CN205039261U - Tax shape device of array antenna and array antenna directional diagram - Google Patents

Abstract

The utility model discloses an array antenna and a shaping device for the pattern of the array antenna, which overcomes the problems of low fitting degree and the like in the shaping of the existing array antenna. The device includes: an input end, which obtains the direction diagram of each array element of the array antenna under the coupling condition; a population generator, which randomly generates an initial population according to the direction diagram of each array element under the coupling condition; Each individual calculates the fitness value, and selects the roulette strategy or the elite selection strategy according to the fitness value; the regeneration optimizer, according to the roulette strategy or the elite selection strategy selected by the selector, selects the regeneration individual with the fitness, and The regenerated individual is optimized to generate a new individual; the output end is to output the optimal pattern of the array antenna when the preset end rule is met. The beam coverage of the X-band antenna array of the utility model is wider, and the practicability is better.

Description

Array antenna and shaping device for array antenna pattern

technical field

The utility model relates to the technical field of communication antennas, in particular to an array antenna and a shaping device for the pattern of the array antenna.

Background technique

With the rapid development of modern microwave communication, satellite communication and aerospace technology, the current society has higher and higher requirements for communication systems, especially the beam width and accuracy of the antenna pattern are getting higher and higher. These requirements are a great test for antenna design and development.

The radiation pattern of the antenna can reflect the radiation characteristics of the antenna. In general, the antenna pattern can represent the distribution pattern of the power or field strength of the electromagnetic wave radiated by the antenna in various directions in space. When synthesizing the pattern of the array antenna, the target pattern required by the design is firstly given, and then the amplitude and phase of the optimal solution for each antenna element to shape the target pattern are calculated by using a suitable algorithm. Finally, according to this group of data as excitation, the pattern of the array antenna can be formed into the required target shape.

An excellent algorithm becomes very important to design an array antenna with high efficiency and high pattern accuracy. Classical genetic algorithms are often used in the synthetic design of antenna patterns. The fitness of the genetic algorithm reflects the fitting degree of the synthetic pattern and the target pattern. The higher the fitness, the more accurate the amplitude and phase values calculated by the algorithm, and the higher the fitting degree of the pattern. Conversely, the lower the fitness of the genetic algorithm is, the lower the accuracy of the amplitude and phase values calculated by the genetic algorithm is, which indicates that the fitting degree of the pattern is lower.

The main process of the classic genetic algorithm to solve the synthesis of array antennas is to generate the initial population randomly at first. Then, the above randomly generated initial population is substituted into the preset expression to calculate the array direction function. Make the difference with the corresponding target direction map, and weight the difference according to the importance of different directions, take the absolute value sum of the difference in different directions in each individual, and then take the inverse to get the fitness value of each individual . According to a single selection strategy (such as roulette or elite selection), the regenerated individuals are selected according to their fitness. Crossover of regenerated individuals can make the next generation population inherit the genes of the previous generation. According to a certain crossover probability and crossover algorithm, new individuals are generated. In order to avoid inbreeding and fall into local optimum, according to a certain mutation probability and mutation algorithm, the cross-generated individuals are mutated to generate new individuals again. Finally, it is judged whether the stop condition is reached, such as whether the maximum genetic algebra is reached or whether the fitness value meets the preset requirements. If the stop condition is met, the optimal value is output, otherwise, return to recalculate the fitness value of the individual until the stop condition is met.

In the classic genetic algorithm, when the roulette selection operator acts on the population, it can protect the diversity of the population genes, but it cannot guarantee that the performance of the offspring is always better than that of the parents, and the evolution of the population will be repeated, or even temporarily The retrogression phenomenon slows down the convergence speed of the algorithm. Elite selection can ensure that the performance of the children is not worse than that of the parents, and can speed up the convergence speed, but premature convergence problems may occur. Therefore, when the classical genetic algorithm solves the problem of array antenna shaping, its fitness is generally below 0.07, and the degree of fitting between the target pattern and the synthetic pattern and the size of the sidelobe cannot fully satisfy the designer.

Moreover, in the current antenna design, the coverage of the common cosecant square extended beam is generally below 55°, and the coverage of the beam is relatively limited, which has been difficult to meet the increasingly high communication requirements.

Utility model content

The technical problem to be solved by the utility model is to overcome the problems of low fitting degree and limited antenna beam coverage in the prior art when the array antenna is shaped.

