CN117933094A - Sparse method and device of antenna array elements, electronic equipment and storage medium - Google Patents

Abstract

The application provides a sparse method, a sparse device, electronic equipment and a storage medium of antenna array elements, wherein the method comprises the following steps: according to the received signals of a plurality of array elements in the target array antenna, a first pattern function of the target array antenna is determined, a target optimization function is constructed according to the first pattern function and a preset expected pattern function corresponding to a preset array element sparsity rate, a plurality of array element populations are initialized according to the preset array element sparsity rate, the preset expected pattern function in the target optimization function is solved according to the plurality of array element populations, a genetic algorithm is adopted to carry out iterative updating on the plurality of array element populations, a second pattern function is determined according to the target optimization function when a preset iteration stop condition is reached, and whether array element sparsity is carried out on the target array antenna by adopting the array element populations corresponding to the second pattern function is determined according to the first pattern function and the second pattern function. The linear frequency diversity array is subjected to antenna array element sparseness, and the antenna cost is reduced.

Description

Sparse method and device of antenna array elements, electronic equipment and storage medium

Technical Field

The present application relates to the field of antenna technologies, and in particular, to a method and apparatus for thinning antenna elements, an electronic device, and a storage medium.

Background

In order to provide measurement and control management service with high precision and high sensitivity, a large array antenna with functions of full airspace coverage, multi-target tracking and the like becomes the first choice in the face of the rapid increase of the performance requirements of a future measurement and control system.

The huge number of units and extremely high signal processing complexity severely limit the development and the growth of a large array antenna, so that the cost consumption of the array antenna is greatly reduced in order to ensure the characteristics of high resolution, high sensitivity and the like of the array antenna, and the array element sparsification technology of the array antenna can reduce the number of array elements according to percentage and reduce the complexity of a feed channel and a feed network.

However, the research on array element sparsification technology is conducted on phased arrays, and the research on linear Frequency diversity arrays (Frequency DIVERSE ARRAY, FDA) is quite limited.

Disclosure of Invention

In view of this, the embodiment of the application provides a method, a device, an electronic device and a storage medium for sparse array elements of an antenna, so as to sparse array elements of a linear frequency diversity array.

In a first aspect, an embodiment of the present application provides a method for thinning an antenna array element, including:

determining a first pattern function of a target array antenna according to received signals of a plurality of array elements in the target array antenna, wherein the target array antenna is a linear frequency diversity array antenna;

Constructing a target optimization function according to the first pattern function and a preset expected pattern function corresponding to a preset array element sparsity;

initializing a plurality of array element populations according to the preset array element sparsity, and respectively solving the preset expected pattern function in the target optimization function according to the plurality of array element populations, wherein each array element population comprises: a plurality of target array elements corresponding to the preset array element sparsity rate in the plurality of array elements;

Adopting a genetic algorithm to carry out iterative updating on the array element populations, and determining a second pattern function according to the target optimization function when a preset iteration stop condition is reached, wherein the preset iteration stop condition is that the value of the target optimization function does not exceed a preset error threshold value, or the iteration times reach an iteration times threshold value;

and determining whether to perform array element sparseness on the target array antenna by adopting an array element population corresponding to the second pattern function according to the first pattern function and the second pattern function.

In an optional implementation manner, the determining, according to the first pattern function and the second pattern function, whether to perform array element sparseness on the target array antenna by using an array element population corresponding to the second pattern function includes:

Respectively carrying out pattern simulation according to the first pattern function and the second pattern function to obtain a first pattern and a second pattern;

And determining whether to adopt an array element population corresponding to the second directional diagram function according to the first directional diagram and the second directional diagram, and performing array element sparseness on the target array antenna.

In an optional implementation manner, the determining, according to the first pattern and the second pattern, whether to use the array element population corresponding to the second pattern function, to perform array element sparseness on the target array antenna includes:

performing main lobe comparison according to the main lobe width in the first direction diagram and the main lobe width in the second direction diagram to obtain a main lobe comparison result;

performing side lobe comparison according to the side lobe level value in the first direction diagram and the side lobe level value in the second direction diagram to obtain a side lobe comparison result;

And determining whether to adopt an array element population corresponding to the second directional diagram function according to the main lobe comparison result and the side lobe comparison result, and performing array element sparseness on the target array antenna.

In an optional implementation manner, the constructing a target optimization function according to the first pattern function and a preset expected pattern function corresponding to a preset array element sparsity ratio includes:

And constructing the target optimization function aiming at a preset distance angle observation space according to the first pattern function and the preset expected pattern function.

In an alternative embodiment, the determining the first pattern function of the target array antenna according to the received signals of the plurality of array elements in the target array antenna includes:

According to a plurality of preset signal processing chains, respectively performing signal processing on the received signals of each array element to obtain a plurality of output signals of each array element;

according to the plurality of output signals of the plurality of array elements, acquiring an array factor of the target array antenna;

And constructing the first pattern function according to the array factor of the target array antenna.

In an optional embodiment, the obtaining the array factor of the target array antenna according to the plurality of output signals of the plurality of array elements includes:

acquiring the weight of each signal processing chain and the weight of a target processing unit in each signal processing chain;

And acquiring the array factors of the target array antenna according to the plurality of output signals of the plurality of array elements, the weight of each signal processing chain and the weight of the target processing unit.

In an alternative embodiment, the iteratively updating the plurality of array element populations using a genetic algorithm includes:

and carrying out iterative updating on the plurality of array element populations according to preset array element population constraint conditions, preset crossover probability, preset variation probability and the preset array element sparsity rate by adopting the genetic algorithm, wherein the preset array element population constraint conditions are used for indicating that the first array element and the last array element in the plurality of array elements are reserved in the iterative updating process.

In a second aspect, an embodiment of the present application further provides a sparse device for antenna array elements, including:

The determining module is used for determining a first directional diagram function of a target array antenna according to the received signals of a plurality of array elements in the target array antenna, wherein the target array antenna is a linear frequency diversity array antenna;

The construction module is used for constructing a target optimization function according to the first pattern function and a preset expected pattern function corresponding to a preset array element sparsity rate;

the solving module is configured to initialize a plurality of array element populations according to the preset array element sparsity, and solve the preset expected pattern function in the objective optimization function according to the plurality of array element populations, where each array element population includes: a plurality of target array elements corresponding to the preset array element sparsity rate in the plurality of array elements;

The iteration module is used for carrying out iteration update on the array element populations by adopting a genetic algorithm, and determining a second pattern function according to the target optimization function when a preset iteration stop condition is reached, wherein the preset iteration stop condition is that the target optimization function does not exceed a preset error threshold, or the iteration times reach an iteration times threshold;

The determining module is further configured to determine, according to the first pattern function and the second pattern function, whether to use an array element population corresponding to the second pattern function to perform array element sparseness on the target array antenna.

