit satellite broadband communication phased array antenna adaptive beam tracking control methods, belong to satellite communication technical field.It includes: obtaining ephemeris information and terminal positioning orientation information calculates initial beam pointing angle, according to signal-to-noise ratio determination tracking mode;When signal-to-noise ratio is higher than the first threshold, adopt closed loop beacon correction tracking mode, utilize downlink signal to carry out high-precision angle of arrival estimation and realize closed loop correction;When signal-to-noise ratio is lower than the second threshold, switch to open loop orbit prediction tracking mode, forecast beam pointing based on ephemeris and terminal attitude;In the middle signal-to-noise ratio region, adopt adaptive fusion tracking of open loop prediction and closed loop correction;Through adaptive Kalman filtering, dynamically adjust fusion weight, and with communication quality optimal as target optimization control parameter;Mode switching adopts gradual weighting transition.The application realizes high-precision, high-reliable beam tracking under low-orbit satellite high dynamic scene.

Description

Technical Field

[0001] This application relates to the field of satellite communication technology, and in particular to an adaptive beam tracking control method for a low-orbit satellite broadband communication phased array antenna. Background Technology

[0002] Low Earth Orbit (LEO) satellite constellations are a core component of satellite internet, offering advantages such as low transmission latency, minimal link loss, and global coverage.

[0003] Low-Earth orbit (LEO) satellites operate at high speeds (approximately 7.5 km / s), with single-satellite transit times typically ranging from a few minutes to a dozen minutes. This places extremely high demands on the beam pointing and tracking capabilities of ground terminal antennas. Phased array antennas, due to their advantages such as flexible beam pointing, fast response speed, and the ability to simultaneously form multiple beams, have become core equipment for LEO satellite broadband communication terminals. However, achieving high-precision and high-reliability tracking of LEO satellites by phased array antenna beams under conditions of high-speed terminal carrier movement and dynamic changes in the channel environment remains a key technical challenge in this field. One related technology, a LEO satellite tracking method based on a phased array antenna, achieves LEO satellite tracking by fusing antenna pointing data and attitude data.

[0004] The aforementioned technologies suffer from the following drawbacks: Existing methods mostly employ a single open-loop orbit prediction and tracking or closed-loop beacon tracking mode, failing to dynamically and adaptively switch according to channel conditions. Open-loop tracking relies on ephemeris accuracy, and pointing deviation increases when ephemeris errors accumulate or terminal attitude changes abruptly; closed-loop tracking relies on downlink signal quality, and the accuracy of angle of arrival estimation decreases or even fails under low signal-to-noise ratio conditions. Summary of the Invention

[0005] To address the aforementioned issues, this application provides an adaptive beam tracking control method for a low-orbit satellite broadband communication phased array antenna.

[0006] The adaptive beam tracking control method for a low-orbit satellite broadband communication phased array antenna provided in this application adopts the following technical solution: An adaptive beam tracking control method for a low-Earth orbit satellite broadband communication phased array antenna includes the following steps: Step S1: Obtain the ephemeris information of the low-orbit satellite and the positioning and orientation information of the ground terminal, calculate the initial beam pointing angle of the target satellite relative to the phased array antenna; obtain the signal-to-noise ratio of the current communication link, and determine the current tracking mode based on the relationship between the signal-to-noise ratio and the preset threshold.

[0007] By employing the above technical solution, ephemeris information combined with terminal positioning and orientation information can be used to calculate the azimuth and elevation angles of the satellite relative to the antenna, providing an initial reference for beam pointing. The signal-to-noise ratio (SNR), as a direct indicator of channel quality, is used to determine which tracking strategy is appropriate for the current link conditions—when the link quality is good, closed-loop tracking relying on signal feedback can be used to achieve high-precision pointing; when the link quality deteriorates, it can switch to open-loop tracking that does not rely on real-time signals to ensure tracking continuity, thus maintaining reliable beam alignment under different channel conditions.