The utility model firstly provides a shaping device for array antenna pattern, including: an input terminal, which obtains the pattern of each array element of the array antenna under the coupling condition; The direction diagram of the random generation initial population; the selector, calculates the fitness value for each individual in the initial population, and selects the roulette wheel strategy or the elite selection strategy according to the fitness value; the regeneration optimizer, according to the The roulette strategy or the elite selection strategy selected by the selector selects the regenerated individual according to the fitness, and optimizes the regenerated individual to generate a new individual; the output terminal outputs the value of the array antenna when the preset end rule is met. Optimal Orientation Map.

Wherein, the device further includes: a feedback controller, which feeds back a recalculation instruction to the selector when the new individual does not meet the preset end rule; the selector recalculates the selected The fitness value mentioned above.

Wherein, the selector includes: a calculation unit, which calculates the fitness value; a selection unit, when the fitness value is less than or equal to a fitness threshold, selects a roulette strategy, otherwise selects an elite selection strategy.

The utility model also provides an array antenna, comprising: a plurality of array element antennas with a distance of half a wavelength from each other, each array element antenna includes a rectangular unit antenna and a connected feeder line, and the feeder line passes through the feed point and the rectangular unit The narrow sides of the antenna are connected.

Wherein, the number of the array element antennas is 10, 14, 16 or 20.

Wherein, the feeder is an octagon with two sets of parallel sides.

Wherein, each element antenna of the array antenna selects a cosecant square spread beam as the target pattern.

Compared with the prior art, the utility model benefits from the precise shaping of the improved self-adaptive genetic algorithm, and the beam coverage width of the antenna pattern can reach 65°. The beam coverage of the X-band antenna array of the utility model is wider, the practicability is better, and it has a very superior application prospect in the aspect of aircraft navigation radar detection.

Other features and advantages of the utility model will be set forth in the following description, and partly become obvious from the description, or can be understood by implementing the technical solution of the utility model. The objectives and other advantages of the present utility model can be realized and obtained through the structures and/or processes particularly pointed out in the specification, claims and drawings.

Description of drawings

The accompanying drawings are used to provide a further understanding of the technical solutions of the utility model or the prior art, and constitute a part of the specification. Wherein, the drawings expressing the embodiments of the utility model are used together with the embodiments of the utility model to explain the technical solution of the utility model, but do not constitute a limitation to the technical solution of the utility model.

FIG. 1 is a schematic flowchart of a method for shaping an array antenna pattern according to an embodiment of the present invention.

FIG. 2 is a schematic diagram of an array antenna according to an embodiment of the present invention.

FIG. 3 is a schematic structural diagram of an array element antenna in an array antenna according to an embodiment of the present invention.

FIG. 4 is a frequency band diagram of an array element antenna in an array antenna according to an embodiment of the present invention.

Fig. 5 is a directional diagram of the array element under the coupling condition in the embodiment of the present invention.

Fig. 6 is a schematic structural diagram of an array antenna shaping device according to an embodiment of the present invention.

Fig. 7 is a graph of the fitness value of the array antenna shaping device according to the embodiment of the present invention when the improved adaptive genetic algorithm is used when the fitness threshold is high.

Fig. 8 is a graph of the fitness value of the array antenna shaping device according to the embodiment of the present invention when the improved adaptive genetic algorithm is used when the fitness threshold is low.

FIG. 9 is a graph of fitness values when the improved adaptive genetic algorithm is applied when the array antenna shaping device according to the embodiment of the present invention does not adopt the elite selection strategy.

Figure 10 is a graph of fitness values when classical genetic algorithm is used.

detailed description

The implementation of the utility model will be described in detail below in conjunction with the accompanying drawings and examples, so as to fully understand and implement the implementation process of how to apply technical means to solve technical problems and achieve corresponding technical effects in the utility model. The embodiments of the utility model and the various features in the embodiments can be combined with each other under the premise of not conflicting, and the formed technical solutions are all within the protection scope of the utility model.

In addition, the steps included in the methods of the embodiments of the present invention shown in the drawings can be executed in a computer system such as a set of computer-executable instructions. Moreover, although the method of the embodiment of the utility model reflects a certain logical order of the technical solution of the utility model during execution in the flow chart shown, generally speaking, the logical sequence is limited to the steps shown in the flow chart. Example shown. In other embodiments of the present utility model, the logic sequence of the technical solution of the present utility model may also be realized in a manner different from that shown in the accompanying drawings.