In an alternative embodiment, the determining module is specifically configured to:

Respectively carrying out pattern simulation according to the first pattern function and the second pattern function to obtain a first pattern and a second pattern;

And determining whether to adopt an array element population corresponding to the second directional diagram function according to the first directional diagram and the second directional diagram, and performing array element sparseness on the target array antenna.

In an alternative embodiment, the determining module is specifically configured to:

performing main lobe comparison according to the main lobe width in the first direction diagram and the main lobe width in the second direction diagram to obtain a main lobe comparison result;

performing side lobe comparison according to the side lobe level value in the first direction diagram and the side lobe level value in the second direction diagram to obtain a side lobe comparison result;

And determining whether to adopt an array element population corresponding to the second directional diagram function according to the main lobe comparison result and the side lobe comparison result, and performing array element sparseness on the target array antenna.

In an alternative embodiment, the construction module is specifically configured to:

And constructing the target optimization function aiming at a preset distance angle observation space according to the first pattern function and the preset expected pattern function.

In an alternative embodiment, the determining module is specifically configured to:

According to a plurality of preset signal processing chains, respectively performing signal processing on the received signals of each array element to obtain a plurality of output signals of each array element;

according to the plurality of output signals of the plurality of array elements, acquiring an array factor of the target array antenna;

And constructing the first pattern function according to the array factor of the target array antenna.

In an alternative embodiment, the determining module is specifically configured to:

acquiring the weight of each signal processing chain and the weight of a target processing unit in each signal processing chain;

And acquiring the array factors of the target array antenna according to the plurality of output signals of the plurality of array elements, the weight of each signal processing chain and the weight of the target processing unit.

In an alternative embodiment, the iteration module is specifically configured to:

and carrying out iterative updating on the plurality of array element populations according to preset array element population constraint conditions, preset crossover probability, preset variation probability and the preset array element sparsity rate by adopting the genetic algorithm, wherein the preset array element population constraint conditions are used for indicating that the first array element and the last array element in the plurality of array elements are reserved in the iterative updating process.

In a third aspect, an embodiment of the present application further provides an electronic device, including: a processor, a memory and a bus, the memory storing machine-readable instructions executable by the processor, the processor and the memory in communication over the bus when the electronic device is running, the processor executing the machine-readable instructions to perform the method of any of the first aspects.

In a fourth aspect, embodiments of the present application also provide a computer-readable storage medium having stored thereon a computer program which, when executed by a processor, performs the method of any of the first aspects.

The application provides a sparse method, a sparse device, electronic equipment and a storage medium of antenna array elements, wherein the method comprises the following steps: according to the received signals of a plurality of array elements in the target array antenna, a first pattern function of the target array antenna is determined, a target optimization function is constructed according to the first pattern function and a preset expected pattern function corresponding to a preset array element sparsity rate, a plurality of array element populations are initialized according to the preset array element sparsity rate, the preset expected pattern function in the target optimization function is solved according to the plurality of array element populations, a genetic algorithm is adopted to carry out iterative updating on the plurality of array element populations, a second pattern function is determined according to the target optimization function when a preset iteration stop condition is reached, and whether array element sparsity is carried out on the target array antenna by adopting the array element populations corresponding to the second pattern function is determined according to the first pattern function and the second pattern function. The linear frequency diversity array is subjected to antenna array element sparseness, and the antenna cost is reduced.

Drawings

In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings that are needed in the embodiments will be briefly described below, it being understood that the following drawings only illustrate some embodiments of the present application and therefore should not be considered as limiting the scope, and other related drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.

Fig. 1 is a schematic flow chart of a method for sparse antenna array elements according to an embodiment of the present application;

fig. 2 is a schematic diagram of an FDA antenna according to an embodiment of the present application;

FIG. 3 is a graph of the position distribution of the optimal array elements after FDA sparse synthesis with logarithmic frequency offset according to an embodiment of the present application;

fig. 4 is a second flow chart of a sparse method of antenna array elements according to an embodiment of the present application;

Fig. 5 is a flowchart illustrating a method for sparse antenna array elements according to an embodiment of the present application;

fig. 6 is a flow chart diagram of a sparse method of antenna array elements according to an embodiment of the present application;

fig. 7 is a three-dimensional view of a full array FDA transmit receive beam pattern with logarithmic frequency offset provided by an embodiment of the present application;

Fig. 8 is a top view of a full array FDA transmit receive beam pattern with logarithmic frequency offset provided by an embodiment of the present application;

fig. 9 is a three-dimensional view of a transmit-receive beam pattern of a sparse array FDA with logarithmic frequency offset provided by an embodiment of the present application;

fig. 10 is a top view of a transmit receive beam pattern of a sparse array FDA with logarithmic frequency offset according to an embodiment of the present application;

FIG. 11 is a cross-sectional view of an angle-dimension normalized transmit-receive beam pattern over a 50km distance before and after FDA sparseness with logarithmic frequency offset provided by an embodiment of the application;

Fig. 12 is a cross-sectional view of a distance dimension normalized transmit-receive beam pattern at a 90 ° angle before and after FDA sparseness with logarithmic frequency offset provided by an embodiment of the present application;

fig. 13 is a schematic structural diagram of a sparse device of an antenna array element according to an embodiment of the present application;

fig. 14 is a schematic structural diagram of an electronic device according to an embodiment of the present application.

Detailed Description

For the purpose of making the objects, technical solutions and advantages of the embodiments of the present application more apparent, the technical solutions of the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present application, and it is apparent that the described embodiments are only some embodiments of the present application, not all embodiments. The components of the embodiments of the present application generally described and illustrated in the figures herein may be arranged and designed in a wide variety of different configurations. Thus, the following detailed description of the embodiments of the application, as presented in the figures, is not intended to limit the scope of the application, as claimed, but is merely representative of selected embodiments of the application. All other embodiments, which can be made by a person skilled in the art without making any inventive effort, are intended to be within the scope of the present application.