[0008] Furthermore, the tracking mode determination in step S1 also includes: when the signal-to-noise ratio is between the first preset threshold and the second preset threshold, a multi-mode fusion adaptive tracking mode is adopted.

[0009] By employing the above technical solution, in the transition region where the signal-to-noise ratio is between two thresholds, it neither relies solely on open-loop prediction nor entirely on closed-loop correction, but rather adaptively fuses the two. This mode fully utilizes the complementary advantages of the two tracking methods, balancing tracking accuracy and stability when channel quality is moderate.

[0010] Step S2: When the signal-to-noise ratio of the communication link is higher than the first preset threshold, the closed-loop beacon correction tracking mode is adopted: the downlink signal of the target satellite is received by the phased array antenna, the real-time estimate of the beam pointing error is obtained by the angle of arrival estimation algorithm, the beam correction amount is generated according to the error estimate, and the current beam pointing is closed-loop corrected.

[0011] By adopting the above technical solution, the downlink signal quality is excellent under high signal-to-noise ratio conditions, and the angle-of-arrival estimation algorithm can provide high-precision beam pointing error estimation. Using this real-time error information for closed-loop correction can effectively eliminate system deviations caused by ephemeris errors, terminal positioning and orientation errors, and atmospheric propagation delays, thereby optimizing beam pointing accuracy and ensuring high throughput for broadband communication.

[0012] Step S3: When the signal-to-noise ratio of the communication link is lower than the second preset threshold, switch to the open-loop orbit prediction and tracking mode: Based on the real-time updated satellite ephemeris and terminal position and attitude information, predict the motion trajectory of the target satellite in the future time window, generate a beam control command sequence, and drive the phased array antenna to perform beam tracking according to the predicted pointing angle.

[0013] By adopting the above technical solution, under low signal-to-noise ratio conditions, the downlink signal is unreliable, and the accuracy of the angle of arrival estimation drops sharply or even produces erroneous observations. At this time, switching to open-loop prediction tracking, which relies entirely on ephemeris models and terminal attitude data for extrapolation, avoids the noise disturbances introduced by closed-loop correction, ensures the continuity and stability of beam pointing, and prevents beam lock-up due to observation outliers.

[0014] Furthermore, the open-loop orbit prediction tracking in step S3 also includes an ephemeris error compensation step: based on the beam pointing error data accumulated during historical tracking, a statistical model of the ephemeris error is established, and the statistical model is used to feedforward compensate the predicted pointing angle.

[0015] By adopting the above technical solution, ephemeris error is one of the main sources of error in open-loop tracking. By statistically analyzing the deviation between the actual pointing and the predicted pointing during historical tracking, a statistical characteristic model of ephemeris error can be established and superimposed on the predicted pointing angle as a feedforward compensation, thereby effectively reducing the system deviation caused by inaccurate ephemeris and improving the accuracy of open-loop tracking.

[0016] Step S4: During the open-loop orbit prediction and tracking process, the angle of arrival is periodically estimated using the downlink signal. The estimated beam pointing error is used as a correction value. The open-loop prediction pointing angle is corrected in real time using an adaptive fusion filtering algorithm. This adaptive fusion filtering algorithm dynamically adjusts the fusion weight between the open-loop prediction value and the closed-loop correction value according to the current communication link quality.

[0017] By adopting the above technical solution, the fusion of open-loop prediction and closed-loop correction fully leverages the complementary advantages of the two tracking methods. The adaptive fusion filtering algorithm dynamically adjusts the fusion weights based on the real-time communication link quality. When the signal quality is good, it increases the confidence level of the closed-loop correction to quickly correct errors, and when the signal quality is poor, it increases the confidence level of the open-loop prediction to ensure the continuity of tracking, thus achieving seamless connection and optimal fusion of the two tracking modes.

[0018] Furthermore, in step S4, the adaptive fusion filtering algorithm is an adaptive Kalman filter algorithm, and its fusion weights are dynamically adjusted according to the current signal-to-noise ratio: the higher the signal-to-noise ratio, the greater the weight of the closed-loop correction value; the lower the signal-to-noise ratio, the greater the weight of the open-loop prediction value.