As shown in FIG. 1 , the method for shaping the array antenna pattern of the present invention mainly includes the following steps.

Step S110, determining a linear array antenna composed of N array elements, and acquiring the pattern of each array element of the array antenna under the coupling condition.

In step S120, an initial population is randomly generated according to the pattern of each array element under the coupling condition. Among them, the initial population is represented by binary.

Step S130, calculating the fitness value for each individual in the initial population, and selecting a roulette strategy or an elite selection strategy according to the fitness value.

Specifically, the randomly generated initial population is substituted into the expression to calculate the array direction function, and the difference between the corresponding direction diagram and the target direction diagram is calculated, and the differences are weighted according to the importance of different directions, and the difference in different directions in each individual is taken as The absolute values of and summed, and then take the inverse to get the fitness value.

The calculation expression of fitness fit is as follows:

$\mathrm{<span\; class="notranslate">\; f</span>}\mathrm{<span\; class="notranslate">\; i</span>}\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; =</span>\frac{\mathrm{<span\; class="notranslate">\; 1</span>}}{\mathrm{<span\; class="notranslate">\; w</span>}\mathrm{<span\; class="notranslate">\; e</span>}\mathrm{<span\; class="notranslate">\; i</span>}\mathrm{<span\; class="notranslate">\; g</span>}\mathrm{<span\; class="notranslate">\; h</span>}\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; \&theta;</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; \&times;</span>\mathrm{<span\; class="notranslate">\; d</span>}\mathrm{<span\; class="notranslate">\; i</span>}\mathrm{<span\; class="notranslate">\; f</span>}\mathrm{<span\; class="notranslate">\; f</span>}<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; \&theta;</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; )</span>}<span\; class="notranslate">\; ,</span><span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 180</span>}<span\; class="notranslate">\; \&le;</span>{\mathrm{<span\; class="notranslate">\; \&theta;</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; \&le;</span>\mathrm{<span\; class="notranslate">\; 180</span>}$

Among them, weight is the weight corresponding to each point, and diff is the absolute error between each point in the composite image and the target orientation map.

$\mathrm{<span\; class="notranslate">\; w</span>}\mathrm{<span\; class="notranslate">\; e</span>}\mathrm{<span\; class="notranslate">\; i</span>}\mathrm{<span\; class="notranslate">\; g</span>}\mathrm{<span\; class="notranslate">\; h</span>}\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; \&theta;</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; =</span>\left\{\begin{array}{cc}{\mathrm{<span\; class="notranslate">\; a</span>}}_{\mathrm{<span\; class="notranslate">\; 1</span>}}<span\; class="notranslate">\; ,</span>& \mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; \&le;</span>\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; \&le;</span>\mathrm{<span\; class="notranslate">\; 114</span>}\\ {\mathrm{<span\; class="notranslate">\; a</span>}}_{\mathrm{<span\; class="notranslate">\; 2</span>}}<span\; class="notranslate">\; ,</span>& \mathrm{<span\; class="notranslate">\; 115</span>}<span\; class="notranslate">\; \&le;</span>\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; \&le;</span>\mathrm{<span\; class="notranslate">\; 168</span>}\\ {\mathrm{<span\; class="notranslate">\; a</span>}}_{\mathrm{<span\; class="notranslate">\; 3</span>}}<span\; class="notranslate">\; ,</span>& \mathrm{<span\; class="notranslate">\; 169</span>}<span\; class="notranslate">\; \&le;</span>\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; \&le;</span>\mathrm{<span\; class="notranslate">\; 179</span>}\\ {\mathrm{<span\; class="notranslate">\; a</span>}}_{\mathrm{<span\; class="notranslate">\; 4</span>}}<span\; class="notranslate">\; ,</span>& \mathrm{<span\; class="notranslate">\; 180</span>}<span\; class="notranslate">\; \&le;</span>\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; \&le;</span>\mathrm{<span\; class="notranslate">\; 240</span>}\\ {\mathrm{<span\; class="notranslate">\; a</span>}}_{\mathrm{<span\; class="notranslate">\; 5</span>}}<span\; class="notranslate">\; ,</span>& \mathrm{<span\; class="notranslate">\; 241</span>}<span\; class="notranslate">\; \&le;</span>\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; \&le;</span>\mathrm{<span\; class="notranslate">\; 360</span>}\end{array}\right.$

Among them, θ is the angle value in the antenna pattern, the range is 0°-360°, here there are 5 values for i, that is, 360° is divided into 5 segments, and the weight a corresponding to the five segments is added, and the weighted value The purpose is to shape more precisely.