In face of the demanding requirements of communication capability, the array antenna can effectively realize the functions of high synthesis gain, ultralow side lobe, beam forming, beam anti-interference, beam scanning and the like, can meet the requirements of modern human civilized high-end communication, and is also a main stream means for improving the performance of the antenna. With the high-speed development of the antenna system carrier, the number of terminal antennas is increased sharply along with the number of carriers, and the working environment of the antennas is also better and more complex, such as an antenna system serving an extraterrestrial space station and a plurality of antenna devices working on a high-speed mobile aircraft. In order to provide measurement and control management service with high precision and high sensitivity, a large array antenna with functions of full airspace coverage, multi-target tracking and the like becomes the first choice in the face of the rapid increase of the performance requirements of a future measurement and control system.

However, the huge number of units and extremely high complexity of signal processing severely limit the development and the growth of a large array antenna, so that the cost consumption of the array antenna is greatly reduced in order to ensure the characteristics of high resolution, high sensitivity and the like of an array system, and the array element number can be reduced according to percentage by the thinning technology of the array antenna element (namely the antenna array element), so that the complexity of a feed channel and a feed network is reduced. Furthermore, in the case of large arrays with a large number of array elements, the computational dimension of digital signal processing is reduced, and the response speed and sensitivity of the system are enhanced. Thus, the sparse research of array antennas has a great opportunity and challenge, and the combination of high performance and low cost is certainly not increasing the importance of research.

For linear Frequency diversity arrays (Frequency DIVERSE ARRAY, FDA), in addition to Frequency offset, array geometry plays an important role in array beamforming, and sparse arrays can greatly reduce cost, which is significant and valuable in practical scenarios. At present, most sparse array researches are conducted on a phased array, but sparse research on FDA is quite limited, in addition, sparse design on the wave beam patterns of the FDA is different from the conventional optimization problem, variables are coupled on objective functions and constraint conditions, and the problem is much more complex than that of the phased array.

Based on the method, the application provides an array element sparse method for an FDA array antenna, combines a transmitting and receiving processing chain, and secondly describes an array comprehensive problem as finding an optimal sparse transmitting and receiving weight vector problem, and the number, the position and the frequency offset of array elements are jointly optimized due to the concept of a sparse array, so that the antenna array element sparseness of a linear frequency diversity array is realized on the premise of keeping the radiation performance of a target array antenna, and the antenna cost is reduced.

Fig. 1 is a schematic flow chart of a method for thinning an antenna array element according to an embodiment of the present application, where an execution body of the embodiment may be an electronic device.

As shown in fig. 1, the method may include:

S101, determining a first pattern function of the target array antenna according to the received signals of a plurality of array elements in the target array antenna.

The target array antenna is a linear frequency diversity array antenna FDA, and comprises a plurality of array elements, namely an antenna array element.

The method comprises the steps that a plurality of array elements in a target array antenna transmit detection signals to a far-field target, the far-field target reflects, signals returned to the array elements are receiving signals of the array elements, receiving signals of the array elements in the target array antenna are obtained, and a first pattern function of the target array antenna is determined according to the receiving signals of the array elements, wherein the first pattern function is a pattern function of the target array antenna.

Fig. 2 is a schematic diagram of an FDA antenna according to an embodiment of the present application, as shown in fig. 2, composed ofThe composition interval of each array element isA frequency offset exists between adjacent array elements of the linear frequency diversity array antenna, taking the 1 st array element as an example, from the 1 st/>The operating frequency of each array element is expressed as:

wherein, Is the reference frequency/>Is/>In order to realize the beam pattern of decoupling distance and angle information, nonlinear frequency offset can be adopted in the linear frequency diversity array antenna, and the scheme provides a linear frequency diversity array antenna with logarithmic frequency offset, in this case, the/>The frequency offset of individual array elements can be expressed as:

wherein, Representation/>Natural logarithm of linear frequency diversity array antenna/>The detection signal emitted by each array element to the far-field target can be expressed as:

Wherein c is the speed of light, Represents the/>The distance of the individual array elements to the far field target can be approximated as:

From the first The array elements transmit detection signals, and far-field targets are in/>Middle reflection, th/>The received signals received by the individual array elements can be expressed as:

wherein, ，/>And/>Distance and angle parameters respectively representing far field target in far field region of linear frequency diversity array antenna,/>Can be understood as the/>Distance between each array element and far-field target,/>Can be understood as the/>The angle between the horizontal position of each array element and the far-field target.

S102, constructing a target optimization function according to the first pattern function and a preset expected pattern function corresponding to a preset array element sparsity.

The preset expected pattern function is a pattern function corresponding to a preset array element sparsity, the pattern function is a pattern function of a sparse array antenna, the sparse array antenna is an antenna array obtained by sparsely obtaining a target array antenna by adopting the preset array element sparsity, and the target optimization function can be expressed as:，/> the integral of the error for the first pattern function and the preset desired pattern function.

It should be noted that the sparseness of the antenna array can be understood as reducing the array elements of the target array antenna, and presetting the sparseness ratioExpressed as:

wherein, Is the array element number of the sparse array antenna,/>Is the number of array elements of the target array antenna.

And S103, initializing a plurality of array element populations according to the preset array element sparsity, and respectively solving a preset expected pattern function in the target optimization function according to the plurality of array element populations.

Initializing to obtain a plurality of array element populations according to a preset array element sparsity, wherein each array element population comprises: a plurality of target array elements corresponding to the preset array element sparsity rate, for example, n antenna array elements, where the preset array element sparsity rate is 50%, may be selected from the following groupFront/>, determined in individual antenna elementsThe 2 antenna elements form an array element group for the target array element, or from/>Post-determination/>, among individual antenna elementsThe 2 antenna elements form an array element group for the target array element, or from/>Determination of intermediate/>, among the individual antenna elementsAnd 2 antenna elements are used as target elements to form an array element population, so that a plurality of target elements can be obtained from a plurality of array elements of the target array antenna according to a preset array element sparsity rate to initialize the target array elements to obtain a plurality of array element populations.

According to each array element population, solving a preset expected pattern function in the target optimization function to obtain a preset expected pattern function corresponding to each array element population, and calculating the target optimization function according to the preset expected pattern function obtained by solving。

For each array element population, the process of solving the preset expected pattern function in the target optimization function is similar to the process of acquiring the first pattern function in step S101, and the preset expected pattern function of each array element population corresponding to the sparse array antenna is determined according to the received signals of the plurality of target array elements in each array element population by acquiring the received signals of the plurality of target array elements in each array element population.

S104, adopting a genetic algorithm to carry out iterative updating on the array element populations, and determining a second pattern function according to a target optimization function when a preset iteration stop condition is reached.

