A method for arranging non-periodic planar antenna array elements

Abstract

The present invention discloses a method for arranging non-periodic planar antenna elements, including: determining a rectangular planar non-periodic antenna array to be established; using an improved genetic iteration method to optimize the arrangement of antenna elements on the entire non-periodic plane, and retaining the optimal individuals of each generation during the iterative loop process; after the iteration is completed, the population generated by the optimal individual determines the relative positions of the antenna elements; arranging the non-periodic planar antenna elements based on the relative positions of the antenna elements; in this improved genetic iteration method, by planning the initial population and calculating the fitness function, excellent individuals that meet the conditions are selected, and relatively excellent population individuals are selected therefrom. On the basis of this population, real number coding operations are performed on it, and the population obtained by taking the real number coding at the local level is transcoded and combined with the population generated after the selection operation to participate in probability variation, thereby completing one round of loop operation, and so on, until the result generated by the loop meets the conditions, and the optimal individual is output.

Description

Technical Field

[0001] The present invention belongs to the field of lidar, and particularly relates to an improved genetic iterative algorithm for arranging non-periodic planar antenna elements. Background Art

[0002] The development of the times promotes the progress of technology. Traditional radars can no longer meet the tracking requirements for some long-range targets. Based on this, phased array radars have emerged. When developing solid-state lidars, the performance of optical phased arrays directly determines the performance of lidar systems. An optical phased array controls the phase of beam emission units through electrical signals and uses the interference of light to achieve beam scanning over long distances, at high speeds, and in large ranges. The equally spaced waveguide antenna array is the research basis for other waveguide element arrangement methods. However, in practical applications, this array antenna structure must simultaneously meet the conditions that there are no grating lobes or the grating lobes are very small, there is no mutual coupling phenomenon between antennas or it can be ignored, and the beam width of the antenna is controlled within a certain range. As a result, it is difficult for this array arrangement form to meet the requirements for system performance in actual work. Once the number of array elements is huge, the sidelobes in the actual data will increase, seriously affecting the quality of the final scanned beam.

[0003] Currently, genetic algorithms, immune algorithms, particle swarm algorithms, ant colony algorithms, simulated annealing algorithms, etc. have all been designed for the design and optimization of antenna element arrangements. Among them, genetic algorithms are divided into binary coding, Gray coding, real number coding, etc. Among them, when dealing with the optimization problems of some continuous functions, binary coding cannot directly reflect the inherent structure of the problem, and due to the randomness and uncertainty of the algorithm, its local search ability is poor. If the individual coding length is short, when deriving the planar array structure using a genetic algorithm, the accuracy will be very low, and it is easy to deviate significantly from the initial population during the iteration process, and the optimal solution obtained may be very different from the actual result. If the individual coding length is long, it will lead to an increase in the search space and memory occupancy. Real number coding has an advantage in local parameter processing, but because there are irrational numbers in its search range, it is easy to generate invalid solutions when dealing with the problem of element distribution, and this coding method is prone to premature convergence of the algorithm. Currently, in the operation of genetic algorithms, there are disadvantages such as high randomness, poor local search ability, and premature convergence when dealing with continuous function problems, which can easily cause large deviations in the results and affect the final operation results. Summary of the Invention

[0004] Objective of the Invention: To solve the problem that the arrangement of equally spaced waveguide antenna arrays is difficult to meet the requirements for system performance in actual work, and the problems of high randomness, poor local search ability, and premature convergence when dealing with continuous functions in the genetic algorithm used to optimize the arrangement of antenna elements. The present invention proposes an improved genetic iterative algorithm for arranging non-periodic planar antenna elements.

[0005] Technical Solution: An improved genetic iterative algorithm for arranging non-periodic planar antenna elements, comprising the following steps:

[0006] Step 1: According to the required number of antenna elements, randomly extract the initial coordinate positions of the antenna elements, and use the initial coordinate positions of the antenna elements as population individuals to create an initial population Q0. The initial population Q0 is a binary string population, and calculate the fitness of each individual in the initial population Q0;

[0007] Step 2: Determine whether the termination requirement is met. If it is met, stop the loop and output the optimal individual; otherwise, execute Step 3;

[0008] Step 3: Use the roulette wheel algorithm to select a certain number of populations, and retain the top 50% of the populations Q1 with high fitness. Convert the population Q1 into the corresponding real number population according to the binary code to real number code algorithm;

[0009] Step 4: Perform single-point crossover of the SBX algorithm in the real number coding for the real number population. After completing one round of SBX algorithm crossover, take the population obtained by real number coding at the local level and generate a new binary code population Q2 according to the real number code to binary code algorithm;

[0010] Step 5: Combine the population Q2 and the population Q1 to generate a new population Q3; perform binary coding crossover of probability on the new population Q3, and then perform mutation operation on the population to generate the next generation population; execute Step 2.