Step S140, according to the selected roulette strategy or elite selection strategy, select regenerated individuals according to fitness, and optimize the regenerated individuals to generate new individuals.

The fitness value is judged by the selection strategy combining roulette selection and elite selection. When the maximum fitness value is less than or equal to the fitness threshold, the regenerated individuals are selected by the roulette method, and the elite strategy is not introduced, which protects the population gene. diversity. And when the maximum fitness value is higher than the fitness threshold, the elite strategy is introduced to speed up the convergence speed and select regenerated individuals. When optimizing the regenerated individuals, first perform crossover processing according to the preset crossover probability and crossover algorithm, and then perform mutation processing on the individuals obtained by crossover according to the preset mutation probability and mutation algorithm to generate new individuals. This enables the next generation of population to inherit the good genes of the previous generation well.

In the roulette selection process, different crossover probabilities and mutation probabilities are selected in the early and late stages of evolution, which helps to speed up convergence and avoid falling into local optimum as much as possible.

Specifically, in the roulette wheel selection process, it is further judged that the fitness value is compared with the preset threshold in the early stage of evolution. The threshold of evolutionary period can be used to distinguish whether it is currently in the early stage of evolution or in the middle and late stage. If the fitness value is less than the preset evolutionary period threshold, the current evolution is considered to be in the early stage; if the fitness value is greater than the preset evolutionary period threshold, the current evolution is considered to be in the middle and late stages.

For the early stage of evolution, the following expressions are used for crossover and mutation respectively:

pc1=pc1+(pc0-0.5)/NG

pm1=pm1-(0.4-pm0)/NG

Among them, pc0 is the initial crossover probability, pm0 is the initial mutation probability, NG is the maximum genetic algebra, pc1 is the linearly changing crossover probability, and pm1 is the linearly changing mutation probability.

In the early stage of evolution, pc1 is greater than pm1 to help speed up the convergence.

For the middle stage of evolution, the following expressions are used for crossover and mutation respectively:

pc2=1/(1+exp(-10/fiy))-0.1

pm2＝0.2/(5*(1+exp(1/fiy)))

Among them, pc2 is the crossover probability of adaptive change, and pm2 is the mutation probability of adaptive change.

In the middle stage of evolution, pc2 is smaller than pm2 to help avoid falling into local optimum.

Step S150, outputting the optimal pattern of the array antenna when the preset end rule is met. When the new individual does not meet the preset end rule, recalculate the fitness value, and return to step S130 to continue.

As shown in Figure 2, a schematic diagram of an array antenna composed of 16 array element antennas in the embodiment of the present invention is provided, and the interval d between each array element antenna is the length of 9GHz half-wavelength, for example, the half-wavelength is 16.667mm. Further, as shown in Figure 3, each element antenna includes a rectangular element antenna and a connected feeder, the feeder is connected to the narrow side of the rectangular element antenna through a feed point, and the feeder part has two sets of parallel sides octagon. The dielectric plate is made of Rogers 5880 material with a certain thickness. When the array shaped antenna is shaped, if the number of arrays is larger, the shaping ability will be stronger. Optionally, the array antenna can also use other numbers of array element antennas, such as 10, or 14, or 20.

For an array antenna composed of 16 element antennas, the _S11 parameters of each element antenna are optimized separately to obtain a good frequency bandwidth, the frequency band ranges from 8.5GHz to 9.8GHz, and the center frequency is 9.05GHz, as shown in Figure 4 The frequency band diagram of the array element antenna is given. Further, the respective directional patterns of the 16 array element antennas under the coupling condition are determined, and FIG. 5 shows an example directional pattern of the array element under the coupling condition. Among them, S ₁₁ is the input reflection coefficient, that is, the input return loss. Since the antenna has been involved in the high-frequency frequency band, the high-frequency frequency band will involve matching problems. If the matching is not good, the input energy will be reflected, which will affect the input power of the entire device and other problems. The input reflection coefficient S ₁₁ reflects the That's the kind of problem.

As shown in FIG. 6 , a device for shaping an array antenna pattern is given, including an input terminal 610 , a population generator 620 , a selector 630 , a regeneration optimizer 640 and an output terminal 650 .