[0019] By employing the above technical solutions, Kalman filtering becomes the optimal linear estimation framework, and adaptive Kalman filtering uses the signal-to-noise ratio (SNR) as an adjustment factor for the observation noise covariance. When the SNR is high, the noise covariance of the observations (closed-loop correction values) is small, the filter gain is large, and the estimated values ​​depend more on the observations; when the SNR is low, the observation noise covariance is large, the filter gain is small, and the estimated values ​​depend more on the predictions (open-loop forecast values). This adaptive mechanism ensures that the fused estimation remains optimal under different channel conditions.

[0020] Furthermore, the adaptive fusion filtering algorithm in step S4 also includes: when the rate of change of the beam pointing error exceeds a preset threshold and the current signal-to-noise ratio is higher than a third preset threshold, it is determined that the terminal motion state has changed abruptly, and the weight of the closed-loop correction value is temporarily increased to speed up the tracking response speed; the third preset threshold is greater than or equal to the second preset threshold.

[0021] By adopting the above technical solution, the terminal carrier (such as a vehicle, ship, or aircraft) may undergo maneuvers such as sharp turns, acceleration, and deceleration during its movement, leading to a sharp increase in the rate of change of beam pointing error. If the signal-to-noise ratio (SNR) is sufficiently high (above the third threshold), it indicates that the closed-loop observation is reliable. By temporarily increasing the closed-loop correction weight, the beam pointing can respond quickly to the terminal's maneuvering changes. If the SNR is too low (below the third threshold), even if a sudden change is detected, the strategy remains primarily open-loop, avoiding beam jitter caused by unreliable observations, thus achieving a reasonable balance between tracking response speed and stability.

[0022] Step S5: Monitor the signal-to-noise ratio, bit error rate, and throughput of the communication link in real time, establish a communication quality evaluation function, and dynamically adjust the beam pointing control parameters of the phased array antenna with the goal of optimal communication quality.

[0023] By adopting the above technical solutions, the ultimate goal of beam tracking is to ensure communication quality, rather than simply aiming accuracy. By establishing a multi-dimensional communication quality evaluation function, indicators such as signal-to-noise ratio, bit error rate, and throughput are comprehensively incorporated into the optimization objective. This ensures that the adjustment of beam control parameters is guided by the optimization of communication quality, rather than blindly pursuing aiming accuracy, thereby achieving a better user experience in actual communication scenarios.

[0024] Furthermore, the communication quality evaluation function in step S5 is: J = w1·SNR + w2·(1-BER) + w3·Throughput, where SNR is the normalized signal-to-noise ratio, BER is the bit error rate, Throughput is the normalized throughput, and w1, w2, and w3 are dynamic weighting coefficients; these dynamic weighting coefficients are adaptively adjusted according to the current communication service type.

[0025] By adopting the above technical solutions, different communication services have varying sensitivities to signal-to-noise ratio (SNR), bit error rate (BER), and throughput. For example, voice services are sensitive to latency and BER, data services are sensitive to throughput, and video services have high requirements for both SNR and throughput. The dynamic weighting coefficients can be adaptively adjusted according to the service type, enabling targeted optimization of beam control parameters under different service scenarios.

[0026] Step S6: Adaptively adjust the control parameters from steps S2 to S5 according to the motion state of the terminal carrier and changes in the channel environment; when the tracking mode needs to be switched, a progressive weighted transition method is used to achieve a smooth switch between open-loop tracking and closed-loop tracking.

[0027] By adopting the above technical solutions, motion state parameters such as the terminal carrier's speed, acceleration, and attitude change rate directly affect the rate of change of beam pointing, while the channel environment (such as rainfall attenuation and multipath fading) directly affects the signal reception quality. Adaptive adjustment of control parameters enables the method to adapt to different motion scenarios and channel conditions. Gradual weighted transitions avoid abrupt parameter changes during mode switching, preventing beam pointing oscillations or loss of lock.