[0011] Further, in Step 3, the conversion of the population Q1 into the corresponding real number population according to the binary code to real number code algorithm comprises the following steps:

[0012] Let the real value m after transcoding be in [a, b], and the population Q1 is a binary string population, expressed as (b0, b1, b2, b3,..., b i ,..., b ξ-1 )2, with a total of ξ values;

[0013] Convert the population Q1 into decimal according to Equation (1):

[0014]

[0015] Obtain the population of all real values m according to Equation (2);

[0016]

[0017] Further, in step 4, the single-point crossover of the SBX algorithm in the real number encoding of the real number population includes the following steps:

[0018] Select two parents m1 and m2 in the real number population, generate a uniformly distributed probability u, and combine the custom distribution index k. Equation (6) can be solved through Equation (5) to calculate the propagation factor.

[0019]

[0020] In Equation (5), D(β) is the probability density function, and the probability u is reflected as the area of the curve from 0 to in the function graph;

[0021] Obtain and then calculate the initial offspring individuals generated by the initial parents:

[0022]

[0023] In the formula, n1 is the offspring individual 1 and n2 is the offspring individual 2;

[0024] And so on to solve all the offspring individuals.

[0025] The crossover process also satisfies:

[0026]

[0027] The present invention also discloses a method for arranging non-periodic planar antenna elements, including the following steps:

[0028] S100: Determine the rectangular planar non-periodic antenna array to be established;

[0029] S200: Use the improved genetic iteration algorithm to optimize the arrangement of the antenna elements on the entire non-periodic plane. During the iterative loop process, retain the optimal individuals of each generation; after the iteration is completed, the population generated by the optimal individuals determines the relative positions of the antenna elements; the improved genetic iteration algorithm is an improved genetic iteration algorithm for arranging non-periodic planar antenna elements;

[0030] S300: Arrange the non-periodic planar antenna elements based on the relative positions of the antenna elements obtained in S200.

[0031] Beneficial effects: Compared with the prior art, the present invention has the following advantages:

[0032] The genetic algorithm of the present invention is suitable for representing a relatively large range of numerical values, taking into account both local reasonable optimization and ensuring global effective search; while achieving large-scale genetic exploration, it ensures concentrated search in a small range, guarantees the diversity of results so that the algorithm will not converge prematurely, and at the same time improves the accuracy and operation efficiency of the operation results. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] Figure 1 is the algorithm flow chart;

[0034] Figure 2 is the evolutionary curve diagram of the fitness function;

[0035] Figure 3 is the azimuth array gain in the direction;

[0036] Figure 4 is the array gain in the elevation angle θ direction;

[0037] Figure 5 is the distribution diagram of the arrangement positions of the antenna elements;

[0038] Figure 6 is the 3D model diagram of the far-field antenna position of the antenna array. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0039] The present invention will be further described below in conjunction with the drawings and embodiments.

[0040] The genetic algorithm does not require prior knowledge of the structural problem, can self-organize and self-adapt. It is an intelligent computer language algorithm with strong robustness and can be used to solve complex unstructured problems. However, in the binary coding process, for some optimization problems of continuous functions, due to its randomness, its performance in local search is average, and it is easy to produce mapping errors after discretization. When the individual coding length is long, although the accuracy can be improved, the decoding difficulty is also increased, and the search space of the genetic algorithm expands rapidly. The present invention is based on the research of an improved genetic algorithm to optimize the arrangement of antenna elements on an optical phased array, and effectively suppresses the side lobes in the structure by changing the element distribution in the genetic algorithm mode. The improved genetic algorithm adopted by the present invention ensures that the calculation results are diverse while not deviating from the normal parameter range by adding various crossover algorithms, making the algorithm more rigorous in local operations than before. While taking into account global operations, the algorithm will not converge prematurely, and can improve the efficiency and accuracy of large-scale operations.

