Multi-constraint intelligent array antenna arrangement method and system

Abstract

The invention discloses a multi-constraint intelligent array antenna arrangement method and system, and belongs to the technical field of array antenna design, and the method comprises the following steps: S1, effective region division; s2, area array element position screening; s3, redistributing areas for the array elements; s4, adaptive value output; and S5, carrying out evolution algorithm iteration. According to the invention, the re-partitioning principle is adopted, the poor uniformity and other performances caused by random arrangement are greatly improved on the premise that the array element spacing requirement is met, and the success rate of array element tracing is improved at the same time; through statistical analysis of the positions of the array elements in a new area, the arrangement rule of the array elements in the range is obtained, tracking of a dense area can be effectively avoided through sequentially determined movement rules, optimization time is greatly shortened, meanwhile, array plane apertures can be effectively utilized by guiding the array elements to be distributed to an area with low array element density, and optimization efficiency is improved. And the performance of the antenna array pattern obtained by final tracing is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of array antenna design, and in particular to a multi-constraint intelligent array antenna arrangement method and system. Background Art

[0002] With the development of satellite-borne platform radar systems, the functional requirements of the systems on the platforms are increasing, and the system index requirements are also getting higher and higher. Often, radars of multiple frequency bands need to be deployed on the same platform. The conventional design of ground-based radars is to use one antenna for each radar, but for moving platforms, especially satellite-borne platforms, in order to meet the requirements of the working frequency band, the limited spatial position and the ultra-wide working bandwidth form a contradiction. To resolve this contradiction, the design of multi-band common aperture antennas has received increasing attention. In the limited platform space, by deploying multiple phased array antennas of different frequency bands, the distribution of low-frequency antennas and high-frequency antennas in the same aperture can be achieved, thereby improving the utilization rate of the aperture.

[0003] In order to layout phased array antennas of different frequency bands, it is necessary to reserve arrangement positions for high-frequency antennas within the low-frequency aperture range. Therefore, there are multiple isolation areas of different shapes inside the low-frequency aperture. Only high-frequency antennas can be arranged in this area, and it is an area that cannot be arranged for low-frequency antennas. In addition, in order to reduce costs, sparse arrangement is often used in phased array design. Therefore, how to reasonably bypass the isolation area and obtain the optimal array element position arrangement scheme to optimize the array pattern performance has become the focus of current design. Since the antenna elements must meet the unit spacing restriction during the sparse optimization of the antenna array, that is, the spacing between any two elements must be greater than the antenna unit size, the sparse arrangement optimization considering the isolation area must be carried out under the constraints of multiple non-arrangeable areas. On the one hand, the array elements cannot be arranged in multiple prohibited arrangement areas, and on the other hand, the array elements need to meet the spacing restriction conditions. The optimization difficulty is greatly increased compared to the situation where there is no isolation area, and the optimization process is very complicated. The above problems need to be solved urgently. To this end, a multi-constraint intelligent array antenna arrangement method and system are proposed. Summary of the invention

[0004] The technical problem to be solved by the present invention is: how to complete the optimal design of sparse array antennas under the premise of satisfying multiple constraints. A multi-constraint intelligent array antenna arrangement method is provided. The optimization process starts with the random position generation of array elements in the arrangable area, and the regional division and screening of the entire area, and then the array elements are regionally reallocated and the positions are optimized according to the characteristics of different areas.

[0005] The present invention solves the above technical problems through the following technical solutions, and the present invention comprises the following steps:

[0006] S1: Effective area division

[0007] Extract the information of the non-arrangeable areas, and divide the whole area into effective areas based on the information. The principle of division is to minimize the number of areas occupied by all non-arrangeable areas and make the area of ​​the divided areas as small as possible.

[0008] S2: Regional array element position screening

[0009] According to the divided valid area, the array elements randomly arranged in the whole area are screened; if the array element is located in the non-arrangeable area, it is marked as a forbidden array element; if the array element is located in the arrangeable area and the distance between adjacent array elements is less than two array element sizes, it is marked as an overlapping array element; the number of array elements in all areas is counted, and the arrangeable area is arranged according to this number, and the arrangement is arranged from large to small according to the number of array elements;

[0010] S3: Array element reallocation area

[0011] Re-allocating the area of ​​the forbidden array elements and overlapping array elements screened out in step S2 so that these array elements are all located in the arrangable area; after determining the area, optimizing the arrangement position of the above array elements so that they do not overlap with all other array elements;

[0012] S4: Fitness value output

[0013] Calculate or set the optimization adaptation value according to the array element arrangement position obtained in step S3;

[0014] S5: Evolutionary Algorithm Iteration

[0015] Regenerate random array element positions and repeat steps S1 to S4, iterating and optimizing according to the evolutionary algorithm.

