Satellite multi-beam rapid forming method based on genetic algorithm

Abstract

The invention relates to a satellite multi-beam rapid forming method based on a genetic algorithm, and the method comprises the steps: inputting a constraint condition, and carrying out the array amplitude tapering, so as to meet the maximum sidelobe level demand of each beam; calculating the size of an array required for forming each wave beam so as to meet the requirement of the wave beam width; and optimizing the position of an array needing to be activated for forming each beam and the corresponding array element amplitude and phase coefficient based on a genetic algorithm. According to the invention, shaping of a plurality of circular wave beams with different sizes and proportional distribution of wave beam power resources are realized, so that the total radiation power of the array is maximized, and meanwhile, the calculation complexity is reduced.

Description

Technical Field

[0001] The present invention belongs to the technical field of satellite communications and relates to a satellite multi-beam rapid formation method based on a genetic algorithm. The method is oriented to a fully flexible satellite and uses a direct radiation array to form multiple circular beams of different sizes through a digital beam forming network to meet the different needs of users in various regions. Background Art

[0002] In recent years, as communication satellites develop towards networking, market uncertainty increases, investment cost-effectiveness improves, and subject to factors such as uneven user distribution and fluctuations in business demand, satellites are gradually developing from fixed-beam analog forwarding to flexible-beam digital processing, and fully flexible satellites have become a new hotspot. Fully flexible satellites include flexibility in coverage, capacity, frequency, connection, business, etc., among which flexible coverage is of great significance for satellite on-orbit mission planning and new market service capabilities. For fully flexible satellites, the use of direct radiation arrays to form multiple circular beams of different sizes can effectively meet the different needs of users in various regions.

[0003] However, according to array antenna theory, the array sizes required to form beams of different widths are different. For the narrowest beam, the entire array may need to participate in the synthesis, while for wider beams, only some array elements need to participate in the synthesis. This leads to the situation that when using an array to form multiple beams of different sizes, if all beams are activated from the center of the array, the amplifiers corresponding to some array elements may be oversaturated, while the amplifiers corresponding to other array elements may be undersaturated. For wider beams, how to select the set of array elements participating in the synthesis to maximize the array's radiated power is a difficult problem that satellite multi-beamforming algorithms urgently need to solve.

[0004] In addition, accurate beam shaping based on constraints such as beam 3dB profile, sidelobe level, and nulling requires sampling of the entire radiation space. As the array becomes larger, the amount of calculation becomes very large. Once multiple beams are formed, the amplitude and phase coefficients of multiple beams need to be jointly optimized under power constraints. The computational complexity will increase exponentially, making the optimization difficult to perform. Therefore, it is urgent to reduce some of the requirements for shaping accuracy and study a method for fast multi-beam formation under multiple constraints.

[0005] Traditional satellite multi-beamforming methods have three important drawbacks:

[0006] (1) Traditional methods do not optimize the array element set involved in the synthesis of multiple beams of different sizes, resulting in low power resource utilization of the entire array;

[0007] (2) The computational complexity of traditional methods to achieve accurate multi-beam shaping under multiple constraints is very high;

[0008] (3) Traditional methods cannot achieve joint optimization of beamforming and power resource allocation. Summary of the invention

[0009] The technical problem solved by the present invention is: to overcome the shortcomings of the prior art and propose a multi-beam rapid forming method based on genetic algorithm, which realizes the shaping of multiple circular beams of different sizes and the proportional allocation of beam power resources under the constraints of beam pointing, size, sidelobe level, power allocation ratio, etc., so as to maximize the total radiation power of the array and reduce the calculation complexity at the same time.

[0010] The solution of the present invention is:

[0011] In a first aspect, the present invention provides a satellite multi-beam rapid formation method based on a genetic algorithm, comprising:

[0012] Inputting constraint conditions, wherein the constraint conditions include array size, array element spacing, array element maximum power, directionality of each beam, beam width, beam maximum sidelobe level, and ratio of power resources allocated to each beam;

[0013] Perform array amplitude taper to meet the maximum sidelobe level requirement for each beam;

[0014] Calculate the array size required to form each beam to meet the beam width requirements;

[0015] The position of the activated array required to form each beam and the corresponding array element amplitude and phase coefficients are optimized based on a genetic algorithm.