Adopting a genetic algorithm to carry out iterative updating on a plurality of array element populations, wherein the array element numbers of the array element populations after iterative updating are unchangedIn each iteration updating process, solving a preset expected pattern function in the target optimization function according to the array element population after iteration updating to obtain a preset expected pattern function corresponding to the array element population after iteration updating, repeating the process until a preset iteration stopping condition is reached, and determining the target optimization function/>, which is obtained when the preset iteration stopping condition is reached, according to the fact that the preset expected pattern function corresponding to the target optimization function is a second pattern function when the preset iteration stopping condition is reachedThe corresponding preset expected pattern function is a second pattern function, wherein for the iterative process of the array element population, the radiation performance difference between the sparse array antenna corresponding to the second pattern function and the target array antenna corresponding to the first pattern function is the smallest.

The preset iteration stop condition is that the value of the target optimization function does not exceed a preset error threshold, or the iteration times reach an iteration times threshold.

It should be noted that, the iterative update of the array element population may be understood as that under the condition that the number of the array elements corresponding to the preset array element sparsity is unchanged, the iterative update is performed on the target array elements in the array element population, for example, the target array elements of the array element population before the iterative update include the array element 1, the array element 2 and the array element 3, and the target array elements of the array element population after the iterative update include the array element 2, the array element 3 and the array element 4, that is, under the condition that the number of the array elements is unchanged, the iterative update is performed on the target array elements in the array element population, where the array element 1, the array element 2, the array element 3 and the array element 4 belong to the array elements of the target array antenna.

S105, determining whether to perform array element sparsity on the target array antenna by adopting an array element population corresponding to the second pattern function according to the first pattern function and the second pattern function.

According to the first directional diagram function and the second directional diagram function, the radiation performance of the target array antenna before and after the sparseness is judged, if the radiation performance difference meets a certain condition, the array element sparseness is determined to be carried out on the target array antenna by adopting the array element population corresponding to the second directional diagram function, namely, under the condition that the radiation performance is basically unchanged, the sparseness scheme of the target array antenna is determined according to the array element population corresponding to the second directional diagram function, so that the complexity of a feed channel and a feed network is reduced, the calculation difficulty of digital signal processing is reduced, and the response speed and the sensitivity of the system are enhanced.

If the radiation performance difference does not meet a certain condition, the fact that the radiation performance of the antenna is reduced when the array element population corresponding to the second directional diagram function is adopted to perform array element sparsity on the target array antenna is indicated, and the array element population corresponding to the second directional diagram function is not adopted to perform array element sparsity on the target array antenna.

Taking a preset array element sparsity rate of 50%, a plurality of array elements of a target array antenna of 60, a frequency offset of a frequency diversity array of logarithmic frequency offset as an example, a transmitting and receiving beam pattern of a second direction pattern, fig. 3 is an optimal array element position distribution diagram after FDA sparse synthesis with logarithmic frequency offset, as shown in fig. 3, an abscissa represents an array element sequence number, an ordinate represents whether an array element is selected or not, an array element selected value of 1 represents an array element left from a full array of the target array antenna to the sparse array antenna, and an selected value of 0 represents an array element discarded in the sparse process.

In the method for sparse antenna array elements of the embodiment, according to the received signals of a plurality of array elements in a target array antenna, a first pattern function of the target array antenna is determined, according to the first pattern function and a preset expected pattern function corresponding to a preset array element sparse rate, a target optimization function is constructed, according to the preset array element sparse rate, a plurality of array element populations are initialized, according to the plurality of array element populations, the preset expected pattern function in the target optimization function is solved, a genetic algorithm is adopted, the plurality of array element populations are subjected to iterative updating, according to the target optimization function when a preset iterative stop condition is met, a second pattern function is determined, and according to the first pattern function and the second pattern function, whether array element sparsity is carried out on the target array antenna by the array element populations corresponding to the second pattern function is determined. The antenna array element sparseness of the linear frequency diversity array is realized while the radiation performance of the antenna is ensured, and the antenna cost is reduced.

Fig. 4 is a second flow chart of a method for sparse array elements of an antenna according to an embodiment of the present application, as shown in fig. 4, in an optional implementation manner, step S105, according to a first pattern function and a second pattern function, determines whether to use an array element population corresponding to the second pattern function to perform array element sparseness on a target array antenna, which may include:

And S201, respectively performing pattern simulation according to the first pattern function and the second pattern function to obtain a first pattern and a second pattern.

S202, determining whether to adopt an array element population corresponding to a second directional diagram function according to the first directional diagram and the second directional diagram, and performing array element sparseness on the target array antenna.

And performing pattern simulation according to the first pattern function to obtain a first pattern, performing pattern simulation according to the second pattern function to obtain a second pattern, comparing the first pattern with the second pattern, determining whether to adopt an array element population corresponding to the second pattern function, and performing array element sparseness on the target array antenna.

In some embodiments, main lobe comparison is performed according to the main lobe width in the first direction diagram and the main lobe width in the second direction diagram, a main lobe comparison result is obtained, side lobe comparison is performed according to the side lobe level value in the first direction diagram and the side lobe level value in the second direction diagram, a side lobe comparison result is obtained, whether an array element population corresponding to the second direction diagram function is adopted or not is determined according to the main lobe comparison result and the side lobe comparison result, and array element sparseness is performed on the target array antenna.

The main lobe width and the side lobe level value in the directional diagram represent the radiation performance of the array antenna, if the main lobe comparison result indicates that the width difference between the main lobe width in the first directional diagram and the main lobe width in the second directional diagram does not exceed the preset width, and the side lobe comparison result indicates that the level difference between the side lobe level value in the first directional diagram and the level value of the side lobe level value in the second directional diagram does not exceed the preset level difference, then determining to use the array element population corresponding to the second directional diagram, performing array element sparseness on the target array antenna, and if the main lobe comparison result indicates that the width difference between the main lobe width in the first directional diagram and the main lobe width in the second directional diagram exceeds the preset width, or the side lobe comparison result indicates that the level difference between the side lobe level value in the first directional diagram and the level value in the second directional diagram exceeds the preset level difference, then determining to not use the array element population corresponding to the second directional diagram, and performing array element sparseness on the target array antenna.