[0016] Furthermore, in the step S1, when the entire domain is divided into effective areas, if the shape of the entire domain envelope is not a rectangle, a plurality of rectangle fitting methods are used to divide the entire domain.

[0017] Furthermore, in the step S1, the sizes of a single rectangular area are L and W, respectively, and the partitioning optimization is performed using these two parameters as optimization variables: first, the initial L and W are set, and the entire domain is divided at this size to obtain a large number of rectangular partitions. After the division, the number of partitions Ns occupied by all non-arrangeable areas is calculated, and L and W are changed to continue iterative optimization until the fitness function obtains the optimal solution.

[0018] Furthermore, the fitness function is set according to the following formula:

[0019] fitness=0.5×LW+0.5×N s

[0020] The optimization goal is to minimize the value of the fitness function.

[0021] Furthermore, in step S2, according to the partition size obtained in step S1, the two-dimensional coordinate range of each partition is obtained, and all the partitions are numbered; the position of each array element is partitioned and judged in turn to obtain the number of the partition where the array element is located; when the array element is located in the arrangeable area, it is judged whether the spacing between the array element and the other array elements meets the spacing requirement; if the spacing requirement is not met, it is marked as an overlapping array element.

[0022] Furthermore, in step S2, when the global envelope shape is not a rectangle, the array element screening located between the partition rectangle and the global boundary when randomly generating the array element position is completed by the elimination method, that is, if the array element is not located in all the arrangable area rectangles at the same time, the array element is set as a forbidden array element.

[0023] Furthermore, in step S3, the specific process of reallocating regions to the pixels is as follows:

[0024] S31: Determine a new area to be allocated for array elements according to the arrangable area ranking table

[0025] First, determine the position of the array element in the sorted list of arrangable areas, then find the arrangable area at the symmetrical position in the list, and use this area as the new area of ​​the array element;

[0026] S32: repeating step S31 to complete the determination of new areas for all array elements;

[0027] S33: determining a movement mode of the array elements to be allocated in the new area according to the arrangement of the array elements in all the new areas: firstly counting the number of array elements in different areas in the new area, and then determining a movement rule of the array elements to be allocated according to the counted number of array elements, in principle, distributing the array elements as evenly as possible;

[0028] S34: determining the movement mode of all the array elements to be allocated according to step S33, and moving all the array elements to be allocated in sequence;

[0029] S35: Check all array elements to be allocated to ensure that all array elements have been allocated; if the array element to be allocated does not find a valid position in the new area, reselect an area adjacent to the area to be allocated and complete the allocation according to the moving method of the area.

[0030] Furthermore, in step S33, the movement pattern of the array elements to be allocated in the new area is determined by the array element densities in different areas of the new area.

[0031] The present invention also discloses a multi-constraint intelligent array antenna arrangement system, which uses the above-mentioned optimization method to optimize the array element position of the sparse array antenna, including:

[0032] The effective area division module is used to extract the information of the non-arrangeable area and divide the whole area into effective areas based on the information;

[0033] The regional array element position screening module is used to screen the array elements randomly arranged in the whole area and sort the arrangable areas;

[0034] An array element reallocation area module is used to reallocate areas for forbidden array elements and overlapping array elements, and optimize the arrangement positions of the array elements;

[0035] A fitness value output module is used to calculate or set the optimized fitness value;

[0036] An evolutionary algorithm iteration module, used to regenerate a random array element set and iterate and optimize according to the evolutionary algorithm;

[0037] The control processing module is used to issue instructions to each module to complete related actions;

[0038] The effective area division module, the regional array element position screening module, the array element reallocation area module, the fitness value output module, and the evolutionary algorithm iteration module are all electrically connected to the control processing module.

