Dynamic digital beamforming method and apparatus for satellites

Abstract

The application provides a dynamic digital beam forming method applied to a satellite, which comprises the following steps: obtaining satellite communication service area information, satellite position and attitude information and satellite-borne antenna state information, wherein the satellite-borne antenna state information comprises a plurality of active element directional diagrams corresponding to a plurality of feeding units respectively; converting the service area from a geographic coordinate system to a satellite-borne antenna coordinate system, determining a main lobe area and a side lobe area, selecting initial angle sampling points to form a main lobe angle sampling set and a side lobe angle sampling set; establishing an optimization model with the minimum upper limit of the beam side lobe as a target; converting the model into a second-order cone programming model for solving to obtain a candidate feeding weight vector; identifying main lobe out-of-tolerance points and side lobe out-of-limit points through the directional diagram, adaptively supplementing angle sampling points and re-solving until the requirements are met to output a current feeding weight vector; and adjusting the signal amplitude and phase of each feeding unit to form a satellite beam. Therefore, the application reduces the side lobe level and optimizes the calculation scale.

Description

Technical Field

[0001] This invention relates to the field of satellite communication technology, and in particular to a dynamic digital beamforming method and apparatus for satellites, applicable to satellite digital processing payloads and satellite communication payloads employing dynamic beamforming technology. Background Technology

[0002] As satellite communication services evolve towards broadband, multi-user, and on-demand services, satellite-to-ground communication systems are constantly approaching the Shannon limit. Given the increasingly scarce spectrum resources, how to improve spectrum efficiency and provide service guarantees for communication services in different regions and at different times under limited spectrum, transmit power, antenna aperture, and onboard processing resources has become a key issue that next-generation satellite communication systems need to address.

[0003] In application scenarios where user distribution and service traffic exhibit spatiotemporal variations, traditional fixed-beam coverage methods struggle to consistently and effectively match satellite resources with actual service demands, often resulting in insufficient communication resources in some areas while resources remain idle in others. Therefore, it is necessary to dynamically configure satellite beam coverage based on changes in service area, user numbers, and service intensity to improve the utilization efficiency of satellite spectrum, power, and antenna resources, and reduce the operating costs of satellite communication systems.

[0004] Beamforming technology controls the amplitude and phase of the signals corresponding to each element in an array antenna or feed array to form a beam in a specific area that meets communication requirements. Current satellite communication payloads primarily employ analog beamforming and digital beamforming.

[0005] Analog beamforming typically adjusts the amplitude and phase relationships between feed channels using RF phase shifters, power dividers, combiners, and analog beamforming networks. This method requires relatively few digital processing resources, but its beamforming capability is usually limited by the RF channels and the structure of the analog beamforming network, resulting in relatively fixed beam characteristics. In some applications, it suffers from high sidelobes and limited multi-beam parallelism. When it is necessary to change the beam coverage area or improve beam scheduling capabilities, it is usually necessary to add RF switches, phase shifters, feed channels, or beamforming branches, making the beamforming network more complex and increasing the satellite payload's quality, power consumption, heat dissipation, and engineering implementation difficulty.

[0006] Digital beamforming adjusts the weighting values ​​of the signals corresponding to each feed channel digitally, enabling it to process feed signals according to predetermined beam coverage requirements. It possesses high dynamic beam configuration and on-orbit reconfiguration capabilities, making it a crucial technology for improving the spectral efficiency and resource utilization of satellite systems. However, if satellites directly employ complex digital beamforming networks, current onboard computing power is insufficient to support them, and power consumption and heat dissipation issues remain unresolved.

[0007] Some existing beamforming methods use ideal array manifolds or establish antenna response models based solely on the geometric positions of the feed elements. These methods fail to adequately reflect the influence of factors such as the coupling between the array feed and the reflector, the mutual coupling between feed elements, and the amplitude and phase errors of the feed channel on the antenna radiation characteristics. This may lead to discrepancies between the theoretical radiation pattern and the actual antenna radiation pattern.

[0008] Therefore, a reasonable balance needs to be struck between beamforming performance, beamforming network complexity, and antenna system power consumption and heat dissipation, and a dynamic digital beamforming method suitable for satellites needs to be provided. This method can combine the radiation characteristics and hardware constraints of actual satellite antennas to dynamically form different types of beams according to user service requirements. While meeting the requirements of computing speed, transmit power and satellite hardware implementation, it can reduce sidelobe levels and optimize the computing scale, thereby improving the real-time performance of satellite beam adjustment and the utilization efficiency of satellite communication resources. Summary of the Invention

[0009] To address the problems existing in the prior art, this invention provides a dynamic digital beamforming method and apparatus for satellites. Under the conditions of satisfying the main lobe gain, main lobe flatness and satellite antenna power constraints, it can minimize the maximum beam response in the side lobe region and realize the beam configuration to adapt to the dynamic changes of satellite communication services in time and space, thereby enabling the satellite antenna to form beam coverage that matches the service requirements.

[0010] To achieve the above-mentioned technical effects, on the one hand, the present invention provides a dynamic digital beamforming method for satellites, comprising the following steps: The system acquires satellite communication service area information, satellite position and attitude information, and onboard antenna status information. The onboard antenna status information includes at least the active element radiation patterns corresponding to multiple feed units. Based on the satellite communication service area information, the system determines the number of beams radiated by the onboard antenna, the coverage area, and the beam pattern, which includes spot beams, area beams, and movable beams. Based on the satellite position and attitude information, the satellite communication service area is transformed from the geographic coordinate system to the onboard antenna coordinate system. Under the onboard antenna coordinate system, at least one main lobe region and at least one side lobe region are determined according to the target area boundary. Initial angle sampling points are selected in the main lobe region and the side lobe region respectively to form the main lobe angle sampling set and the side lobe angle sampling set. A beam weight optimization model is established by using the feed weight vector and the upper bound of the beam sidelobe as optimization variables. The beam weight optimization model aims to minimize the upper bound of the beam sidelobe and includes constraints on the main lobe complex electric field response, the sidelobe complex electric field response, the total antenna power constraint, and the power constraint of a single feed channel. The beam weight optimization model is converted into a second-order cone programming model and solved to obtain candidate feed weight vectors. The radiation pattern corresponding to the candidate feed weight vectors is calculated to identify main lobe error points and side lobe error points. When there are angle points that do not meet the requirements, at least one angle point is selected according to the preset point selection rules to supplement the corresponding main lobe angle sampling set or the side lobe angle sampling set, and the second-order cone programming model is solved again. When all angle points meet the requirements or the number of optimizations reaches the preset limit, the current feed weight vector is output. The amplitude and phase of the corresponding signal of each feed unit are adjusted according to the current feed weight vector output to form a satellite beam corresponding to the satellite communication service area.