The first direction diagram and the second direction diagram may be direction diagrams of a distance angle space, main lobe widths in the first direction diagram and the second direction diagram correspond to far-field targets, the main lobe widths in the first direction diagram and the second direction diagram comprise distance dimension main lobe widths and angle dimension main lobe widths, side lobe level values in the first direction diagram and the second direction diagram correspond to interference factors of non-far-field targets, and side lobe level values in the first direction diagram and the second direction diagram comprise distance dimension side lobe level values and angle dimension side lobe level values.

In this embodiment, the main beam (i.e., the main lobe) is focused in the two-dimensional space of the range angle through the main lobe comparison result and the side lobe comparison result, and the low side lobe level in the region of interest finally generates the second pattern with slightly deteriorated radiation characteristics under the premise of the lowest possible sparsity.

In an optional embodiment, step 102, constructing the objective optimization function according to the first pattern function and a preset expected pattern function corresponding to a preset array element sparsity ratio may include:

And constructing an objective optimization function aiming at a preset distance angle observation space according to the first directional diagram function and a preset expected directional diagram function.

The preset distance angle observation space can be a two-dimensional distance angle space and can be expressed as，/>Representing distance,/>Indicating the angle.

It is worth noting that the first and second patterns observe the integral of the error of the space at a preset distance angleExpressed as:

wherein the first and second patterns observe an integral of the error of the space at a preset distance angle Can be understood as a weighted error function,/>Is a preset weight function designed according to the importance of a preset distance angle observation space, wherein the main lobe position in the preset distance angle observation space is expressed as/>Main lobe widths in the preset distance angle region are respectively expressed as/>And/>。

The objective optimization function isIts value/>Will decrease with iterative updates,/>Representing the array population.

In some embodiments, the preset range angle observation space is evenly divided into a dense set of discrete pointsWherein/>Representing the/>, in distance spaceDistance of individual points,/>Representing the/>, in angular spaceThe angle of the individual points, the objective optimization function can be equivalently expressed as:

The fitness function of the target optimization function is reconstructed as follows:

fig. 5 is a flowchart of a third method for sparse antenna array elements according to an embodiment of the present application, as shown in fig. 5, in an optional implementation manner, step S101, according to received signals of a plurality of array elements in a target array antenna, determines a first pattern function of the target array antenna, which may include:

S301, respectively performing signal processing on the received signals of each array element according to a plurality of preset signal processing chains to obtain a plurality of output signals of each array element.

For each element, the element transmits a probe signal and receives a received signal from a far-field target, e.g.,Each array element transmits a signal, each array element can receive/>Each receiving signal and each array element outputs a signal of/>The sum of the individual received signals, the output signal of the array element can be expressed as:

wherein, Take values from 1 to N.

According to each signal processing chain, signal processing is carried out on the received signal of each array element to obtain an output signal of each array element, and each array element under a plurality of signal processing chains corresponds to a plurality of output signals, wherein one signal processing chain comprises: a plurality of processing units, the plurality of processing units may include: the signal processing circuit comprises an amplifier, a mixer, an analog-to-digital converter, a digital mixer and a low-pass filter which are connected in sequence, wherein different signal processing chains are provided with different digital mixers.

That is, for multiple signal processing chains, the received signal for each element goes into an amplifier and then through a frequency ofThe output of the mixer is passed through an analog-to-digital converter, and the signals output by the analog-to-digital converter are respectively fed into a group of mixers with bandwidths of/>, respectively, in order to compensate the time-varying term caused by frequency offsetIn the digital mixer of (2) >)The output signal of the digital mixer is expressed as:

First, the The output signal of the digital mixer passes through a low-pass filter, and the filtered output signal is as follows:

wherein, For the temporary response of the low-pass filter,/>As a convolution operator, it can be seen that the time-varying term is removed after passing through the low pass filter, thereby solving the inherent time-varying characteristics of the frequency diversity array.

According to the firstAnd a signal processing chain for performing signal processing on the received signal of each array element, wherein the output signal of each array element can be expressed as:

wherein, Represents the/>Weights of digital mixers in the individual signal processing chains, </>The digital mixer in the signal processing chain can be understood as the/>A digital mixer.

S302, according to a plurality of output signals of a plurality of array elements, acquiring an array factor of a target array antenna.

And calculating the accumulated values of the plurality of output signals of each array element, and adding the accumulated values of the plurality of array elements to obtain the array factor of the target array antenna.

In an alternative embodiment, the weight of each signal processing chain and the weight of the target processing unit in each signal processing chain are obtained, and the array factor of the target array antenna is obtained according to the plurality of output signals of the plurality of array elements, the weight of each signal processing chain and the weight of the target processing unit.

Wherein the target processing unit is a digital mixer, and the array factor of the target array antenna can be expressed as:

/>

wherein, ，/>Represents the/>Weights of individual signal processing chains,/>Represents the/>Weights of digital mixers in the individual signal processing chains,/>The value of (1) to N,/>The value of (2) is from 1 to N.

S303, constructing a first directional diagram function according to the array factors of the target array antennas.

The weight vector of the target array antenna can be expressed as:

wherein, 。

In some embodiments of the present invention, in some embodiments,Element entry/>Can be expressed as:

wherein, Expressed by/>The array elements emit detection signals and are formed by the/>The phase of the signal received by each array element.

The steering vector of the target array antenna can be expressed as:

wherein, . The superscripts T and H represent the transpose operator and the conjugate transpose operator, respectively.

The array factor of the target array antenna can be reconstructed as:

the first directional diagram function may be expressed as:

wherein, ，/>Representing the covariance matrix of the target array antenna.

In an alternative embodiment, the iterative updating of the plurality of array element populations using a genetic algorithm includes: and carrying out iterative updating on the plurality of array element populations according to the constraint condition of the preset array element populations, the preset crossover probability, the preset variation probability and the preset array element sparsity rate by adopting a genetic algorithm to obtain updated plurality of array element populations.

Fig. 6 is a flow chart of a method for sparse antenna array elements according to an embodiment of the present application, as shown in fig. 6, in an alternative implementation manner, the method may include:

s401, determining a first pattern function of the target array antenna according to the received signals of a plurality of array elements in the target array antenna.

S402, constructing a target optimization function according to a first pattern function and a preset expected pattern function corresponding to a preset array element sparsity.

S403, initializing a plurality of array element populations according to a preset array element sparsity, and respectively solving a preset expected pattern function in the target optimization function according to the plurality of array element populations.

S404, adopting a genetic algorithm, carrying out iterative updating on the array element populations according to a preset array element population constraint condition, a preset crossover probability, a preset variation probability and a preset array element sparsity rate, and determining a second pattern function according to a target optimization function when a preset iteration stop condition is reached.