[0041] Figure 1 shows a schematic flow chart for optimizing the entire planar rectangular antenna array. Now, in conjunction with Figure 1 , a further description will be given of the method for suppressing the side lobes of the array antenna using the improved genetic algorithm of the present invention. It includes:

[0042] S100: Confirm the target and plan to establish a rectangular planar aperiodic antenna array structure of 18×18; set the spacing between array elements to half a wavelength. From the target, it is known that there are 18 rows of array elements in the azimuth direction and 18 columns of array elements in the elevation direction, and all array elements have equal amplitude and omnidirectional radiation. Set the transmission wavelength to 1.55 μm, the initial azimuth angle and elevation angle are both 0°, and perform a sparse layout of 100 array elements for the structure.

[0043] S200: Initialize the population size NP = 50, the individual gene dimension is L = 324, the maximum number of evolutionary generations G = 200, the crossover probability P c = 0.8, and the mutation probability P m = 0.05. Inspired by the natural selection process, when creating the initial population Q0, randomly generate multiple individuals in the form of binary strings within a certain range, calculate the fitness function for the initial population, that is, the peak sidelobe ratio, and perform a normalization operation after the calculation.

[0044] S300: If the individual result meets the requirements, directly output it.

[0045] S400: If not, according to the loop, select a certain number of populations by roulette wheel selection method, and retain the better half of the populations Q1 among them, and convert the binary code to a real number code. When performing the transcoding operation, assume that the real value m after transcoding belongs to [a, b], and a group of binary string populations is (b0, b1, b2, b3,..., b i ,..., b ξ-1 )2, with a total of ξ values. First, convert it to decimal:

[0046]

[0047] Obtain the population of all real values m according to Equation (2).

[0048]

[0049] Then perform a single-point crossover operation of real number encoding SBX; this crossover part is to simulate the single-point crossover in the genetic algorithm of binary encoding with the SBX algorithm. Assume that the decoded real value of the parent individual 1 in the genetic algorithm loop is m1, the decoded value of the parent individual 2 is m2, the decoded value of the offspring individual 1 is n1, and the decoded value of the offspring individual 2 is n2, which satisfies Equation (3), that is, the average of the crossover real values before and after decoding is equal.

[0050]

[0051] If the propagation factor β is used to represent the ratio of the distance between children and parents, it can be expressed as:

[0052]

[0053] The distance between β and 1 determines their approximation degree. The closer it is to 1, the closer the offspring is to their parent. The relational expression satisfied by β is:

[0054]

[0055] D(β) is expressed as a probability distribution function, and k represents a distribution index, which can be customized. The larger the distribution index, the higher the probability of offspring-parent inheritance. When using the probability density function to calculate the probability, set the probability to u, and solve equation (6) from the relationship satisfied by the probability distribution function D(β);

[0056]

[0057] Equation (6) is obtained based on equation (5) and the relational expression (7) satisfied by the probability and the probability density function:

[0058]

[0059] When performing the real-coded SBX operator, the specific process is as follows:

[0060] Select two parents m1 and m2, generate a uniformly distributed random number u, that is, the probability u, within [0, 1), and combine the customized distribution index k to calculate the propagation factor β through equation (6).

[0061] Obtain After that, calculate the initial offspring individuals generated by the initial parents:

[0062]

[0063] All offspring individuals can be solved by analogy according to this rule.

[0064] After completing one round of SBX crossover, the population obtained by using real-number encoding at the local level is used to convert the real-number code to binary code to generate a new binary population Q2. At the same time, it is combined with the population Q1 selected by roulette wheel selection to form a new population Q3. The new population participates in binary-coded crossover of probabilities, and the binary values of the chromosome genes are designed to be exchanged at the selected crossover positions. After the loop instruction is completed, the population is randomly mutated, that is, the binary value at this position is inverted, thereby generating new optimized individuals, and gradually eliminating the individual units with low fitness. Through continuous iteration and optimization design, the finally generated genetic population can evolve to a situation close to or even containing the optimal solution. While ensuring that the sparsity rate remains unchanged, the optimal individuals of each generation are retained in the loop. After the iteration is completed, the population generated by the optimal individuals determines the relative positions of the antenna array elements, and sparse arraying is carried out accordingly, which is convenient to achieve the purpose of reasonably reducing the antenna elements and lowering the production cost, as well as improving the computing efficiency and solution quality of the optical phased array antenna unit.

[0065] Observe Figure 2 , after 40 iterations, the structure has undergone a period of data adjustment and found a breakthrough after about 110 iterations. Continue to optimize the array structure. Finally, after the iteration reaches 160 times, the function array layout is basically completed and no longer mutates.