[0011] Furthermore, the satellite communication service area information includes the location, boundary, service intensity, service type, and service duration of the service area; the satellite position and attitude information includes the satellite orbital position and satellite attitude; and the onboard antenna status information also includes the onboard antenna line-of-sight direction, operating frequency, antenna size, number of feed elements, allowable scanning range, upper limit of total antenna power, and upper limit of power per feed channel.

[0012] Furthermore, the main lobe region is an antenna angle region determined according to the service area that needs to meet preset beam gain, complex electric field response, or gain flatness requirements; the side lobe region is an angle region within the service field of view of the satellite antenna that needs to limit the beam response, excluding the main lobe region; the service area is the geographical area that the satellite communication system needs to provide communication coverage for or the area where the moving target is located. Specifically, for point beam services, the main lobe region is determined based on the target direction and the required beamwidth; for area beam services, the service area boundary is determined as the main lobe region; and for movable beam services, the area where the moving target is located is determined as the main lobe region.

[0013] Furthermore, the main lobe angle sampling set is M = {Ωp, p = 1, 2, ..., P}. MThe sidelobe angle sampling set is S={Ωq, q=1,2,…,P}. S The main lobe region and the side lobe regions may use the same or different initial sampling intervals. Based on the business needs of each business area, a target complex electric field response and an allowable response deviation are set for the main lobe sampling points in the main lobe angle sampling set.

[0014] Furthermore, the method further includes: constructing an array response vector corresponding to the angular position based on the radiation pattern of the active unit corresponding to the multiple feed units; The step of constructing the array response vector corresponding to the angular position based on the radiation pattern of the active unit corresponding to the multiple feed units further includes: Let E be the complex electric field response of the active element at the angular position Ω of the nth feed element. n If (Ω), then the initial array response vector at the angular position Ω is: e(Ω) = [E1(Ω), E2(Ω), ..., E N (Ω)]ᵀ; A feed channel calibration matrix is ​​established, and the array response vector a(Ω) is obtained based on the feed channel calibration matrix; the feed channel calibration matrix is ​​a diagonal matrix, and its nth diagonal element represents the amplitude correction coefficient and phase correction coefficient of the nth feed channel.

[0015] Furthermore, the beam weight optimization model aims to minimize the upper bound t of the beam sidelobe and satisfies the following constraints: sidelobe complex electric field response constraint, main lobe complex electric field response constraint, total antenna power constraint, and single feed channel power constraint. The sidelobe complex electric field response constraint is specifically defined as follows: for any angle sampling point Ωq in the sidelobe angle sampling set S, the following condition is met: ; The main lobe complex electric field response constraint is specifically defined as follows: for any angle sampling point Ωp in the main lobe angle sampling set M, the following condition is met: ; The specific antenna total power constraint is as follows: ; The specific power constraint for the single-feed channel is: |wn| ≤ √Pn,max, n=1,2,…,N; Where w is the feed weight vector; a(Ω) is the array response vector; Ωq is the angle sampling point in the sidelobe angle sampling set; Ωp is the angle sampling point in the main lobe angle sampling set; dp is the target complex electric field response of the main lobe sampling point Ωp; εp is the allowable response deviation of the main lobe sampling point Ωp; Pmax is the total allowable input power of the spaceborne antenna; and Pn,max is the maximum allowable input power of the nth feed channel.

[0016] Further, the step of converting the beam weight optimization model into a second-order cone programming model and solving it to obtain the candidate feed weight vector includes: The sidelobe complex electric field response constraint is converted into a L2 constraint; and the main lobe complex electric field response constraint, the L2 constraint, the total antenna power constraint, and the single-feed channel power constraint are combined to form a second-order cone programming model. The second-order cone programming model is solved using the interior point method or convex optimization algorithm to obtain the candidate power supply weight vector; When solving the second-order cone programming model using the interior-point method, the following steps are included: Set the initial iteration point, maximum number of iterations, and convergence tolerance; Calculate the original residual, dual residual, and complementary gap corresponding to the initial iteration point; Determine whether the original residual, dual residual, and complementary gap satisfy the convergence condition; If the convergence condition is not met, calculate the search direction and step size; Update the original variables, dual variables, and slack variables, and recalculate the original residuals, dual residuals, and complementary gaps based on the updated original variables, dual variables, and slack variables; Repeat the above judgment and update until the convergence condition is met or the maximum number of iterations is reached.

[0017] Further, the step of calculating the radiation pattern corresponding to the candidate feed weight vector and identifying the main lobe error point and the side lobe error point includes: A dense set of complex kernel angles is established in the main lobe region and the side lobe region by using an angle interval smaller than the initial angle sampling interval; The complex electric field response at each angular position in the set of compact kernel angles is calculated based on the candidate feed weight vector; For the verification angle position located within the main lobe region, the main lobe response deviation is calculated based on the complex electric field response. When the main lobe response deviation is greater than the main lobe verification tolerance, the verification angle position is determined as a candidate point for main lobe violation. For the verification angle position located within the sidelobe region, the sidelobe response excess is calculated based on the complex electric field response. When the sidelobe response excess is greater than the sidelobe verification tolerance, the verification angle position is determined as a candidate point for sidelobe violation. At least one angle point is selected from the main lobe violation candidate points and the side lobe violation candidate points respectively as the main lobe excess point and the side lobe excess point; wherein, the selected angle points include: angle points with the largest number of main lobe response deviations or side lobe response excesses, angle points corresponding to local peaks in the radiation pattern, angle points where the response descent rate at the main lobe edge does not meet the requirements, and angle points that are more than a preset distance from the existing angle sampling points. The main lobe out-of-range points are added to the main lobe angle sampling set, and the side lobe out-of-range points are added to the side lobe angle sampling set.

[0018] Furthermore, the method also includes: Whether to update the feed weight vector is determined based on at least one of the following changes: change in the service area boundary, change in the service area center location, change in service intensity, change in the location of the moving target, and change in beam pointing accuracy error; wherein, a corresponding update threshold is set for each of the different changes. When any change reaches the corresponding update threshold, the main lobe region and side lobe region are redefined, and beam weight optimization is re-executed.