S405, determining whether to use an array element population corresponding to the second pattern function to perform array element sparseness on the target array antenna according to the first pattern function and the second pattern function.

The method comprises the steps of determining a target array antenna, wherein a preset array element population constraint condition is used for indicating a first array element and a last array element in a plurality of array elements of the target array antenna in an iterative updating process, and the influence of the first array element and the last array element on the radiation performance of the target array antenna is large, so that the influence on the antenna performance can be reduced while the array elements are sparse through the preset array element population constraint condition when the population is iteratively updated.

Adopting a genetic algorithm, and carrying out iterative updating on a plurality of array element populations according to a preset array element population constraint condition, a preset crossover probability, a preset variation probability and a preset array element sparsity rate, wherein the array element numbers of the array element populations after iterative updating are unchangedThe ratio of the number of the array elements of the array element population after iteration update to the number of the array elements of the target array antenna is not changed to a preset array element sparseness ratio, and the target array elements in the array element population after iteration update comprise the first array element and the last array element of the target array antenna.

It should be noted that, for the explanation of the steps S401 to S405, reference may be made to the specific implementation process of the steps S101 to S105, which is not described herein.

The preset crossover probability and the preset mutation probability can be understood as probabilities of iteratively updating the array population in a genetic algorithm, and particularly, reference may be made to related descriptions in the prior art.

As an example, D represents the array element population, G represents the iteration number threshold,Representing the array element number corresponding to the preset array element sparsity rate,/>And/>The preset crossover probability and the preset mutation probability are respectively represented, and the process of acquiring the second pattern function is described below by combining a genetic algorithm, and the method comprises the following steps:

Step 1: at the position of And/>Under the constraint of (1), initializing D array element populations and simultaneously setting g=1 (representing the first-generation array element population)/>And/>Representing the first and last element of the reserved target array antenna, respectively.

Step 2: for each array element population, calculating the fitness function of the target optimization functionIs a value of (2).

Step 3: selecting a function that produces the smallest fitnessIs a population of array elements.

Step 4: fitness function according to D array element populationsAnd selecting a target array element population to be subjected to iterative updating from the D populations.

Can be according to fitness functionAnd selecting a certain number of target array element populations serving as target array element populations to be subjected to iterative updating from the D populations in the order from small values to large values.

Step 5: at the time of guaranteeingIn the case of a constant, according to probability/>, respectivelyAnd/>Crossing and mutating the target array element population and updating the target array element populationUpdated as/>+1 Is/>=/>+1。

Step 6: and (5) repeating the steps 2-5 until the iteration times threshold is reached or the target optimization function meets the preset error threshold.

Step 7: determining an optimal receiving and transmitting weight vector according to the target optimization function when the iteration number threshold is reached or the target optimization function meets the preset error threshold。

The second pattern function may be written as:

on the basis of the embodiment, the scheme of the application is described by taking a software platform Windows 11 operating system and MATLAB R2019b of a simulation experiment of pattern simulation as an example.

Parameters of the simulation experiment are set as follows: the uniform linear FDA antenna with the array element number of N=60 is full array FDA, the sparsity is 50%, namely 30 array elements are selected from the full array FDA to form the sparse array FDA, wherein the full array FDA is a target array antenna, and the sparse array FDA is a sparse array antenna corresponding to the second pattern function.

Operating frequencyThe frequency step can be set to 10kHz, array element spacing/>The preset distance angle space is set as follows: 10 Km is less than or equal to R is less than or equal to 90Km,0 degree is less than or equal to/>Less than or equal to 180 degrees. The space of interest (i.e. the main lobe) is locatedThe parameters in the genetic algorithm are set as/>，/>，/>，，/>For the iteration number threshold,/>To initialize the array element population number,/>And/>Respectively representing a preset crossover probability and a preset mutation probability.

Fig. 7 is a three-dimensional view of a transmitting and receiving beam pattern of a full-array FDA with logarithmic frequency offset according to an embodiment of the present application, and fig. 8 is a top view of a transmitting and receiving beam pattern of a full-array FDA with logarithmic frequency offset according to an embodiment of the present application, where, as shown in fig. 7 and 8, the abscissa is distance, represents a position of a far-field target in a distance dimension, the ordinate is km, the ordinate is angle, represents a position of the far-field target in an angle dimension, and the unit is degree.

Based on the measurements of fig. 7 and 8, the full-array FDA has a main lobe width in the distance dimension of 7.2km and a main lobe width in the angle dimension of 4 °.

Fig. 9 is a three-dimensional view of a transmit-receive beam pattern of a sparse array FDA with logarithmic frequency offset according to an embodiment of the present application, and fig. 10 is a top view of a transmit-receive beam pattern of a sparse array FDA with logarithmic frequency offset according to an embodiment of the present application, where, as shown in fig. 9 and 10, the abscissa indicates a distance, the ordinate indicates km, the ordinate indicates an angle, the ordinate indicates a position of a target in an angle, and the unit indicates degree.

As can be seen from fig. 9, the sparse array FDA has only one main lobe in the range angle space (consistent with the full array FDA), which indicates that in the FDA with nonlinear frequency offset, the directional diagram of decoupling of the range angle information can be maintained after the sparse.

Based on the measurement of fig. 9 and 10, the main lobe width of the sparse array FDA in the sparse synthesis is 8km, the main lobe width of the angle dimension is 5 °, and the main lobe width is slightly larger than that of the full array FDA.

Based on measurement comparison between fig. 9 and 10 and fig. 7 and 8, it was found that after sparseness, peak side lobe level slightly increased and main lobe width slightly widened. For example, the side lobe level of the full array FDA with logarithmic frequency offset is 10log10 (0.04185) = -13.8dB. The side lobe level of the frequency diversity array after sparse synthesis is 10log10 (0.05187) = -12.9dB, and 0.9 dB is added. That is, the number of linear FDA elements is essentially reduced, i.e., 50% of the elements are saved, at the cost of slightly increased peak sidelobe levels.

Fig. 11 is a cross-sectional view of an angle-dimension normalized transmit-receive beam pattern with logarithmic frequency offset over a 50km distance before and after FDA sparse, where a dotted line represents a cross-sectional view of an angle-dimension normalized transmit-receive beam pattern over a 50km distance for a full-array FDA before sparse, and a solid line represents a cross-sectional view of an angle-dimension normalized transmit-receive beam pattern over a 50km distance for a sparse-array FDA after sparse, according to an embodiment of the present application. Wherein the cross-sectional view of the normalized transmit receive beam pattern is a cross-sectional view of the pattern.