[0016] Preferably, the beam width is the beam size.

[0017] Preferably, the position of the activated array required to form each beam and the corresponding array element amplitude and phase coefficient are optimized based on a genetic algorithm, and the method is as follows:

[0018] a) Initializing the row and column positions of the activation array required to form each beam in the entire array according to the array size required for each beam to form a population;

[0019] b) Given the position of the array required to be activated for each beam, that is, the row and column position where the array required to be activated starts in the entire array, the phase coefficient of the array required to be activated for each beam is calculated according to the beam pointing requirement and the beam scanning principle, and the initial amplitude coefficient of the array required to be activated for each beam is calculated according to the beam maximum sidelobe level requirement;

[0020] c) weighting the initial amplitude coefficient of the array required to activate each beam to obtain an amplitude coefficient that satisfies two constraints: the maximum power of the array element and the ratio of the power resources allocated to each beam;

[0021] d) Calculate the fitness function of each individual in the population;

[0022] e) Perform selection, crossover and mutation operations on the population in sequence to obtain the next generation population;

[0023] f) Determine whether the maximum number of iterations has been reached, if not, return to b); if the maximum number of iterations has been reached, end, and output the position of the array required to activate each beam obtained by the current population and the corresponding array element amplitude and phase coefficient.

[0024] Preferably, array amplitude tapering is performed using an array synthesis method.

[0025] Preferably, the array synthesis method includes but is not limited to Dolph-Chebyshev synthesis method and Taylor synthesis method.

[0026] Preferably, an array synthesis method is used to calculate the array size required to form each beam.

[0027] Preferably, the fitness function of each individual in the population is the maximum radiation power of the array.

[0028] Preferably, in step e), a roulette wheel and elite retention selection operation is performed on the population.

[0029] In a second aspect, the present invention provides a terminal device, comprising:

[0030] a memory for storing instructions executed by at least one processor;

[0031] The processor is used to execute instructions stored in the memory to implement the method described in the first aspect above.

[0032] In a third aspect, the present invention provides a computer-readable storage medium, wherein the computer-readable storage medium stores computer instructions. When the computer instructions are executed on a computer, the computer executes the method described in the first aspect.

[0033] The beneficial effects of the present invention compared with the prior art are:

[0034] (1) The present invention proposes a satellite multi-beam forming method based on genetic algorithm, which can not only realize accurate shaping and meet the coverage requirements of circular beams with different directions and sizes, but also meet the requirements of the maximum sidelobe level of the beam and achieve a higher system carrier-to-interference ratio;

[0035] (2) The present invention can effectively reduce the solution scale of the optimization algorithm, reduce the algorithm complexity, and improve the solution speed by optimizing the activation positions of different beam arrays and combining the rapid calculation of array amplitude and phase coefficients;

[0036] (3) The present invention optimizes and adjusts the amplitude coefficient of each beam participating in the synthesis array. Under the constraint of the maximum power of the array element, it can also achieve proportional distribution of power between beams, thereby meeting the demand for flexible adjustment of the power of the fully flexible satellite. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] Figure 1 The flowchart of the satellite multi-beam rapid formation method based on genetic algorithm is shown;

[0038] Figure 2 Schematic diagram of beam coverage;

[0039] Figure 3 is the array element radiation power distribution;

[0040] Figure 4 is the radiated power of each beam. DETAILED DESCRIPTION

[0041] The present invention will be further described below in conjunction with the accompanying drawings.