[0028] Furthermore, the progressive weighted transition method in step S6 is as follows: within the transition time window, the control parameters smoothly change from the current mode parameters to the target mode parameters according to a preset transition function. The transition function can be any one of a linear function, a sigmoid function, or an exponential function.

[0029] By adopting the above technical solutions, the linear function transition is simple and direct, the S-shaped function transition is smooth with gradual changes in the start and end phases, and the exponential function transition has a fast start and a slow end. The three transition functions can be flexibly selected according to the actual application scenario, ensuring the stability of beam pointing during mode switching.

[0030] Furthermore, it also includes beam widening and power adaptive adjustment steps: when the estimated value of the beam pointing error exceeds the preset error threshold, the beam width of the phased array antenna is adaptively increased to maintain link connectivity, and the transmit power is increased accordingly to compensate for the gain loss caused by beam widening.

[0031] By adopting the above technical solution, although increasing the beamwidth will reduce the antenna gain when the beam pointing error is large, it can improve the beam coverage and avoid link interruption due to excessive pointing deviation. At the same time, by increasing the transmit power to compensate for the gain loss, a balance is achieved between pointing accuracy and link maintenance, thus improving the robustness of the link.

[0032] Furthermore, the method is executed in real time in the digital beamforming processor of the phased array antenna, and the update period of the beam pointing angle is no more than 10 milliseconds.

[0033] By adopting the above technical solution, the angular velocity of low-Earth orbit satellites relative to ground terminals can reach several degrees per second, requiring beam pointing control to have an extremely high update rate. An update cycle of less than 10 milliseconds can ensure real-time tracking accuracy of beam pointing during the high-speed transit of low-Earth orbit satellites, meeting the stringent requirements of broadband communication for beam alignment.

[0034] In summary, this application includes at least one of the following beneficial effects: 1. By using a signal-to-noise ratio-driven multi-mode adaptive switching mechanism, the optimal pointing is achieved by using high-precision closed-loop correction at high signal-to-noise ratios, switching to a robust open-loop prediction and maintenance link at low signal-to-noise ratios, and adopting adaptive fusion of open-loop and closed-loop at medium signal-to-noise ratios, thus achieving the best balance between tracking accuracy and stability under different channel conditions.

[0035] 2. The open-loop prediction value and the closed-loop correction value are dynamically fused by an adaptive Kalman filter algorithm. The fusion weight is adaptively adjusted according to the real-time signal-to-noise ratio. When the signal is good, more reliance is placed on the closed-loop correction to eliminate errors, and when the signal is poor, more reliance is placed on the open-loop prediction to maintain stability. This achieves the optimal complementarity of the two tracking methods.

[0036] 3. By driving beam control parameter optimization through a multi-dimensional communication quality evaluation function, communication quality indicators such as signal-to-noise ratio, bit error rate, and throughput are incorporated into the control objectives, upgrading beam tracking from "optimal pointing accuracy" to "optimal communication quality," thereby improving the user experience in actual communication scenarios.

[0037] 4. A smooth switching of tracking modes is achieved through a progressive weighted transition, avoiding beam pointing oscillations and link interruptions caused by parameter mutations, and ensuring communication continuity during mode switching.

[0038] 5. By using beam widening and adaptive power adjustment, the system gains coverage in exchange for increased pointing error, and maintains link connectivity through power compensation, thereby improving the robustness and availability of the system. Attached Figure Description

[0039] Figure 1 This is a flowchart illustrating the overall process of the method described in this application. Figure 2 This is a schematic diagram illustrating the mode switching between open-loop orbit prediction tracking and closed-loop beacon correction tracking in an embodiment of this application; Figure 3 This is a block diagram illustrating the principle of the adaptive fusion filtering algorithm in the embodiments of this application; Figure 4 This is a flowchart illustrating the control parameter optimization driven by the communication quality evaluation function in this embodiment. Detailed Implementation

[0040] The present application will be further described in detail below with reference to the accompanying drawings.