Based on the measurements made in fig. 11, the side lobe level of the FDA with logarithmic frequency offset in the angular dimension was increased by 8.7 dB (from-26.2 dB to-17.5 dB) before and after the sparse implementation was applied.

Fig. 12 is a cross-sectional view of a distance dimension normalized transmit-receive beam pattern with logarithmic frequency offset at an angle of 90 ° before and after FDA sparse, where a dotted line represents a cross-sectional view of a distance dimension normalized transmit-receive beam pattern with a full-array FDA before sparse at 90 °, and a solid line represents a cross-sectional view of a distance dimension normalized transmit-receive beam pattern with a sparse array FDA after sparse at 90 °.

Based on the measurements made in fig. 12, the side lobe level of the linear FDA with logarithmic frequency offset was increased by 0.9 dB (from-13.8 dB to-12.9 dB) in the distance dimension before and after the application of the sparse implementation.

In summary, the FDA with logarithmic frequency offset can save 50% of array elements, and the increment of sidelobe level is smaller than 1.0 dB, that is, the cost of the FDA with logarithmic frequency offset can be reduced essentially by the cost of increasing sidelobe level and widening main lobe width after sparse synthesis.

Based on the same inventive concept, the embodiment of the application also provides a sparse device of the antenna array element corresponding to the sparse method of the antenna array element, and because the principle of solving the problem of the device in the embodiment of the application is similar to that of the sparse method of the antenna array element in the embodiment of the application, the implementation of the device can refer to the implementation of the method, and the repetition is omitted.

Fig. 13 is a schematic structural diagram of a sparse device of an antenna array element according to an embodiment of the present application, where the sparse device may be integrated in an electronic device.

As shown in fig. 13, the apparatus may include:

A determining module 501, configured to determine a first pattern function of a target array antenna according to received signals of a plurality of array elements in the target array antenna, where the target array antenna is a linear frequency diversity array antenna;

the construction module 502 is configured to construct a target optimization function according to the first pattern function and a preset expected pattern function corresponding to a preset array element sparsity;

the solving module 503 is configured to initialize a plurality of array element populations according to a preset array element sparsity, and solve a preset expected pattern function in the objective optimization function according to the plurality of array element populations, where each array element population includes: presetting a plurality of target array elements corresponding to the array element sparsity rate in the plurality of array elements;

the iteration module 504 is configured to perform iterative update on the multiple array element populations by using a genetic algorithm, determine a second pattern function according to a target optimization function when a preset iteration stop condition is reached, where the preset iteration stop condition is that the target optimization function does not exceed a preset error threshold, or the iteration number reaches an iteration number threshold;

The determining module 501 is further configured to determine, according to the first pattern function and the second pattern function, whether to use an array element population corresponding to the second pattern function to perform array element sparseness on the target array antenna.

In an alternative embodiment, the determining module 501 is specifically configured to:

Respectively carrying out pattern simulation according to the first pattern function and the second pattern function to obtain a first pattern and a second pattern;

And determining whether to adopt an array element population corresponding to the second pattern function according to the first pattern and the second pattern, and performing array element sparseness on the target array antenna.

In an alternative embodiment, the determining module 501 is specifically configured to:

performing main lobe comparison according to the main lobe width in the first direction diagram and the main lobe width in the second direction diagram to obtain a main lobe comparison result;

Performing side lobe comparison according to the side lobe level value in the first direction diagram and the side lobe level value in the second direction diagram to obtain a side lobe comparison result;

And determining whether to adopt an array element population corresponding to the second directional diagram function according to the main lobe comparison result and the side lobe comparison result, and performing array element sparseness on the target array antenna.

In an alternative embodiment, the construction module 502 is specifically configured to:

And constructing an objective optimization function aiming at a preset distance angle observation space according to the first directional diagram function and a preset expected directional diagram function.

In an alternative embodiment, the determining module 501 is specifically configured to:

according to a plurality of preset signal processing chains, respectively performing signal processing on the received signals of each array element to obtain a plurality of output signals of each array element;

According to a plurality of output signals of a plurality of array elements, acquiring an array factor of a target array antenna;

And constructing a first directional diagram function according to the array factors of the target array antenna.

In an alternative embodiment, the determining module 501 is specifically configured to:

Acquiring the weight of each signal processing chain and the weight of a target processing unit in each signal processing chain;

and acquiring array factors of the target array antenna according to the plurality of output signals of the plurality of array elements, the weight of each signal processing chain and the weight of the target processing unit.

In an alternative embodiment, the iteration module 504 is specifically configured to:

And carrying out iterative updating on the plurality of array element populations by adopting a genetic algorithm according to preset array element population constraint conditions, preset crossover probability, preset variation probability and preset array element sparsity, wherein the preset array element population constraint conditions are used for indicating that a first array element and a last array element in the plurality of array elements are reserved in the iterative updating process.

The process flow of each module in the apparatus and the interaction flow between the modules may be described with reference to the related descriptions in the above method embodiments, which are not described in detail herein.

Fig. 14 is a schematic structural diagram of an electronic device according to an embodiment of the present application, as shown in fig. 14, where the device includes: the electronic device comprises a processor 601, a memory 602 and a bus 603, the memory 602 storing machine readable instructions executable by the processor 601, the processor 601 and the memory 602 communicating over the bus 603 when the electronic device is running, the processor 601 executing the machine readable instructions to perform the method described above.

The embodiment of the application also provides a computer readable storage medium, wherein the computer readable storage medium stores a computer program, and the computer program is executed by a processor when the computer program is executed by the processor, and the processor executes the method.

In an embodiment of the present application, the computer program may further execute other machine readable instructions when executed by a processor to perform the method as described in other embodiments, and the specific implementation of the method steps and principles are referred to in the description of the embodiments and are not described in detail herein.

In the embodiments provided in the present application, it should be understood that the disclosed apparatus and method may be implemented in other manners. The above-described apparatus embodiments are merely illustrative, for example, the division of the units is merely a logical function division, and there may be other manners of division in actual implementation, and for example, multiple units or components may be combined or integrated into another system, or some features may be omitted, or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed with each other may be through some communication interface, device or unit indirect coupling or communication connection, which may be in electrical, mechanical or other form.

The units described as separate units may or may not be physically separate, and units shown as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

In addition, each functional unit in the embodiments provided in the present application may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit.