[0019] On the other hand, the present invention also provides a spaceborne dynamic digital beamforming apparatus, the apparatus being used to implement the method as described in the first aspect, comprising: The information acquisition module is used to acquire satellite communication service area information, satellite position and attitude information, and onboard antenna status information; the onboard antenna status information includes at least the active element radiation patterns corresponding to multiple feed units; the number of beams radiated by the onboard antenna, coverage area, and beam pattern are determined based on the satellite communication service area information, and the beam pattern includes spot beams, area beams, and movable beams; The region mapping module is used to transform the satellite communication service area from the geographic coordinate system to the spaceborne antenna coordinate system according to the satellite position and attitude information. Under the spaceborne antenna coordinate system, at least one main lobe region and at least one side lobe region are determined according to the boundary of the target area. Initial angle sampling points are selected in the main lobe region and the side lobe region respectively to form the main lobe angle sampling set and the side lobe angle sampling set. The constraint construction module is used to establish a beam weight optimization model by taking the feed weight vector and the upper bound of the beam sidelobe as optimization variables. The beam weight optimization model aims to minimize the upper bound of the beam sidelobe and includes main lobe complex electric field response constraints, sidelobe complex electric field response constraints, total antenna power constraints, and single feed channel power constraints. The optimization and solution module is used to convert the beam weight optimization model into a second-order cone programming model and solve it to obtain candidate feed weight vectors. The weight verification module is used to calculate the radiation pattern corresponding to the candidate feed weight vector, identify the main lobe error point and the side lobe error point; when there are angle points that do not meet the requirements, at least one angle point is selected according to the violation amount from large to small to supplement the corresponding main lobe angle sampling set or the side lobe angle sampling set, and then sent back to the optimization solution module for model solution; when all angle points meet the requirements or the number of optimizations reaches the preset limit, the current feed weight vector is output; so that the antenna can adjust the amplitude and phase of the signal corresponding to each feed unit according to the output current feed weight vector to form a satellite beam corresponding to the satellite communication service area.

[0020] Compared with the prior art, the dynamic digital beamforming method and apparatus for satellites provided by the present invention have the following advantages: (1) The present invention can dynamically adjust the satellite beam according to changes in service area and service intensity to form a point beam, a regional beam, a mobile beam or a multi-region coverage beam.

[0021] (2) The present invention uses the active element radiation pattern of each feed unit to construct the beam response model, which can reflect the interaction between the array feed and the reflector, the mutual coupling of the feed units and the influence of the installation platform on the radiation performance, and improve the accuracy of the actual antenna radiation pattern.

[0022] (3) By adopting an adaptive constraint sampling method, the angle points that play a major role in limiting the radiation pattern index can be gradually added to the optimization model. This reduces the possibility of missing local peaks in side lobes and out-of-range points at the edges of the main lobe, while avoiding the introduction of too many constraints at once by a fixed dense grid.

[0023] (4) Under the condition of meeting the requirements of main lobe gain or main lobe response flatness, the present invention reduces the maximum beam response in the side lobe region, which is beneficial to reduce spatial interference between co-frequency beams or adjacent coverage areas.

[0024] (5) In the process of beam weight optimization, the present invention incorporates the total power of the antenna and the power of a single feed channel to ensure that the optimization results meet the power carrying capacity of the satellite antenna and the feed channel.

[0025] (6) For multiple coverage areas carrying the same service content, the present invention can use the same power supply weight vector to form multiple spatial main lobes; for multiple coverage areas carrying independent service content, corresponding power supply weight vectors can be generated respectively. Attached Figure Description

[0026] Figure 1 A flowchart illustrating the steps of the dynamic digital beamforming method applied to satellites according to an embodiment of the present invention; Figure 2A detailed flowchart of the dynamic digital beamforming method applied to satellites provided in an embodiment of the present invention; Figure 3 A flowchart of solving a second-order cone programming model for a dynamic digital beamforming method applied to satellites, provided in an embodiment of the present invention; Figure 4 A schematic block diagram of the spaceborne dynamic digital beamforming device provided in another embodiment of the present invention; Figure 5 A schematic diagram of the nadir beam simulation results of the dynamic digital beamforming method applied to satellites according to an embodiment of the present invention; Figure 6 This is a schematic diagram of the edge beam simulation results of the dynamic digital beamforming method applied to satellites according to an embodiment of the present invention; Figure 7 A schematic diagram of the point beam simulation results of the dynamic digital beamforming method applied to satellites according to an embodiment of the present invention; Figure 8 A schematic diagram of the regional beam simulation results of the dynamic digital beamforming method applied to satellites according to an embodiment of the present invention; Figure 9 This is a schematic diagram of multiple point beam simulation results of the dynamic digital beamforming method applied to satellites according to an embodiment of the present invention. Detailed Implementation

[0027] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0028] It should be noted that references to "an embodiment," "embodiment," "example embodiment," etc., in this specification refer to the described embodiment including specific features, structures, or characteristics, but not every embodiment must include these specific features, structures, or characteristics. Furthermore, such expressions do not refer to the same embodiment. Moreover, when describing specific features, structures, or characteristics in conjunction with embodiments, whether or not explicitly described, it is indicated that incorporating such features, structures, or characteristics into other embodiments is within the knowledge of those skilled in the art.

[0029] Furthermore, certain terms are used in the specification and subsequent claims to refer to specific components or parts. Those skilled in the art will understand that manufacturers may use different names or terms to refer to the same component or part. This specification and subsequent claims do not distinguish components or parts by differences in name, but rather by differences in function. The terms "comprising" and "including" used throughout the specification and subsequent claims are open-ended and should be interpreted as "including but not limited to." Additionally, the term "connection" here includes any direct and indirect electrical connection means. Indirect electrical connection means include connections made through other means.

[0030] Figures 1-2 This invention illustrates a dynamic digital beamforming method for satellites, provided by an embodiment of the present invention. The method determines the main lobe region and side lobe region based on the satellite communication service area and the satellite's operating status. It establishes an actual antenna response model using the active element radiation pattern of each feed unit, and solves for the amplitude and phase weights of the feed units while satisfying constraints on the main lobe response, side lobe response, total power, and single-channel power. The specific steps are as follows: S101: Acquire satellite communication service area information, satellite position and attitude information, and onboard antenna status information; the onboard antenna status information includes at least the active element radiation patterns corresponding to each of the multiple feed units; determine the number of beams radiated by the onboard antenna, coverage area, and beam pattern based on the satellite communication service area information, wherein the beam pattern includes point beams, area beams, and movable beams.