[0039] Compared with the prior art, the present invention has the following advantages:

[0040] 1. The area division method is used to distinguish the isolated area (non-arrangeable area) from the arrangeable area, so that the subsequent element position optimization can bypass the isolated area and improve the efficiency of sparse optimization after considering the isolated area;

[0041] 2. The intelligent area division method can efficiently obtain the effective area while minimizing the size of the area, thereby improving the positioning accuracy of area allocation, improving the success rate of allocation and optimization efficiency;

[0042] 3. Adopting the principle of re-partitioning, the performance such as poor uniformity caused by random arrangement is greatly improved under the premise of meeting the requirements of array element spacing, and the success rate of array element tracking is improved at the same time; through statistical analysis of the array element positions in the new area, the arrangement law of the array elements in the range is obtained, and the movement law determined in sequence can effectively avoid tracking in dense areas, greatly shortening the optimization time. At the same time, guiding the array elements to be allocated to the area with low array element density can effectively utilize the array aperture and improve the performance of the antenna array pattern obtained by the final tracking. The array antenna obtained by this method can have low sidelobe and high gain characteristics. When this method is applied to the satellite-borne radar system, it can effectively improve the aperture utilization efficiency of the platform and increase the working distance of the radar. . BRIEF DESCRIPTION OF THE DRAWINGS

[0043] Figure 1 is a flow chart of a method for arranging multi-constrained intelligent array antennas in Embodiment 1 of the present invention;

[0044] Figure 2 This is a schematic diagram of global area division obtained after the effective area is intelligently divided in the first embodiment of the present invention;

[0045] Figure 3 is the arrangement of array element positions after array elements are reallocated in the second embodiment of the present invention;

[0046] Figure 4 is the normal φ=0° tangent plane pattern at 3 GHz after the array antenna is optimized in the second embodiment of the present invention;

[0047] Figure 5 is the arrangement of array element positions after array elements are reallocated in the third embodiment of the present invention;

[0048] Figure 6 This is the sectional radiation pattern of the optimized array antenna in the third embodiment of the present invention when the φ=0° sectional radiation scans 45° at 10 GHz. DETAILED DESCRIPTION

[0049] The following is a detailed description of an embodiment of the present invention. This embodiment is implemented on the premise of the technical solution of the present invention, and a detailed implementation method and a specific operation process are given, but the protection scope of the present invention is not limited to the following embodiment.

[0050] Embodiment 1

[0051] This embodiment provides a multi-constraint intelligent array antenna arrangement method, which ultimately obtains the array element distribution of the optimal sparse array and the array radiation pattern with the lowest side lobe. Figure 1 As shown, Figure 1 This is a flow chart of the multi-constraint intelligent array antenna arrangement method in this embodiment. This embodiment is based on a genetic algorithm. The algorithm uses mathematical methods and computer simulation operations to convert the problem-solving process into a process similar to the crossover and mutation of chromosome genes in biological evolution. The optimization process starts with the random position generation of array elements in the arrangable area, and the regional division and screening of the entire area, and then the regional redistribution and position optimization of the array elements are performed according to the characteristics of different areas.

[0052] This embodiment solves the above technical problems through the following technical solutions, and this embodiment includes the following steps:

[0053] S1: Intelligent division of effective areas

[0054] like Figure 2 As shown, Figure 2This is a schematic diagram of the global area division obtained after the effective area is intelligently divided in this embodiment; extract the non-arrangeable area information, and divide the entire area into effective areas based on the information. The division principle is: minimize the number of areas occupied by all non-arrangeable areas and make the area of ​​the divided areas as small as possible;

[0055] S2: Regional array element position screening

[0056] According to the divided valid area, the array elements randomly arranged in the whole area are screened; if the array element is located in the non-arrangeable area, it is marked as a forbidden array element; if the array element is located in the arrangeable area and the distance between adjacent array elements is less than two array element sizes, it is marked as an overlapping array element; the number of array elements in all areas is counted, and the arrangeable area is arranged according to this number, and the arrangeable area is arranged from large to small according to the number of array elements to obtain a sorted table of arrangeable areas;

[0057] S3: Array element reallocation area

[0058] Re-allocating the area of ​​the forbidden array elements and overlapping array elements screened out in step S2 so that these array elements are all located in the arrangable area; after determining the area, optimizing the arrangement position of the above array elements so that they do not overlap with all other array elements;

[0059] S4: Fitness value output

[0060] Calculate or set the optimization adaptation value according to the array element arrangement position obtained in step S3;

[0061] S5: Evolutionary Algorithm Iteration

[0062] Regenerate random array element positions and repeat steps S1 to S4, iterating and optimizing according to the evolutionary algorithm.