The input end 610 is used to obtain the pattern of each element of the array antenna under the coupling condition.

The population generator 620 is connected to the input terminal 610, and randomly generates an initial population according to the direction diagram of each array element under the coupling condition. Wherein, the initial population may be expressed in binary.

The selector 630, connected to the population generator 620, calculates the fitness value for each individual in the initial population, and selects the roulette strategy or the elite selection strategy according to the comparison result between the fitness value and the fitness preset.

The regeneration optimizer 640 is connected to the selector 630, selects the regeneration individual according to the roulette strategy or the elite selection strategy selected by the selector 630, and cooperates with the fitness, and performs crossover and mutation optimization on the regeneration individual to generate a new individual.

The fitness value is judged by the selection strategy combining roulette selection and elite selection. When the maximum fitness value is less than or equal to the fitness threshold, the regenerated individuals are selected by the roulette method, and the elite strategy is not introduced, which protects the population gene. diversity. And when the maximum fitness value is higher than the fitness threshold, the elite strategy is introduced to speed up the convergence speed and select the regenerated individuals. When optimizing the regenerated individuals, first perform crossover processing according to the preset crossover probability and crossover algorithm, and then perform mutation processing on the individuals obtained by crossover according to the preset mutation probability and mutation algorithm to generate new individuals. This enables the next generation of population to inherit the good genes of the previous generation well.

In the roulette selection process, different crossover probabilities and mutation probabilities are selected in the early and late stages of evolution, which helps to speed up convergence and avoid falling into local optimum as much as possible.

Specifically, in the roulette wheel selection process, it is further judged that the fitness value is compared with the preset threshold in the early stage of evolution. The threshold of evolutionary period can be used to distinguish whether it is currently in the early stage of evolution or in the middle and late stage. If the fitness value is less than the preset evolutionary period threshold, the current evolution is considered to be in the early stage; if the fitness value is greater than the preset evolutionary period threshold, the current evolution is considered to be in the middle and late stages.

For the early stage of evolution, the following expressions are used for crossover and mutation respectively:

pc1=pc1+(pc0-0.5)/NG

pm1=pm1-(0.4-pm0)/NG

Among them, pc1 is the crossover probability of linear change, and pm1 is the mutation probability of linear change.

In the early stage of evolution, pc1 is greater than pm1 to help speed up the convergence.

For the middle stage of evolution, the following expressions are used for crossover and mutation respectively:

pc2=1/(1+exp(-10/fiy))-0.1

pm2＝0.2/(5*(1+exp(1/fiy)))

$\mathrm{<span\; class="notranslate">\; f</span>}\mathrm{<span\; class="notranslate">\; i</span>}\mathrm{<span\; class="notranslate">\; the\; y</span>}<span\; class="notranslate">\; =</span>\frac{\mathrm{<span\; class="notranslate">\; E.</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; f</span>}\mathrm{<span\; class="notranslate">\; i</span>}\mathrm{<span\; class="notranslate">\; t</span>}\mathrm{<span\; class="notranslate">\; no</span>}\mathrm{<span\; class="notranslate">\; e</span>}\mathrm{<span\; class="notranslate">\; the\; s</span>}\mathrm{<span\; class="notranslate">\; the\; s</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; +</span>\mathrm{<span\; class="notranslate">\; 1</span>}}{\sqrt{\mathrm{<span\; class="notranslate">\; D.</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; f</span>}\mathrm{<span\; class="notranslate">\; i</span>}\mathrm{<span\; class="notranslate">\; t</span>}\mathrm{<span\; class="notranslate">\; no</span>}\mathrm{<span\; class="notranslate">\; e</span>}\mathrm{<span\; class="notranslate">\; the\; s</span>}\mathrm{<span\; class="notranslate">\; the\; s</span>}<span\; class="notranslate">\; )</span>}}$

Among them, pc2 is the crossover probability of adaptive change, and pm2 is the mutation probability of adaptive change. E(fitness) is the mean value of fitness, and D(fitness) is the variance of fitness. In the middle stage of evolution, pc2 is smaller than pm2 to help avoid falling into local optimum.

The output terminal 650 is connected to the regeneration optimizer 640, and outputs the optimal pattern of the array antenna when the preset end rule is met. Wherein, the preset end rule is, for example, whether the genetic algebra of crossover mutation reaches the maximum genetic algebra or whether the fitness value meets the requirement.