[0031] The satellite communication service area information includes the location, boundaries, service intensity, service type, and service duration of the service area; this information can be obtained through a ground network management system, an onboard service monitoring module, a service prediction system, or the number of user terminals accessing the system. The satellite position and attitude information includes the satellite's orbital position and attitude; the onboard antenna status information also includes the onboard antenna's line-of-sight direction, operating frequency, antenna size, number of feed elements, allowable scanning range, maximum total antenna power, and maximum power per feed channel.

[0032] Furthermore, it is also possible to obtain the amplitude and phase calibration parameters, temperature status, and device aging correction parameters of the onboard feed channel.

[0033] S102: Based on the satellite position and attitude information, the satellite communication service area is transformed from the geographic coordinate system to the satellite antenna coordinate system. Under the satellite antenna coordinate system, at least one main lobe region and at least one side lobe region are determined according to the boundary of the target area. Initial angle sampling points are selected in the main lobe region and the side lobe region respectively to form the main lobe angle sampling set and the side lobe angle sampling set.

[0034] After obtaining the above information in step S101, the number of beams, coverage area, and beam pattern of the satellite-borne antenna are determined based on the satellite communication service area information. The beam patterns include point beams, area beams, and movable beams. The main lobe region and side lobe regions are divided according to the corresponding application scenarios. For point beam services, the main lobe region is determined centered on the target direction and based on the required beamwidth. For services with relatively concentrated areas, area beams are selected, and the service area boundary is determined as the main lobe region, while other areas are determined as side lobe regions. If the service area is relatively dispersed and multiple beams need to be generated simultaneously, multiple main lobe regions need to be divided. The size of the region determines whether it is multiple point beams or a point beam + area beam pattern. Other areas surrounding the main lobe region and within the maximum effective range of the satellite-borne antenna are considered side lobe regions. For application scenarios with spatiotemporal variation characteristics, movable beams need to be selected. The area where the moving target is located is determined as the main lobe region, while other areas are considered side lobe regions. The real-time calculated output beam direction is adjusted according to the target's location. Provided that the capacity and AEIRP requirements are met, the above application scenarios can also be combined simultaneously. This invention can propose corresponding beam allocation schemes according to business needs.

[0035] In this embodiment, the active element pattern method antenna response model is used as input. The core idea of ​​this method is to excite the nth feed element and connect the other feed elements to a matched load to obtain the complex electric field pattern of the nth feed element at different angular positions.

[0036] Furthermore, the method further includes: constructing an array response vector corresponding to the angular position based on the radiation pattern of the active unit corresponding to the multiple feed units; The step of constructing the array response vector corresponding to the angular position based on the radiation pattern of the active unit corresponding to the multiple feed units further includes: Let E be the complex electric field response of the active element at the angular position Ω of the nth feed element. n If (Ω), then the initial array response vector at the angular position Ω is: e(Ω) = [E1(Ω), E2(Ω), ..., E N (Ω)]ᵀ;Without considering the feed channel error, the beam response model can be directly established based on e(Ω); When considering the amplitude and phase errors of the feed channel, a feed channel calibration matrix is ​​established, and the array response vector is obtained based on the feed channel calibration matrix. The feed channel calibration matrix is ​​a diagonal matrix, and its nth diagonal element represents the amplitude correction coefficient and phase correction coefficient of the nth feed channel. Finally, the array response vector a(Ω) is obtained.

[0037] Let the power supply weight vector be: w=[w1,w2,…,wN]ᵀ; The complex electric field response F(Ω) of the spaceborne antenna at the angular position Ω is: ; Active element radiation patterns can be obtained through full-wave simulation, anechoic chamber testing, or on-orbit calibration, and can be pre-stored in the memory of the ground processing system for on-demand uploading or the onboard processor.

[0038] The main lobe region is an antenna angle region determined according to the service area, which needs to meet preset beam gain, complex electric field response, or gain flatness requirements; the side lobe region is an angle region within the service field of view of the satellite antenna, excluding the main lobe region, where beam response needs to be limited; the service area is the geographical area where the satellite communication system needs to provide communication coverage or the area where the moving target is located; wherein, for point beam services, the main lobe region is determined with the target direction as the center and according to the required beamwidth; for area beam services, the service area boundary is determined as the main lobe region; for movable beam services, the area where the moving target is located is determined as the main lobe region.

[0039] In this embodiment, the main lobe angle sampling set is set as M = {Ωp, p = 1, 2, ..., P}. M The sidelobe angle sampling set is S={Ωq, q=1,2,…,P}. S The main lobe region and the side lobe region may use the same or different initial sampling intervals; for the main lobe edge and the edge of the satellite coverage field of view where the radiation pattern changes rapidly, a smaller initial sampling interval can be used; for regions where the radiation pattern changes relatively gently, a larger initial sampling interval can be used.

[0040] Based on the business needs of each business area, a target complex electric field response d is set for the main lobe sampling point Ωp in the main lobe angle sampling set. p and allowable response deviation ε p For beamforming regions requiring uniform gain within the main lobe area, the same target complex electric field amplitude can be set for each sampling point within the main lobe area. For multiple service areas with different service intensities, different target complex electric field amplitudes can be set according to service intensity, capacity requirements, or beam priority.

[0041] S103: Using the feed weight vector and the upper bound of the beam sidelobe as optimization variables, establish a beam weight optimization model; the beam weight optimization model aims to minimize the upper bound of the beam sidelobe and includes main lobe complex electric field response constraints, sidelobe complex electric field response constraints, total antenna power constraints, and single feed channel power constraints.

[0042] That is, the beam weight optimization model in this embodiment aims to maximize the main lobe gain and reduce the beam sidelobes (the sidelobe level in the sidelobe region is less than the upper limit of the level), while taking into account the main lobe complex electric field response constraints, the sidelobe complex electric field response constraints, and the power constraints of the single feed unit.