[0063] Furthermore, in the step S1, when the entire domain is intelligently divided, if the envelope shape of the entire domain is not a rectangle, a plurality of rectangles can be used to fit the domain; for example, when the shape of the entire domain is a circle, a plurality of rectangles are used to fit the circle, and the vertices of the edge rectangles (the two vertices of the edge rectangles located on the periphery) are inscribed in the circular boundary, and the area enclosed by the edge rectangle and the circular boundary is shaped like a fan, and this area is not included in the arrangeable area.

[0064] Furthermore, in the step S1, when the whole domain is intelligently divided, the whole domain is divided into a plurality of identical rectangular areas, and the sizes of the rectangular areas are L and W respectively, and the division optimization is performed with these two parameters as optimization variables: first, the initial L and W are set, and the whole domain is divided into a plurality of rectangular partitions with this size, and after the division, the number of partitions Ns occupied by all the non-arrangeable areas is calculated, and L and W are changed to continue iterative optimization until the fitness function obtains the optimal solution; the fitness function can be set according to the following formula:

[0065] fitness=0.5×LW+0.5×N s

[0066] The optimization goal is to minimize the value of the fitness function. The divided area is a rectangle, which is determined by L and W. These two parameters are set as optimization variables and take their minimum values. The smaller the values ​​of the two are, the smaller the area of ​​the rectangle is, and the more accurate the fitting shape is.

[0067] In this embodiment, in step S2, the two-dimensional coordinate range of each partition is obtained according to the partition size obtained in step S1, and all the partitions are numbered; the position of each array element is partitioned and judged in turn to obtain the number of the partition where the array element is located; when the array element is located in the arrangeable area, it is judged whether the spacing between the array element and the other array elements meets the spacing requirement; if the spacing requirement is not met, it is marked as an overlapping array element.

[0068] Furthermore, in step S2, when the global shape is not a rectangle, the array element screening located between the partition rectangle and the global boundary when randomly generating the array element position is completed by the elimination method, that is, if the array element is not located in all the arrangable area rectangles at the same time, the array element is set as a forbidden array element.

[0069] Furthermore, in step S3, the specific process of reallocating regions to the pixels is as follows:

[0070] S31: Determine a new area to be allocated for array elements according to the arrangable area ranking table

[0071] First, determine the position of the array element in the sorting table of arrangable areas. Then, to ensure the uniformity of array element distribution, find the arrangable area at the symmetrical position in the table and use this area as the new area of ​​the array element.

[0072] S32: repeating step S31 to complete the determination of new areas for all array elements;

[0073] S33: determining a movement mode of the array elements to be allocated in the new area according to the arrangement of the array elements in all the new areas: firstly counting the number of array elements in different areas in the new area, and then determining a movement rule of the array elements to be allocated according to the counted number of array elements, in principle, distributing the array elements as evenly as possible;

[0074] S34: According to the method in step S33, the movement mode of all the array elements to be allocated is determined, and all the array elements to be allocated are moved in sequence;

[0075] S35: Check all array elements to be allocated to ensure that all array elements have been allocated; if the array element to be allocated does not find a valid position in the new area, reselect an area adjacent to the area to be allocated and complete the allocation according to the moving method of the area.

[0076] Furthermore, in step S33, the movement rule of the array element to be allocated in the new area is determined by the array element density in different areas of the new area. For example, when the number of array elements in the upper half of the new area reaches twice the number of array elements in the lower half, the array element to be allocated moves from bottom to top in the new area to find a suitable position, and vice versa; when the number of array elements in the left half of the new area reaches twice the number of array elements in the right half, the array element to be allocated moves from right to left in the new area to find a suitable position, and vice versa; when the difference between the number of array elements on both sides is less than 30% of the number of array elements on one side, the array element to be allocated moves from the lower left to the upper right to find a suitable position.

[0077] Embodiment 2

[0078] This embodiment optimizes the sidelobe level of the rectangular aperture antenna pattern with a length and width of 400 mm and 370 mm respectively, and sets the x-axis and y-axis as Figure 3 As shown. The array antenna to be optimized operates in the 2-4 GHz frequency band, with 40 array elements and an element size of 30 mm. There are 3 non-arrangeable areas in the whole domain, and all array elements are arranged in the remaining areas as independent units. The time interval of the array element moving in the new area is 2 mm along the x-axis and y-axis. The directional pattern to be optimized is the normal φ=0° section of the antenna array, and the target sidelobe is below -10 dB. During the optimization process, the parameters to be optimized are composed of the position coordinates of 40 array elements. A set of array element sets are randomly generated as the initial value and substituted into the optimization process. Regional division, intelligent screening and element re-partitioning operations are performed in turn until the generated element coordinates are valid. During the optimization, the fitness function is constructed with the target sidelobe of the antenna, and the initial value of the array element coordinates and the fitness function are substituted into the genetic algorithm for calculation. During the process, the newly generated array element coordinates are subject to regional division, intelligent screening and element re-partitioning operations. The number of populations is set to 400, and the number of optimizations is set to 100.