[0042] like Figure 1 As shown, the multi-beam rapid forming method based on genetic algorithm of the present invention mainly includes the following steps:

[0043] (1) Input constraints / requirements such as array size, element spacing, maximum element power, the pointing direction of each beam, beam width (i.e., beam size), maximum beam sidelobe level, and the ratio of power resources allocated to each beam;

[0044] (2) Select an array synthesis method, such as Dolph-Chebyshev synthesis or Taylor synthesis, to perform array amplitude tapering to meet the requirement of maximum sidelobe level for each beam;

[0045] (3) Given an array synthesis method, calculate the array size required to form each beam to meet the beam width requirements;

[0046] (4) Genetic optimization is used to determine the position of the array activation required to form each beam and the corresponding array element amplitude and phase coefficients:

[0047] a) Given the array size required for each beam, the row and column positions of the activation array required to form each beam in the entire array are initialized to form a population;

[0048] b) Given the position of each beam activation array (i.e., the row and column position where the required activation array starts in the entire array), according to the beam pointing requirements, the phase coefficient of the array activated by each beam is calculated according to the beam scanning principle, and according to the beam maximum sidelobe level requirements, the initial amplitude coefficient of the array activated by each beam is calculated according to the selected array synthesis method;

[0049] c) weighting the initial amplitude coefficient of the array activated by each beam to obtain an amplitude coefficient that satisfies two constraints: the maximum power of the array element and the ratio of the power resources allocated to each beam;

[0050] d) Calculate the fitness function of each individual in the population (i.e., the maximum radiation power of the array);

[0051] e) Perform selection, crossover and mutation operations on the population in sequence to obtain the next generation population;

[0052] f) Determine whether the maximum number of iterations has been reached;

[0053] i. If not, return to b);

[0054] ii. Otherwise, execute (5);

[0055] (5) Output the optimal array activation position and array amplitude and phase coefficients for each beam.

[0056] The present invention realizes the shaping of multiple circular beams of different sizes and the proportional allocation of beam power resources under the constraints of beam pointing, size, sidelobe level, power allocation ratio, etc., thereby maximizing the total radiation power of the array while reducing the calculation complexity.

[0057] The present invention also provides a terminal device, comprising: a memory for storing instructions executed by at least one processor; and a processor for executing the instructions stored in the memory to implement the above method.

[0058] The present invention also provides a computer-readable storage medium, wherein the computer-readable storage medium stores computer instructions. When the computer instructions are executed on a computer, the computer executes the above method.

[0059] Example:

[0060] This embodiment considers two coverage areas with different demand densities. The area with a smaller demand density is covered by 7 wide beams with a width of 2°, and the area with a larger demand density is covered by 19 narrow beams with a width of 1°. The frequency plan is arranged by using a four-color multiplexing method, such as Figure 2 As shown in the figure. The array size is 70×70, the array element spacing is 0.8686λ, and the frequency is 18.9GHz. The maximum sidelobe level requirement is -20dB, the transmit power allocated to each beam is equal, and the maximum array element power is 10W. In the genetic algorithm, the population size is 100, the crossover probability is 0.8, the mutation probability is 0.01, and the maximum number of iterations is 1000.

[0061] (1) According to the above settings, input the constraints / requirements such as array size, array element spacing, maximum array element power, the direction of each beam, beam width (i.e., beam size), maximum beam sidelobe level, and the ratio of power resources allocated to each beam;

[0062] (2) Select an array synthesis method. In this embodiment, Taylor synthesis method is selected to perform array amplitude tapering to meet the requirement of maximum sidelobe level of each beam.

[0063] (3) Given an array synthesis method, calculate the array size required to form each beam to meet the beam width requirements;

[0064] (4) Genetic optimization is used to determine the position of the array activation required to form each beam and the corresponding array element amplitude and phase coefficients:

[0065] a) Given the array size required for each beam, the row and column positions of the activation array required to form each beam in the entire array are initialized to form individuals. Each individual is integer-coded and has a size of twice the number of beams, thereby forming a population;

[0066] b) Given the position of each beam activation array (i.e., the row or column position where the required activation array starts in the entire array), the phase coefficient of the array activated by each beam is calculated according to the beam pointing requirement and the beam scanning principle, and the initial amplitude coefficient of the array activated by each beam is calculated according to the maximum sidelobe level requirement of the beam and the Taylor synthesis method;

[0067] c) weighting the initial amplitude coefficient of the array activated by each beam to obtain an amplitude coefficient that satisfies two constraints: the maximum power of the array element and the ratio of the power resources allocated to each beam;

[0068] d) Calculate the fitness function of each individual in the population (i.e., the maximum radiation power of the array);

[0069] e) Perform roulette wheel and elite retention selection operations, single-point crossover and mutation operations on the population in sequence to obtain the next generation population;

[0070] f) Determine whether the maximum number of iterations has been reached;

[0071] i. If not, return to b);

[0072] ii. Otherwise, execute (5);

[0073] (5) Output the optimal array activation position and array amplitude and phase coefficients for each beam.