[0043] This embodiment determines the objective function based on the input region information and spaceborne antenna information, and establishes and solves the beam weight optimization model. Specifically, the beam weight optimization model aims to minimize the upper bound t of the beam sidelobes and satisfies the following constraints: sidelobe complex electric field response constraint, main lobe complex electric field response constraint, total antenna power constraint, and single-feed channel power constraint. The sidelobe complex electric field response constraint is specifically defined as follows: for any angle sampling point Ωq in the sidelobe angle sampling set S, the following condition is met: ; The main lobe complex electric field response constraint is specifically defined as follows: for any angle sampling point Ωp in the main lobe angle sampling set M, the following condition is met: ; The specific antenna total power constraint is as follows: ; The specific power constraint for the single-feed channel is: |wn| ≤ √Pn,max, n=1,2,…,N; Where w is the feed weight vector; a(Ω) is the array response vector; Ωq is the angle sampling point in the sidelobe angle sampling set; Ωp is the angle sampling point in the main lobe angle sampling set; dp is the target complex electric field response of the main lobe sampling point Ωp; εp is the allowable response deviation of the main lobe sampling point Ωp; Pmax is the total allowable input power of the spaceborne antenna; and Pn,max is the maximum allowable input power of the nth feed channel.

[0044] S104: Convert the beam weight optimization model into a second-order cone programming model and solve it to obtain candidate feed weight vectors; calculate the radiation pattern corresponding to the candidate feed weight vectors, and identify the main lobe error point and the side lobe error point; when there are angle points that do not meet the requirements, select at least one angle point according to the preset point selection rules to supplement the corresponding main lobe angle sampling set or side lobe angle sampling set, and resolve the second-order cone programming model; the point selection rules in this embodiment are specifically selected according to the violation amount from large to small; when all angle points meet the requirements or the number of optimizations reaches the preset number of optimizations limit, output the current feed weight vector.

[0045] Further, the step of converting the beam weight optimization model into a second-order cone programming model and solving it to obtain candidate feed weight vectors includes: converting the sidelobe complex electric field response constraint into a L2 constraint; and combining the main lobe complex electric field response constraint, the L2 constraint, the total antenna power constraint, and the single feed channel power constraint to form a second-order cone programming model; and using the interior point method or other convex optimization algorithms capable of solving second-order cone programming problems to solve the second-order cone programming model to obtain candidate feed weight vectors. When the magnitude of the target complex electric field of the main lobe is normalized to 1, t represents the ratio of the magnitude of the complex electric field of the side lobe to the magnitude of the complex electric field of the target main lobe. The complex modulus constraint can be converted into a 2-norm constraint composed of the real and imaginary parts. It can be represented as: ; Therefore, the beam weight optimization model can be converted into a second-order cone programming model.

[0046] like Figure 3 As shown, solving the second-order cone programming model using the interior point method includes: Set the initial iteration point, maximum number of iterations, and convergence tolerance; Calculate the original residual, dual residual, and complementary gap corresponding to the initial iteration point; Determine whether the original residual, dual residual, and complementary gap satisfy the convergence condition; If the convergence condition is not met, calculate the search direction and step size; Update the original variables, dual variables, and slack variables, and recalculate the original residuals, dual residuals, and complementary gaps based on the updated original variables, dual variables, and slack variables; Repeat the above judgment and update until the convergence condition is met or the maximum number of iterations is reached.

[0047] Further, the step of calculating the radiation pattern corresponding to the candidate feed weight vector and identifying the main lobe error point and the side lobe error point includes: A dense complex kernel angle set is established in the main lobe and side lobe regions using an angle interval smaller than the initial angle sampling interval. The complex electric field response at each angle position in the dense complex kernel angle set is calculated based on the candidate feed weight vector. For a kernel angle position located within the main lobe region, the main lobe response deviation is calculated based on the complex electric field response. When the main lobe response deviation is greater than the main lobe complex tolerance, the kernel angle position is determined as a main lobe violation candidate point. For a kernel angle position located within the side lobe region, the side lobe response exceedance is calculated based on the complex electric field response. When the side lobe response exceedance is greater than the side lobe complex tolerance... The verification angle position is determined as a candidate point for sidelobe violation; at least one angle point is selected from the candidate points for main lobe violation and the candidate points for sidelobe violation as the main lobe excess point and the sidelobe excess point, respectively; wherein, the selected angle points include: angle points with the largest number of main lobe response deviations or sidelobe response excesses, angle points corresponding to local peaks in the radiation pattern, angle points where the response descent rate at the main lobe edge does not meet the requirements, and angle points that are more than a preset distance from existing angle sampling points; the main lobe excess points are added to the main lobe angle sampling set, and the sidelobe excess points are added to the sidelobe angle sampling set.

[0048] In practice, after obtaining the candidate feed weight vector, a dense complex kernel angle set V is established in the main lobe region and the side lobe region by adopting an angle interval smaller than the initial angle sampling interval.

[0049] The complex electric field response at each angular position in the complex angle set V is calculated based on the candidate feed weight vector.

[0050] For the complex angle position Ωv located within the main lobe region, calculate the main lobe response deviation: For the complex angle position Ωv located within the main lobe region, calculate the main lobe response deviation: ; When em(Ωv)>δm, the verification angle position is determined as the main lobe violation candidate point.

[0051] For the core angle position Ωv located within the sidelobe region, calculate the sidelobe response overlimit: ; When es(Ωv)>δs, the verification angle position is determined as a candidate point for sidelobe violation.

[0052] Wherein, δm and δs are the main lobe nuclear tolerance and the side lobe nuclear tolerance, respectively.

[0053] Select at least one of the following angle points from the candidate violation points as the violation angle point: 1. The preset number of angle points where the main lobe response deviation or side lobe response exceeds the maximum limit; 2. The angle point corresponding to the local peak of the radiation pattern; 3. Angle points where the response descent rate at the edge of the main lobe does not meet the requirements; 4. Angle points that are more than the preset distance from the existing angle sampling points.

[0054] The non-compliant angle points of the main lobe are added to the main lobe angle sampling set M, and the non-compliant angle points of the side lobes are added to the side lobe angle sampling set S, and then the model is re-optimized.

[0055] Repeat the optimization and direction pattern verification until any of the following termination conditions are met: 1. There are no non-compliant angle points where the response deviation exceeds the preset review tolerance; 2. The number of adaptive updates has reached the preset limit; 3. Optimize calculation time to reach the preset time limit.