[0079] like Figure 3-4 As shown, Figure 3 is the arrangement of array element positions after array elements are redistributed in this embodiment, Figure 4 It is the normal φ=0° section radiation pattern at 3 GHz after the array antenna in this embodiment is optimized.

[0080] From the above, it can be seen that the optimization process of this embodiment takes less than 4 hours, the normal sidelobe level of the optimized array is less than -14 dB, and no grating lobe appears within the range of ±90°.

[0081] Embodiment 3

[0082] This embodiment optimizes the sidelobe level of the circular aperture antenna pattern with a radius of 0.6 m, and sets the x-axis and y-axis as Figure 5As shown. The array antenna to be optimized operates in the 8-12GHz frequency band, with 100 array elements and an element size of 24.5mm. There are 7 circular non-arrangeable areas in the whole domain, and all array elements are arranged in the remaining areas as independent units. The time interval of the array element moving in the new area is 2mm along the x-axis and y-axis. The directional pattern to be optimized is the φ=0° scanning 45° section of the antenna array, and the target sidelobe is below -10dB. During the optimization process, the parameters to be optimized are composed of the position coordinates of 100 array elements. A set of array element sets are randomly generated as the initial value and substituted into the optimization process. The regional division, intelligent screening and array element re-partitioning operations are performed in turn until the generated array element coordinates are valid. During the optimization, the fitness function is constructed with the target sidelobe of the antenna, and the initial value of the array element coordinates and the fitness function are substituted into the genetic algorithm for calculation. During the process, the newly generated array element coordinates are subject to regional division, intelligent screening and array element re-partitioning operations. The number of populations is set to 200, and the number of optimizations is set to 100 times.

[0083] like Figure 5-6 As shown, Figure 5 is the arrangement of array element positions after array elements are redistributed in this embodiment, Figure 6 It is the sectional radiation pattern of the optimized array antenna in this embodiment when the φ=0° sectional scanning is 45° at 10 GHz.

[0084] From the above, it can be seen that the time used for the optimization process of this embodiment is less than 6 hours, the normal sidelobe level of the optimized array is achieved below -14dB, and no grating lobe appears within the range of ±90°.

[0085] In summary, the multi-constraint intelligent array antenna arrangement method in the above embodiment adopts the re-zoning principle, which greatly improves the performance such as poor uniformity caused by random arrangement while meeting the requirements of array element spacing, and at the same time improves the success rate of array element tracking; through statistical analysis of the array element positions in the new area, the arrangement rules of the array elements in the range are obtained, and the movement rules determined in sequence can effectively avoid tracking in dense areas, greatly shortening the optimization time, and guiding the array elements to be allocated to the area with low array element density can effectively utilize the array aperture and improve the antenna array radiation pattern performance obtained by the final tracking. The array antenna obtained by this method can have low sidelobe and high gain characteristics. When this method is applied to a satellite-borne radar system, it can effectively improve the aperture utilization efficiency of the platform and increase the working distance of the radar.

[0086] Although the embodiments of the present invention have been shown and described above, it is to be understood that the above embodiments are exemplary and are not to be construed as limitations of the present invention. A person skilled in the art may change, modify, replace and vary the above embodiments within the scope of the present invention.

Claims

1. A multi-constraint intelligent array antenna arrangement method, characterized in that: The following steps are involved: S1: Effective area division Extract the information of the non-arrangeable areas, and divide the whole area into effective areas based on the information. The principle of division is to minimize the number of areas occupied by all non-arrangeable areas and make the area of ​​the divided areas as small as possible. S2: Regional array element position screening According to the divided valid area, the array elements randomly arranged in the whole area are screened; if the array element is located in the non-arrangeable area, it is marked as a forbidden array element; if the array element is located in the arrangeable area and the distance between adjacent array elements is less than two array element sizes, it is marked as an overlapping array element; the number of array elements in all areas is counted, and the arrangeable area is arranged according to this number, and the arrangeable area is arranged from large to small according to the number of array elements to obtain a sorted table of arrangeable areas; S3: Array element reallocation area Re-allocating the area of ​​the forbidden array elements and overlapping array elements screened out in step S2 so that these array elements are all located in the arrangable area; after determining the area, optimizing the arrangement position of the above array elements so that they do not overlap with all other array elements; S4: Fitness value output Calculate or set the optimization adaptation value according to the array element arrangement position obtained in step S3; S5: Evolutionary Algorithm Iteration Regenerate random array element positions and repeat steps S1 to S4, iterating and optimizing according to the evolutionary algorithm.