[0041] This application discloses an adaptive beam tracking control method for a low-orbit satellite broadband communication phased array antenna.

[0042] The method includes the following steps: Step S1: Initial pointing calculation and tracking mode determination The system acquires ephemeris information of low-Earth orbit satellites (such as two-line orbital elements TLE or real-time updated precise ephemeris) and positioning and orientation information of ground terminals (such as GNSS position and attitude angles of inertial navigation units). Based on the satellite orbital dynamics model and the terminal's position and attitude, it calculates the initial beam pointing angle (azimuth and elevation angles) of the target satellite relative to the phased array antenna. Simultaneously, it acquires the received signal-to-noise ratio (SNR_cur) of the current communication link and compares it with a preset first threshold SNR_high (e.g., 15dB) and a second threshold SNR_low (e.g., 8dB). If SNR_cur > SNR_high, it is determined to be in closed-loop tracking mode; if SNR_cur < SNR_low, it is determined to be in open-loop tracking mode; if SNR_low ≤ SNR_cur ≤ SNR_high, it is determined to be in multi-mode fusion tracking mode.

[0043] Step S2: Closed-loop beacon correction tracking mode When the signal-to-noise ratio (SNR) of the communication link is higher than a first preset threshold SNR_high (e.g., 15dB), a closed-loop beacon correction tracking mode is adopted. In this mode, the downlink beacon signal of the target satellite is received using a phased array antenna array, and the real-time accurate estimate of the direction of arrival (DOA) is obtained through an angle of arrival (DOA) estimation algorithm (such as MUSIC, ESPRIT, or EKF). The estimated DOA is compared with the current beam pointing angle to obtain the pointing error. This error is used as feedback input to the beam controller (such as a PID or Kalman filter) to generate a beam correction command, which drives the digital beamformer to adjust the beam pointing in real time, forming a closed-loop control. Because the DOA estimation accuracy is high at high SNR (the error is usually less than 0.1°), closed-loop correction can quickly eliminate various system deviations and ensure that the beam is accurately aligned with the satellite.

[0044] Angle of arrival estimation algorithms can employ multiple signal classification (MUSIC), rotation-invariant technique (ESPRIT), or extended Kalman filter (EKF). MUSIC is suitable for high-resolution estimation in multipath environments, ESPRIT utilizes the rotation invariance of the array for fast estimation with low computational cost, and EKF can fuse historical state information for recursive estimation, making it suitable for dynamic scenarios.

[0045] Step S3: Open-loop orbit prediction and tracking mode When the signal-to-noise ratio (SNR) of the communication link falls below the second preset threshold SNR_low (e.g., 8dB), the system switches to open-loop orbit prediction and tracking mode. In this mode, real-time feedback no longer relies on downlink signals. Instead, it uses the latest acquired satellite ephemeris (such as TLE or precise ephemeris) and the real-time position and attitude provided by the terminal GNSS / INS to predict the satellite's trajectory within a future time window (e.g., 30 seconds) using orbit prediction models such as SGP4 / SDP4. This generates a time series of beam pointing angles, which is then sent as open-loop control commands to the beamformer. Since it does not involve a closed-loop signal processing system, this mode is unaffected by the variance explosion in DOA estimation under low SNR conditions and can smoothly maintain beam pointing.

[0046] During open-loop tracking, a simultaneous ephemeris error compensation step is performed: The beam pointing error (the difference between the actual received signal direction and the predicted pointing angle) is recorded at each moment in the historical tracking process. A statistical model of the ephemeris error (such as mean, variance, and autocorrelation coefficient) is established. This statistical model is then used to feedforward correct the future predicted pointing angle, reducing system bias caused by inaccurate ephemeris data. For example, if historical statistics show a constant deviation of +0.3° in the azimuth direction for the predicted pointing angle, then 0.3° is subtracted from the predicted pointing angle to obtain the corrected command value.