[0074] By using the proposed method, the array element radiation power distribution tends to be uniform, such as Figure 3As shown, the radiation power of the entire array can be effectively improved. By comparing the optimized and expected beam profiles, it is found that the method proposed in the present invention can achieve accurate beamforming and meet the constraints of beam pointing and beam width. By observing the three-dimensional radiation pattern of the synthetic beam, it can be found that the proposed method can meet the maximum sidelobe level requirement through the selected array synthesis method. In addition, by optimizing and adjusting the amplitude coefficient of the array activated by each beam, the beam power achieved by the method proposed in the present invention is equal (such as Figure 4 ), which is consistent with the required power distribution ratio.

[0075] Parts of the present invention that are not described in detail belong to common knowledge among those skilled in the art.

Claims

1. A satellite multi-beam rapid formation method based on genetic algorithm, characterized in that: include: Inputting constraint conditions, wherein the constraint conditions include array size, array element spacing, array element maximum power, directionality of each beam, beam width, beam maximum sidelobe level, and ratio of power resources allocated to each beam; Perform array amplitude tapering to meet the maximum sidelobe level requirement for each beam; Calculate the array size required to form each beam to meet the beam width requirements; The position of the activated array required to form each beam and the corresponding array element amplitude and phase coefficients are optimized based on a genetic algorithm.

2. The method for rapid satellite multi-beam formation based on genetic algorithm according to claim 1, characterized in that: The beam width is the beam size.

3. The method for rapid satellite multi-beam formation based on genetic algorithm according to claim 1, characterized in that: The genetic algorithm is used to optimize the position of the array activation required to form each beam and the corresponding array element amplitude and phase coefficients. The method is as follows: a) Initializing the row and column positions of the activation array required to form each beam in the entire array according to the array size required for each beam to form a population; b) Given the position of the array required to be activated for each beam, that is, the row and column position where the array required to be activated starts in the entire array, the phase coefficient of the array required to be activated for each beam is calculated according to the beam pointing requirement and the beam scanning principle, and the initial amplitude coefficient of the array required to be activated for each beam is calculated according to the beam maximum sidelobe level requirement; c) weighting the initial amplitude coefficient of the array required to activate each beam to obtain an amplitude coefficient that satisfies two constraints: the maximum power of the array element and the ratio of the power resources allocated to each beam; d) Calculate the fitness function of each individual in the population; e) Perform selection, crossover and mutation operations on the population in sequence to obtain the next generation population; f) Determine whether the maximum number of iterations has been reached, if not, return to b); if the maximum number of iterations has been reached, end, and output the position of the array required to activate each beam obtained by the current population and the corresponding array element amplitude and phase coefficient.

4. The method for rapid satellite multi-beam formation based on genetic algorithm according to claim 1, characterized in that: Array amplitude tapering is performed using array synthesis methods.

5. The method for rapid satellite multi-beam formation based on genetic algorithm according to claim 4, characterized in that: The array synthesis method includes but is not limited to Dolph-Chebyshev synthesis method and Taylor synthesis method.

6. The method for rapid satellite multi-beam formation based on genetic algorithm according to claim 1, characterized in that: The array synthesis method is used to calculate the array size required to form each beam.

7. The method for rapid satellite multi-beam formation based on genetic algorithm according to claim 3, characterized in that: The fitness function of each individual in the population is the maximum radiation power of the array.

8. The method for rapid satellite multi-beam formation based on genetic algorithm according to claim 3, characterized in that: In step e), roulette wheel and elite retention selection operations are performed on the population.

9. A terminal device, characterized in that: include: a memory for storing instructions executed by at least one processor; A processor, configured to execute instructions stored in a memory to implement a method according to any one of claims 1 to 8.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer instructions, and when the computer instructions are executed on a computer, the computer is enabled to execute the method according to any one of claims 1 to 8.

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