The functions, if implemented in the form of software functional units and sold or used as a stand-alone product, may be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application may be embodied essentially or in a part contributing to the prior art or in a part of the technical solution, in the form of a software product stored in a storage medium, comprising several instructions for causing a computer device (which may be a personal computer, a server, a network device, etc.) to perform all or part of the steps of the method according to the embodiments of the present application. And the aforementioned storage medium includes: a usb disk, a removable hard disk, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), a magnetic disk, or an optical disk, or other various media capable of storing program codes.

It should be noted that: like reference numerals and letters in the following figures denote like items, and thus once an item is defined in one figure, no further definition or explanation of it is required in the following figures, and furthermore, the terms "first," "second," "third," etc. are used merely to distinguish one description from another and are not to be construed as indicating or implying relative importance.

Finally, it should be noted that: the above examples are only specific embodiments of the present application, and are not intended to limit the scope of the present application, but it should be understood by those skilled in the art that the present application is not limited thereto, and that the present application is described in detail with reference to the foregoing examples: any person skilled in the art may modify or easily conceive of the technical solution described in the foregoing embodiments, or perform equivalent substitution of some of the technical features, while remaining within the technical scope of the present disclosure; such modifications, changes or substitutions do not depart from the spirit and scope of the corresponding technical solutions. Are intended to be encompassed within the scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.

Claims (10)

1. A method for sparsely forming antenna elements, comprising:

determining a first pattern function of a target array antenna according to received signals of a plurality of array elements in the target array antenna, wherein the target array antenna is a linear frequency diversity array antenna;

Constructing a target optimization function according to the first pattern function and a preset expected pattern function corresponding to a preset array element sparsity;

initializing a plurality of array element populations according to the preset array element sparsity, and respectively solving the preset expected pattern function in the target optimization function according to the plurality of array element populations, wherein each array element population comprises: a plurality of target array elements corresponding to the preset array element sparsity rate in the plurality of array elements;

Adopting a genetic algorithm to carry out iterative updating on the array element populations, and determining a second pattern function according to the target optimization function when a preset iteration stop condition is reached, wherein the preset iteration stop condition is that the value of the target optimization function does not exceed a preset error threshold value, or the iteration times reach an iteration times threshold value;

and determining whether to perform array element sparseness on the target array antenna by adopting an array element population corresponding to the second pattern function according to the first pattern function and the second pattern function.

2. The method of claim 1, wherein the determining, according to the first pattern function and the second pattern function, whether to perform element sparseness on the target array antenna using an element population corresponding to the second pattern function includes:

Respectively carrying out pattern simulation according to the first pattern function and the second pattern function to obtain a first pattern and a second pattern;

And determining whether to adopt an array element population corresponding to the second directional diagram function according to the first directional diagram and the second directional diagram, and performing array element sparseness on the target array antenna.

3. The method of claim 2, wherein determining whether to use the array element population corresponding to the second pattern function according to the first pattern and the second pattern, and performing array element sparseness on the target array antenna comprises:

performing main lobe comparison according to the main lobe width in the first direction diagram and the main lobe width in the second direction diagram to obtain a main lobe comparison result;

performing side lobe comparison according to the side lobe level value in the first direction diagram and the side lobe level value in the second direction diagram to obtain a side lobe comparison result;

And determining whether to adopt an array element population corresponding to the second directional diagram function according to the main lobe comparison result and the side lobe comparison result, and performing array element sparseness on the target array antenna.

4. The method according to claim 1, wherein the constructing the objective optimization function according to the first pattern function and a preset expected pattern function corresponding to a preset array element sparsity ratio includes:

And constructing the target optimization function aiming at a preset distance angle observation space according to the first pattern function and the preset expected pattern function.

5. The method of claim 1, wherein determining the first pattern function for the target array antenna based on the received signals for the plurality of array elements in the target array antenna comprises:

According to a plurality of preset signal processing chains, respectively performing signal processing on the received signals of each array element to obtain a plurality of output signals of each array element;

according to the plurality of output signals of the plurality of array elements, acquiring an array factor of the target array antenna;

And constructing the first pattern function according to the array factor of the target array antenna.

6. The method of claim 5, wherein the obtaining the array factor of the target array antenna from the plurality of output signals of the plurality of array elements comprises:

acquiring the weight of each signal processing chain and the weight of a target processing unit in each signal processing chain;

And acquiring the array factors of the target array antenna according to the plurality of output signals of the plurality of array elements, the weight of each signal processing chain and the weight of the target processing unit.

7. The method of any of claims 1-6, wherein iteratively updating the plurality of array element populations using a genetic algorithm comprises:

and carrying out iterative updating on the plurality of array element populations according to preset array element population constraint conditions, preset crossover probability, preset variation probability and the preset array element sparsity rate by adopting the genetic algorithm, wherein the preset array element population constraint conditions are used for indicating that the first array element and the last array element in the plurality of array elements are reserved in the iterative updating process.

8. A sparse device for antenna elements, comprising:

The determining module is used for determining a first directional diagram function of a target array antenna according to the received signals of a plurality of array elements in the target array antenna, wherein the target array antenna is a linear frequency diversity array antenna;

The construction module is used for constructing a target optimization function according to the first pattern function and a preset expected pattern function corresponding to a preset array element sparsity rate;

the solving module is configured to initialize a plurality of array element populations according to the preset array element sparsity, and solve the preset expected pattern function in the objective optimization function according to the plurality of array element populations, where each array element population includes: a plurality of target array elements corresponding to the preset array element sparsity rate in the plurality of array elements;

The iteration module is used for carrying out iteration update on the array element populations by adopting a genetic algorithm, and determining a second pattern function according to the target optimization function when a preset iteration stop condition is reached, wherein the preset iteration stop condition is that the target optimization function does not exceed a preset error threshold, or the iteration times reach an iteration times threshold;

The determining module is further configured to determine, according to the first pattern function and the second pattern function, whether to use an array element population corresponding to the second pattern function to perform array element sparseness on the target array antenna.

9. An electronic device, comprising: a processor, a memory and a bus, the memory storing machine readable instructions executable by the processor, the processor and the memory in communication over the bus when the electronic device is running, the processor executing the machine readable instructions to perform the method of sparsing antenna elements according to any one of claims 1 to 7.

10. A computer readable storage medium, characterized in that the computer readable storage medium has stored thereon a computer program which, when executed by a processor, performs the method of sparsing antenna array elements according to any of claims 1 to 7.