[0066] Combine Figure 3 and Figure 4 , the azimuth peak sidelobe size is -20.85 dB, the elevation peak sidelobe size is -21.68 dB, and the amplitude of the sidelobe change is very large. Generally, the influence of the azimuth horizontal sidelobe on the array structure is smaller than that of the elevation vertical direction. The more sidelobes, the greater the actual influence on the overall structure. The influence of the sidelobe can be reduced by reducing the size of the object.

[0067] Those skilled in the art should understand that the embodiments of the present application can be provided as a method, a system, or a computer program product. Therefore, the present application can take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present application can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0068] This application is described with reference to the flowcharts and / or block diagrams of methods, apparatuses (systems), and computer program products according to embodiments of the present application. It should be understood that each flow and / or block in the flowchart and / or block diagram, as well as the combination of flows and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to generate a machine, such that the instructions executed by the processor of the computer or other programmable data processing devices generate means for implementing the functions specified in one flow Figure 1 one flow or multiple flows and / or blocks Figure 1 or means for implementing the functions specified in multiple blocks.

[0069] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, such that the instructions stored in the computer-readable memory generate a manufactured article including instruction means, and the instruction means implement the functions specified in one flow Figure 1 one flow or multiple flows and / or blocks Figure 1 or means for implementing the functions specified in multiple blocks.

[0070] These computer program instructions can also be loaded onto a computer or other programmable data processing device, such that a series of operation steps are executed on the computer or other programmable device to generate a computer-implemented process, and thus the instructions executed on the computer or other programmable device provide steps for implementing the functions specified in one flow Figure 1 one flow or multiple flows and / or blocks Figure 1 or means for implementing the functions specified in multiple blocks.

[0071] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit them. Although the present invention has been described in detail with reference to the above embodiments, those of ordinary skill in the art should understand that: it is still possible to modify the specific implementation manners of the present invention or make equivalent replacements, and any modification or equivalent replacement that does not depart from the spirit and scope of the present invention shall be covered by the protection scope of the claims of the present invention.

Claims

1. A method for arranging non - periodic planar antenna array elements, characterized in that: It includes the following steps: S100: Determine the rectangular planar aperiodic antenna array to be established; S200: Use an improved genetic iteration method to optimize the arrangement of antenna elements on the entire aperiodic plane. During the iterative loop process, retain the optimal individuals of each generation; after the iteration is completed, the population generated by the optimal individual determines the relative positions of the antenna elements; S300: Arrange the aperiodic planar antenna elements based on the relative positions of the antenna elements obtained in S200; Among them, the improved genetic iteration method includes the following steps: Step 1: According to the required number of elements, randomly extract the initial coordinate positions of the elements, and use the initial coordinate positions of the elements as population individuals to create an initial population Q0. This initial population Q0 is a binary string population, and calculate the fitness of each individual in the initial population Q0; Step 2: Determine whether the termination requirement is met. If it is met, stop the loop and output the optimal individual; otherwise, execute Step 3; Step 3: Use the roulette wheel algorithm to select a certain number of populations, and retain the top 50% of the populations Q1 with high fitness. Convert the population Q1 into the corresponding real number population according to the binary code to real number code algorithm; Step 4: Perform single-point crossover of the SBX algorithm in the real number coding for the real number population. After completing one round of SBX algorithm crossover, take the population obtained by the real number coding at the local level and generate a new binary code population Q2 according to the real number code to binary code algorithm; Step 5: Combine the population Q2 and the population Q1 to generate a new population Q3; perform binary coding crossover of probabilities on the new population Q3, and then perform mutation operations on the population to generate the next generation population; execute Step 2; Among them, the single-point crossover of the SBX algorithm in the real number coding for the real number population includes the following steps: Select two parents m1 and m2 from the real population, generate a uniformly distributed probability u, and combine the custom distribution index k. Equation (6) can be solved through Equation (5) to calculate the propagation factor In formula (5), D(β) is the probability density function, and the probability u is reflected as the area of the curve from 0 to in the function graph; Obtain After that, calculate the initial offspring individuals generated by the initial parents: In the formula, n1 is the offspring individual 1, and n2 is the offspring individual 2; And so on to solve for all offspring individuals; The crossover process also satisfies:

2. The method for arranging non - periodic planar antenna array elements according to claim 1, characterized in that: In Step 3, the conversion of the population Q1 into the corresponding real number population according to the binary code to real number code algorithm includes the following steps: Suppose the real value m after transcoding belongs to [a, b], and the population Q1 is a binary string population, represented as (b0, b1, b2, b3,..., b i ,…, b ξ-1 )2, with a total of ξ values; Convert the population Q1 into decimal according to Equation (1): Obtain the population of all real values m according to Equation (2);

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