The shaping device of the embodiment of the utility model further includes a feedback controller. The controller feeds back a recalculation instruction to the selector 630 when the new individual does not meet the preset end rule. The selector 630 recalculates the fitness value according to the recalculation instruction, and then performs strategy selection according to the recalculated fitness value.

The selector 630 includes a calculation unit and a selection unit. The calculation unit calculates the fitness value. Specifically, the initial population randomly generated by the population generator 620 is substituted into the expression to calculate the array direction function, and the difference is made with the target direction map, and the difference is weighted according to the importance of different directions, and different directions in each individual are taken The sum of the absolute values of the difference, and then take the reciprocal to get the fitness value. The selection unit compares the fitness value calculated by the fitness calculation unit with the fitness threshold. If the obtained fitness value is less than or equal to the fitness threshold, it chooses to adopt the roulette strategy. If the obtained fitness value is greater than the fitness threshold, it chooses Elite selection strategy.

The regeneration optimizer 640 includes a regeneration unit, a crossover unit and a mutation unit. The regeneration unit selects regeneration individuals according to the roulette wheel strategy or the elite selection strategy with fitness. The crossover unit is connected with the regeneration unit, and the regenerated individuals are crossed, and new individuals are generated according to a certain crossover probability and crossover algorithm; in this way, the next generation population can inherit the genes of the previous generation. The mutation unit is connected with the crossover unit, and the crossover individuals are mutated according to a certain mutation probability and mutation algorithm to generate new individuals, so as to avoid inbreeding and fall into local optimum.

Applications:

First of all, the cosecant square expansion beam is selected as the target pattern. The specific indicators of the target pattern are: -3dB width range is 0°-12°, -10dB beam width is 65°, beam coverage is 65°, and the frequency band range is 8.5GHz～9.8GHz, the center frequency is 9.05GHz.

As shown in Figure 2, an array antenna including 16 element antennas is provided, and the interval d of each element antenna is 16.667mm at a half wavelength of 9.05 GHz. The element antennas in the array antenna include rectangular element antennas and connected As for the feeder line, as shown in Fig. 3, the length L of the rectangular element antenna of the microstrip is 12.6 mm, the width W is 9.45 mm, and the width w ₁ = 7.2975 mm for determining the position of the feed point. Furthermore, the feeder is an octagon with two sets of parallel sides, the dimensions shown are total length L ₁ =15.96 mm, total width w ₃ =6.93 mm, and width w ₂ of the feed point connected to the rectangular unit =0.42 mm The width w ₄ =1.302mm of the parallel opposite ends far away from the rectangular unit, the length L ₂ of the part with the width w ₃ =6.93mm =11.34mm, and the height L ₃ of the trapezoid formed by w ₃ and w ₄ =2.31mm. The dielectric board is Rogers 5880 with a thickness of 1.575mm.

Optimize the _S11 parameters of each element antenna to obtain a good frequency bandwidth. The frequency band range is 8.5GHz to 9.8GHz, and the center frequency is 9.05GHz. As shown in Figure 4, the 16 element antennas in the array antenna are given An example of the respective orientation maps for the coupled condition. Substituting the pattern of each element antenna obtained above into the shape-forming device shown in Figure 6 and the shape-forming method shown in Figure 1 that applied the improved adaptive genetic algorithm for processing, the fitness value of each individual is calculated, and the adaptation The calculation expression of degree fit is as follows:

$\mathrm{<span\; class="notranslate">\; f</span>}\mathrm{<span\; class="notranslate">\; i</span>}\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; =</span>\frac{\mathrm{<span\; class="notranslate">\; 1</span>}}{\mathrm{<span\; class="notranslate">\; w</span>}\mathrm{<span\; class="notranslate">\; e</span>}\mathrm{<span\; class="notranslate">\; i</span>}\mathrm{<span\; class="notranslate">\; g</span>}\mathrm{<span\; class="notranslate">\; h</span>}\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; \&theta;</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; \&times;</span>\mathrm{<span\; class="notranslate">\; d</span>}\mathrm{<span\; class="notranslate">\; i</span>}\mathrm{<span\; class="notranslate">\; f</span>}\mathrm{<span\; class="notranslate">\; f</span>}<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; \&theta;</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; )</span>}<span\; class="notranslate">\; ,</span><span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 180</span>}<span\; class="notranslate">\; \&le;</span>{\mathrm{<span\; class="notranslate">\; \&theta;</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; \&le;</span>\mathrm{<span\; class="notranslate">\; 180</span>}$

Among them, weight is the weight corresponding to each point, and diff is the absolute error between each point in the synthetic map and the required pattern.