2. The multi-constraint intelligent array antenna arrangement method according to claim 1, characterized in that: In the step S1, when the entire domain is divided into effective areas, if the shape of the entire domain envelope is not a rectangle, a plurality of rectangle fitting methods are used for division.

3. The multi-constraint intelligent array antenna arrangement method according to claim 2, characterized in that: In step S1, the sizes of a single rectangular area are L and W, respectively, and the partitioning optimization is performed using these two parameters as optimization variables: first, the initial L and W are set, and the entire domain is divided into a number of rectangular partitions at this size. After the division, the number of partitions Ns occupied by all non-arrangeable areas is calculated, and L and W are changed to continue iterative optimization until the fitness function obtains the optimal solution.

4. The multi-constraint intelligent array antenna arrangement method according to claim 3, characterized in that: The fitness function is set according to the following formula: fitness=0.5×LW+0.5×N s The optimization goal is to minimize the value of the fitness function.

5. The multi-constraint intelligent array antenna arrangement method according to claim 1, characterized in that: In step S2, according to the partition size obtained in step S1, the two-dimensional coordinate range of each partition is obtained, and all the partitions are numbered; the position of each array element is partitioned and judged in turn to obtain the number of the partition where the array element is located; when the array element is located in the arrangeable area, it is judged whether the spacing between the array element and the other array elements meets the spacing requirement; if the spacing requirement is not met, it is marked as an overlapping array element.

6. The multi-constraint intelligent array antenna arrangement method according to claim 1, characterized in that: In step S2, when the global envelope shape is not a rectangle, the array element screening located between the partition rectangle and the global boundary when randomly generating the array element position is completed by the elimination method, that is, if the array element is not located in all the arrangable area rectangles at the same time, the array element is set as a prohibited array element.

7. The multi-constraint intelligent array antenna arrangement method according to claim 4, characterized in that: In step S3, the specific process of reallocating regions to the pixels is as follows: S31: Determine a new area to be allocated for array elements according to the arrangable area ranking table First, determine the position of the array element in the sorted list of arrangable areas, then find the arrangable area at the symmetrical position in the list, and use this area as the new area of ​​the array element; S32: repeating step S31 to complete the determination of new areas for all array elements; S33: determining a movement mode of the array elements to be allocated in the new area according to the arrangement of the array elements in all the new areas: firstly counting the number of array elements in different areas in the new area, and then determining a movement rule of the array elements to be allocated according to the counted number of array elements, in principle, distributing the array elements as evenly as possible; S34: determining the movement mode of all the array elements to be allocated according to step S33, and moving all the array elements to be allocated in sequence; S35: Check all array elements to be allocated to ensure that all array elements have been allocated; if the array element to be allocated does not find a valid position in the new area, reselect an area adjacent to the area to be allocated and complete the allocation according to the moving method of the area.

8. The multi-constraint intelligent array antenna arrangement method according to claim 7, characterized in that: In step S33, the movement rule of the array elements to be allocated in the new area is determined by the density of array elements in different areas in the new area.

9. A multi-constraint intelligent array antenna arrangement system, characterized in that: The optimization method according to any one of claims 1 to 8 is used to optimize the element positions of the sparse array antenna, comprising: The effective area division module is used to extract the information of the unarrangeable area and divide the whole area into effective areas based on the information; The regional array element position screening module is used to screen the array elements randomly arranged in the whole area and sort the arrangeable areas; An array element reallocation area module is used to reallocate areas for forbidden array elements and overlapping array elements, and optimize the arrangement positions of the array elements; A fitness value output module is used to calculate or set the optimized fitness value; An evolutionary algorithm iteration module, used to regenerate a random array element set and iterate and optimize according to the evolutionary algorithm; The control processing module is used to issue instructions to each module to complete related actions; The effective area division module, the regional array element position screening module, the array element reallocation area module, the fitness value output module, and the evolutionary algorithm iteration module are all electrically connected to the control processing module.

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