[0056] Using the above method, it is not necessary to add all the dense angle sampling points to the initial optimization model. Instead, angle constraints that affect the radiation pattern indicators are gradually added based on the radiation pattern verification results.

[0057] The optimized feed weights are input into the digital beamforming network to control the amplitude and phase of the signal corresponding to each feed unit.

[0058] This embodiment determines whether to update the beam weights based on at least one of the following changes: 1. Changes in business area boundaries; 2. Changes in the central location of the business area; 3. Changes in business intensity; 4. Change in the position of the moving target; 5. Change in beam pointing accuracy error; Set corresponding update thresholds for different changes. When any change reaches the corresponding update threshold, redetermine the main lobe region and side lobe region, and re-execute beam weight optimization. When the change does not reach the update threshold, continue to use the current beam weights to avoid frequent beam switching caused by small service fluctuations or measurement noise.

[0059] S105: Adjust the amplitude and phase of the corresponding signal of each feed unit according to the current output feed weight vector to form a satellite beam corresponding to the satellite communication service area. If the service requirements change, determine whether to update the feed weight vector of each feed unit; if an update is required, redetermine the main lobe region and side lobe region and update the feed weight vector of each feed unit; if no update is required, continue to use the current feed weight vector.

[0060] In this embodiment, when there are multiple separate service areas carrying the same service signal, a single power supply weight vector can be used to form multiple main lobes; when multiple coverage areas carry independent service content, corresponding power supply weight vectors can be generated respectively.

[0061] Figure 4 This invention illustrates a spaceborne dynamic digital beamforming apparatus 100 according to another embodiment of the present invention. The apparatus 100 is used to implement the methods described in the above embodiments and includes an information acquisition module 10, a region mapping module 20, a constraint construction module 30, an optimization solution module 40, and a weight verification module 50, wherein: The information acquisition module 10 is used to acquire satellite communication service area information, satellite position and attitude information, and onboard antenna status information; the onboard antenna status information includes at least the active element radiation pattern corresponding to each of the multiple feed units; and determines the number of beams, coverage area, and beam pattern radiated by the onboard antenna based on the satellite communication service area information, wherein the beam pattern includes point beams, area beams, and movable beams.

[0062] The region mapping module 20 is used to transform the satellite communication service area from the geographic coordinate system to the spaceborne antenna coordinate system according to the satellite position and attitude information, and to determine at least one main lobe region and at least one side lobe region according to the target region boundary in the spaceborne antenna coordinate system; and to select initial angle sampling points in the main lobe region and the side lobe region respectively to form the main lobe angle sampling set and the side lobe angle sampling set.

[0063] The constraint construction module 30 is used to establish a beam weight optimization model by taking the feed weight vector and the upper bound of the beam sidelobe as optimization variables. The beam weight optimization model aims to minimize the upper bound of the beam sidelobe and includes main lobe complex electric field response constraints, sidelobe complex electric field response constraints, total antenna power constraints, and single feed channel power constraints.

[0064] The optimization solution module 40 is used to convert the beam weight optimization model into a second-order cone programming model and solve it to obtain the candidate feed weight vector.

[0065] The weight verification module 50 is used to calculate the radiation pattern corresponding to the candidate feed weight vector, identify the main lobe error point and the side lobe error point; when there are angle points that do not meet the requirements, at least one angle point is selected according to the violation amount from large to small to supplement the corresponding main lobe angle sampling set or the side lobe angle sampling set, and then sent back to the optimization solution module 40 for model solution; when all angle points meet the requirements or the number of optimizations reaches the preset number of times limit, the current feed weight vector is output; so that the antenna can adjust the amplitude and phase of the signal corresponding to each feed unit according to the output current feed weight vector to form a satellite beam corresponding to the satellite communication service area.

[0066] The spaceborne antenna can be an array-fed reflector antenna, a direct-radiating array antenna, or other antennas with multiple independently controllable feed elements.

[0067] The above modules can be implemented through software, hardware, firmware, or a combination thereof. The ground processing system can transmit service requirements or coverage area inputs to the satellite, and the entire beam weight optimization process can be executed by the onboard processor, which inputs the calculated amplitude weights to the onboard antenna to form the target beam.

[0068] To verify the effectiveness of the embodiments of the present invention, an array-fed reflector antenna is used as an example. The satellite is located in GEO orbit, the feed array is arranged in a triangle, and the antenna operates in the Ka band. Figure 5 This is a simulation result of a point beam beneath a GEO satellite located at the center of the equator. The target service area at the center of the satellite's coverage area is set as the main lobe region, and the remaining serviceable field of view is set as the side lobe region. The method of this embodiment can generate a point beam at the center of the satellite's coverage area, with a 3dB beamwidth of 1.2°, a beam gain of up to 40.73 dBi, and a maximum sidelobe gain of 20.79 dBi, without interfering with co-frequency beams outside the coverage area.

[0069] Figure 6 Simulation results are shown where the target area at the edge of the satellite service field of view is set as the main lobe region. This invention enables beam scanning within the satellite field of view, forming a point beam at the edge of the coverage area with a beam center angle (AZ, EL) of (4.5°, 0°), a 3dB beamwidth of 1.2°, and a beam gain of up to 39.4 dBi, without the sidelobes interfering with other beams. Simultaneously, Figure 5 and Figure 6 The results show that the embodiments of the present invention can generate beams from Figure 5 The position in the middle is smoothly and continuously moved to Figure 6 The device features beam agility, allowing the algorithm to adaptively adjust the beam direction based on changes in the target's position. It is suitable for communication and tracking scenarios involving mobile targets such as aircraft, ships, and satellites.

[0070] like Figure 7 As shown, the method of the present invention can achieve coverage of service areas with different width spot beams. The 3dB beamwidth is 1.7°, the beam gain can reach 40.5dBi, and the sidelobe region gain is more than 20 dBi lower than the service area gain. It is suitable for normalized coverage of service areas of different scales.

[0071] like Figure 8As shown, a preset continuous geographical region is transformed to the coordinate system of the satellite antenna to establish a main lobe region of corresponding shape. Within the main lobe region, the same or graded target complex electric field responses are set to obtain a regional beam. The beamforming scheme proposed in this invention can realize regional beams of different shapes within the satellite's field of view. The maximum gain within the regional beam is 37.7 dBi, and the gain flatness is less than 0.5 dB, making it suitable for fixed-user broadcast communication scenarios.