[0047] Step S4: Adaptive Fusion Tracking Mode During the open-loop trajectory prediction and tracking process (including during the execution of step S3 and the fusion mode under medium signal-to-noise ratio conditions), the angle of arrival is estimated periodically (e.g., every 100ms) using the downlink signal. The estimated beam pointing error is used as the closed-loop correction amount, and the open-loop prediction pointing angle is corrected in real time through an adaptive fusion filtering algorithm.

[0048] The adaptive fusion filtering algorithm uses adaptive Kalman filtering (AKF), and its core equation is: State prediction: x̂(k|k-1) = Φ·x̂(k-1) + B·u(k) Where x̂ is the estimated beam pointing angle, Φ is the state transition matrix (determined by satellite orbital dynamics), and u is the control input (open-loop predicted pointing angle).

[0049] Observation update: x̂(k) = x̂(k|k-1) + K(k)·[z(k) - H·x̂(k|k-1)] Where z is the observed value (closed-loop angle of arrival estimate), H is the observation matrix, and K is the Kalman gain.

[0050] The adaptive mechanism is manifested in dynamically adjusting the observation noise covariance matrix R based on the current signal-to-noise ratio (SNR_cur), where R = R0 / (SNR_cur / SNR_ref). The higher the SNR, the smaller R, the larger the Kalman gain K, and the greater the weight of the closed-loop correction value; conversely, the lower the SNR, the larger R, the smaller K, and the greater the weight of the open-loop prediction value. This adaptive mechanism ensures that the fusion estimation remains optimal under different channel conditions.

[0051] Simultaneously, when the rate of change of the beam pointing error exceeds a preset threshold (e.g., 0.5° / s), it is determined that a sudden change in the terminal's motion state has occurred (e.g., a sharp turn by the carrier). At this point, it is necessary to determine whether the current signal-to-noise ratio (SNR) is higher than a third preset threshold (this threshold is not lower than a second preset threshold). Only when the SNR is higher than the third threshold is the weight of the closed-loop correction value temporarily increased (e.g., the Kalman gain is amplified by 1.5 times) to accelerate the tracking response speed; if the SNR is lower than the third threshold, even if a sudden change is detected, the strategy of primarily relying on open-loop prediction is maintained to avoid beam jitter caused by unreliable closed-loop observations. After the sudden change determination is restored, the weights gradually return to normal values.

[0052] Step S5: Communication quality evaluation and parameter optimization Real-time monitoring of signal-to-noise ratio (SNR), bit error rate (BER), and throughput (Throughput) of the communication link is used to establish a communication quality evaluation function. J = w1·SNR_norm + w2·(1-BER_norm) + w3·Throughput_norm Where SNR_norm, BER_norm, and Throughput_norm are the normalized values ​​(range 0~1) of each indicator, and w1, w2, and w3 are dynamic weighting coefficients. The dynamic weighting coefficients are adaptively adjusted according to the current communication service type: w2 is increased for voice services (sensitive to bit error rate), w3 is increased for data services (sensitive to throughput), and the three weights are evenly distributed for video services.

[0053] With the goal of maximizing the communication quality evaluation function J or minimizing the loss function, the beam pointing control parameters of the phased array antenna (such as the proportional coefficient, integral coefficient, derivative coefficient of PID control, and beam scanning step size) are dynamically adjusted so that beam tracking control directly serves communication quality optimization.

[0054] Step S6: Adaptive parameter adjustment and smooth mode switching Based on the motion state of the terminal carrier (velocity, acceleration, rate of attitude change) and changes in the channel environment (such as rainfall attenuation, multipath fading), the control parameters in steps S2 to S5 are adaptively adjusted. For example, when the carrier speed increases, the update frequency of the beam pointing angle is increased; when the channel attenuation increases, the fusion weight of the open-loop prediction is increased.