$\mathrm{<span\; class="notranslate">\; w</span>}\mathrm{<span\; class="notranslate">\; e</span>}\mathrm{<span\; class="notranslate">\; i</span>}\mathrm{<span\; class="notranslate">\; g</span>}\mathrm{<span\; class="notranslate">\; h</span>}\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; \&theta;</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; =</span>\left\{\begin{array}{c}{\mathrm{<span\; class="notranslate">\; a</span>}}_{\mathrm{<span\; class="notranslate">\; 1</span>}}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; \&le;</span>\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; \&le;</span>\mathrm{<span\; class="notranslate">\; 114</span>}\\ {\mathrm{<span\; class="notranslate">\; a</span>}}_{\mathrm{<span\; class="notranslate">\; 2</span>}}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; 115</span>}<span\; class="notranslate">\; \&le;</span>\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; \&le;</span>\mathrm{<span\; class="notranslate">\; 168</span>}\\ {\mathrm{<span\; class="notranslate">\; a</span>}}_{\mathrm{<span\; class="notranslate">\; 3</span>}}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; 169</span>}<span\; class="notranslate">\; \&le;</span>\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; \&le;</span>\mathrm{<span\; class="notranslate">\; 179</span>}\\ {\mathrm{<span\; class="notranslate">\; a</span>}}_{\mathrm{<span\; class="notranslate">\; 4</span>}}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; 180</span>}<span\; class="notranslate">\; \&le;</span>\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; \&le;</span>\mathrm{<span\; class="notranslate">\; 240</span>}\\ {\mathrm{<span\; class="notranslate">\; a</span>}}_{\mathrm{<span\; class="notranslate">\; 5</span>}}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; 241</span>}<span\; class="notranslate">\; \&le;</span>\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; \&le;</span>\mathrm{<span\; class="notranslate">\; 360</span>}\end{array}\right.$

Among them, θ is the angle value in the antenna pattern, the range is 0°-360°, here there are 5 values for i, that is, 360° is divided into 5 segments, and the weight a corresponding to the five segments is added, and the weighted value The purpose is for more precise shaping.

Using the process shown in Figure 1 to form the pattern of the 9.05GHz center frequency point, the amplitude and phase of the optimal solution required to form the array antenna into the target pattern can be calculated.

In the shape-forming method process using the improved adaptive genetic algorithm, the population size is set to 300, the number of amplitude encoding bits is 7, the number of phase encoding bits is 9, and the number of genetic algebra is 2000.

The following shows the advantages of applying the improved adaptive algorithm to shape the array antenna through the comparison of four sets of graphic results.

The first is to use the improved adaptive genetic algorithm to set a higher threshold (0.065) and obtain the fitness and synthetic direction graph obtained by the elite retention strategy, as shown in Figure 7, the results show that the fitness is above 0.09, and the synthetic direction The shape of the image is perfect, the side lobe is well suppressed, and the lobe width can reach 65°. Then change the high threshold to a low threshold (0.05) under the same conditions, and observe the results, as shown in Figure 8. At this time, the fitness is around 0.07, and the shape of the synthetic direction map is not ideal. Then, under the same conditions, the elite retention strategy is removed, and the observed results are shown in Figure 9. At this time, the fitness is around 0.05, and the shape of the synthetic direction map is even less ideal. Finally, the fitness value obtained by using the classical genetic algorithm and the synthetic pattern of the antenna, as shown in Figure 10, the fitness can only reach about 0.06, and the main lobe jitter of the synthetic pattern is obvious, and the side lobe is large.

It can be seen from this that, compared with the classical genetic algorithm, the introduction of the improved adaptive genetic algorithm for the shaping operation, due to the addition of the threshold setting and the elite retention strategy, has greatly improved the shaping accuracy of the antenna, and can also Get our target wide beam pattern.

On the basis of the roulette method, the utility model proposes a selection strategy combining roulette selection and elite selection for genetic operation. When the maximum fitness value is lower than the threshold, the roulette method is used for selection, and the elite strategy is not introduced to protect the genetic diversity of the population. And when the maximum fitness value is higher than the threshold, an elite strategy is introduced into the algorithm to speed up the convergence. The improved adaptive genetic algorithm can improve the fitness value calculated by the traditional genetic algorithm from less than 0.07 to more than 0.09 when solving the problem of array antenna shaping, which greatly improves the fitting degree of the pattern.