[0072] like Figure 9 As shown, three separate service areas are set up, and corresponding target complex electric field responses are set according to the service intensity of each service area. When the three service areas carry the same service signal, a single feed weight vector is used to form three main lobes. This invention can simultaneously form multiple point beams within the satellite field of view according to service requirements. The gain and beamwidth of the three point beams are different depending on the service intensity. The gains of the three beams are 35.6 dBi, 34.5 dBi, and 34.4 dBi, respectively, and the beamwidths are 0.9°, 1.1°, and 1.2°, respectively. There is no mutual interference between the beams, and the sidelobe level in the remaining area is more than 20 dBi lower than the main lobe level. It is suitable for scenarios with uneven terminal distribution, can achieve wide-area unbalanced coverage, and meet the service needs of large-area dispersed multi-users.

[0073] In summary, the dynamic digital beamforming method and apparatus for satellites provided by this invention acquires satellite communication service area information, satellite position and attitude information, and onboard antenna state information including active element radiation patterns. It then transforms the service area into the onboard antenna coordinate system and forms main lobe and side lobe angle sampling sets. A beam weight optimization model is established with the goal of minimizing the upper bound of the beam sidelobes, and candidate feed weight vectors are obtained through second-order cone programming. Furthermore, a dense kernel angle set is used to identify main lobe out-of-range points and side lobe out-of-range points, adaptively supplementing constraints and resolving the problem. Finally, a feed weight vector that meets the requirements is output, forming the satellite beam. This method can reduce the maximum beam response in the sidelobe region while satisfying constraints on main lobe response, side lobe response, total antenna power, and single feed channel power, thereby improving the real-time performance of beamforming and the accuracy of the actual antenna radiation pattern. It is applicable to various dynamic beamforming application scenarios such as point beams, area beams, and movable beams.

[0074] Numerous specific details are set forth in the specification provided herein. However, it will be understood that embodiments of the invention may be practiced without these specific details. In some instances, well-known methods, structures, and techniques have not been shown in detail so as not to obscure the understanding of this specification.

[0075] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Furthermore, it should be pointed out that the scope of the methods and apparatuses in the embodiments of the present invention is not limited to performing functions in the order shown or discussed, but may also include performing functions substantially simultaneously or in the reverse order, depending on the functions involved. For example, the described methods may be performed in a different order than described, and various steps may be added, omitted, or combined. Additionally, features described with reference to certain examples may be combined in other examples.

[0076] Of course, the present invention may have other various embodiments. Without departing from the spirit and essence of the present invention, those skilled in the art can make various corresponding changes and modifications according to the present invention, but these corresponding changes and modifications should all fall within the protection scope of the appended claims.

Claims

1. A dynamic digital beamforming method for satellites, characterized in that, Including the following steps: The system acquires satellite communication service area information, satellite position and attitude information, and onboard antenna status information. The onboard antenna status information includes at least the active element radiation patterns corresponding to multiple feed units. Based on the satellite communication service area information, the system determines the number of beams radiated by the onboard antenna, the coverage area, and the beam pattern, which includes spot beams, area beams, and movable beams. Based on the satellite position and attitude information, the satellite communication service area is transformed from the geographic coordinate system to the onboard antenna coordinate system. Under the onboard antenna coordinate system, at least one main lobe region and at least one side lobe region are determined according to the target area boundary. Initial angle sampling points are selected in the main lobe region and the side lobe region respectively to form the main lobe angle sampling set and the side lobe angle sampling set; A beam weight optimization model is established by using the feed weight vector and the upper bound of the beam sidelobe as optimization variables. The beam weight optimization model aims to minimize the upper bound of the beam sidelobe and includes constraints on the main lobe complex electric field response, the sidelobe complex electric field response, the total antenna power constraint, and the power constraint of a single feed channel. The beam weight optimization model is converted into a second-order cone programming model and solved to obtain candidate feed weight vectors. The radiation pattern corresponding to the candidate feed weight vectors is calculated to identify main lobe error points and side lobe error points. When there are angle points that do not meet the requirements, at least one angle point is selected according to the preset point selection rules to supplement the corresponding main lobe angle sampling set or the side lobe angle sampling set, and the second-order cone programming model is solved again. When all angle points meet the requirements or the number of optimizations reaches the preset limit, the current feed weight vector is output. The amplitude and phase of the corresponding signal of each feed unit are adjusted according to the current feed weight vector output to form a satellite beam corresponding to the satellite communication service area.

2. The method according to claim 1, characterized in that, The satellite communication service area information includes the location, boundary, service intensity, service type, and service duration of the service area; the satellite position and attitude information includes the satellite orbital position and satellite attitude; the onboard antenna status information also includes the onboard antenna line-of-sight direction, operating frequency, antenna size, number of feed elements, allowable scanning range, upper limit of total antenna power, and upper limit of power per feed channel.

3. The method according to claim 1, characterized in that, The main lobe region is the antenna angle region that needs to meet preset beam gain, complex electric field response, or gain flatness requirements based on the service area; the side lobe region is the angle region within the field of view that the satellite antenna can serve, excluding the main lobe region, where beam response needs to be limited; the service area is the geographical area that the satellite communication system needs to provide communication coverage for or the area where the moving target is located. Specifically, for point beam services, the main lobe region is determined based on the target direction and the required beamwidth; for area beam services, the service area boundary is determined as the main lobe region; and for movable beam services, the area where the moving target is located is determined as the main lobe region.

4. The method according to claim 1, characterized in that, The main lobe angle sampling set is M = {Ωp, p = 1, 2, ..., P}. M The sidelobe angle sampling set is S={Ωq, q=1,2,…,P}. S The main lobe region and the side lobe regions may use the same or different initial sampling intervals. Based on the business needs of each business area, a target complex electric field response and an allowable response deviation are set for the main lobe sampling points in the main lobe angle sampling set.

5. The method according to claim 1, characterized in that, The method further includes: constructing an array response vector corresponding to the angular position based on the radiation pattern of the active unit corresponding to the multiple feed units; The step of constructing the array response vector corresponding to the angular position based on the radiation pattern of the active unit corresponding to the multiple feed units further includes: Let E be the complex electric field response of the active element at the angular position Ω of the nth feed element. n If (Ω), then the initial array response vector at the angular position Ω is: e(Ω) = [E1(Ω), E2(Ω), ..., E N (Ω)]ᵀ; A feed channel calibration matrix is ​​established, and the array response vector a(Ω) is obtained based on the feed channel calibration matrix; the feed channel calibration matrix is ​​a diagonal matrix, and its nth diagonal element represents the amplitude correction coefficient and phase correction coefficient of the nth feed channel.