[0055] When the tracking mode needs to be switched (e.g., SNR drops from above SNR_high to between SNR_high and SNR_low, or further to below SNR_low), a gradual weighted transition method is used to achieve a smooth switch between open-loop and closed-loop tracking. Within the transition time window T_trans (e.g., 500ms), the control parameters smoothly change from the current mode parameters to the target mode parameters according to a preset transition function. The transition function can be a linear function f(t)=t / T_trans, a sigmoid function f(t)=1 / (1+e^(-k·(t-T_trans / 2))), or an exponential function f(t)=1-e^(-t / τ). Linear transition is simple and direct, sigmoid transition has a smooth start and end, and exponential transition has a fast start and slow end; the appropriate function can be selected based on the actual scenario.

[0056] Beamwidth and adaptive power adjustment When the estimated beam pointing error exceeds a preset error threshold (such as 1 / 3 of the beamwidth), the beamwidth of the phased array antenna is adaptively increased to maintain link connectivity, while the transmit power is increased accordingly to compensate for the gain loss caused by beam widening.

[0057] Specifically, the beamwidth is determined based on the ratio of the current pointing error estimate Δθ to the current half-power beamwidth θ3dB: when Δθ / θ3dB ∈ (1 / 3, 1 / 2], the beamwidth is widened to 1.2 times its original value; when Δθ / θ3dB ∈ (1 / 2, 2 / 3], it is widened to 1.5 times; and when Δθ / θ3dB > 2 / 3, it is widened to 2 times. Simultaneously, the transmit power is compensated according to the inverse square relationship between antenna gain and beamwidth, i.e., P_new = P_old × (θ_new / θ_old)², to maintain the equivalent isotropic radiated power (EIRP) essentially unchanged. After the pointing error returns to normal, the beamwidth and transmit power are gradually restored to their normal values. This mechanism achieves a balance between pointing accuracy and link maintenance, improving the robustness of the link.

[0058] Example of implementation parameters This method is executed in real time in the digital beamforming processor (FPGA or DSP) of the phased array antenna. The basic control command for the beam pointing angle is updated every 10ms to ensure a rapid response to the high-speed motion of low-Earth orbit satellites; the Kalman filter fusion correction is updated every 100ms, and between two fusion updates, the beam command at the intermediate moment is generated based on the extrapolation result of the current optimal estimate, thus balancing real-time performance and estimation accuracy.

[0059] Example parameters: SNR_high=15dB, SNR_low=8dB, third preset threshold=10dB, Kalman filter update period 100ms, mode switching transition time window 500ms, beamwidth trigger error threshold is 1 / 3 of half power beamwidth.

[0060] The above description is merely a preferred embodiment of this application and is not intended to limit this application. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the protection scope of this application.

Claims

1. An adaptive beam tracking control method for a low-Earth orbit satellite broadband communication phased array antenna, characterized in that, Includes the following steps: Step S1: Obtain the ephemeris information of the low-orbit satellite and the positioning and orientation information of the ground terminal, calculate the initial beam pointing angle of the target satellite relative to the phased array antenna; obtain the signal-to-noise ratio of the current communication link, and determine the current tracking mode based on the relationship between the signal-to-noise ratio and the preset threshold. Step S2: When the signal-to-noise ratio of the communication link is higher than the first preset threshold, the closed-loop beacon correction tracking mode is adopted: the downlink signal of the target satellite is received by the phased array antenna, the real-time estimate of the beam pointing error is obtained by the angle of arrival estimation algorithm, the beam correction amount is generated according to the error estimate, and the current beam pointing is corrected in a closed loop. Step S3: When the signal-to-noise ratio of the communication link is lower than the second preset threshold, switch to the open-loop orbit prediction and tracking mode: Based on the real-time updated satellite ephemeris and terminal position and attitude information, predict the motion trajectory of the target satellite in the future time window, generate a beam control command sequence, and drive the phased array antenna to perform beam tracking according to the predicted pointing angle; Step S4: During the open-loop trajectory prediction and tracking process, the downlink signal is periodically used to estimate the angle of arrival. The estimated beam pointing error is used as a correction value. The open-loop prediction pointing angle is corrected in real time by an adaptive fusion filtering algorithm. The adaptive fusion filtering algorithm dynamically adjusts the fusion weight between the open-loop prediction value and the closed-loop correction value according to the current communication link quality. Step S5: Monitor the signal-to-noise ratio, bit error rate, and throughput of the communication link in real time, establish a communication quality evaluation function, and dynamically adjust the beam pointing control parameters of the phased array antenna with the goal of optimal communication quality. Step S6: Adaptively adjust the control parameters from steps S2 to S5 according to the motion state of the terminal carrier and changes in the channel environment; when the tracking mode needs to be switched, a progressive weighted transition method is used to achieve a smooth switch between open-loop tracking and closed-loop tracking.