Based on the improved adaptive genetic algorithm, the cosecant square beam is designed. The parameters of the target pattern are -3dB width range of 0°-12°, -10dB beam width of 65°, beam coverage of 65°, and frequency band range of 8.5GHz～9.8GHz, the center frequency is 9.05GHz. Thanks to the precise shaping of the improved adaptive genetic algorithm, the beam coverage width of the antenna pattern can reach 65°. The X-band antenna array designed in this paper has a wider beam coverage and better practicability. It has a very good application prospect.

Aiming at the problems of low fitting degree and limited antenna beam coverage in the prior art, the utility model uses a selection strategy combining roulette selection and elite selection to carry out genetic operation, and the maximum fitness value is lower than Or equal to the fitness threshold, the roulette method is used for selection without introducing elite strategies, which protects the diversity of population genes. When the maximum fitness value is higher than the threshold, an elite strategy is introduced for selection, which speeds up the convergence speed. In order to obtain a larger beam coverage width of the antenna pattern, the utility model designs a rectangular patch array antenna, which can meet the shape of the cosecant square expanded beam. In the embodiment of the utility model, the cosecant square expansion beam is selected as the target pattern, and the specific indicators of the target pattern are: the -3dB width range is 0°-12°, the -10dB beam width is 65°, and the beam coverage is 65° , the frequency band ranges from 8.5GHz to 9.8GHz, and the center frequency is 9.05GHz.

Those skilled in the art should understand that each component of the device provided by the above-mentioned embodiment of the present invention, and each step in the method, they can be concentrated on a single computing device, or distributed among multiple computing devices. on the network. Alternatively, they may be implemented in program code executable by a computing device. Therefore, they can be stored in a storage device to be executed by a computing device, or they can be fabricated into individual integrated circuit modules, or multiple modules or steps can be fabricated into a single integrated circuit module for implementation. As such, the invention is not limited to any specific combination of hardware and software.

Although the disclosed embodiments of the present utility model are as above, the content described is only an embodiment adopted to facilitate understanding of the technical solutions of the present utility model, and is not intended to limit the present utility model. Anyone skilled in the field of the utility model can make any modifications and changes in the form and details of the implementation without departing from the spirit and scope disclosed by the utility model, but the patent protection scope of the utility model , must still be subject to the scope defined by the appended claims.

Claims (7)

1. a size enlargement apparatus for array aerial direction figure, is characterized in that, comprising:

Input, obtains the directional diagram of each array element of array antenna under coupling condition;

Population maker, according to the directional diagram stochastic generation initial population of each array element under described coupling condition;

Selector, for each individuality in described initial population calculates fitness value, and selects roulette wheel dish strategy or elitist selection strategy according to described fitness value;

Regeneration optimizer, the roulette wheel dish strategy selected by described selector or elitist selection strategy, coordinate fitness to select regeneration individual, and be optimized described regeneration individuality, generate new individuality;

Output, exports the optimal direction figure of described array antenna when meeting default end rules.

2. size enlargement apparatus according to claim 1, is characterized in that, this device also comprises:

Feedback controller, when described individuality does not newly meet described default end rules, recalculates instruction to described selector feedback;

Described selector according to described in recalculate instruction and recalculate described fitness value.

3. size enlargement apparatus according to claim 1, is characterized in that, described selector comprises:

Computing unit, calculates described fitness value;

Selected cell, when described fitness value is less than or equal to fitness threshold value, selects roulette wheel dish strategy, otherwise selects elitist selection strategy.

4. an array antenna, is characterized in that, comprising:

Be spaced the multiple array-element antenna of distance for half-wavelength, each array-element antenna described comprises rectangular element antenna and connected feeder line, and described feeder line is connected with the narrow limit of rectangular element antenna by feedback point.

5. array antenna according to claim 4, is characterized in that, the quantity of described array-element antenna is 10,14,16 or 20.

6. array antenna according to claim 4, is characterized in that, described feeder line is the octagon with two groups of parallel edges.

7. array antenna as claimed in claim 4, is characterized in that, the selected cosecant of an each array-element antenna square expansion wave beam for described array antenna is target direction figure.