6. The method according to claim 1, characterized in that, The beam weight optimization model aims to minimize the upper bound t of the beam sidelobe and satisfies the following constraints: sidelobe complex electric field response constraint, main lobe complex electric field response constraint, total antenna power constraint, and single-feed channel power constraint. The sidelobe complex electric field response constraint is specifically defined as follows: for any angle sampling point Ωq in the sidelobe angle sampling set S, the following condition is met: ; The main lobe complex electric field response constraint is specifically defined as follows: for any angle sampling point Ωp in the main lobe angle sampling set M, the following condition is met: ; The specific antenna total power constraint is as follows: ; The specific power constraint for the single-feed channel is: |wn| ≤ √Pn,max, n=1,2,…,N; Where w is the feed weight vector; a(Ω) is the array response vector; Ωq is the angle sampling point in the sidelobe angle sampling set; Ωp is the angle sampling point in the main lobe angle sampling set; dp is the target complex electric field response of the main lobe sampling point Ωp; εp is the allowable response deviation of the main lobe sampling point Ωp; Pmax is the total allowable input power of the spaceborne antenna; and Pn,max is the maximum allowable input power of the nth feed channel.

7. The method according to claim 6, characterized in that, The step of converting the beam weight optimization model into a second-order cone programming model and solving it to obtain candidate feed weight vectors includes: The sidelobe complex electric field response constraint is converted into a L2 constraint; and the main lobe complex electric field response constraint, the L2 constraint, the total antenna power constraint, and the single-feed channel power constraint are combined to form a second-order cone programming model. The second-order cone programming model is solved using the interior point method or convex optimization algorithm to obtain the candidate power supply weight vector; When solving the second-order cone programming model using the interior-point method, the following steps are included: Set the initial iteration point, maximum number of iterations, and convergence tolerance; Calculate the original residual, dual residual, and complementary gap corresponding to the initial iteration point; Determine whether the original residual, dual residual, and complementary gap satisfy the convergence condition; If the convergence condition is not met, calculate the search direction and step size; Update the original variables, dual variables, and slack variables, and recalculate the original residuals, dual residuals, and complementary gaps based on the updated original variables, dual variables, and slack variables; Repeat the above judgment and update until the convergence condition is met or the maximum number of iterations is reached.

8. The method according to claim 1, characterized in that, The calculation of the radiation pattern corresponding to the candidate feed weight vector, and the identification of main lobe error points and side lobe error points, includes: A dense set of complex kernel angles is established in the main lobe region and the side lobe region by using an angle interval smaller than the initial angle sampling interval; The complex electric field response at each angular position in the set of compact kernel angles is calculated based on the candidate feed weight vector; For the verification angle position located within the main lobe region, the main lobe response deviation is calculated based on the complex electric field response. When the main lobe response deviation is greater than the main lobe verification tolerance, the verification angle position is determined as a candidate point for main lobe violation. For the verification angle position located within the sidelobe region, the sidelobe response excess is calculated based on the complex electric field response. When the sidelobe response excess is greater than the sidelobe verification tolerance, the verification angle position is determined as a candidate point for sidelobe violation. At least one angle point is selected from the main lobe violation candidate points and the side lobe violation candidate points respectively as the main lobe excess point and the side lobe excess point; wherein, the selected angle points include: angle points with the largest number of main lobe response deviations or side lobe response excesses, angle points corresponding to local peaks in the radiation pattern, angle points where the response descent rate at the main lobe edge does not meet the requirements, and angle points that are more than a preset distance from the existing angle sampling points. The main lobe out-of-range points are added to the main lobe angle sampling set, and the side lobe out-of-range points are added to the side lobe angle sampling set.

9. The method according to claim 1, characterized in that, The method further includes: Whether to update the feed weight vector is determined based on at least one of the following changes: change in the service area boundary, change in the service area center location, change in service intensity, change in the location of the moving target, and change in beam pointing accuracy error; wherein, a corresponding update threshold is set for each of the different changes. When any change reaches the corresponding update threshold, the main lobe region and side lobe region are redefined, and beam weight optimization is re-executed.

10. A spaceborne dynamic digital beamforming device, characterized in that, The apparatus is used to implement the method as described in any one of claims 1 to 9, comprising: The information acquisition module is used to acquire satellite communication service area information, satellite position and attitude information, and onboard antenna status information; the onboard antenna status information includes at least the active element radiation patterns corresponding to multiple feed units; the number of beams radiated by the onboard antenna, coverage area, and beam pattern are determined based on the satellite communication service area information, and the beam pattern includes spot beams, area beams, and movable beams; The region mapping module is used to transform the satellite communication service area from the geographic coordinate system to the spaceborne antenna coordinate system according to the satellite position and attitude information, and to determine at least one main lobe region and at least one side lobe region according to the target region boundary in the spaceborne antenna coordinate system. Initial angle sampling points are selected in the main lobe region and the side lobe region respectively to form the main lobe angle sampling set and the side lobe angle sampling set; The constraint construction module is used to establish a beam weight optimization model by taking the feed weight vector and the upper bound of the beam sidelobe as optimization variables. The beam weight optimization model aims to minimize the upper bound of the beam sidelobe and includes main lobe complex electric field response constraints, sidelobe complex electric field response constraints, total antenna power constraints, and single feed channel power constraints. The optimization and solution module is used to convert the beam weight optimization model into a second-order cone programming model and solve it to obtain candidate feed weight vectors. The weight verification module is used to calculate the radiation pattern corresponding to the candidate feed weight vector and identify the main lobe error point and the side lobe error point. When there are angle points that do not meet the requirements, at least one angle point is selected from the largest to the smallest violation amount to supplement the corresponding main lobe angle sampling set or the side lobe angle sampling set, and then sent back to the optimization solution module for model solution; when all angle points meet the requirements or the number of optimizations reaches the preset limit, the current feed weight vector is output; so that the antenna can adjust the amplitude and phase of the corresponding signal of each feed unit according to the output current feed weight vector to form a satellite beam corresponding to the satellite communication service area.

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