2. The adaptive beam tracking control method for low-orbit satellite broadband communication phased array antennas according to claim 1, characterized in that, The tracking mode determination in step S1 further includes: when the signal-to-noise ratio is between the first preset threshold and the second preset threshold, the multi-mode fusion adaptive tracking mode described in step S4 is adopted.

3. The adaptive beam tracking control method for low-orbit satellite broadband communication phased array antennas according to claim 1, characterized in that, The open-loop orbit prediction and tracking described in step S3 also includes an ephemeris error compensation step: based on the beam pointing error data accumulated during historical tracking, a statistical model of the ephemeris error is established, and the statistical model is used to perform feedforward compensation on the predicted pointing angle.

4. The adaptive beam tracking control method for low-orbit satellite broadband communication phased array antennas according to claim 1, characterized in that, The angle of arrival estimation algorithm can be any one of the following: multiple signal classification algorithm, rotation-invariant signal parameter estimation algorithm, or extended Kalman filter algorithm.

5. The adaptive beam tracking control method for a low-orbit satellite broadband communication phased array antenna according to claim 1, characterized in that, The adaptive fusion filtering algorithm described in step S4 is an adaptive Kalman filter algorithm, whose fusion weights are dynamically adjusted according to the current signal-to-noise ratio: the higher the signal-to-noise ratio, the greater the weight of the closed-loop correction value; the lower the signal-to-noise ratio, the greater the weight of the open-loop prediction value.

6. The adaptive beam tracking control method for a low-orbit satellite broadband communication phased array antenna according to claim 5, characterized in that, The adaptive fusion filtering algorithm in step S4 further includes: when the rate of change of the beam pointing error exceeds a preset threshold and the current signal-to-noise ratio is higher than a third preset threshold, it is determined that the terminal motion state has changed abruptly, and the weight of the closed-loop correction value is temporarily increased to speed up the tracking response speed; the third preset threshold is greater than or equal to the second preset threshold.

7. The adaptive beam tracking control method for a low-orbit satellite broadband communication phased array antenna according to claim 1, characterized in that, The communication quality evaluation function in step S5 is: J = w1·SNR + w2·(1-BER) + w3·Throughput, where SNR is the normalized signal-to-noise ratio, BER is the bit error rate, Throughput is the normalized throughput, and w1, w2, and w3 are dynamic weighting coefficients; the dynamic weighting coefficients are adaptively adjusted according to the current communication service type.

8. The adaptive beam tracking control method for a low-orbit satellite broadband communication phased array antenna according to claim 1, characterized in that, The progressive weighted transition method described in step S6 is as follows: within the transition time window, the control parameters smoothly change from the current mode parameters to the target mode parameters according to a preset transition function, wherein the transition function is any one of a linear function, a sigmoid function, or an exponential function.

9. The adaptive beam tracking control method for a low-orbit satellite broadband communication phased array antenna according to claim 1, characterized in that, It also includes beam widening and power adaptive adjustment steps: when the estimated value of the beam pointing error exceeds the preset error threshold, the beam width of the phased array antenna is adaptively increased to maintain link connectivity, and the transmit power is increased accordingly to compensate for the gain loss caused by beam widening.

10. The adaptive beam tracking control method for a low-orbit satellite broadband communication phased array antenna according to claim 1, characterized in that, The method is executed in real time in the digital beamforming processor of the phased array antenna, and the update period of the beam pointing angle is no more than 10 milliseconds.

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